Sentiment Analysis in Social Media Platforms: The Contribution of Social Relationships

The massive amount of data in social media platforms is a key source for companies to analyze customer sentiment and opinions. Many existing sentiment analysis approaches solely rely on textual contents of a sentence (e.g. words) for sentiment identification. Consequently, current sentiment analysis systems are ineffective for analyzing contents in social media because people may use non-standard language (e.g., abbreviations, misspellings, emoticons or multiple languages) in online platforms. Inspired by the attribution theory that is grounded in social psychology, we propose a sentiment analysis framework that considers the social relationships among users and contents. We conduct experiments to compare the proposed approach against the existing approaches on a dataset collected from Facebook. The results indicate that we can more accurately classify sentiment of sentences by utilizing social relationships

THE THREE-DIMENSIONAL STRUCTURE OF LIGNITE HUMIC ACID FERMENTATION TEMPERATURE BASED ON MATLAB

Abstract: The temperature is one of the most important factors in the traditional fermentation. Wireless temperature monitoring system is used for real-time monitoring on the three lignite fermentation heap temperature of YUNNAN GREENTECH CO., LTD. in this experiment, lignite humic acid content, organic matter content and PH are tracked and detected. It reflect directly internal temperature changes of the fermentation heap through three-dimensional structure of the fermentation heap temperature mapping, especially of every lignite layers at certain point. More fermentation heap monitoring points, other indicators of organic fertilizer assessment besides humic acid content, organic matter content and PH can be selected to be monitored in further study. A more scientific and comprehensive lignite fermentation law can be explored to guide production practices better, and improve production quality and yield

Coreset Selection with Prioritized Multiple Objectives

Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms. It strives to identify a small subset from large-scale data, so that training only on the subset practically performs on par with full data. When coreset selection is applied in realistic scenes, under the premise that the identified coreset has achieved comparable model performance, practitioners regularly desire the identified coreset can have a size as small as possible for lower costs and greater acceleration. Motivated by this desideratum, for the first time, we pose the problem of "coreset selection with prioritized multiple objectives", in which the smallest coreset size under model performance constraints is explored. Moreover, to address this problem, an innovative method is proposed, which maintains optimization priority order over the model performance and coreset size, and efficiently optimizes them in the coreset selection procedure. Theoretically, we provide the convergence guarantee of the proposed method. Empirically, extensive experiments confirm its superiority compared with previous strategies, often yielding better model performance with smaller coreset sizes

HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts

In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our work is motivated by an important observation that it is, in many cases, possible to achieve temporally robust predictive performance via hyperparameter optimization. Based on this observation, we leverage the `worst-case-oriented' philosophy from the robust optimization literature to help find such robust hyperparameter configurations. HyperTime imposes a lexicographic priority order on average validation loss and worst-case validation loss over chronological validation sets. We perform a theoretical analysis on the upper bound of the expected test loss, which reveals the unique advantages of our approach. We also demonstrate the strong empirical performance of the proposed method on multiple machine learning tasks with temporal distribution shifts.Comment: 19 pages, 7 figure

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework

This technical report presents AutoGen, a new framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools. AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as automated agent chats. We provide many diverse examples of how developers can easily use AutoGen to effectively solve tasks or build applications, ranging from coding, mathematics, operations research, entertainment, online decision-making, question answering, etc.Comment: 28 page

A virus-like particle of the hepatitis B virus preS antigen elicits robust neutralizing antibodies and T cell responses in mice

The preS antigen of hepatitis B virus (HBV) corresponds to the N-terminal polypeptide in the large (L) antigen in addition to the small (S) antigen. The virus-like particle (VLP) of the S antigen is widely used as a vaccine to protect the population from HBV infection. The presence of the S antigen and its antibodies in patient blood has been used as markers to monitor hepatitis B. However, there is very limited knowledge about the preS antigen. We generated a preS VLP that is formed by a chimeric protein between preS and hemagglutinin (HA), and the matrix protein M1 of influenza virus. The HBV preS antigen is displayed on the surface of preS VLP. Asn112 and Ser98 of preS in VLP were found to be glycosylated and O-glycosylation of Ser98 has not been reported previously. The preS VLP shows a significantly higher immunogenicity than recombinant preS, eliciting robust anti-preS neutralizing antibodies. In addition, preS VLP is also capable of stimulating preS-specific CD8+ and CD4+ T cell responses in Balb/c mice and HBV transgenic mice. Furthermore, preS VLP immunization provided protection against hydrodynamic transfection of HBV DNA in mice. The data clearly suggest that this novel preS VLP could elicit robust immune responses to the HBV antigen, and can be potentially developed into prophylactic and therapeutic vaccines

