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We investigate the implications of the quantized vectorial and axial charges in the lattice Hamiltonian of multi-flavor staggered fermions in $(1+1)$ dimensions. These lattice charges coincide with those of the $U(1)_V$ and $U(1)_A$ global symmetries of Dirac fermions in the continuum limit, whose perturbative chiral anomaly matches the non-Abelian Onsager algebra on the lattice. In this note, we focus on the lattice models that flow to continuum quantum field theories of Dirac fermions that are free from the perturbative chiral anomaly between $U(1)_V$ and $U(1)_A$. In a lattice model that flows to two Dirac fermions, we identify quadratic Hamiltonian deformations that can gap the system while fully preserving both the vectorial and axial charges on the lattice. These deformations flow to the usual symmetry-preserving Dirac mass terms in the continuum. Additionally, we propose a lattice model that flows to the chiral fermion $3-4-5-0$ model in the continuum by using these lattice charges, and we discuss the multi-fermion interactions that can generate a mass gap in the paradigm of symmetric mass generation., Comment: 28 pages; comments and suggestions welcome

This study aims to exploit the analogy of vortex dynamics in a 2D ideal fluid and 2D non-neutral plasma. Numerical simulations using contour dynamics with adaptive refinement are conducted to study the dynamics of one or more vortices within a background vortex, and results are compared with experiments using magnetized non-neutral electron plasmas confined in a Penning-Malmberg trap. This comparison is based on the isomorphism between the 2D Euler equations for ideal fluid and the 2D drift-Poisson equations for non-neutral plasma, where the the fluid vorticity and stream function correspond to the plasma charge density and electric potential. Agreement and discrepancy between simulation and experiment are observed and discussed.

Text-based person search aims to retrieve the matched pedestrians from a large-scale image database according to the text description. The core difficulty of this task is how to extract effective details from pedestrian images and texts, and achieve cross-modal alignment in a common latent space. Prior works adopt image and text encoders pre-trained on unimodal data to extract global and local features from image and text respectively, and then global-local alignment is achieved explicitly. However, these approaches still lack the ability of understanding visual details, and the retrieval accuracy is still limited by identity confusion. In order to alleviate the above problems, we rethink the importance of visual features for text-based person search, and propose VFE-TPS, a Visual Feature Enhanced Text-based Person Search model. It introduces a pre-trained multimodal backbone CLIP to learn basic multimodal features and constructs Text Guided Masked Image Modeling task to enhance the model's ability of learning local visual details without explicit annotation. In addition, we design Identity Supervised Global Visual Feature Calibration task to guide the model learn identity-aware global visual features. The key finding of our study is that, with the help of our proposed auxiliary tasks, the knowledge embedded in the pre-trained CLIP model can be successfully adapted to text-based person search task, and the model's visual understanding ability is significantly enhanced. Experimental results on three benchmarks demonstrate that our proposed model exceeds the existing approaches, and the Rank-1 accuracy is significantly improved with a notable margin of about $1\%\sim9\%$. Our code can be found at https://github.com/zhangweifeng1218/VFE_TPS.

We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language modeling and diffusion approaches to support two key music creation workflows: controlled music generation and post-production editing. For controlled music generation, our system enables vocal music generation with performance controls from multi-modal inputs, including style descriptions, audio references, musical scores, and voice prompts. For post-production editing, it offers interactive tools for editing lyrics and vocal melodies directly in the generated audio. We encourage readers to listen to demo audio examples at https://team.doubao.com/seed-music "https://team.doubao.com/seed-music"., Comment: Seed-Music technical report, 20 pages, 5 figures

