Scallop: A Language for Neurosymbolic Programming

We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. We evaluate Scallop on a suite of eight neurosymbolic applications from the literature. Our evaluation demonstrates that Scallop is capable of expressing algorithmic reasoning in diverse and challenging AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain knowledge, and yields solutions that are comparable or superior to state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions outperform these models in aspects such as runtime and data efficiency, interpretability, and generalizability

LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision

We propose LASER, a neuro-symbolic approach to learn semantic video representations that capture rich spatial and temporal properties in video data by leveraging high-level logic specifications. In particular, we formulate the problem in terms of alignment between raw videos and spatio-temporal logic specifications. The alignment algorithm leverages a differentiable symbolic reasoner and a combination of contrastive, temporal, and semantics losses. It effectively and efficiently trains low-level perception models to extract fine-grained video representation in the form of a spatio-temporal scene graph that conforms to the desired high-level specification. In doing so, we explore a novel methodology that weakly supervises the learning of video semantic representations through logic specifications. We evaluate our method on two datasets with rich spatial and temporal specifications: 20BN-Something-Something and MUGEN. We demonstrate that our method learns better fine-grained video semantics than existing baselines

Experience of Compiling a Chinese-Russian Scientific Dictionary in Journalism

Современные цифровые библиотеки содержат много многоязычной информации. Чтобы улучшить точность поиска, упрощается обмен информацией между людьми, а люди часто просматривают словарь. Однако на многих азиатских языках отсутствует такой словарь. Исследования является современная терминологическая система СМИ Китайской Народной Республики и России, опыт создания «Китайско-русского медиасловаря» 《新闻学大辞典》 (Синвенсюэ Дацидиан, Словарь журналистики). Это хорошо структурированный, инклюзивный, простой в использовании, новаторский контент инструмента для новостей.Modern digital libraries contain a lot of multilingual information. To improve the accuracy of the search, the exchange of information between people is simplified, and people often look up the dictionary. However, many Asian languages lack such a vocabulary. research is the modern terminological system of the media of the People's Republic of China and Russia，the experience of creating the "Chinese-Russian Media Dictionary" 《新闻学大辞典》（Xinwenxue Dacidian, Dictionary of Journalism). It is a well-structured, inclusive, easy-to-use, innovative news tool content

Experience of Compiling a Chinese-Russian Scientific Dictionary of Dissert Research in Journalism

Современные цифровые библиотеки содержат много многоязычной информации. Чтобы улучшить точность поиска, упрощается обмен информацией между людьми, а люди часто просматривают словарь. Однако на многих азиатских языках отсутствует такой словарь. Исследования современной терминологической системы СМИ Китайской Народной Республики и России, опыт создания «Китайско-русского медиа словаря» ........ (Синвенсюэ Дацидиан, Словарь журналистики) — это хорошо структурированный, инклюзивный, простой в использовании, новаторский контент инструмента для новостей.Modern digital libraries contain a lot of multilingual information. To improve the accuracy of the search, the exchange of information between people is simplified, and people often look up the dictionary. However, many Asian languages lack such a vocabulary. Research is the modern terminological system of the media of the People’s Republic of China and Russia, the experience of creating the «Chinese-Russian Media Dictionary»《 新闻学大辞典》 (Xinwenxue Dacidian, Dictionary of Journalism). It is a well-structured, inclusive, easy-to-use, innovative news tool content

Improving the Performance of PCA-Based Chiller Sensor Fault Detection by Sensitivity Analysis for the Training Data Set

An improved approach of fault detection for chiller sensors is presented based on the sensitivity analysis for the original data set used to train the Principal Component Analysis (PCA) model. Sensor faults are inevitable due to the aging, environment, location and so on. Meanwhile, because of the wide range of operational conditions, the fault of a certain sensor is very difficult to be directly detected by its own historical data. PCA is a multivariate data-based statistical analysis method and it is very useful for the sensor fault detection in HVAC&R. The undetectable zone of a certain sensor by Q-statistic is derived from the definition of Q-statistic which is usually employed as a boundary to detect the sensor fault situation. Due to the similar style between Q-statistic and Hawkins’ TH2, the undetectable zone by Hawkins’ TH2 is also obtained. Undetectable zone is a predictive index to indicate the detectability of different sensors by different statistics. Since undetectable zone is the character of the original training data set, it can indicate the quality for the selected training data. One field data set is employed to validate the presented approach. Results show that the undetectable zone of a certain sensor by Q-statistic is quite different from that by Hawkins’ TH2. Therefore, the undetectable zone can be used to improving the performance of PCA-based chiller sensor fault detection by choosing different fault detection statistics with less undetectable zone for different sensor