Text Analytics Methods for Sentence-level Sentiment Analysis

Opinions have important effects on the process of decision making. With the explosion of text information on networks, sentiment analysis, which aims at predicting the opinions of people about specific entities, has become a popular tool to make sense of countless text information. There are multiple approaches for sentence-level sentiment analysis, including machine-learning methods and lexicon-based methods. In this MSc thesis we studied two typical sentiment analysis techniques -- AFINN and RNTN, which are also the representation of lexicon-based and machine-learning methods, respectively. The assumption of a lexicon-based method is that the sum of sentiment orientation of each word or phrase predicts the contextual sentiment polarity. AFINN is a word list with sentiment strength ranging from -5 to +5, which is constructed with the inclusion of Internet slang and obscene words. With AFINN, we extract sentiment words from sentences and sentiment scores are then assigned to these words. The sentiment of a sentence is aggregated as the sum of scores from all its words. The Stanford Sentiment Treebank is a corpus with labeled parse trees, which provides the community with the possibility to train compositional models based on supervised machine learning techniques. The labels of Stanford Sentiment Treebank involve 5 categories: negative, somewhat negative, neutral, somewhat positive and positive. Compared to the standard recursive neural network (RNN) and Matrix-Vector RNN, Recursive Neural Tensor Network (RNTN) is a more powerful composition model to compute compositional vector representations for input sentences. Dependent on the Stanford Sentiment Treebank, RNTN can predict the sentiment of input sentences by its computed vector representations. With the benchmark datasets that cover diverse data sources, we carry out a thorough comparison between AFINN and RNTN. Our results highlight that although RNTN is much more complicated than AFINN, the performance of RNTN is not better than that of AFINN. To some extent, AFINN is more simple, more generic and takes less computation resources than RNTN in sentiment analysis

Learned video compression with intra-guided enhancement and implicit motion information

Although learned approaches to video compression have been proposed with promising results, hand-engineered video codecs are still unbeaten. On the other hand, learned image compression has already surpassed traditional image codecs. In this paper, we propose a learned video compression system that mimics part of the pipeline of traditional codecs while leveraging learned image compression. It comprises two main modules: a learned intra-frame compression module, and a learned inter-frame compression module that is conditioned on intra-coded frames. These modules use separate learned probability models for entropy coding. The intra-frame codec uses a variant of nonlocal attention layers. Regarding the inter-frame codec, we propose an implicit motion information mechanism, and an enhancement of the inter-frame predictions by leveraging the high quality information of intra-coded frames. On the learned probability model side, we propose to use the reference frames as additional conditioning information. We used this system as our submitted entry for the 2021 Challenge on Learned Image Compression (CLIC). In our experiments, we show the effectiveness of our system and its components via a set of ablation studies.acceptedVersionPeer reviewe

Adaptation and Attention for Neural Video Coding

Neural image coding represents now the state-of-The-Art image compression approach. However, a lot of work is still to be done in the video domain. In this work, we propose an end-To-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention. Our codec is organized as an intra-frame codec paired with an inter-frame codec. As one architectural novelty, we propose to train the inter-frame codec model to adapt the motion estimation process based on the resolution of the input video. A second architectural novelty is a new neural block that combines concepts from split-Attention based neural networks and from DenseNets. Finally, we propose to overfit a set of decoder-side multiplicative parameters at inference time. Through ablation studies and comparisons to prior art, we show the benefits of our proposed techniques in terms of coding gains. We compare our codec to VVC/H.266 and RLVC, which represent the state-of-The-Art traditional and end-To-end learned codecs, respectively, and to the top performing end-To-end learned approach in 2021 CLIC competition, E2E_T_OL. Our codec clearly outperforms E2E_T_OL, and compare favorably to VVC and RLVC in some settings.acceptedVersionPeer reviewe

