A Wave Splitter with Simple Structure Based on Biaxial Anisotropic Medium

A TE/TM wave splitter based on biaxial anisotropic slab is designed. According to the total reflection and total transmission of TE/TM waves incident from isotropic medium into biaxial anisotropic medium, we propose a wave splitter with simple structure and little limitation on both constitutive parameters and incident angle. All the results are verified by simulation

Kosmos-2: Grounding Multimodal Large Language Models to the World

We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new capabilities of perceiving object descriptions (e.g., bounding boxes) and grounding text to the visual world. Specifically, we represent refer expressions as links in Markdown, i.e., ``[text span](bounding boxes)'', where object descriptions are sequences of location tokens. Together with multimodal corpora, we construct large-scale data of grounded image-text pairs (called GrIT) to train the model. In addition to the existing capabilities of MLLMs (e.g., perceiving general modalities, following instructions, and performing in-context learning), Kosmos-2 integrates the grounding capability into downstream applications. We evaluate Kosmos-2 on a wide range of tasks, including (i) multimodal grounding, such as referring expression comprehension, and phrase grounding, (ii) multimodal referring, such as referring expression generation, (iii) perception-language tasks, and (iv) language understanding and generation. This work lays out the foundation for the development of Embodiment AI and sheds light on the big convergence of language, multimodal perception, action, and world modeling, which is a key step toward artificial general intelligence. Data, demo, and pretrained models are available at https://aka.ms/kosmos-2.Comment: 20 page

How do Service Quality, Value, Pleasure, and Satisfaction Create Loyalty to Smart Dockless Bike-Sharing Systems?

Purpose – The purpose of this research is to investigate which factors influence users’ loyalty to smart dockless bike-sharing systems (DBSS). Design/methodology/approach – A sample of 374 subjects who had previously experienced smart DBSS was obtained and the partial least squares (PLS) method was performed to analyze the measurement and structural models. Findings – The results indicated that customer loyalty was the final construct produced by perceived green value, customer satisfaction, and perceived pleasure, which were found to be partial mediators between service quality and customer loyalty. Originality/value – This study (1) provides a new perspective from which to examine consumer loyalty behavior in a smart DBSS context, (2) it identifies the role of service quality as an important stimulus, (3) it confirms that perceived pleasure not only has an effect on customer loyalty, but also acts as an excellent mediator, and (4) it provides empirical evidence for smart DBSS providers to enhance the adoption of green transport and to strengthen customer loyalty to smart DBSS

Preparation and Identification of α-Amylase Inhibitory Peptides from Mung Bean Protein

In this study, sequential hydrolysis with pepsin followed by trypsin was conducted on total protein and protein fractions from mung bean. The difference in α-amylase inhibitory activity among the resulting hydrolysates was compared and the underlying reason was analyzed in terms of degree of hydrolysis, amino acid composition and molecular mass. The results showed that the total protein hydrolysate had the highest α-amylase inhibitory activity (16.51%). Compared with its fractions, the total protein showed the highest content of hydrophobic amino acids (32.68%) and degree of hydrolysis (6.28%), and the molecular mass of its hydrolysate was the lowest (< 20 kDa). Therefore, the total protein was selected to prepare α-amylase inhibitory peptides. Finally, 17 peptides with potential α-amylase inhibitory activity were discovered by the isolation and identification of peptides from mung bean protein. This study suggests that mung bean protein is a better food source of α-amylase inhibitory peptides than its protein fractions, which can be used in blood glucose-lowering functional foods or drugs

Interaction of Isoflavones with β-Conglycinin and Its Effect on the Structure and Potential Allergenicity of Their Complex

