Efficient, edge-aware, combined color quantization and dithering

Abstract—In this paper we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on opposite sides of an edge. The importance of each pixel is weighted according to its saliency. To rapidly minimize the cost function, we use a modified multi-scale iterative conditional mode (ICM) algorithm which updates one pixel a time while keeping other pixels unchanged. As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one. To address this problem, we initialize ICM with a palette generated by a modified median-cut method. Compared to previous approaches, our method can produce high quality results with fewer visual artifacts but also requires significantly less computational effort. Index Terms—Color quantization, dithering, optimization-based image processing. I

On Effectively Learning of Knowledge in Continual Pre-training

Pre-trained language models (PLMs) like BERT have made significant progress in various downstream NLP tasks. However, by asking models to do cloze-style tests, recent work finds that PLMs are short in acquiring knowledge from unstructured text. To understand the internal behaviour of PLMs in retrieving knowledge, we first define knowledge-baring (K-B) tokens and knowledge-free (K-F) tokens for unstructured text and ask professional annotators to label some samples manually. Then, we find that PLMs are more likely to give wrong predictions on K-B tokens and attend less attention to those tokens inside the self-attention module. Based on these observations, we develop two solutions to help the model learn more knowledge from unstructured text in a fully self-supervised manner. Experiments on knowledge-intensive tasks show the effectiveness of the proposed methods. To our best knowledge, we are the first to explore fully self-supervised learning of knowledge in continual pre-training

Flameless combustion with liquid fuel: A review focusing on fundamentals and gas turbine application

Flameless combustion has been developed to reduce emissions whilst retaining thermal efficiencies in combustion systems. It is characterized with its distinguished features, such as suppressed pollutant emission, homogeneous temperature distribution, reduced noise and thermal stress for burners and less restriction on fuels (since no flame stability is required). Recent research has shown the potential of flameless combustion in the power generation industry such as gas turbines. In spite of its potential, this technology needs further research and development to improve its versatility in using liquid fuels as a source of energy. In this review, progress toward application of the flameless technique is presented with emphasis on gas turbines. A systematic analysis of the state-of-the-art and the major technical and physical challenges in operating gas turbines with liquid fuels in a flameless combustion mode is presented. Combustion characteristics of flameless combustion are explained along with a thorough review of modelling and simulation of the liquid fuel fed flameless combustion. A special focus is given to the relevant research on applications to the inner turbine burners. The paper is concluded by highlighting recent findings and pointing out several further research directions to improve the flameless combustion application in gas turbines, including in-depth flow and combustion mechanisms, advanced modelling, developed experimental technology and comprehensive design methods aiming at gas turbine flameless combustors

EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning

Expressing universal semantics common to all languages is helpful in understanding the meanings of complex and culture-specific sentences. The research theme underlying this scenario focuses on learning universal representations across languages with the usage of massive parallel corpora. However, due to the sparsity and scarcity of parallel data, there is still a big challenge in learning authentic ``universals'' for any two languages. In this paper, we propose EMMA-X: an EM-like Multilingual pre-training Algorithm, to learn (X)Cross-lingual universals with the aid of excessive multilingual non-parallel data. EMMA-X unifies the cross-lingual representation learning task and an extra semantic relation prediction task within an EM framework. Both the extra semantic classifier and the cross-lingual sentence encoder approximate the semantic relation of two sentences, and supervise each other until convergence. To evaluate EMMA-X, we conduct experiments on XRETE, a newly introduced benchmark containing 12 widely studied cross-lingual tasks that fully depend on sentence-level representations. Results reveal that EMMA-X achieves state-of-the-art performance. Further geometric analysis of the built representation space with three requirements demonstrates the superiority of EMMA-X over advanced models.Comment: Accepted by NeurIPS 202

Crosstalk of nervous and immune systems in pancreatic cancer

Pancreatic cancer is a highly malignant tumor known for its extremely low survival rate. The combination of genetic disorders within pancreatic cells and the tumor microenvironment contributes to the emergence and progression of this devastating disease. Extensive research has shed light on the nature of the microenvironmental cells surrounding the pancreatic cancer, including peripheral nerves and immune cells. Peripheral nerves release neuropeptides that directly target pancreatic cancer cells in a paracrine manner, while immune cells play a crucial role in eliminating cancer cells that have not evaded the immune response. Recent studies have revealed the intricate interplay between the nervous and immune systems in homeostatic condition as well as in cancer development. In this review, we aim to summarize the function of nerves in pancreatic cancer, emphasizing the significance to investigate the neural-immune crosstalk during the advancement of this malignant cancer

3-Chloro-6-(3,5-dimethyl-1H-pyrazol-1-yl)picolinic acid–triphenyl­phosphine oxide (1/1)

In the title 1:1 adduct, C11H10ClN3O2·C18H15OP, the dihedral angle between the pyridine and pyrazole rings is 10.3 (2)°. The two components of the adduct are linked by an O—H⋯O hydrogen bond

Active Learning on a Programmable Photonic Quantum Processor

Training a quantum machine learning model generally requires a large labeled dataset, which incurs high labeling and computational costs. To reduce such costs, a selective training strategy, called active learning (AL), chooses only a subset of the original dataset to learn while maintaining the trained model's performance. Here, we design and implement two AL-enpowered variational quantum classifiers, to investigate the potential applications and effectiveness of AL in quantum machine learning. Firstly, we build a programmable free-space photonic quantum processor, which enables the programmed implementation of various hybrid quantum-classical computing algorithms. Then, we code the designed variational quantum classifier with AL into the quantum processor, and execute comparative tests for the classifiers with and without the AL strategy. The results validate the great advantage of AL in quantum machine learning, as it saves at most $85\%$ labeling efforts and $91.6\%$ percent computational efforts compared to the training without AL on a data classification task. Our results inspire AL's further applications in large-scale quantum machine learning to drastically reduce training data and speed up training, underpinning the exploration of practical quantum advantages in quantum physics or real-world applications