We discuss heavy particles that can be used to pin down the faithful Standard Model (SM) gauge group and their patterns in the SM effective field theory (SMEFT). These heavy particles are not invariant under a specific $\mathbb{Z}_6$ subgroup of $SU(3)_c\times SU(2)_L \times U(1)_Y$, which however acts trivially on all the SM particles, hence the faithful SM gauge group remains undetermined. Different realizations of the faithful SM gauge group correspond to different spectra of heavy particles, and they also correspond to distinct sets of line operators with one-form global symmetry acting on them. We show that the heavy particles not invariant under the $\mathbb{Z}_6$ group cannot appear in tree-level ultraviolet completions of SMEFT, this enforces us to consider one-loop UV completions of SMEFT to identify the $\mathbb{Z}_6$ non-invariant heavy particles. We demonstrate with examples that correlations between Wilson coefficients provide an efficient way to examine models with $\mathbb{Z}_6$ non-invariant heavy particles. Finally, we prove that all the scalars that can trigger electroweak symmetry breaking must be invariant under the $\mathbb{Z}_6$ group, hence they cannot be used to probe the faithful SM gauge group., Comment: v3: JHEP version, the original title "The Standard Model Gauge Group, SMEFT, and Generalized Symmetries" is adjusted in the published version, some improvements

Multi-modality magnetic resonance imaging data with various sequences facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC). The lack of publicly available, comprehensive datasets limits advancements in diagnosis, treatment planning, and the development of machine learning algorithms for NPC. Addressing this critical need, we introduce the first comprehensive NPC MRI dataset, encompassing MR axial imaging of 277 primary NPC patients. This dataset includes T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences, totaling 831 scans. In addition to the corresponding clinical data, manually annotated and labeled segmentations by experienced radiologists offer high-quality data resources from untreated primary NPC.

Our recent works revisit the proof of chiral symmetry breaking in the confining phase of four-dimensional QCD-like theories, i.e. $SU(N_c)$ gauge theories with $N_f$ flavors of vectorlike quarks in the fundamental representation. The analysis relies on the structure of 't Hooft anomaly matching and persistent mass conditions for theories with same $N_c$ and different $N_f$. In this paper, we work out concrete examples with $N_c=3$ and $N_c=5$ to support and elucidate the results in the companion papers. Within the same examples, we also test some claims made in earlier works., Comment: 53 pages, many equations, companion paper of arXiv:hep-th/2212.02930 and another paper that appears simultaneously

We demonstrate that chiral symmetry breaking occurs in the confining phase of QCD-like theories with $N_c$ colors and $N_f$ flavors. Our proof is based on a novel strategy, called `downlifting', by which solutions of the 't Hooft anomaly matching and persistent mass conditions for a theory with $N_f-1$ flavors are constructed from those of a theory with $N_f$ flavors, while $N_c$ is fixed. By induction, chiral symmetry breaking is proven for any $N_f\geq p_{min}$, where $p_{min}$ is the smallest prime factor of $N_c$. The proof can be extended to $N_f

When legged robots physically interact with obstacles in applications such as search and rescue through rubble and planetary exploration across Martain rocks, even the most advanced ones struggle because they lack a fundamental framework to model the robot-obstacle physical interaction paralleling artificial potential fields for obstacle avoidance. To remedy this, recent studies established a novel framework - potential energy landscape modeling - that explains and predicts the destabilizing transitions across locomotor modes from the physical interaction between robots and obstacles, and governs a wide range of complex locomotion. However, this framework was confined to the laboratory because we lack methods to obtain the potential energy landscape in unknown environments. Here, we explore the feasibility of introducing this framework to such environments. We showed that a robot can reconstruct the potential energy landscape for unknown obstacles by measuring the obstacle contact forces and resulting torques. To elaborate, we developed a minimalistic robot capable of sensing contact forces and torques when propelled against a pair of grass-like obstacles. Despite the forces and torques not being fully conservative, they well-matched the potential energy landscape gradients, and the reconstructed landscape well-matched ground truth. In addition, we found that using normal forces and torques and head oscillation inspired by cockroach observations further improved the estimation of conservative ones. Our study will finally inspire free-running robots to achieve low-effort, "zero-shot" traversing clustered, large obstacles in real-world applications by sampling contact forces and torques and reconstructing the landscape around its neighboring states in real time.