In this study, the interaction mechanism of isoflavones with β-conglycinin (BGG) and its effect on the structure and potential allergenicity of their complex were investigated. Fluorescence spectroscopy and circular dichroism spectroscopy were used to analyze the types of quenching, the number of binding sites, the types of forces and the secondary structure content of BGG complexes with one of the two soy isoflavones genistein (Gen) and daidzein (Dai). The structure of isoflavone-BGG complexes prepared under different conditions was characterized, and the potential allergenicity of the complexes were detected by enzyme linked immunosorbent assay (ELISA) and Western blotting. The result showed that isoflavones caused a static quench of the fluorescence of BGG, and the interaction was dominated by hydrophobic interactions, with one binding site. Furthermore, the interaction with isoflavones induced an increase in the polarity of the amino acid microenvironment of BGG, which led to unfolding of the peptide chain and a looser structure. ELISA and immunoblotting of digestion products showed that the potential allergenicity of the protein was enhanced by the binding of isoflavones. The results of this study would be helpful to understand how isoflavones affect the allergenicity of allergenic proteins in complex food matrices, and be significant for its further development and utilization for allergenicity reduction

ToMBench: Benchmarking Theory of Mind in Large Language Models

Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others. Recent research has sparked a debate over whether large language models (LLMs) exhibit a form of ToM. However, existing ToM evaluations are hindered by challenges such as constrained scope, subjective judgment, and unintended contamination, yielding inadequate assessments. To address this gap, we introduce ToMBench with three key characteristics: a systematic evaluation framework encompassing 8 tasks and 31 abilities in social cognition, a multiple-choice question format to support automated and unbiased evaluation, and a build-from-scratch bilingual inventory to strictly avoid data leakage. Based on ToMBench, we conduct extensive experiments to evaluate the ToM performance of 10 popular LLMs across tasks and abilities. We find that even the most advanced LLMs like GPT-4 lag behind human performance by over 10% points, indicating that LLMs have not achieved a human-level theory of mind yet. Our aim with ToMBench is to enable an efficient and effective evaluation of LLMs' ToM capabilities, thereby facilitating the development of LLMs with inherent social intelligence.Comment: Under revie

Flames: Benchmarking Value Alignment of Chinese Large Language Models

The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, however, fall short of effectively uncovering safety vulnerabilities in LLMs. Despite numerous models achieving high scores and 'topping the chart' in these evaluations, there is still a significant gap in LLMs' deeper alignment with human values and achieving genuine harmlessness. To this end, this paper proposes the first highly adversarial benchmark named Flames, consisting of 2,251 manually crafted prompts, ~18.7K model responses with fine-grained annotations, and a specified scorer. Our framework encompasses both common harmlessness principles, such as fairness, safety, legality, and data protection, and a unique morality dimension that integrates specific Chinese values such as harmony. Based on the framework, we carefully design adversarial prompts that incorporate complex scenarios and jailbreaking methods, mostly with implicit malice. By prompting mainstream LLMs with such adversarially constructed prompts, we obtain model responses, which are then rigorously annotated for evaluation. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames, particularly in the safety and fairness dimensions. Claude emerges as the best-performing model overall, but with its harmless rate being only 63.08% while GPT-4 only scores 39.04%. The complexity of Flames has far exceeded existing benchmarks, setting a new challenge for contemporary LLMs and highlighting the need for further alignment of LLMs. To efficiently evaluate new models on the benchmark, we develop a specified scorer capable of scoring LLMs across multiple dimensions, achieving an accuracy of 77.4%. The Flames Benchmark is publicly available on https://github.com/AIFlames/Flames

PSTPIP2 Inhibits the Inflammatory Response and Proliferation of Fibroblast-Like Synoviocytes in vitro

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease and its pathogenesis remains unclear. Fibroblast-like synoviocytes (FLSs) play an important role in the pathogenesis of RA. Proline-serine-threonine phosphatase interacting protein 2 (PSTPIP2) is an adaptor protein, which is associated with auto-inflammatory disease. In this study, we selected adjuvant-induced arthritis (AIA) as animal model to study the role of PSTPIP2 in FLSs. We found that the expression of PSTPIP2 was significantly down-regulated in synovial tissues and FLSs of AIA rat compared with normal group. And overexpression of PSTPIP2 could inhibit the proliferation and inflammatory response of FLSs. Moreover, the proliferation and inflammatory response of FLSs were promoted with PSTPIP2 silencing treatment. In terms of mechanism, we found that the expression of PSTPIP2 was closely related to NF-κB signaling pathway. Overall, our results suggested that PSTPIP2 inhibits the proliferation and inflammatory response of FLSs, which might be closely related to NF-κB signaling pathway