BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching

Financial time series have historically been assumed to be a martingale process under the Random Walk hypothesis. Instead of making investment decisions using the raw prices alone, various multimodal pattern matching algorithms have been developed to help detect subtly hidden repeatable patterns within the financial market. Many of the chart-based pattern matching tools only retrieve similar past chart (PC) patterns given the current chart (CC) pattern, and leaves the entire interpretive and predictive analysis, thus ultimately the final investment decision, to the investors. In this paper, we propose an approach of ranking similar PC movements given the CC information and show that exploiting this as additional features improves the directional prediction capacity of our model. We apply our ranking and directional prediction modeling methodologies on Bitcoin due to its highly volatile prices that make it challenging to predict its future movements.Comment: 5 pages, 2 figures, KDD 2023 Machine Learning in Finance worksho

Reconstruction of multiplex networks via graph embeddings

Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations for a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper of network science. Here, we develop a machine learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques.Comment: 12 pages, 10 figures, 2 table

LMCanvas: Object-Oriented Interaction to Personalize Large Language Model-Powered Writing Environments

Large language models (LLMs) can enhance writing by automating or supporting specific tasks in writers' workflows (e.g., paraphrasing, creating analogies). Leveraging this capability, a collection of interfaces have been developed that provide LLM-powered tools for specific writing tasks. However, these interfaces provide limited support for writers to create personal tools for their own unique tasks, and may not comprehensively fulfill a writer's needs -- requiring them to continuously switch between interfaces during writing. In this work, we envision LMCanvas, an interface that enables writers to create their own LLM-powered writing tools and arrange their personal writing environment by interacting with "blocks" in a canvas. In this interface, users can create text blocks to encapsulate writing and LLM prompts, model blocks for model parameter configurations, and connect these to create pipeline blocks that output generations. In this workshop paper, we discuss the design for LMCanvas and our plans to develop this concept.Comment: Accepted to CHI 2023 Workshop on Generative AI and HC

Atypical occurrence of anti-Ma2-associated encephalitis after breast cancer surgery and COVID-19

In this report, we present a rare case of anti-Ma2-associated encephalitis concurrent with coronavirus disease 2019 (COVID-19) following breast cancer surgery. The patient exhibited minimal clinical symptoms of COVID-19 infection but developed seizures and altered mental status after surgery, leading to diagnosis of a classic paraneoplastic syndrome. This case highlights the possibility of paraneoplastic neurological syndrome even after cancer surgery and the need for careful consideration of post-acute infection syndromes when neurological symptoms occur following an infection

In silico identification of metabolic engineering strategies for improved lipid production in Yarrowia lipolytica by genome-scale metabolic modeling

Background Yarrowia lipolytica, an oleaginous yeast, is a promising platform strain for production of biofuels and oleochemicals as it can accumulate a high level of lipids in response to nitrogen limitation. Accordingly, many metabolic engineering efforts have been made to develop engineered strains of Y. lipolytica with higher lipid yields. Genome-scale model of metabolism (GEM) is a powerful tool for identifying novel genetic designs for metabolic engineering. Several GEMs for Y. lipolytica have recently been developed; however, not many applications of the GEMs have been reported for actual metabolic engineering of Y. lipolytica. The major obstacle impeding the application of Y. lipolytica GEMs is the lack of proper methods for predicting phenotypes of the cells in the nitrogen-limited condition, or more specifically in the stationary phase of a batch culture. Results In this study, we showed that environmental version of minimization of metabolic adjustment (eMOMA) can be used for predicting metabolic flux distribution of Y. lipolytica under the nitrogen-limited condition and identifying metabolic engineering strategies to improve lipid production in Y. lipolytica. Several well-characterized overexpression targets, such as diglyceride acyltransferase, acetyl-CoA carboxylase, and stearoyl-CoA desaturase, were successfully rediscovered by our eMOMA-based design method, showing the relevance of prediction results. Interestingly, the eMOMA-based design method also suggested non-intuitive knockout targets, and we experimentally validated the prediction with a mutant lacking YALI0F30745g, one of the predicted targets involved in one-carbon/methionine metabolism. The mutant accumulated 45% more lipids compared to the wild-type. Conclusion This study demonstrated that eMOMA is a powerful computational method for understanding and engineering the metabolism of Y. lipolytica and potentially other oleaginous microorganisms.This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF2017R1E1A1A01073523) and Industrial Strategic technology development program, 20002734 funded by the Ministry of Trade, Industry & Energy (MI, Korea

A Multi-Platform Collection of Social Media Posts about the 2022 U.S. Midterm Elections

Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.Comment: 8 pages, 3 figures, forthcoming in ICWSM2

Chromatin accessibility of circulating CD8(+) T cells predicts treatment response to PD-1 blockade in patients with gastric cancer

Although tumor genomic profiling has identified small subsets of gastric cancer (GC) patients with clinical benefit from anti-PD-1 treatment, not all responses can be explained by tumor sequencing alone. We investigate epigenetic elements responsible for the differential response to anti-PD-1 therapy by quantitatively assessing the genome-wide chromatin accessibility of circulating CD8(+) T cells in patients' peripheral blood. Using an assay for transposase-accessible chromatin using sequencing (ATAC-seq), we identify unique open regions of chromatin that significantly distinguish anti-PD-1 therapy responders from non-responders. GC patients with high chromatin openness of circulating CD8(+) T cells are significantly enriched in the responder group. Concordantly, patients with high chromatin openness at specific genomic positions of their circulating CD8(+) T cells demonstrate significantly better survival than those with closed chromatin. Here we reveal that epigenetic characteristics of baseline CD8(+) T cells can be used to identify metastatic GC patients who may benefit from anti-PD-1 therapy. Anti-PD-1 therapy could induce a durable response in patients with gastric cancer, however biomarkers to predict response to immunotherapy are generally lacking. Here the authors report that openness of chromatin in circulating CD8(+) T cells predicts treatment outcome in patients with metastatic gastric cancer treated with pembrolizumab

Search for supersymmetry in proton-proton collisions at <mml:msqrt>s</mml:msqrt>=13 TeV in events with high-momentum Z bosons and missing transverse momentum

A search for new physics in events with two highly Lorentz-boosted Z bosons and large missing transverse momentum is presented. The analyzed proton-proton collision data, corresponding to an integrated luminosity of 137 fb(-1), were recorded at s = 13 TeV by the CMS experiment at the CERN LHC. The search utilizes the substructure of jets with large radius to identify quark pairs from Z boson decays. Backgrounds from standard model processes are suppressed by requirements on the jet mass and the missing transverse momentum. No significant excess in the event yield is observed beyond the number of background events expected from the standard model. For a simplified supersymmetric model in which the Z bosons arise from the decay of gluinos, an exclusion limit of 1920 GeV on the gluino mass is set at 95% confidence level. This is the first search for beyond-standard-model production of pairs of boosted Z bosons plus large missing transverse momentum.Peer reviewe