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We develop semantics and syntax for bicategorical type theory. Bicategorical type theory features contexts, types, terms, and directed reductions between terms. This type theory is naturally interpreted in a class of structured bicategories. We start by developing the semantics, in the form of comprehension bicategories . Examples of comprehension bicategories are plentiful; we study both specific examples as well as classes of examples constructed from other data. From the notion of comprehension bicategory, we extract the syntax of bicategorical type theory, that is, judgment forms and structural inference rules. We prove soundness of the rules by giving an interpretation in any comprehension bicategory. The semantic aspects of our work are fully checked in the Coq proof assistant, based on the UniMath library.

The rise of misinformation on social networks creates serious problems for public awareness, policy-making, and trust in society. Social media content is getting more complex, often including text, metadata, and multimedia. This makes it essential to have smart systems that can classify misinformation using various signals. This paper introduces a machine learning approach to check the misinformation that uses the MuMiN (Multilingual Multimodal Fact-Checked Misinformation) dataset. This dataset contains annotated claims, supporting evidence, user tweets, and fact-check labels. Structured preprocessing pipeline applied to get the dataset ready for analysis. The textual and structural features were extracted as features. Three machine learning models, Random Forest (RF), Gradient Boosting (GB), and a Stacking Classifier were developed and assessed. These models were evaluated using key performance metrics. The experimental findings indicate that the stacking ensemble regularly surpasses the individual base classifiers, attaining an accuracy rating of 89.12%. This highlights the advantages of combining models to manage complex, noisy, and multimodal social media data. This study emphasizes the value of merging multimodal feature representations with ensemble learning methods for effective and scalable misinformation detection on online platforms.

A “checked” syllable usually refers to one with a short vowel and an oral or glottal coda, which results impressionistically in a “short” and “abrupt” quality. Although common in languages of the world, it is unclear how to characterize checked syllables phonetically. In this study, we investigated the acoustic features of checked syllables in citation and sandhi forms in Xiapu Min, an under-documented language from China. We conducted a production experiment and analyzed the F0, phonatory quality, vowel duration, and vowel quality in checked syllables. The results show that, in citation tones, checked syllables are realized with distinct F0 contours from unchecked syllables, along with glottalization in the end and a shorter duration overall. In sandhi tones, checked syllables lose their distinct F0 contours and become less glottalized. However, the shorter duration of checked syllables is retained in sandhi forms. This study lays out the acoustic properties that tend to be associated with checked syllables and can be used when testing checked syllables in other language varieties. The fact that in Xiapu Min sandhi checked tones become less glottalized but preserve their shorter duration suggests that, when checked syllables become unchecked diachronically, glottalization might be lost prior to duration lengthening.

Many foundational program verification tools have been developed to build machine-checked program correctness proofs, a majority of which are based on Hoare logic. Their program logics, their assertion languages, and their underlying programming languages can be formalized by either a shallow embedding or a deep embedding. Tools like early versions of Verified Software Toolchain (before 2018) choose different shallow embeddings to formalize their program logic. But the pros and cons of these different embeddings were not yet well studied. Therefore, we want to study the impact of the program logic’s embedding on logic’s proof rules in this paper. This paper considers a set of useful extended proof rules, which aided the proof automation in VST, and four different logic embeddings: one deep embedding and three common shallow embeddings. We prove the validity of these extended rules under these embeddings and discuss their main challenges. Furthermore, we propose a method to lift existing shallowly embedded logics to deeply embedded ones to greatly simplify proofs of extended rules in VST. We implemented our theory in VST by lifting the originally shallowly embedded VST to our deeply embedded VST and establishing these extended rules.

We propose a class of locally Lipschitz functions with piecewise structure for use as Lyapunov functions for hybrid dynamical systems. Subject to some regularity of the dynamics, we show that Lyapunov inequalities can be checked only on a dense set and thus we avoid checking them at points of nondifferentiability of the Lyapunov function. Connections to other classes of locally Lipschitz or piecewise regular functions are also discussed, and applications to hybrid dynamical systems are included.

In this paper, we aim to address a relevant estimation problem that aviation professionals encounter in their daily operations. Specifically, aircraft load planners require information on the expected number of checked bags for a flight several hours prior to its scheduled departure to properly palletize and load the aircraft. However, the checked baggage prediction problem has not been sufficiently studied in the literature, particularly at the flight level. Existing prediction approaches have not properly accounted for the different impacts of overestimating and underestimating checked baggage volumes on airline operations. Therefore, we propose a custom loss function, in the form of a piecewise quadratic function, which aligns with airline operations practice and utilizes machine learning algorithms to optimize checked baggage predictions incorporating the new loss function. We consider multiple linear regression, LightGBM, and XGBoost, as supervised learning algorithms. We apply our proposed methods to baggage data from a major airline and additional data from various US government agencies. We compare the performance of the three customized supervised learning algorithms. We find that the two gradient boosting methods (i.e., LightGBM and XGBoost) yield higher accuracy than the multiple linear regression; XGBoost outperforms LightGBM while LightGBM requires much less training time than XGBoost. We also investigate the performance of XGBoost on samples from different categories and provide insights for selecting an appropriate prediction algorithm to improve baggage prediction practices. Our modeling framework can be adapted to address other prediction challenges in aviation, such as predicting the number of standby passengers or no-shows.

