Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices

Predicting stock market is vital for investors and policymakers, acting as a barometer of the economic health. We leverage social media data, a potent source of public sentiment, in tandem with macroeconomic indicators as government-compiled statistics, to refine stock market predictions. However, prior research using tweet data for stock market prediction faces three challenges. First, the quality of tweets varies widely. While many are filled with noise and irrelevant details, only a few genuinely mirror the actual market scenario. Second, solely focusing on the historical data of a particular stock without considering its sector can lead to oversight. Stocks within the same industry often exhibit correlated price behaviors. Lastly, simply forecasting the direction of price movement without assessing its magnitude is of limited value, as the extent of the rise or fall truly determines profitability. In this paper, diverging from the conventional methods, we pioneer an ECON. The framework has following advantages: First, ECON has an adept tweets filter that efficiently extracts and decodes the vast array of tweet data. Second, ECON discerns multi-level relationships among stocks, sectors, and macroeconomic factors through a self-aware mechanism in semantic space. Third, ECON offers enhanced accuracy in predicting substantial stock price fluctuations by capitalizing on stock price movement. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility

Personalized Federated Learning via ADMM with Moreau Envelope

Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergence on heterogeneous data. However, most existing PFL frameworks require strong assumptions for convergence. In this paper, we propose an alternating direction method of multipliers (ADMM) for training PFL models with Moreau envelope (FLAME), which achieves a sublinear convergence rate, relying on the relatively weak assumption of gradient Lipschitz continuity. Moreover, due to the gradient-free nature of ADMM, FLAME alleviates the need for hyperparameter tuning, particularly in avoiding the adjustment of the learning rate when training the global model. In addition, we propose a biased client selection strategy to expedite the convergence of training of PFL models. Our theoretical analysis establishes the global convergence under both unbiased and biased client selection strategies. Our experiments validate that FLAME, when trained on heterogeneous data, outperforms state-of-the-art methods in terms of model performance. Regarding communication efficiency, it exhibits an average speedup of 3.75x compared to the baselines. Furthermore, experimental results validate that the biased client selection strategy speeds up the convergence of both personalized and global models.Comment: 15 page

ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility

Analisis Adopsi Inovasi Teknologi Pertanian Berbasis Padi di Sumatera Selatan dalam Perspektif Komunikasi

Analysis of Adoption of Agricultural Technology Innovation Rice-based Farming in Sumatra inthe perspective of communications. Assessment Institute of Agricultural Technology (AIAT) South Sumatrahas produced innovative rice-based farming technology in various agroecosystem. However, adoption ratesare still relatively low. Evaluation of four assessments aimed to identify the factors that predominantly affectthe adoption of technological innovation based local-specific farming rice and to know the level of adoption.This activity is carried out in OKI, East OKU and Banyuasin regencies with 67 respondents interviewedin July-September 2007. The results of this assessment showed that the factors that influence the adoption oftechnological innovations such as the level of selective exposure of technology innovation, cosmopolite,triability, complexity of technology and agricultural extension intensity. The average adoption index for thepacket of rice cultivation technology was 50.32%. As many as 93.02% of respondents have positive perceptionsof the researcher-extension AIAT South Sumatra as the communicator in delivering information technology.Most respondents (80%) expressed a desire to obtain agricultural information generated AIAT South Sumatra.Key words: Adoption, innovation, rice, communication Balai Pengkajian Teknologi Pertanian (BPTP) Sumatera Selatan sudah menghasilkan inovasi teknologipertanian berbasis padi di berbagai agroekosistem. Namun tingkat adopsinya masih relatif rendah. Evaluasi terhadapempat pengkajian ini bertujuan untuk mengidentifikasi faktor-faktor yang dominan mempengaruhi proses adopsiinovasi teknologi pertanian spesifik lokasi berbasis padi dan mengetahui tingkat adopsinya. Kegiatan ini dilakukan diKabupaten OKI, OKU Timur dan Banyuasin dengan mewawancarai 67 orang responden pada bulan Juli – September2007. Berdasarkan hasil analisis deskriptif kualitatif diketahui bahwa (1) adopsi inovasi teknologi budidaya tanamanpadi di Sumatera Selatan dipengaruhi oleh tingkat kebutuhan petani terhadap inovasi teknologi, sifat kekosmopolitanpetani, triabilitas dan kompleksitas teknologi dan intensitas pembinaan, (2) indeks adopsi inovasi petani terhadappaket teknologi budidaya padi kondisinya beragam tergantung pada jenis kegiatan, (3) petani di Sumatera Selatanumumnya memberikan apresiasi positif terhadap peneliti-penyuluh BPTP Sumatera Selatan, terlihat dari tingginyaminat petani untuk mendapatkan berbagai media informasi pertanian BPTP Sumatera Selatan, dan (4) temuankajian ini mengindikasikan faktor komunikasi memegang peran utama yang dapat mempengaruhi adopsi teknologi

