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Contrastive learning (CL) has been widely investigated with various learning mech- anisms and achieves strong capability in learning representations of data in a self-supervised manner using unlabeled data. A common fashion of contrastive learning on this line is employing large-sized encoders to achieve comparable performance as the supervised learning counterpart. Despite the success of the labelless training, current contrastive learning algorithms failed to achieve good performance with lightweight (compact) models, e.g., MobileNet, while the re- quirements of the heavy encoders impede the energy-efficient computation, espe- cially for resource-constrained AI applications. Motivated by this, we propose a new self-supervised CL scheme, named SACL-XD, consisting of two technical components, Slimmed Asymmetrical Contrastive Learning (SACL) and Cross- Distillation (XD), which collectively enable efficient CL with compact models. While relevant prior works employed a strong pre-trained model as the teacher of unsupervised knowledge distillation to a lightweight encoder, our proposed method trains CL models from scratch and outperforms them even without such an expensive requirement. Compared to the SoTA lightweight CL training (dis- tillation) algorithms, SACL-XD achieves 1.79% ImageNet-1K accuracy improve- ment on MobileNet-V3 with 64⇥ training FLOPs reduction. Code is available at https://github.com/mengjian0502/SACL-XD. 

We apply the joint threshold and transverse momentum dependent (TMD) factorization theorem to introduce new threshold-TMD distribution functions, including threshold-TMD parton distribution functions (PDFs) and fragmentation functions (FFs). We apply Soft-Collinear Effective Theory and renormalization group methods to carry out QCD evolution for both threshold-TMD PDFs and FFs. We show the universality of threshold-TMD functions among three standard processes, i.e. the Drell-Yan production inppcollisions, semi-inclusive deep-inelastic scattering and back-to-back two hadron production ine⁺e⁻collisions. In the end, we present the numerical predictions for different threshold-TMD functions and also transverse momentum distributions atpp,ep, ande⁺e⁻collisions.

 

CsYbSe2 has an ideal triangular-lattice geometry with pronounced two-dimensionality, pseudospin-1/2 nature, and the absence of structural disorder. These excellent characteristics favor a quantum spin-liquid realization in this material. In this work, we applied quasihydrostatic compression methods to explore the structural behaviors. Our study reveals that CsYbSe2 undergoes a structural transition around 24 GPa, accompanied by a large volume collapse of ΔV /V0∼13%. The ambient hexagonal structure with the space group P63/mmcis lowered to the tetragonal structure (P4/mmm) under high pressure. Meanwhile, the color of CsYbSe2 changes gradually from red to black before the transition. Dramatic pressure-induced changes are clarified by the electronic structure calculations from the first principles, which indicate that the initial insulating ground state turns metallic in a squeezed lattice. These findings highlight Yb-based dichalcogenide delafossites as an intriguing material to probe novel quantum effects under high pressure. 

Topological mosaic pattern (TMP) can be formed in two-dimensional (2D) moiré superlattices, a set of periodic and spatially separated domains with distinct topologies that give rise to periodic edge states on the domain walls. In this study, we demonstrate that these periodic edge states play a crucial role in determining global topological properties. By developing a continuum model for periodic edge states with 𝐶6⁢𝑧 and 𝐶3⁢𝑧 rotational symmetry, we predict that a global topological phase transition at the charge neutrality point (CNP) can be driven by the size of domain walls and moiré period. The Wannier representation analysis reveals that these periodic edge states are fundamentally chiral 𝑝𝑥±𝑖⁢𝑝𝑦 orbitals. The interplay between on-site chiral orbital rotation and neighboring hopping among chiral orbitals leads to band inversion and a topological phase transition. Our work establishes a general model for tuning local and global topological phases, paving the way for future research on strongly correlated topological flat minibands within topological mosaic patterns.