Gravitational ultrarelativistic interaction of classical particles in the context of unification of interactions

The response of the ultrarelativistic particle with spin in a Schwarzschild field to the gravitomagnetic components as measured by the comoving observer is investigated. The dependence of the particle's spin-orbit acceleration on the Lorentz \gamma - factor and the spin orientation is studied. The concrete circular ultrarelativistic orbit of radius r=3m is considered as a partial solution of the Mathisson-Papapetrou equations and as the corresponding high-energy quantum state of the Dirac particle. Numerical estimates for protons and electrons near black holes are given. A tendency of gravitational and electromagnetic interactions to approach in quantitative terms at ultrarelativistic velocities is discussedComment: 16 page

Quasifree Knockout Of Deuterons In The ⁶Li(α,αd)⁴He Reaction At 23.6 MeV

α−d correlations in quasi-elastic scattering of 23.6-MeV α particles on the deuteron cluster of the ⁶Li target were measured in and off the principal reaction plane. Despite the low c.m. energy of 14.2 MeV, the impulse approximation provides a reasonable description of the quasifree process. Computations were based on the asymptotic α−d S-state wave function and on the cluster-model wave function of ⁶Li. Insensitivity of the fits to the details of the ⁶Li cluster-model wave function indicates an extreme surface reaction mechanism. The full width at half-maximum of the spectator momentum distribution was found to be 48±6 MeV/c. By comparing the experimental cross section for the quasifree process at the maximum of the angular correlation ((d2σ/dΩddΩ)=68±9 mb/sr² at θ=25°,θ(d)=45°) with the corresponding cross section for the free process, the probability of finding ⁶Li as an α−d cluster was evaluated

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common method for training recurrent neural networks, back-propagation through time (BPTT), requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. This becomes computationally expensive or even infeasible when used with long sequences. Importantly, biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states (consider days, months, or years.) However, humans are often reminded of past memories or mental states which are associated with the current mental state. We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state. Based on this principle, we study a novel algorithm which only back-propagates through a few of these temporal skip connections, realized by a learned attention mechanism that associates current states with relevant past states. We demonstrate in experiments that our method matches or outperforms regular BPTT and truncated BPTT in tasks involving particularly long-term dependencies, but without requiring the biologically implausible backward replay through the whole history of states. Additionally, we demonstrate that the proposed method transfers to longer sequences significantly better than LSTMs trained with BPTT and LSTMs trained with full self-attention.Comment: To appear as a Spotlight presentation at NIPS 201

Deep Complex Networks

At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech Spectrum Prediction using the TIMIT dataset. We achieve state-of-the-art performance on these audio-related tasks

Extreme Weather Events in Ukraine: Occurrence and Changes

Extreme weather is in the attention focus of many scientists and managers during the last decades. The leading aspect of these phenomena investigations in the recent years is the risk of material and human losses and damage mitigation. Especially, the interest is with regard to effects of weather extremities on natural systems and social processes such as land use practices, water resources management, emergency management, and planning. The main objectives of the investigations are clarifying of spectrum, space and time regularities of extreme weather events occurring in Ukraine as well as their intensity, duration, daily and seasonal variation, spreading, recurrence in the regions, and their changes analyzed. Applying statistical and geographical space–time analyses, the main regularities of the extreme weather events’ occurrence have been described as well as the trends and intensity of the extreme weather regime changes in Ukraine have been calculated and assessed

Neutrino Mass^2 Inferred from the Cosmic Ray Spectrum and Tritium Beta Decay

An earlier prediction of a cosmic ray neutron line right at the energy of the knee of the cosmic ray spectrum was based on the speculation that the electron neutrino is a tachyon whose mass is reciprocally related to the energy of the knee, $E_k$. Given the large uncertainty in $E_k$, the values of ${m_\nu}^2$ corresponding to it are consistent with values recently reported in tritium beta decay experiments.Comment: Published as Phys. Lett. B 493 (2000) 1-

APL And The Numerical Solution Of High-Order Linear Differential Equations

An Nth‐order linear ordinary differential equation is rewritten as a first‐order equation in an N×N matrix. Taking advantage of the matrix manipulation strength of the APL language this equation is then solved directly, yielding a great simplification over the standard procedure of solving N coupled first‐order scalar equations. This eases programming and results in a more intuitive algorithm. Example applications of a program using the technique are given from quantum mechanics and control theory

Gravitational and electromagnetic fields of a charged tachyon

An axially symmetric exact solution of the Einstein-Maxwell equations is obtained and is interpreted to give the gravitational and electromagnetic fields of a charged tachyon. Switching off the charge parameter yields the solution for the uncharged tachyon which was earlier obtained by Vaidya. The null surfaces for the charged tachyon are discussed.Comment: 8 pages, LaTex, To appear in Pramana- J. Physic