You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model

Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow inference speed and increased cost for severing. While some certain predictions benefit from the full complexity of the large-scale model, not all of inputs need the same amount of computation to conduct, potentially leading to computation resource waste. To handle this challenge, early exiting is proposed to adaptively allocate computational power in term of input complexity to improve inference efficiency. The existing early exiting strategies usually adopt output confidence based on intermediate layers as a proxy of input complexity to incur the decision of skipping following layers. However, such strategies cannot apply to encoder in the widely-used unified architecture with both encoder and decoder due to difficulty of output confidence estimation in the encoder. It is suboptimal in term of saving computation power to ignore the early exiting in encoder component. To handle this challenge, we propose a novel early exiting strategy for unified visual language models, which allows dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely \textbf{MuE}. By decomposing the image and text modalities in the encoder, MuE is flexible and can skip different layers in term of modalities, advancing the inference efficiency while minimizing performance drop. Experiments on the SNLI-VE and MS COCO datasets show that the proposed approach MuE can reduce expected inference time by up to 50\% and 40\% while maintaining 99\% and 96\% performance respectively

Diversity and antibacterial and antioxidant activities of fungal endophytes from the roots of Eucalyptus deglupta

In this study, 45 endophytic fungal strains were isolated from the roots of Eucalyptus deglupta. Among them, 16 distinct strains were identified and classified into 14 different genera (Celoporthe, Aspergillus, Castanediella, Chaetomium, Biscogniauxia, Sordariales, Pestalotiopsis, Clitopilus, Cylindrocladiella, Calonectria, Trichoderma, Xylaria, Neofusicoccum and Pleosporales) according to their morphological characteristics and molecular information. The genera Aspergillus and Calonectria were the dominant endophytic fungi in the roots of E. deglupta. In addition, the antibacterial and antioxidant activities of the 16 endophytic fungi isolated from the roots of E. deglupta were evaluated. All the strains displayed inhibitory activities against Agrobacterium tumefaciens, Bacillus subtilis, Escherichia coli, and Xanthomonas vesicatoria. Strains Edf-1 to Edf-4, Edf-11 and Edf-12 demonstrated strong inhibitory activity against R. solanacearum with plaque diameters between 5 and 10 mm. The crude extract of Edf-14 had inhibitory activity against all tested bacteria. Five strains, Edf-1 to Edf-5, demonstrated a strong scavenging capacity for 2,2-diphenyl-1-picrylhydrazyl (DPPH), with IC50 values of 0.26 ± 0.04, 0.11 ± 0.03, 0.20 ± 0.05, 0.10 ± 0.04 and 0.14 ± 0.02 mg/mL, respectively. Hence, endophytic fungi isolated from the roots of E. deglupta showed antibacterial and antioxidant activities, providing a theoretical foundation for further isolation and identification of specific active components

Modular Synthesis and First Antimicrobial Investigation of Mycoleptodiscin A and Simplified Indolosesquiterpenoids

The structure-activity relationship of the structurally unusual indolosesquiterpenoid mycoleptodiscin A is unknown due to natural scarcity and inefficient synthesis. A modular approach leveraging Larock indole synthesis has been established to access mycoleptodiscin A and a divergent collection of unprecedented drimanyl indoles. This tactic features the utilization of a commercially available and inexpensive (+)-sclareolide, modularity, purification-economy, and scalability, which facilitates the first biological evaluation of mycoleptodiscin A and related pseudo-natural products for identification of promising new leads

Facile and Divergent Synthesis and Antifungal Evaluation of Drimane Meroterpenoids by Merging Decarboxylative Borylation and Suzuki Coupling

Based on the easily accessible and inexpensive sclareol, the bench stable drimanyl BPin was achieved through oxidative degradation and decarboxylative borylation. The following Suzuki coupling of the borate intermediate was developed as a powerful platform for a large variety of drimane meroterpenoids, non-natural mimics and ring-distorted motifs. Key features include mild conditions, operational facility, broad substrate scope, scalability, and good chemofidelity and stereofidelity as well as the easy availability of the various coupling partners. Expedient formal synthesis of a large number of complex natural products is feasible via the current methodology. The high degree of practicality of the current chemistry bodes well for the discovery sciences towards pharmaceutically important meroterpenoids or leads through detailed SAR study. The facile accessibility to drimane meroterpenoids and mimics allowed the unprecedented evaluation of these chemical entities as antifungal agents. The promising activity of the non-natural mimics may open a new window for structural optimization and the identification of new targets.</p