Adaptation and Attention for Neural Video Coding

Neural image coding represents now the state-of-The-Art image compression approach. However, a lot of work is still to be done in the video domain. In this work, we propose an end-To-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention. Our codec is organized as an intra-frame codec paired with an inter-frame codec. As one architectural novelty, we propose to train the inter-frame codec model to adapt the motion estimation process based on the resolution of the input video. A second architectural novelty is a new neural block that combines concepts from split-Attention based neural networks and from DenseNets. Finally, we propose to overfit a set of decoder-side multiplicative parameters at inference time. Through ablation studies and comparisons to prior art, we show the benefits of our proposed techniques in terms of coding gains. We compare our codec to VVC/H.266 and RLVC, which represent the state-of-The-Art traditional and end-To-end learned codecs, respectively, and to the top performing end-To-end learned approach in 2021 CLIC competition, E2E_T_OL. Our codec clearly outperforms E2E_T_OL, and compare favorably to VVC and RLVC in some settings.acceptedVersionPeer reviewe

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Text Analytics Methods for Sentence-level Sentiment Analysis

Opinions have important effects on the process of decision making. With the explosion of text information on networks, sentiment analysis, which aims at predicting the opinions of people about specific entities, has become a popular tool to make sense of countless text information. There are multiple approaches for sentence-level sentiment analysis, including machine-learning methods and lexicon-based methods. In this MSc thesis we studied two typical sentiment analysis techniques -- AFINN and RNTN, which are also the representation of lexicon-based and machine-learning methods, respectively. The assumption of a lexicon-based method is that the sum of sentiment orientation of each word or phrase predicts the contextual sentiment polarity. AFINN is a word list with sentiment strength ranging from -5 to +5, which is constructed with the inclusion of Internet slang and obscene words. With AFINN, we extract sentiment words from sentences and sentiment scores are then assigned to these words. The sentiment of a sentence is aggregated as the sum of scores from all its words. The Stanford Sentiment Treebank is a corpus with labeled parse trees, which provides the community with the possibility to train compositional models based on supervised machine learning techniques. The labels of Stanford Sentiment Treebank involve 5 categories: negative, somewhat negative, neutral, somewhat positive and positive. Compared to the standard recursive neural network (RNN) and Matrix-Vector RNN, Recursive Neural Tensor Network (RNTN) is a more powerful composition model to compute compositional vector representations for input sentences. Dependent on the Stanford Sentiment Treebank, RNTN can predict the sentiment of input sentences by its computed vector representations. With the benchmark datasets that cover diverse data sources, we carry out a thorough comparison between AFINN and RNTN. Our results highlight that although RNTN is much more complicated than AFINN, the performance of RNTN is not better than that of AFINN. To some extent, AFINN is more simple, more generic and takes less computation resources than RNTN in sentiment analysis

A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method

Functional differentiation related to decomposing complex carbohydrates of intestinal microbes between two wild zokor species based on 16SrRNA sequences

Abstract Background The intestinal microbes in mammals play a key role in host metabolism and adaptation. As a subterranean rodent, zokor digs tunnels for foraging and mating. These digging activities of zokors increase the energy expenditure relative to their aboveground counterparts. However, relatively little is known regarding intestinal microbes of zokor and how they make full use of limited food resources underground for high energy requirements. Results Eospalax cansus and Eospalax rothschildi had distinct intestinal microbes. Although the composition of intestinal microbes is similar in two species, the proportion of bacterium are distinctly different between them. At phylum level, 11 phyla were shared between two species. Firmicutes and Bacteroidota were two dominant microbes in both of two species, while Eospalax cansus have a significantly high proportion of Firmicutes/Bacteroidota than that of Eospalax rothschildi. At genus level, norank_f_Muribaculaceae were dominant microbes in both of two zokor species. The relative abundance of 12 genera were significantly different between two species. Some bacterium including unclassified_f__Lachnospiraceae, Lachnospiraceae_NK4A136_group, Ruminococcus and Eubacterium_siraeum_group associated with cellulose degradation were significantly enriched in Eospalax cansus. Although alpha diversity was with no significant differences between Eospalax cansus and Eospalax rothschildi, the intestinal microbes between them are significant distinct in PCoA analysis. We have found that trapping location affected the alpha diversity values, while sex and body measurements had no effect on alpha diversity values. PICRUSt metagenome predictions revealed significant enrichment of microbial genes involved in carbohydrate metabolism in Eospalax cansus rather than Eospalax rothschildi. Conclusions Our results demonstrate that Eospalax cansus harbor a stronger ability of fermentation for dietary plants than Eospalax rothschildi. The stronger ability of fermentation and degradation of cellulose of intestinal microbes of Eospalax cansus may be a long-time adaptation to limited food resources underground