Note On Certain Inequalities for Neuman Means

In this paper, we give the explicit formulas for the Neuman means $N_{AH}$, $N_{HA}$, $N_{AC}$ and $N_{CA}$, and present the best possible upper and lower bounds for theses means in terms of the combinations of harmonic mean $H$, arithmetic mean $A$ and contraharmonic mean $C$.Comment: 9 page

Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

Colloidal toxic trace metals in urban riverine and estuarine waters of Yantai City, southern coast of North Yellow Sea

The environmental characteristics of colloidal toxic trace metals Cd, Cu and Pb in riverine and estuarine waters collected from two urban rivers of Yantai City in eastern China, the Guangdang and Xin'an Rivers, were investigated using a modified centrifugal ultrafiltration (CUF) method in conjunction with acid extraction and inductively coupled plasma mass spectrometry. The target metals in dissolved pool were divided into four CUF fractions, i.e. <1 kDa, 1-3 kDa, 3-10 kDa and 10 kDa-0.2 mu m, and the results showed that colloidal Cd, Cu and Pb were dominated by 1-10 kDa (1-3 and 3-10 kDa), 1-3 kDa and 10 kDa-0.2 lm fractions, respectively. The coagulation/flocculation of low-molecular-weight (1-10 kDa) colloidal Cd and Cu in the estuaries was obvious and strong, while the enrichment of dissolved Pb in the 10 kDa-0.2 lm fraction may be mainly related to its biogeochemical interactions with Fe-oxides, which is easy to occur in macromolecular colloids. In addition, the actual molecular weight cutoffs (MWCOs) of the three used CUF units with nominal MWCOs of 1, 3 and 10 kDa were determined to be 4.9, 8.5 and 33.9 kDa, respectively, indicating that membrane calibration is essential for explaining the actual fraction of dissolved trace metals and verifying the integrity of ultrafiltration membrane. Overall, the results in this study provide a further understanding of the heterogeneity in biogeochemical features, migration and fate of toxic trace metals in aquatic ecosystems, especially that of the river-sea mixing zone. (C) 2019 Elsevier B.V. All rights reserved

D4AM: A General Denoising Framework for Downstream Acoustic Models

The performance of acoustic models degrades notably in noisy environments. Speech enhancement (SE) can be used as a front-end strategy to aid automatic speech recognition (ASR) systems. However, existing training objectives of SE methods are not fully effective at integrating speech-text and noisy-clean paired data for training toward unseen ASR systems. In this study, we propose a general denoising framework, D4AM, for various downstream acoustic models. Our framework fine-tunes the SE model with the backward gradient according to a specific acoustic model and the corresponding classification objective. In addition, our method aims to consider the regression objective as an auxiliary loss to make the SE model generalize to other unseen acoustic models. To jointly train an SE unit with regression and classification objectives, D4AM uses an adjustment scheme to directly estimate suitable weighting coefficients rather than undergoing a grid search process with additional training costs. The adjustment scheme consists of two parts: gradient calibration and regression objective weighting. The experimental results show that D4AM can consistently and effectively provide improvements to various unseen acoustic models and outperforms other combination setups. Specifically, when evaluated on the Google ASR API with real noisy data completely unseen during SE training, D4AM achieves a relative WER reduction of 24.65% compared with the direct feeding of noisy input. To our knowledge, this is the first work that deploys an effective combination scheme of regression (denoising) and classification (ASR) objectives to derive a general pre-processor applicable to various unseen ASR systems. Our code is available at https://github.com/ChangLee0903/D4AM