507 research outputs found
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 â¶Li(α,αd)âŽHe Reaction At 23.6 MeV
αâd correlations in quasi-elastic scattering of 23.6-MeV α particles on the deuteron cluster of the â¶Li 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 αâd S-state wave function and on the cluster-model wave function of â¶Li. Insensitivity of the fits to the details of the â¶Li 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 ((d2Ï/dΩddΩ)=68±9 mb/srÂČ at Ξ=25°,Ξ(d)=45°) with the corresponding cross section for the free process, the probability of finding â¶Li as an αâd 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 spaceâtime analyses, the main regularities of the extreme weather eventsâ 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, . Given the large uncertainty in , the values of
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 Nthâorder linear ordinary differential equation is rewritten as a firstâorder equation in an NĂN 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 firstâorder 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
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