arXiv subjects

Ping Song

Publications and source records attributed to Ping Song.

4 recordsLinked to original sources

arXiv2025

Strain energy enhanced room-temperature magnetocaloric effect in second-order magnetic transition materials

Large magnetic entropy change (deltaSM) can realize a prominent heat transformation under the magnetic field and directly strengthen the efficacy of the magnetocaloric effect, which provides a pioneering environmentally friendly solid-state strategy to improve refrigeration capacities and efficiencies. The second-order magnetic transition (SOMT) materials have broader deltaSM peaks without thermal hysteresis compared with most first-order magnetic transition materials, making them highly attractive in magnetic refrigeration, especially in the room temperature range. Here, we report a significant enhancement of deltaSM at room temperature in single-crystal Mn5Ge3. In this SOMT system, we realize a 60% improvement of -deltaSM from 3.5 J/kgK to 5.6 J/kgK at T = 300K. This considerable enhancement of deltaSM is achieved by intentionally introducing strain energy through high-pressure constrained deformation. Both experimental results and Monte Carlo simulations demonstrate that the enhancement of deltaSM originates from the microscopic strain and lattice deformation induced by strain energy after deformation. This strain energy will reconstruct the energy landscape of this ferromagnetic system and enhance magnetization, resulting in a giant intensity of magnetocaloric responses. Our findings provide an approach to increase magnetic entropy change and may give fresh ideas for exploring advanced magnetocaloric materials.

cond-mat.mtrl-sci↗

arXiv2024

MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding

We introduce MUSE-VL, a Unified Vision-Language Model through Semantic discrete Encoding for multimodal understanding and generation. Recently, the research community has begun exploring unified models for visual generation and understanding. However, existing vision tokenizers (e.g., VQGAN) only consider low-level information, which makes it difficult to align with language tokens. This results in high training complexity and necessitates a large amount of training data to achieve optimal performance. Additionally, their performance is still far from dedicated understanding models. This paper proposes Semantic Discrete Encoding (SDE), which effectively aligns the information of visual tokens and language tokens by adding semantic constraints to the visual tokenizer. This greatly reduces the amount of training data and improves the performance of the unified model. With the same LLM size, our method improved the understanding performance by 4.8% compared to the previous SOTA Emu3 and surpassed the dedicated understanding model LLaVA-NeXT 34B by 3.7%. Our model also surpasses the existing unified models on visual generation benchmarks.

cs.CV↗

arXiv2022

Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization

Open-World Instance Segmentation (OWIS) is an emerging research topic that aims to segment class-agnostic object instances from images. The mainstream approaches use a two-stage segmentation framework, which first locates the candidate object bounding boxes and then performs instance segmentation. In this work, we instead promote a single-stage framework for OWIS. We argue that the end-to-end training process in the single-stage framework can be more convenient for directly regularizing the localization of class-agnostic object pixels. Based on the single-stage instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss. We show that such a consistency loss could alleviate the problem of incomplete instance annotation -- a common problem in the existing OWIS datasets. We also show that the proposed loss lends itself to an effective solution to semi-supervised OWIS that could be considered an extreme case that all object annotations are absent for some images. Our extensive experiments demonstrate that the proposed method achieves impressive results in both fully-supervised and semi-supervised settings. Compared to SOTA methods, the proposed method significantly improves the $AP_{100}$ score by 4.75\% in UVO$\rightarrow$UVO setting and 4.05\% in COCO$\rightarrow$UVO setting. In the case of semi-supervised learning, our model learned with only 30\% labeled data, even outperforms its fully-supervised counterpart with 50\% labeled data. The code will be released soon.

cs.CV↗

arXiv2019

Broadband and wide-temperature-range thermal emitter with super-hydrophobicity based on oxidized high-entropy film

This paper demonstrates broadband and wide-temperature-range thermal emitter with super-hydrophobicity based on an oxidized high-entropy film. The annealed (NiCuCrFeSi)O high-entropy film can serve as a mid-infrared emitter due to its three characteristics: i) High blackbody-like emission in a broadband mid-infrared region from 2.5 um to 20 um; ii) High emittance (more than 90%) in a wide temperature range from 300 K to 1000 K; iii) Super-hydrophobicity with high contact angle (CA) of 151 degree and low title angle (TA) of 5 degree. These characteristics originate from the recrystallization of as-deposited film and formation of cavernous structure with a plethora of nanoscale polyhedrons and holes after an annealing process, supported by the results of XRD and SEM/EDX. This work may provide new ideas for applications of thermal emitters operating at substantially wide temperature range.

physics.app-ph↗