16,426 research outputs found
Neutrino mixing in the seesaw model
In the seesaw model with hierarchical Dirac masses, the neutrino mixing angle
exhibits the behavior of a narrow resonance. In general, the angle is strongly
suppressed, but it can be maximal for special parameter values. We delineate
the small regions in which this happens, for the two flavor problem. On the
other hand, the physical neutrino masses are hierarchical, in general, except
in a large part of the region in which the mixing angle is sizable, where they
are nearly degenerate. Our general analysis is also applicable to the RGE of
neutrino mass matrix, where we find analytic solutions for the running of
physical parameters, in addition to a complex RGE invariant relating them. It
is also shown that, if one mixing angle is small, the three neutrino problem
reduces to two, two flavor problems.Comment: 19 pages, 4 figures; added new sections on RGE effects and universal
seesaw; version to appear in EPJ
Weyl points and topological nodal superfluids in a face-centered cubic optical lattice
We point out that a face-centered cubic (FCC) optical lattice, which can be
realised by a simple scheme using three lasers, provides one a highly
controllable platform for creating Weyl points and topological nodal
superfluids in ultracold atoms. In non-interacting systems, Weyl points
automatically arise in the Floquet band structure when shaking such FCC
lattices, and sophisticated design of the tunnelling is not required. More
interestingly, in the presence of attractive interaction between two hyperfine
spin states, which experience the same shaken FCC lattice, a three-dimensional
topological nodal superfluid emerges, and Weyl points show up as the gapless
points in the quasiparticle spectrum. One could either create a double Weyl
point of charge 2, or split it to two Weyl points of charge 1, which can be
moved in the momentum space by tuning the interactions. Correspondingly, the
Fermi arcs at the surface may be linked with each other or separated as
individual ones.Comment: 5 pages, 2 figures in the main text; 2 pages, 2 figures in the
supplemental materia
A parallel VLSI architecture for a digital filter of arbitrary length using Fermat number transforms
A parallel architecture for computation of the linear convolution of two sequences of arbitrary lengths using the Fermat number transform (FNT) is described. In particular a pipeline structure is designed to compute a 128-point FNT. In this FNT, only additions and bit rotations are required. A standard barrel shifter circuit is modified so that it performs the required bit rotation operation. The overlap-save method is generalized for the FNT to compute a linear convolution of arbitrary length. A parallel architecture is developed to realize this type of overlap-save method using one FNT and several inverse FNTs of 128 points. The generalized overlap save method alleviates the usual dynamic range limitation in FNTs of long transform lengths. Its architecture is regular, simple, and expandable, and therefore naturally suitable for VLSI implementation
VLSI architectures for computing multiplications and inverses in GF(2-m)
Finite field arithmetic logic is central in the implementation of Reed-Solomon coders and in some cryptographic algorithms. There is a need for good multiplication and inversion algorithms that are easily realized on VLSI chips. Massey and Omura recently developed a new multiplication algorithm for Galois fields based on a normal basis representation. A pipeline structure is developed to realize the Massey-Omura multiplier in the finite field GF(2m). With the simple squaring property of the normal-basis representation used together with this multiplier, a pipeline architecture is also developed for computing inverse elements in GF(2m). The designs developed for the Massey-Omura multiplier and the computation of inverse elements are regular, simple, expandable and, therefore, naturally suitable for VLSI implementation
A VLSI pipeline design of a fast prime factor DFT on a finite field
A conventional prime factor discrete Fourier transform (DFT) algorithm is used to realize a discrete Fourier-like transform on the finite field, GF(q sub n). A pipeline structure is used to implement this prime factor DFT over GF(q sub n). This algorithm is developed to compute cyclic convolutions of complex numbers and to decode Reed-Solomon codes. Such a pipeline fast prime factor DFT algorithm over GF(q sub n) is regular, simple, expandable, and naturally suitable for VLSI implementation. An example illustrating the pipeline aspect of a 30-point transform over GF(q sub n) is presented
Temperature and strain characterization of regenerated gratings
Both temperature and strain characterization of seed and regenerated gratings with and without post annealing is reported. The high temperature regeneration has significant impact on thermal characterization and mechanical strength of gratings while the post annealing has little effect. The observed difference is evidence of viscoelastic changes in glass structure. © 2013 Optical Society of America
Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation
Authoring dynamic garment shapes for character animation on body motion is one of the fundamental steps in the CG industry. Established workflows are either time and labor consuming (i.e., manual editing on dense frames with controllers), or lack keyframe-level control (i.e., physically-based simulation). Not surprisingly, garment authoring remains a bottleneck in many production pipelines. Instead, we present a deep-learning-based approach for semi-automatic authoring of garment animation, wherein the user provides the desired garment shape in a selection of keyframes, while our system infers a latent representation for its motion-independent intrinsic parameters (e.g., gravity, cloth materials, etc.). Given new character motions, the latent representation allows to automatically generate a plausible garment animation at interactive rates. Having factored out character motion, the learned intrinsic garment space enables smooth transition between keyframes on a new motion sequence. Technically, we learn an intrinsic garment space with an motion-driven autoencoder network, where the encoder maps the garment shapes to the intrinsic space under the condition of body motions, while the decoder acts as a differentiable simulator to generate garment shapes according to changes in character body motion and intrinsic parameters. We evaluate our approach qualitatively and quantitatively on common garment types. Experiments demonstrate our system can significantly improve current garment authoring workflows via an interactive user interface. Compared with the standard CG pipeline, our system significantly reduces the ratio of required keyframes from 20% to 1 -- 2%
A single chip VLSI Reed-Solomon decoder
A new VLSI design of a pipeline Reed-Solomon decoder is presented. The transform decoding technique used in a previous design is replaced by a time domain algorithm. A new architecture that implements such an algorithm permits efficient pipeline processing with minimum circuitry. A systolic array is also developed to perform erasure corrections in the new design. A modified form of Euclid's algorithm is implemented by a new architecture that maintains the throughput rate with less circuitry. Such improvements result in both enhanced capability and a significant reduction in silicon area, therefore making it possible to build a pipeline (31,15)RS decoder on a single VLSI chip
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