An Empirical Study on Challenging Math Problem Solving with GPT-4

Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language across numerous science and engineering fields. While several prior works have investigated solving elementary mathematics using LLMs, this work explores the frontier of using GPT-4 for solving more complex and challenging math problems. We evaluate various ways of using GPT-4. Some of them are adapted from existing work, and one is \MathChat, a conversational problem-solving framework newly proposed in this work. We perform the evaluation on difficult high school competition problems from the MATH dataset, which shows the advantage of the proposed conversational approach

Unraveling the mechanisms of intervertebral disc degeneration: an exploration of the p38 MAPK signaling pathway

Intervertebral disc (IVD) degeneration (IDD) is a worldwide spinal degenerative disease. Low back pain (LBP) is frequently caused by a variety of conditions brought on by IDD, including IVD herniation and spinal stenosis, etc. These conditions bring substantial physical and psychological pressure and economic burden to patients. IDD is closely tied with the structural or functional changes of the IVD tissue and can be caused by various complex factors like senescence, genetics, and trauma. The IVD dysfunction and structural changes can result from extracellular matrix (ECM) degradation, differentiation, inflammation, oxidative stress, mechanical stress, and senescence of IVD cells. At present, the treatment of IDD is basically to alleviate the symptoms, but not from the pathophysiological changes of IVD. Interestingly, the p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway is involved in many processes of IDD, including inflammation, ECM degradation, apoptosis, senescence, proliferation, oxidative stress, and autophagy. These activities in degenerated IVD tissue are closely relevant to the development trend of IDD. Hence, the p38 MAPK signaling pathway may be a fitting curative target for IDD. In order to better understand the pathophysiological alterations of the intervertebral disc tissue during IDD and offer potential paths for targeted treatments for intervertebral disc degeneration, this article reviews the purpose of the p38 MAPK signaling pathway in IDD

Advances in the application of co-culture strategies in organoids

As a good in vitro research model, organoids are more and more widely used in the biomedical field. By developing self-assembled 3D structures using various tissue culture techniques, organoids can rebuild the high complexity of cells in the inherent structure of the organ, and are therefore unanimously used to study mechanisms regulating body development and disease, high-throughput drug screening, and personalized treatment and so on. To better recapitulate cell-to-cell interactions within the microenvironment, co-culture strategies have been extended to more cell types, and their rapid development offers broader prospects for organoids and paves the way for the treatment of human diseases and regenerative medicine. This review discussed the role of co-culture strategies in organoid generation, and focused on the application of various cellular components and microorganisms in organoid construction, thereby providing reference and help for scholars to construct and develop organoids with a higher degree of in vivo simulation

Weakly coupled lithospheric extension in southern Tibet

AbstractWest–east extension is a prominent tectonic feature of southern and central Tibet despite ongoing north–south (N–S) convergence between India and Eurasia. Knowledge of deep structure beneath the N–S trending rifts is key to evaluating models proposed for their origin, including gravitational collapse, oblique convergence along the arcuate plate boundary, and mantle upwelling. We model direct S and Moho-reflected SsPmp phases at teleseismic distances to constrain variations in crustal thickness across the major rifts crossed by a ∼900-km long, W–E broadband array in the Lhasa Terrane. Crustal thicknesses are ∼70–80 km. However, Moho depth decreases by ∼10 km within a horizontal distance of 100 km west of the Yadong–Gulu rift (YGR) and Nyainquentanghla mountains (NQTL). This Moho uplift, taken with deep, extensional focal mechanisms and reduced seismic velocity in the upper mantle, suggests that asthenospheric upwelling has significantly contributed to the pattern of extension across the YGR and NQTL. The ∼100-km separation between surface rift and Moho uplift is likely enabled by partial decoupling across a ductile middle crust