Thermal diffusivity is a key thermophysical parameter that characterizes the rate of heat propagation through a material under transient thermal conditions. For molten salts, including halide systems, this property is of particular importance for the design and optimization of high-temperature processes. Rare-earth metal halides, such as cerium(III) chloride, are of interest due to their specific structural features and their role as model systems in nuclear technology research. Owing to its electrochemical similarity to plutonium, CeCl₃ is widely employed in experimental studies simulating actinide behavior in pyrochemical processing of spent nuclear fuel. While the thermal diffusivity of pure alkali halides is relatively well studied, the introduction of trivalent cations such as Ce³⁺ leads to significant structural rearrangements in the melt, making direct extrapolation from pure salts unreliable. This work focuses on the thermal diffusivity of binary CeCl₃-MCl (M = Li, Na, K, Rb, Cs) systems over a wide temperature range, using calculated values based on experimental data for thermal conductivity, density, and specific heat capacity. The results reveal a pronounced dependence of the thermal diffusivity on the cationic composition and temperature. The observed trends are interpreted in terms of changes in molar mass, ionic mobility, interionic interaction energies, and structural organization within the melts. The findings provide valuable input for validating molecular dynamics simulations, as well as for developing predictive models of heat and mass transfer in high-temperature applications, including pyrochemical nuclear fuel processing and thermal energy storage systems.
Interactive Artificial Intelligent(AI) assistant systems are designed to offer timely guidance to help human users to complete a variety tasks. One of the remaining challenges is to understand user's mental states during the task for more personalized guidance. In this work, we analyze users' mental states during task executions and investigate the capabilities and challenges for large language models to interpret user profiles for more personalized user guidance.
This paper shows how to use large-scale pre-trained language models to extract character roles from narrative texts without training data. Queried with a zero-shot question-answering prompt, GPT-3 can identify the hero, villain, and victim in diverse domains: newspaper articles, movie plot summaries, and political speeches.
Text detoxification is a style transfer task of creating neutral versions of toxic texts. In this paper, we use the concept of text editing to build a two-step tagging-based detoxification model using a parallel corpus of Russian texts. With this model, we achieved the best style transfer accuracy among all models in the RUSSE Detox shared task, surpassing larger sequence-to-sequence models.
Neural network approaches have been applied to computational morphology with great success, improving the performance of most tasks by a large margin and providing new perspectives for modeling. This paper starts with a brief introduction to computational morphology, followed by a review of recent work on computational morphology with neural network approaches, to provide an overview of the area. In the end, we will analyze the advantages and problems of neural network approaches to computational morphology, and point out some directions to be explored by future research and study.
This paper describes our system that was designed for Humor evaluation within the SemEval-2020 Task 7. The system is based on convolutional neural network architecture. We investigate the system on the official dataset, and we provide more insight to model itself to see how the learned inner features look.
The paper proves that the number of k-skip-n-grams for a corpus of size $L$ is $$\frac{Ln + n + k' - n^2 - nk'}{n} \cdot \binom{n-1+k'}{n-1}$$ where $k' = \min(L - n + 1, k)$.
In this work, we focus on effectively leveraging and integrating information from concept-level as well as word-level via projecting concepts and words into a lower dimensional space while retaining most critical semantics. In a broad context of opinion understanding system, we investigate the use of the fused embedding for several core NLP tasks: named entity detection and classification, automatic speech recognition reranking, and targeted sentiment analysis.
We present a new open-source parallel corpus consisting of news articles collected from the Bianet magazine, an online newspaper that publishes Turkish news, often along with their translations in English and Kurdish. In this paper, we describe the collection process of the corpus and its statistical properties. We validate the benefit of using the Bianet corpus by evaluating bilingual and multilingual neural machine translation models in English-Turkish and English-Kurdish directions.
Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.
This paper evaluates the impact of various event extraction systems on automatic pathway curation using the popular mTOR pathway. We quantify the impact of training data sets as well as different machine learning classifiers and show that some improve the quality of automatically extracted pathways.
We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset.
We propose to train bi-directional neural network language model(NNLM) with noise contrastive estimation(NCE). Experiments are conducted on a rescore task on the PTB data set. It is shown that NCE-trained bi-directional NNLM outperformed the one trained by conventional maximum likelihood training. But still(regretfully), it did not out-perform the baseline uni-directional NNLM.
In this paper, we present a novel approach for medical synonym extraction. We aim to integrate the term embedding with the medical domain knowledge for healthcare applications. One advantage of our method is that it is very scalable. Experiments on a dataset with more than 1M term pairs show that the proposed approach outperforms the baseline approaches by a large margin.
This paper presents the steps involved in creating an electronic lexical knowledge base from the 1987 Penguin edition of Roget's Thesaurus. Semantic relations are labelled with the help of WordNet. The two resources are compared in a qualitative and quantitative manner. Differences in the organization of the lexical material are discussed, as well as the possibility of merging both resources.
The hypereosinophilic syndromes (HES) are a group of disorders marked by the sustained overproduction of eosinophils, resulting in multiple organ damage. We report a 55-year-old lady presented with sudden onset of left-sided limb weakness and hypereosinophilia. Cerebral computerised tomography scan showed multiple small infarctions in bilateral corona radiata and right thalamus. A transesophageal echocardiogram revealed endomyocardial damage with mural thrombus suggesting Loeffler endocarditis. The multiple cerebral infarctions were probably due to cardiac thromboembolism. Treatment with prednisolone led to significant clinical improvement. This case illustrates hypereosinophilia should be considered in patients with multiple cerebral infarctions.