Energy-Momentum Tensor and Related Experimental Analysis of Electromagnetic Waves in Media

We find that the energy-momentum tensor of electromagnetic waves in media is very similar to that of ordinary fluids, and concepts such as density, pressure, and energy transfer rate can be similarly defined. On this basis, we conducted a detailed theoretical analysis on the mean momentum and equivalent mass of photons in the medium, the relationship between pressure and polarization of beams, the influence of polarization energy and magnetization energy of the medium, the Bernoulli effect of beams and the energy-momentum tensor of beams in moving media. We also obtain a conservation new energy-momentum tensor based on the interaction term between the electromagnetic field and the medium. From this energy-momentum tensor, we can derive both the Minkowski momentum and the Abraham momentum simultaneously. We find that Minkowski momentum is actually a canonical momentum that considers the influence of the interaction between electromagnetic waves and media, while Abraham momentum is actually a mechanical momentum that does not consider the influence of the interaction between electromagnetic waves and media. Based on the theory obtained in this paper, we have provided theoretical explanations for Jones'experiment of light pressure in a medium, Ashkin's free liquid surface deformation experiment, Weilong's optical fiber deformation experiment, and frequency shift measurement experiment. The theory obtained in this paper can self-consistently explain the above experiments simultaneously. Unlike the Minkowski and Abraham tensors, according to the energy-momentum tensor proposed in this paper, a beam in a medium also generates a pressure on its side, and the direction of this pressure is related to the polarization of the beam. The findings of this paper may shed new light on the application of light.Comment: Page 29, Figure

InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion

This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most existing research on HOI synthesis lacks comprehensive whole-body interactions with dynamic objects, e.g., often limited to manipulating small or static objects. Our task is significantly more challenging, as it requires modeling dynamic objects with various shapes, capturing whole-body motion, and ensuring physically valid interactions. To this end, we propose InterDiff, a framework comprising two key steps: (i) interaction diffusion, where we leverage a diffusion model to encode the distribution of future human-object interactions; (ii) interaction correction, where we introduce a physics-informed predictor to correct denoised HOIs in a diffusion step. Our key insight is to inject prior knowledge that the interactions under reference with respect to contact points follow a simple pattern and are easily predictable. Experiments on multiple human-object interaction datasets demonstrate the effectiveness of our method for this task, capable of producing realistic, vivid, and remarkably long-term 3D HOI predictions.Comment: ICCV 2023; Project Page: https://sirui-xu.github.io/InterDiff

Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation

Resource-constrained perception systems such as edge computing and vision-for-robotics require vision models to be both accurate and lightweight in computation and memory usage. While knowledge distillation is a proven strategy to enhance the performance of lightweight classification models, its application to structured outputs like object detection and instance segmentation remains a complicated task, due to the variability in outputs and complex internal network modules involved in the distillation process. In this paper, we propose a simple yet surprisingly effective sequential approach to knowledge distillation that progressively transfers the knowledge of a set of teacher detectors to a given lightweight student. To distill knowledge from a highly accurate but complex teacher model, we construct a sequence of teachers to help the student gradually adapt. Our progressive strategy can be easily combined with existing detection distillation mechanisms to consistently maximize student performance in various settings. To the best of our knowledge, we are the first to successfully distill knowledge from Transformer-based teacher detectors to convolution-based students, and unprecedentedly boost the performance of ResNet-50 based RetinaNet from 36.5% to 42.0% AP and Mask R-CNN from 38.2% to 42.5% AP on the MS COCO benchmark.Comment: ICML 202

MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information-based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rote memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities

Human Papillomaviruses and Papillomatosis Lesions of the Female Lower Genital Tract

Objective: The objective of this study was to determine whether human papillomavirus (HPV) infections are involved in the development of papillomatosis lesions of the lower female genital tract

Aligning Large Multimodal Models with Factually Augmented RLHF

Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounded by the multimodal information in context. To address the multimodal misalignment issue, we adapt the Reinforcement Learning from Human Feedback (RLHF) from the text domain to the task of vision-language alignment, where human annotators are asked to compare two responses and pinpoint the more hallucinated one, and the vision-language model is trained to maximize the simulated human rewards. We propose a new alignment algorithm called Factually Augmented RLHF that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options, which alleviates the reward hacking phenomenon in RLHF and further improves the performance. We also enhance the GPT-4-generated training data (for vision instruction tuning) with previously available human-written image-text pairs to improve the general capabilities of our model. To evaluate the proposed approach in real-world scenarios, we develop a new evaluation benchmark MMHAL-BENCH with a special focus on penalizing hallucinations. As the first LMM trained with RLHF, our approach achieves remarkable improvement on the LLaVA-Bench dataset with the 94% performance level of the text-only GPT-4 (while previous best methods can only achieve the 87% level), and an improvement by 60% on MMHAL-BENCH over other baselines. We opensource our code, model, data at https://llava-rlhf.github.io.Comment: Preprin

Establishment and characterization of immortalized human eutopic endometrial stromal cells.