The phonation variation in Shanghainese is influenced by both phonemic phonation contrast and global prosodic context. This study investigated the phonetic realization of checked and unchecked syllables at four different prosodic positions (sandhi-medial, sandhi-final, phrase-final, and IP-final). By analyzing both acoustic and articulatory voice measures, we achieved a better understanding of the nature of checkedness contrast and prosodic boundaries: (1) Different phonetic correlates are associated with the two laryngeal functions: The checkedness contrast is mostly distinguished by the relative degree of glottal constriction, but the prosodic boundaries are mostly associated with periodicity and noise measures. (2) The checkedness contrast is well maintained in all prosodic contexts, suggesting that the controls for the local checkedness contrast are rather independent of global prosody.

Terrestrial, satellite, and internet High Definition (HD) and Ultra High Definition (UHD) broadcasting have experienced notable advancements in recent years, yet the potential within the DTV Essential Hidden Area (DEHA) in each frame remains largely untapped. This study focuses on exploring and harnessing the capabilities of the DEHA via the introduction of a DEHA decoder. Through experimental analysis, the proposed decoder effectively reveals previously unnoticed DEHA in diverse HDTV broadcasting systems. By incorporating overlaid three-digit values, DEHA text, a 16 × 15 checked board pattern, and a QR code, the decoder enables the easy identification and extraction of the embedded DEHA service. Furthermore, the research investigates the potential of utilizing the embedded DEHA for transmitting program-related metadata, encompassing technical information, specific camera details, and post-production technologies. Leveraging a checked pattern block image, a seamless and efficient transfer mechanism within the embedded DEHA is established. The utilization of the embedded DEHA holds promising opportunities for enhancing DTV service, leveraging its inherent synchronization advantages within video content. Moreover, compliance with established broadcasting standards, such as ATSC, ISDB, and DVB, ensures compatibility and interoperability. This study emphasizes the significance of the DEHA in terrestrial, satellite, and internet broadcasting, unveiling new possibilities for innovation and improvement in the industry.

The use of CAD/CAM systems have greatly optimized material utilization of garment styles from plain fabrics. Processing of intricate pattern materials is still work and material consuming. Increased material consumption is obtained because of two reasons: necessity to match pattern on ready garments and unfixed textile material structure. The fabric loss related to the pattern matching necessity is the most difficult to reduce. A style of classical women jacket was tested to compare efficiency of plain and checked fabric markers for sizes 36 – 54. It was determined that marker length is dependent on the conformity of the size of the checked fabric repeat and the length of the longest components placed in a sectioned marker. Reducing the length of the style slightly it was possible to reduce marker length considerably for certain size markers. Marker length, fabric consumption and, with it, product costs can be reduced conforming the length of the style to the size of the checked fabric repeat. Length tolerance – acceptable slight variations of the length of the style which does not change design and visual perception of the style should be determined by designers. Specialized software could be developed to vary the length of the style in the interval of length tolerance to reduce fabric consumption and with it product costs of certain production orders. Improving manufacturing efficiency and reducing fabric use, the styles from checked materials could be included in garment collections more often.

Objectives The purpose of this study was to estimate the incidence of metabolic syndrome (MS) in a 5-year follow-up adult population in Taiwan who were examined at the Major Health Screening Center, and to assess possible socioeconomic determinants of the syndrome in this sample. Methods The longitudinal study included 9,389 adults, aged 35–74 years, who visited the Major Health Screening Center from 1998–2002, and were followed up for 5 years. Results The 5-year cumulative incidence of MS in this sample was 11.37%, and the weighted incidence was 12.46%; 14.95% for men and 9.89% for women, respectively. After adjustment for behavioral and habits, family history, gender and age, education level was associated with the incidence of MS. With middle school and lower as a baseline, the incidence of MS for high school, junior college, and college and above was OR 0.80, 95% CI 0.64–1.00; OR 0.80, 95% CI 0.62–1.03 and OR 0.65, 95% CI 0.50–0.83, respectively. Conclusions The standardized cumulative incidence of MS was 12.46%. Lower education level was an important socioeconomic determinant of MS in women.