Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory

Numerical computing of the rank of a matrix is a fundamental problem in scientific computation. The datasets generated by the internet often correspond to the analysis of high-dimensional sparse matrices. Notwithstanding recent advances in the promotion of traditional singular value decomposition (SVD), an efficient estimation algorithm for the rank of a high-dimensional sparse matrix is still lacking. Inspired by the controllability theory of complex networks, we converted the rank of a matrix into maximum matching computing. Then, we established a fast rank estimation algorithm by using the cavity method, a powerful approximate technique for computing the maximum matching, to estimate the rank of a sparse matrix. In the merit of the natural low complexity of the cavity method, we showed that the rank of a high-dimensional sparse matrix can be estimated in a much faster way than SVD with high accuracy. Our method offers an efficient pathway to quickly estimate the rank of the high-dimensional sparse matrix when the time cost of computing the rank by SVD is unacceptable.Comment: 10 pages, 4 figure

Economic Burden for Lung Cancer Survivors in Urban China.

BackgroundWith the rapid increase in the incidence and mortality of lung cancer, a growing number of lung cancer patients and their families are faced with a tremendous economic burden because of the high cost of treatment in China. This study was conducted to estimate the economic burden and patient responsibility of lung cancer patients and the impact of this burden on family income.MethodsThis study uses data from a retrospective questionnaire survey conducted in 10 communities in urban China and includes 195 surviving lung cancer patients diagnosed over the previous five years. The calculation of direct economic burden included both direct medical and direct nonmedical costs. Indirect costs were calculated using the human capital approach, which measures the productivity lost for both patients and family caregivers. The price index was applied for the cost calculation.ResultsThe average economic burden from lung cancer was $43,336 per patient, of which the direct cost per capita was$42,540 (98.16%) and the indirect cost per capita was $795 (1.84$30,277 per capita, which accounted for 171% of the household annual income, a percentage that fell to 107% after subtracting the compensation from medical insurance.ConclusionsThe economic burden for lung cancer patients is substantial in the urban areas of China, and an effective control strategy to lower the cost is urgently needed

Construction of stable Ta3N5/g-C3N4 metal/non-metal nitride hybrids with enhanced visible-light photocatalysis

In this paper, a novel Ta3N5/g-C3N4 metal/non-metal nitride hybrid was successfully synthesized by a facile impregnation method. The photocatalytic activity of Ta3N5/g-C3N4 hybrid nitrides was evaluated by the degradation of organic dye rhodamine B (RhB) under visible light irradiation, and the result indicated that all Ta3N5/g-C3N4 samples exhibited distinctly enhanced photocatalytic activities for the degradation of RhB than pure g-C3N4. The optimal Ta3N5/g-C3N4 composite sample, with Ta3N5 mass ratio of 2%, demonstrated the highest photocatalytic activity, and its degradation rate constant was 2.71 times as high as that of pure g-C3N4. The enhanced photocatalytic activity of this Ta3N5/g-C3N4 metal/metal-free nitride was predominantly attributed to the synergistic effect which increased visible-light absorption and facilitated the efficient separation of photoinduced electrons and holes. The Ta3N5/g-C3N4 hybrid nitride exhibited excellent photostability and reusability. The possible mechanism for improved photocatalytic performance was proposed. Overall, this work may provide a facile way to synthesize the highly efficient metal/metal-free hybrid nitride photocatalysts with promising applications in environmental purification and energy conversion

Text Generation with Efficient (Soft) Q-Learning

Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models. This paradigm relies on direct supervision examples, which is not applicable to many applications, such as generating adversarial attacks or generating prompts to control language models. Reinforcement learning (RL) on the other hand offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. Yet previous RL algorithms for text generation, such as policy gradient (on-policy RL) and Q-learning (off-policy RL), are often notoriously inefficient or unstable to train due to the large sequence space and the sparse reward received only at the end of sequences. In this paper, we introduce a new RL formulation for text generation from the soft Q-learning perspective. It further enables us to draw from the latest RL advances, such as path consistency learning, to combine the best of on-/off-policy updates, and learn effectively from sparse reward. We apply the approach to a wide range of tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation. Experiments show our approach consistently outperforms both task-specialized algorithms and the previous RL methods. On standard supervised tasks where MLE prevails, our approach also achieves competitive performance and stability by training text generation from scratch.Comment: Code available at https://github.com/HanGuo97/soft-Q-learning-for-text-generatio

ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models

Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data. Yet, real-world data-to-text problems often suffer from various data-scarce issues: one may have access to only a handful of or no training examples, and/or have to rely on examples in a different domain or schema. To fill this gap, we propose Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse settings by making efficient use of any given (or no) examples. ASDOT consists of two steps, data disambiguation and sentence fusion, both of which are amenable to be solved with off-the-shelf pretrained language models (LMs) with optional finetuning. In the data disambiguation stage, we employ the prompted GPT-3 model to understand possibly ambiguous triples from the input data and convert each into a short sentence with reduced ambiguity. The sentence fusion stage then uses an LM like T5 to fuse all the resulting sentences into a coherent paragraph as the final description. We evaluate extensively on various datasets in different scenarios, including the zero-/few-/full-shot settings, and generalization to unseen predicates and out-of-domain data. Experimental results show that ASDOT consistently achieves significant improvement over baselines, e.g., a 30.81 BLEU gain on the DART dataset under the zero-shot setting.Comment: Findings of EMNLP 202