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1. Energy-momentum tensor densities in the bag model

   https://doi.org/10.22323/1.352.0210  

   Neubelt, M. J.; Sampino, A.; Hudson, J.; Tezgin, Kemal; Schweitzer, P. (July 2019, XXVII International Workshop on Deep-Inelastic Scattering and Related Subjects (DIS2019))

   The form factors of the energy-momentum tensor can be accessed via studies of generalized parton distributions in hard exclusive reactions. In this talk we present recent results on the energy-momentum tensor form factors and densities in the bag model formulated in the large-Nc limit. The simplicity and lucidity of this quark model allow us to investigate many general concepts which have recently attracted interest, including pressure, shear forces and angular momentum density inside the nucleon. The results from the bag model are theoretically consistent, and comply with all general requirements. 

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2. On the Conservation of Turbulence Energy in Turbulence Transport Models

   https://doi.org/10.3847/1538-4357/ac596e  

   Wang, B.-B.; Zank, G. P.; Adhikari, L.; Zhao, L.-L. (April 2022, The Astrophysical Journal)

   Abstract Zank et al. developed models describing the transport of low-frequency incompressible and nearly incompressible turbulence in inhomogeneous flows. The formalism was based on expressing the fluctuating variables in terms of the Elsässar variables and then taking “moments” subject to various closure hypotheses. The turbulence transport models are different according to whether the plasma beta regime is large, of order unity, or small. Here, we show explicitly that the three sets of turbulence transport models admit a conservation representation that resembles the well-known WKB transport equation for Alfvén wave energy density after introducing appropriate definitions of the “pressure” associated with the turbulent fluctuations. This includes introducing a distinct turbulent pressure tensor for 3D incompressible turbulence (the large plasma beta limit) and pressure tensors for quasi-2D and slab turbulence (the plasma beta order-unity or small regimes) that generalize the form of the WKB pressure tensor. Various limits of the different turbulent pressure tensors are discussed. However, the analogy between the conservation form of the turbulence transport models and the WKB model is not close for multiple reasons, including that the turbulence models express fully nonlinear physical processes unlike the strictly linear WKB description. The analysis presented here both serves as a check on the validity and correctness of the turbulence transport models and also provides greater transparency of the energy dissipation term and the “turbulent pressure” in our models, which is important for many practical applications. 

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3. Tensor category describing anyons in the quantum Hall effect and quantization of conductance

   https://doi.org/10.1142/S0129055X24610075  

   Bachmann, Sven; Corbelli, Matthew; Fraas, Martin; Ogata, Yoshiko (May 2025, Reviews in Mathematical Physics)

   In this study, we examine the quantization of Hall conductance in an infinite plane geometry. We consider a microscopic charge-conserving system with a pure, gapped infinite-volume ground state. While Hall conductance is well-defined in this scenario, existing proofs of its quantization have relied on assumptions of either weak interactions, or properties of finite volume ground state spaces, or invertibility. Here, we assume that the conditions necessary to construct the braided [Formula: see text]-tensor category which describes anyonic excitations are satisfied, and we demonstrate that the Hall conductance is rational if the tensor category is finite. 

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4. Tensor Slices: FPGA Building Blocks For The Deep Learning Era

   https://doi.org/10.1145/3529650  

   Arora, Aman; Ghosh, Moinak; Mehta, Samidh; Betz, Vaughn; John, Lizy K. (December 2022, ACM Transactions on Reconfigurable Technology and Systems)

   FPGAs are well-suited for accelerating deep learning (DL) applications owing to the rapidly changing algorithms, network architectures and computation requirements in this field. However, the generic building blocks available on traditional FPGAs limit the acceleration that can be achieved. Many modifications to FPGA architecture have been proposed and deployed including adding specialized artificial intelligence (AI) processing engines, adding support for smaller precision math like 8-bit fixed point and IEEE half-precision (fp16) in DSP slices, adding shadow multipliers in logic blocks, etc. In this paper, we describe replacing a portion of the FPGA’s programmable logic area with Tensor Slices. These slices have a systolic array of processing elements at their heart that support multiple tensor operations, multiple dynamically-selectable precisions and can be dynamically fractured into individual multipliers and MACs (multiply-and-accumulate). These slices have a local crossbar at the inputs that helps with easing the routing pressure caused by a large block on the FPGA. Adding these DL-specific coarse-grained hard blocks to FPGAs increases their compute density and makes them even better hardware accelerators for DL applications, while still keeping the vast majority of the real estate on the FPGA programmable at fine-grain. 

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5. A magnetic analog of pressure–strain interaction

   https://doi.org/10.1063/5.0241603  

   Barbhuiya, M_Hasan; Cassak, P_A (December 2024, Physics of Plasmas)

   We study the evolution equation for magnetic energy density for a non-relativistic magnetized plasma in the (Lagrangian) reference frame comoving with the electron bulk velocity. Analyzing the terms that arise due to the ideal electric field, namely, perpendicular electron compression and magnetic field line bending, we recast them to reveal a quantity with a functional form analogous to the often-studied pressure–strain interaction term that describes one piece of internal energy density evolution of the species in a plasma, except with the species pressure tensor replaced by the magnetic stress tensor. We dub it the “magnetic stress–strain interaction.” We discuss decompositions of the magnetic stress–strain interaction analogous to those used for pressure–strain interaction. These analogies facilitate the interpretation of the evolution of the various forms of energy in magnetized plasmas and should be useful for a wide array of applications, including magnetic reconnection, turbulence, collisionless shocks, and wave–particle interactions. We display and analyze all the terms that can change magnetic energy density in the Lagrangian reference frame of the electrons using a particle-in-cell simulation of magnetic reconnection. 

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