Phases of Chiral Gauge Theories

We discuss the behavior of two non-supersymmetric chiral SU(N) gauge theories, involving fermions in the symmetric and antisymmetric two-index tensor representations respectively. In addition to global anomaly matching, we employ a recently proposed inequality constraint on the number of effective low energy (massless) degrees of freedom of a theory, based on the thermodynamic free energy. Several possible zero temperature phases are consistent with the constraints. A simple picture for the phase structure emerges if these theories choose the phase, consistent with global anomaly matching, that minimizes the massless degree of freedom count defined through the free energy. This idea suggests that confinement with the preservation of the global symmetries through the formation of massless composite fermions is in general not preferred. While our discussion is restricted mainly to bilinear condensate formation, higher dimensional condensates are considered for one case. We conclude by commenting briefly on two related supersymmetric chiral theories.Comment: 23 pages, 2 figures, ReVTeX, improved forma

A Novel Dynamic Event-triggered Mechanism for Dynamic Average Consensus

This paper studies a challenging issue introduced in a recent survey, namely designing a distributed event-based scheme to solve the dynamic average consensus (DAC) problem. First, a robust adaptive distributed event-based DAC algorithm is designed without imposing specific initialization criteria to perform estimation task under intermittent communication. Second, a novel adaptive distributed dynamic event-triggered mechanism is proposed to determine the triggering time when neighboring agents broadcast information to each other. Compared to the existing event-triggered mechanisms, the novelty of the proposed dynamic event-triggered mechanism lies in that it guarantees the existence of a positive and uniform minimum inter-event interval without sacrificing any accuracy of the estimation, which is much more practical than only ensuring the exclusion of the Zeno behavior or the boundedness of the estimation error. Third, a composite adaptive law is developed to update the adaptive gain employed in the distributed event-based DAC algorithm and dynamic event-triggered mechanism. Using the composite adaptive update law, the distributed event-based solution proposed in our work is implemented without requiring any global information. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.Comment: 9 pages, 8 figure

A novel dynamic event-triggered mechanism for dynamic average consensus

This paper studies a challenging issue introduced in a recent survey, namely designing a distributed event-based scheme to solve the dynamic average consensus (DAC) problem. First, a robust adaptive distributed event-based DAC algorithm is designed without imposing specific initialization criteria to perform estimation task under intermittent communication. Second, a novel adaptive distributed dynamic event-triggered mechanism is proposed to determine the triggering time when neighboring agents broadcast information to each other. Compared to the existing event-triggered mechanisms, the novelty of the proposed dynamic event-triggered mechanism lies in that it guarantees the existence of a positive and uniform minimum inter-event interval without sacrificing any accuracy of the estimation, which is much more practical than only ensuring the exclusion of the Zeno behavior or the boundedness of the estimation error. Third, a composite adaptive law is developed to update the adaptive gain employed in the distributed event-based DAC algorithm and dynamic event-triggered mechanism. Using the composite adaptive update law, the distributed event-based solution proposed in our work is implemented without requiring any global information. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.</p

Adaptive Distributed Formation-Containment Control on Switching Directed Networks:A Dynamic Triggering Framework

This article addresses the time-varying formation-containment control problem for networked second-order systems with unknown nonlinear dynamics. The communication topology among agents is switching and directed. To simultaneously achieve formation configuration for leaders and accomplish containment behavior for followers, adaptive distributed formation and containment control schemes are developed. In the control framework design, the neural-network control technique is utilized to approximate the unknown nonlinear dynamics. The update frequency and computation resources of the controllers are reduced by dynamic triggering mechanisms. It is proved that no agent exhibits the Zeno behavior on the time-varying asymmetric communication topology. Moreover, no prior knowledge of global information is needed in controller and triggering mechanism implementations. All system parameters can be easily and flexibly chosen. Finally, numerical examples are presented to illustrate the effectiveness of the proposed control schemes.</p

Electroweak Physics for Color Superconductivity

We construct the effective theories describing the electroweak interactions for the low energy excitations associated with the color superconductive phases of QCD at high matter density. The main result, for the 3 flavor case, is that the quasiparticle Goldstone boson $\pi^0$ decay into two physical massless photons is identical to the zero density case once we use the new Goldstone decay constant and the modified electric charge $\widetilde{e}=e \cos\theta$, with $\tan\theta =2e/\sqrt{3}g_s$ and $g_s$ the strong coupling constant. For 2 flavors we find that the coupling of the quarks to the neutral vector boson $Z^0$ is modified with respect to the zero density case. We finally point out possible applications of our result to the physics of compact objects.Comment: 23 pages, 1 Figure, RevTex. More discussion and references adde

