The bosonized Chiral Schwinger model (CSM) is quantized on the light-front (LF). The physical Hilbert space of CSM is obtained directly once the constraints on the LF phase space are eliminated. The discussion of the degenerate vacua and the absence in the CSM of the theta-vacua, as ...
Light-front (LF) quantization in the light-cone (LC) gauge is used to construct a renormalizable theory of the standard model. The framework derived earlier for QCD is extended to the Glashow-Weinberg-Salam (GWS) model of electroweak interaction theory. The Lorentz condition is automatically ...
An DNN accelerator may include a PE array performing MAC operations. The PE array may include PEs capable of MAC operations on quantized values. A PE may include subtractors for sub
We then derive a novel, analytic, extended-source solution to the multilayer search-light problem by quantizing the diffusion Green's function. This allows the application of the diffusion multipole model to material layers several orders of magnitude thinner than previously possible and creates ...
In the sense of third quantization a Friedman minisuperspace has been quantized. Within this model coherent states are constructed and Heisenberg's uncertainty relation is investigated. This simple model shows a dominance of quantum effe...
The light-front quantization of the bosonized Schwinger model is discussed in the continuum formulation. The proposal, successfully used earlier for describing the spontaneous symmetry breaking on the light-front, of separating first the scalar field into the dynamical condensate and the fluctuation fiel...
Shared cryptographic bits via quantized quandrature phase amplitudes of light We propose a novel quantum cryptographic protocol without using polarized photons. The protocol consists of an optical coupler and four nonorthogonal coher... M Yi,J Seberry,Y Zheng - 《Optics Communications》 被引量: 30...
Possibly shedding some light: I am able to solve the error withing AutoAWQ by setting the fuse_layers parameter to False. model = AutoAWQForCausalLM.from_quantized(quant_path, quant_file, safetensors=True, fuse_layers=False) I tested it for both TheBloke/CodeLlama-7B-AWQ and TheBloke...
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the b
1 Properties of solitons. a. Linear energy bands (grey) for the model described in the main text at Ωz/2π=0.3 together with the nonlinear eigenvalue of a soliton (red) for increasing nonlinearity. Dashed line marks a nonlinear bifurcation point, with a corresponding slope change. b. ...