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As technological advancements continue to expand the capabilities of multi unmanned-aerial-vehicle systems (mUAV), human operators face challenges in scalability and efficiency due to the complex cognitive load and operations associated with motion adjustments and team coordination. Such cognitive demands limit the feasible size of mUAV teams and necessitate extensive operator training, impeding broader adoption. This paper developed a Hand Gesture Based Interactive Control (HGIC), a novel interface system that utilize computer vision techniques to intuitively translate hand gestures into modular commands for robot teaming. Through learning control models, these commands enable efficient and scalable mUAV motion control and adjustments. HGIC eliminates the need for specialized hardware and offers two key benefits: 1) Minimal training requirements through natural gestures; and 2) Enhanced scalability and efficiency via adaptable commands. By reducing the cognitive burden on operators, HGIC opens the door for more effective large-scale mUAV applications in complex, dynamic, and uncertain scenarios. HGIC will be open-sourced after the paper being published online for the research community, aiming to drive forward innovations in human-mUAV interactions.

Network speeds grow quickly in the modern cloud, so SmartNICs are introduced to offload network processing tasks, even application logic. However, typical multicore SmartNICs such as BlueFiled-2 are only capable of processing control-plane tasks with their embedded CPU that has limited memory bandwidth and computing power. On the other hand, hot cloud applications evolve, such that a limited number of fixed hardware engines in a SmartNIC cannot satisfy the requirements of cloud applications. Therefore, SmartNIC programmers call for a programmable datapath accelerator (DPA) to process network traffic at line rate. However, no existing work has unveiled the performance characteristics of the existing DPA. To this end, we present the first architectural characterization of the latest DPA-enhanced BF3 SmartNIC. Our evaluation results indicate that BF3's DPA is much wimpier than the off-path Arm and the host CPU. However, we still identify that DPA has three unique architectural characteristics that unleash the performance potential of DPA. Specifically, we demonstrate how to take advantage of DPA's three architectural characteristics regarding computing, networking, and memory subsystems. Then we propose three important guidelines for programmers to fully unleash the potential of DPA. To demonstrate the effectiveness of our approach, we conduct detailed case studies regarding each guideline. Our case study on key-value aggregation service achieves up to 4.3$\times$ higher throughput by using our guidelines to optimize memory combinations.

We investigate the chiral phase transition within a sphere under a uniform background magnetic field. The Nambu--Jona-Lasinio (NJL) model is employed and the MIT boundary condition is imposed for the spherical confinement. Using the wave expansion method, the diagonalizable Hamiltonian and energy spectrum are derived for the system. By solving the gap equation in the NJL model, the influence of magnetic field on quark matter in a sphere is studied. It is found that inverse magnetic catalysis occurs at small radii, while magnetic catalysis occurs at large radii. Additionally, both magnetic catalysis and inverse magnetic catalysis are observed at the intermediate radii ($R\approx4$ fm).

We propose a method to calculate the $q\bar{q}$ light front wave functions (LFWFs) of photon at low-virtuality, i.e., the light front amplitude of $\gamma^*\rightarrow q\bar{q}$ at low $Q^2$, based on a light front projection approach. We exemplify this method using a contact interaction model within Dyson-Schwinger equations formalism and obtain the nonperturbative photon $q\bar{q}$ LFWFs. In this case, we find the nonperturbative effects are encoded in the enhanced quark mass and a dressing function of covariant quark-photon vertex, as compared to the leading order quantum electrodynamics photon $q\bar{q}$ LFWFs. We then use nonperturbative-effect modified photon $q\bar{q}$ LFWFs to study the inclusive deep inelastic scattering HERA data in the framework of the color dipole model. The results demonstrate that the theoretical description of data at low $Q^2$ can be significantly improved once the nonperturbative corrections are included in the photon LFWFs., Comment: 11 pages, 4 figures

The famous partition theorem of Euler states that partitions of $n$ into distinct parts are equinumerous with partitions of $n$ into odd parts. Another famous partition theorem due to MacMahon states that the number of partitions of $n$ with all parts repeated at least once equals the number of partitions of $n$ where all parts must be even or congruent to $3 \pmod 6$. These partition theorems were further extended by Glaisher, Andrews, Subbarao, Nyirenda and Mugwangwavari. In this paper, we utilize the Chinese remainder theorem to prove a comprehensive partition theorem that encompasses all existing partition theorems. We also give a natural generalization of Euler's theorem based on a special complete residue system. Furthermore, we establish interesting congruence connections between the partition function $p(n)$ and related partition functions.

Multimodal data, which can comprehensively perceive and recognize the physical world, has become an essential path towards general artificial intelligence. However, multimodal large models trained on public datasets often underperform in specific industrial domains. This paper proposes a multimodal federated learning framework that enables multiple enterprises to utilize private domain data to collaboratively train large models for vertical domains, achieving intelligent services across scenarios. The authors discuss in-depth the strategic transformation of federated learning in terms of intelligence foundation and objectives in the era of big model, as well as the new challenges faced in heterogeneous data, model aggregation, performance and cost trade-off, data privacy, and incentive mechanism. The paper elaborates a case study of leading enterprises contributing multimodal data and expert knowledge to city safety operation management , including distributed deployment and efficient coordination of the federated learning platform, technical innovations on data quality improvement based on large model capabilities and efficient joint fine-tuning approaches. Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management. The established federated learning cooperation ecosystem is expected to further aggregate industry, academia, and research resources, realize large models in multiple vertical domains, and promote the large-scale industrial application of artificial intelligence and cutting-edge research on multimodal federated learning.