Application of Fast Deviation Correction Algorithm Based on Shape Matching Algorithm in Component Placement

For contradiction PC template matching between accuracy and speed, combined with the advantages of FPGA high speed parallel computing. This paper presents a FPGA-based rapid correction shape matching algorithm. Mainly in the FPGA, using shape matching and least squares method to calculate the angular deviation chip components. Use single instruction stream algorithm acceleration. Experimental results show that compared with traditional PC template matching algorithms, this algorithm to further improve the correction accuracy and greatly reducing correction time. And SMT machine vision correction can be obtained in a stable and efficient use

Low Energy Theory for 2 flavors at High Density QCD

We construct the effective Lagrangian describing the low energy excitations for Quantum Chromodynamics with two flavors at high density. The non-linear realization framework is employed to properly construct the low energy effective theory. The light degrees of freedom, as required by 't Hooft anomaly conditions, contain massless fermions which we properly include in the effective Lagrangian. We also provide a discussion of the linearly realized Lagrangian.Comment: 17 pages, RevTeX format, references added. To appear in Phys. Rev.

HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding

Object categories are typically organized into a multi-granularity taxonomic hierarchy. When classifying categories at different hierarchy levels, traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios. Recent studies integrating Vision-Language Models (VLMs) with class hierarchies have shown promise, yet they fall short of fully exploiting the hierarchical relationships. These efforts are constrained by their inability to perform effectively across varied granularity of categories. To tackle this issue, we propose a novel framework (HGCLIP) that effectively combines CLIP with a deeper exploitation of the Hierarchical class structure via Graph representation learning. We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category. After passing through a graph encoder, the textual features incorporate hierarchical structure information, while the image features emphasize class-aware features derived from prototypes through the attention mechanism. Our approach demonstrates significant improvements on 11 diverse visual recognition benchmarks. Our codes are fully available at https://github.com/richard-peng-xia/HGCLIP

DM-MIMO: Diffusion Models for Robust Semantic Communications over MIMO Channels

This paper investigates robust semantic communications over multiple-input multiple-output (MIMO) fading channels. Current semantic communications over MIMO channels mainly focus on channel adaptive encoding and decoding, which lacks exploration of signal distribution. To leverage the potential of signal distribution in signal space denoising, we develop a diffusion model over MIMO channels (DM-MIMO), a plugin module at the receiver side in conjunction with singular value decomposition (SVD) based precoding and equalization. Specifically, due to the significant variations in effective noise power over distinct sub-channels, we determine the effective sampling steps accordingly and devise a joint sampling algorithm. Utilizing a three-stage training algorithm, DM-MIMO learns the distribution of the encoded signal, which enables noise elimination over all sub-channels. Experimental results demonstrate that the DM-MIMO effectively reduces the mean square errors (MSE) of the equalized signal and the DM-MIMO semantic communication system (DM-MIMO-JSCC) outperforms the JSCC-based semantic communication system in image reconstruction

Selective protein degradation through tetrazine ligation of genetically incorporated unnatural amino acids

Small molecule‐responsive tags for targeted protein degradation are valuable tools for fundamental research and drug target validation. Here, we show that genetically incorporated unnatural amino acids bearing a strained alkene or alkyne functionality can act as a minimalist tag for targeted protein degradation. Specifically, we observed the degradation of strained alkene‐ or alkyne‐containing kinases and E2 ubiquitin‐conjugating enzymes upon treatment with hydrophobic tetrazine conjugates. The extent of the induced protein degradation depends on the identity of the target protein, unnatural amino acid, and tetrazine conjugate, as well as the site of the unnatural amino acid in the target protein. Mechanistic studies revealed proteins undergo proteasomal degradation after tetrazine tethering, and the identity of tetrazine conjugates influences the dependence of ubiquitination on protein degradation. This work provides an alternative approach for targeted protein degradation and mechanistic insight, facilitating the future development of more effective targeted protein degradation strategies