PROBLEM(#br)The application of primary eutopic endometrial cells from endometriosis patients in research is restricted for short life span, dedifferentiation of hormone responsiveness.(#br)METHOD OF STUDY(#br)Human telomerase reverse transcriptase (hTERT)-induced immortalized cells (iheESCs) were infected by lentivirus. mRNA level was examined by qRT-PCR, and protein expression was quantified by Western blot. CCK-8 and EdU assay were assigned to assess the proliferation. The migration and invasion of cells were assessed by transwell assay. Clone formation assay and nude mouse tumorigenicity assay were used to evaluate colony-formation and tumorigenesis abilities.(#br)RESULTS(#br)hTERT mRNA and protein were significantly expressed higher in iheESCs compared to primary cells. iheESCs grew without morphological change for 42 passages which is much longer than 18 passages of primary cells. There was no obvious difference between primary cells and iheESCs in growth, mobility, and chromosome karyotype. Furthermore, the expression of epithelial-mesenchymal transition (EMT) markers and estrogen/progesterone receptors remained unchanged. The decidualization of iheESCs could be induced by progesterone and cAMP. Estrogen increased the proliferation and mobility of iheESCs, and lipopolysaccharides (LPS) induced the IL-1β and IL-6 promoting inflammatory response. The colony-forming ability of iheESCs, like primary cells, was lower than Ishikawa cells. In addition, tumorigenicity assay indicated that iheESCs were unable to trigger tumor formation in BALB/c nude mouse.(#br)CONCLUSIONS(#br)This study established and characterized iheESCs that kept the cellular physiology of primary cells and were not available with tumorigenic ability. Thus, iheESCs would be useful as in vitro cell model to investigate pathogenesis of endometriosis

Anti-Inflammatory Effects of the Bioactive Compound Ferulic Acid Contained in Oldenlandia diffusa

Objectives. This study aimed to identify the active compounds in Oldenlandia diffusa (OD) decoction and the compounds absorbed into plasma, and to determine whether the absorbed compounds derived from OD exerted any anti-inflammatory effects in rats with collagen induced arthritis (CIA). Methods. The UPLC-PDA (Ultra Performance Liquid Chromatography Photo-Diode Array) method was applied to identify the active compounds both in the decoction and rat plasma. The absorbable compound was administered to the CIA rats, and the effects were dynamically observed. X-ray films of the joints and HE stain of synovial tissues were analyzed. The levels of IL-1β and TNF-α in the rats from each group were measured by means of ELISA. The absorbed compound in the plasma of CIA rats was identified as ferulic acid (FA), following OD decoction administration. Two weeks after the administration of FA solution or OD decoction, the general conditions improved compared to the model group. The anti-inflammatory effect of FA was inferior to that of the OD decoction (P<0.05), based on a comparison of IL-1β TNF-α levels. FA from the OD decoction was absorbed into the body of CIA rats, where it elicited anti-inflammatory responses in rats with CIA. Conclusions. These results suggest that FA is the bioactive compound in OD decoction, and FA exerts its effects through anti-inflammatory pathways

Boosting Superior Lithium Storage Performance of Alloy‐Based Anode Materials via Ultraconformal Sb Coating–Derived Favorable Solid‐Electrolyte Interphase

Alloy materials such as Si and Ge are attractive as high‐capacity anodes for rechargeable batteries, but such anodes undergo severe capacity degradation during discharge–charge processes. Compared to the over‐emphasized efforts on the electrode structure design to mitigate the volume changes, understanding and engineering of the solid‐electrolyte interphase (SEI) are significantly lacking. This work demonstrates that modifying the surface of alloy‐based anode materials by building an ultraconformal layer of Sb can significantly enhance their structural and interfacial stability during cycling. Combined experimental and theoretical studies consistently reveal that the ultraconformal Sb layer is dynamically converted to Li3Sb during cycling, which can selectively adsorb and catalytically decompose electrolyte additives to form a robust, thin, and dense LiF‐dominated SEI, and simultaneously restrain the decomposition of electrolyte solvents. Hence, the Sb‐coated porous Ge electrode delivers much higher initial Coulombic efficiency of 85% and higher reversible capacity of 1046 mAh g−1 after 200 cycles at 500 mA g−1, compared to only 72% and 170 mAh g−1 for bare porous Ge. The present finding has indicated that tailoring surface structures of electrode materials is an appealing approach to construct a robust SEI and achieve long‐term cycling stability for alloy‐based anode materials

In Situ Construction of an Ultrarobust and Lithiophilic Li-Enriched Li–N Nanoshield for High-Performance Ge-Based Anode Materials

Alloy-based materials are promising anodes for rechargeable batteries because of their higher theoretical capacities in comparison to graphite. Unfortunately, the huge volume changes during cycling cause serious structural degradation and undesired parasitic reactions with electrolytes, resulting in fragile solid-electrolyte interphase formation and serious capacity decay. This work proposes to mitigate the volume changes and suppress the interfacial reactivity of Ge anodes without sacrificing the interfacial Li+ transport, through in situ construction of an ultrarobust and lithiophilic Li-enriched Li–N nanoshield, which demonstrated improved chemical, electrochemical, mechanical, and environmental stability. Therefore, it can serve as a versatile interlayer to facilitate Li+ transport and effectively block the attack of electrolyte solvents, thus boosting the long-term cycle stability and fast charging capability of Ge anodes. This work offers an alternative methodology to tune the interfaces of other electrode materials as well by screening for more N-containing compounds that can react with Li+ during battery operation