Some Issues in a Gauge Model of Unparticles

We address in a recent gauge model of unparticles the issues that are important for consistency of a gauge theory, i.e., unitarity and Ward identity of physical amplitudes. We find that non-integrable singularities arise in physical quantities like cross section and decay rate from gauge interactions of unparticles. We also show that Ward identity is violated due to the lack of a dispersion relation for charged unparticles although the Ward-Takahashi identity for general Green functions is incorporated in the model. A previous observation that the unparticle's (with scaling dimension d) contribution to the gauge boson self-energy is a factor (2-d) of the particle's has been extended to the Green function of triple gauge bosons. This (2-d) rule may be generally true for any point Green functions of gauge bosons. This implies that the model would be trivial even as one that mimics certain dynamical effects on gauge bosons in which unparticles serve as an interpolating field.Comment: v1:16 pages, 3 figures. v2: some clarifications made and presentation improved, calculation and conclusion not modified; refs added and updated. Version to appear in EPJ

Parameter-Efficient Fine-Tuning without Introducing New Latency

Parameter-efficient fine-tuning (PEFT) of pre-trained language models has recently demonstrated remarkable achievements, effectively matching the performance of full fine-tuning while utilizing significantly fewer trainable parameters, and consequently addressing the storage and communication constraints. Nonetheless, various PEFT methods are limited by their inherent characteristics. In the case of sparse fine-tuning, which involves modifying only a small subset of the existing parameters, the selection of fine-tuned parameters is task- and domain-specific, making it unsuitable for federated learning. On the other hand, PEFT methods with adding new parameters typically introduce additional inference latency. In this paper, we demonstrate the feasibility of generating a sparse mask in a task-agnostic manner, wherein all downstream tasks share a common mask. Our approach, which relies solely on the magnitude information of pre-trained parameters, surpasses existing methodologies by a significant margin when evaluated on the GLUE benchmark. Additionally, we introduce a novel adapter technique that directly applies the adapter to pre-trained parameters instead of the hidden representation, thereby achieving identical inference speed to that of full fine-tuning. Through extensive experiments, our proposed method attains a new state-of-the-art outcome in terms of both performance and storage efficiency, storing only 0.03% parameters of full fine-tuning

Deformation of a Trapped Fermi Gas with Unequal Spin Populations

The real-space densities of a polarized strongly-interacting two-component Fermi gas of $^6$Li atoms reveal two low temperature regimes, both with a fully-paired core. At the lowest temperatures, the unpolarized core deforms with increasing polarization. Sharp boundaries between the core and the excess unpaired atoms are consistent with a phase separation driven by a first-order phase transition. In contrast, at higher temperatures the core does not deform but remains unpolarized up to a critical polarization. The boundaries are not sharp in this case, indicating a partially-polarized shell between the core and the unpaired atoms. The temperature dependence is consistent with a tricritical point in the phase diagram.Comment: Accepted for publication in Physical Review Letter

Audeosynth: music-driven video montage

We introduce music-driven video montage, a media format that offers a pleasant way to browse or summarize video clips collected from various occasions, including gatherings and adventures. In music-driven video montage, the music drives the composition of the video content. According to musical movement and beats, video clips are organized to form a montage that visually reflects the experiential properties of the music. Nonetheless, it takes enormous manual work and artistic expertise to create it. In this paper, we develop a framework for automatically generating music-driven video montages. The input is a set of video clips and a piece of background music. By analyzing the music and video content, our system extracts carefully designed temporal features from the input, and casts the synthesis problem as an optimization and solves the parameters through Markov Chain Monte Carlo sampling. The output is a video montage whose visual activities are cut and synchronized with the rhythm of the music, rendering a symphony of audio-visual resonance.postprin

Steering vector estimation and beamforming under uncertainties

In this paper, we propose a new method for estimating the steering vector under uncertainties, which is utilized for improving the robustness of beamforming. We show that the desired steering vector can be estimated in closed form from a convex optimization problem by making use of the subspace principle. As this method is developed based on an extended version of the orthonormal PAST (OPAST), the steering vector can be recursively estimated with very low complexity and moving sources can be handled. To further improve the performance of beamforming, the uncertainty of the array covariance matrix is taken into account. Numerical results demonstrate that the proposed method performs well in the presence of uncertainties. © 2012 IEEE.published_or_final_versio

Electric Flux Tube in Magnetic Plasma

In this paper we study a methodical problem related to the magnetic scenario recently suggested and initiated by the authors \cite{Liao_ES_mono} to understand the strongly coupled quark-gluon plasma (sQGP): the electric flux tube in monopole plasma. A macroscopic approach, interpolating between Bose condensed (dual superconductor) and classical gas medium is developed first. Then we work out a microscopic approach based on detailed quantum mechanical calculation of the monopole scattering on electric flux tube, evaluating induced currents for all partial waves. As expected, the flux tube looses its stability when particles can penetrate it: we make this condition precise by calculating the critical value for the product of the flux tube size times the particle momentum, above which the flux tube dissolves. Lattice static potentials indicate that flux tubes seem to dissolve at $T>T_{dissolution} \approx 1.3 T_c$. Using our criterion one gets an estimate of the magnetic density $n\approx 4.4 \sim 6.6 fm^{-3}$ at this temperature.Comment: New version with new referecences added and minor changes. 15 pages, 8 figure

Multiplexing More Data Streams in the MU-MISO Downlink by Interference Exploitation Precoding

In this paper, we focus on the constructive interference (CI) precoding for the scenario when the number of streams simultaneously transmitted by the base station (BS) is larger than that of transmit antennas at the BS, and derive the optimal precoding structure by employing the pseudo inverse. We show that the optimal pre-scaling vector in IE precoding is equal to a linear combination of the right singular vectors that correspond to zero singular values of the coefficient matrix. By formulating the dual problem, we further show that the optimal precoding matrix can be expressed as a function of the dual variables in a closed form, and an equivalent quadratic programming (QP) formulation is derived for computational complexity reduction. Numerical results validate our analysis and demonstrate significant performance improvements for interference exploitation precoding in the considered scenario