A model explaining neutrino masses and the DAMPE cosmic ray electron excess

We propose a flavored $U(1)_{e\mu}$ neutrino mass and dark matter~(DM) model to explain the recent DArk Matter Particle Explorer (DAMPE) data, which feature an excess on the cosmic ray electron plus positron flux around 1.4 TeV. Only the first two lepton generations of the Standard Model are charged under the new $U(1)_{e\mu}$ gauge symmetry. A vector-like fermion $\psi$, which is our DM candidate, annihilates into $e^{\pm}$ and $\mu^{\pm}$ via the new gauge boson $Z'$ exchange and accounts for the DAMPE excess. We have found that the data favors a $\psi$ mass around 1.5~TeV and a $Z'$ mass around 2.6~TeV, which can potentially be probed by the next generation lepton colliders and DM direct detection experiments.Comment: 7 pages, 3 figures. V2: version accepted by Physics Letters

4-Chloro-N′-(2-hy­droxy-4-meth­oxy­benzyl­idene)benzohydrazide methanol monosolvate

The title compound, C15H13ClN2O3·CH3OH, was synthesized by the condensation reaction of 2-hy­droxy-4-meth­oxy­benzaldehyde with 4-chloro­benzohydrazide in methanol. The Schiff base mol­ecule displays a trans configuration with respect to the C=N and C—N bonds. The dihedral angle between the two benzene rings is 5.3 (2)°. In the crystal, mol­ecules are linked by N—H⋯O and O—H⋯O hydrogen-bond inter­actions into chains running parallel to the a axis. An intra­molecular O—H⋯N hydrogen bond is observed

Empirical Research on the Impact of Personalized Recommendation Diversity

Personalized recommendation has important implications in raising online shopping efficiency and increasing product sales. There has been wide interest in finding ways to provide more efficient personalized recommendations. Most existing studies focus on how to improve the accuracy of the recommendation algorithms, or are more concerned on ways to increase consumer satisfaction. Unlike these studies, our study focuses on the process of decision-making, using long tail theory as a basis, to reveal the mechanisms involved in consumers’ adoption of recommendations. This paper analyzes the effect of personalized recommendations from two angles: product sales and ratings, and tries to point out differences in consumer preferences between mainstream products and niche products, high rating products and low rating products, search products and experience products. The study verifies that consumers demand diversity in the recommended content, and also provides suggestions on how to better plan and operate a personalized recommendation system

VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding

Large-scale pre-trained models have achieved remarkable success in various computer vision tasks. A standard approach to leverage these models is to fine-tune all model parameters for downstream tasks, which poses challenges in terms of computational and storage costs. Recently, inspired by Natural Language Processing (NLP), parameter-efficient transfer learning has been successfully applied to vision tasks. However, most existing techniques primarily focus on single-task adaptation, and despite limited research on multi-task adaptation, these methods often exhibit suboptimal training and inference efficiency. In this paper, we first propose an once-for-all Vision Multi-Task Adapter (VMT-Adapter), which strikes approximately O(1) training and inference efficiency w.r.t task number. Concretely, VMT-Adapter shares the knowledge from multiple tasks to enhance cross-task interaction while preserves task-specific knowledge via independent knowledge extraction modules. Notably, since task-specific modules require few parameters, VMT-Adapter can handle an arbitrary number of tasks with a negligible increase of trainable parameters. We also propose VMT-Adapter-Lite, which further reduces the trainable parameters by learning shared parameters between down- and up-projections. Extensive experiments on four dense scene understanding tasks demonstrate the superiority of VMT-Adapter(-Lite), achieving a 3.96%(1.34%) relative improvement compared to single-task full fine-tuning, while utilizing merely ~1% (0.36%) trainable parameters of the pre-trained model.Comment: Accepted to AAAI202

A common origin of multi-messenger spectral anomaly of galactic cosmic rays

Recent observations of cosmic rays (CRs) have revealed a two-component anomaly in the spectra of primary and secondary particles, as well as their ratios, prompting investigation into their common origin. In this study, we incorporate the identification of slow diffusion zones around sources as a common phenomenon into our calculations, which successfully reproduces all previously described anomalies except for the positron spectrum. Crucially, our research offers a clear physical picture of the origin of CR: while high-energy ($\textrm{>200~GV}$, including the knee) particles are primarily produced by fresh accelerators and are confined to local regions, low energy ($\textrm{<200~GV}$) components come from distant sources and travel through the outer diffusive zone outside of the galactic disk. This scenario can be universally applied in the galactic disk, as evidenced by ultra-high energy diffuse $\rm\gamma$-ray emissions detected by the AS$\rm\gamma$ experiment. Furthermore, our results predict that the spectrum of diffuse $\rm\gamma$-ray is spatial-dependent, resting with local sources, which can be tested by LHAASO experiment.Comment: 9 pages, 7 figures, accepted by PhysRev