Computing the degree of semantic relatedness words is a key functionality many language applications such as search, clustering, and disambiguation. Previous approaches to computing mostly used static resources, while essentially ignoring their temporal aspects. We believe that considerable amount information can also be found in studying patterns word usage over time. Consider, for instance, newspaper archive spanning years. Two "war" "peace" might rarely co-occur same articles, yet use...

The problem we tackle in this work is, given a present news event, to generate plausible future event that can be caused by the event. We new methodology for modeling and predicting such events using machine learning data mining techniques. Our Pundit algorithm generalizes examples of causality pairs infer predictor. To obtain precise labeled examples, mine 150 years articles, apply semantic natural language techniques titles containing certain predefined patterns. For generalization, model...

We describe and evaluate methods for learning to forecast forthcoming events of interest from a corpus containing 22 years news stories. consider the examples identifying significant increases in likelihood disease outbreaks, deaths, riots advance occurrence these world. provide details studies, including automated extraction generalization sequences corpora multiple web resources. predictive power approach on real-world withheld system.

Query auto-completion (QAC) is a common feature in modern search engines. High quality QAC candidates enhance experience by saving users time that otherwise would be spent on typing each character or word sequentially.

User behavior on the Web changes over time. For example, queries that people issue to search engines, and underlying informational goals behind vary In this paper, we examine how model predict temporal user behavior. We develop a modeling framework adapted from physics signal processing can be used time-varying using smoothing trends. also explore other dynamics of behaviors, such as detection periodicities surprises. learning procedure construct models users' activities based features...

The Electrocardiogram (ECG) is performed routinely by medical personnel to identify structural, functional and electrical cardiac events. Many attempts were made automate this task using machine learning algorithms including classic supervised deep neural networks, reaching state-of-the-art performance. ECG signal conveys the specific activity of each subject thus extreme variations are observed between patients. These challenging for algorithms, impede generalization. In work, we propose a...

We address the task of Named Entity Disambiguation (NED) for noisy text. present WikilinksNED, a large-scale NED dataset text fragments from web, which is significantly noisier and more challenging than existing news-based datasets. To capture limited local context surrounding each mention, we design neural model train it with novel method sampling informative negative examples. also describe new way initializing word entity embeddings that improves performance. Our outperforms...

In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of graph's nodes and edges over time incorporates dynamics framework different graph prediction tasks. joint loss function creates by learning to combine its historical embeddings, such it optimizes per given task (e.g., link prediction). The is initialized using static which are then aligned representations at points, eventually adapted optimization. evaluate...

Our world is constantly evolving, and so the content on web. Consequently, our languages, often said to mirror world, are dynamic in nature. However, most current contextual language models static cannot adapt changes over time. In this work, we propose a temporal model called TempoBERT, which uses time as an additional context of texts. technique based modifying texts with information performing masking - specific for supplementary information. We leverage approach tasks semantic change...

End stage renal disease (ESRD) describes the most severe of chronic kidney (CKD), when patients need dialysis or transplant. There is often a delay in recognizing, diagnosing, and treating various etiologies CKD. The objective present study was to employ machine learning algorithms develop prediction model for progression ESRD based on large-scale multidimensional database.This analyzed 10,000,000 medical insurance claims from 550,000 patient records using commercial health database....

The queries people issue to a search engine and the results clicked following query change over time. For example, after earthquake in Japan March 2011, japan spiked popularity issuing were more likely click government-related than they would prior earthquake. We explore modeling prediction of such temporal patterns Web behavior. develop framework adapted from physics signal processing harness it predict behavior using smoothing, trends, periodicities, surprises. Using current past...

The Electrocardiogram (ECG) is performed routinely by medical personell to identify structural, functional and electrical cardiac events. Many attempts were made automate this task using machine learning algorithms. Numerous supervised algorithms proposed, requiring manual feature extraction. Lately, deep neural networks also proposed for reaching state-of-the-art results. ECG signal conveys the specific activity of each subject thus extreme variations are observed between patients. These...

Pretrained language models based on the transformer architecture have shown great success in NLP.Textual training data often comes from web and is thus tagged with time-specific information, but most ignore this information.They are trained textual alone, limiting their ability to generalize temporally.In work, we extend key component of architecture, i.e., self-attention mechanism, propose temporal attention - a time-aware mechanism.Temporal can be applied any model requires input texts...

Accurate prediction of changing web page content improves a variety retrieval and related components. For example, given such algorithm one can both design better crawling strategy that only recrawls pages when necessary as well proactive mechanism for personalization pushes associated with user revisitation directly to the user. While many techniques modeling change have focused simply on past frequency, our work goes beyond by additionally studying usefulness in of: page's content; degree...

Designing a new drug is lengthy and expensive process. As the space of potential molecules very large (Polishchuk, P. G.; Madzhidov, T. I.; Varnek, A. Estimation size drug-like chemical based on GDB-17 data. J. Comput.-Aided Mol. Des. 2013, 27, 675–679 10.1007/s10822-013-9672-4), common technique during discovery to start from molecule which already has some desired properties. An interdisciplinary team scientists generates hypothesis about required changes prototype. In this work, we...

Abstract Despite effective medications, rates of uncontrolled hypertension remain high. Treatment protocols are largely based on randomized trials and meta‐analyses these studies. The objective this study was to test the utility machine learning big data in gaining insight into treatment hypertension. We applied techniques such as decision trees neural networks, identify determinants that contribute success drug a large set patients. also identified concomitant drugs not considered have...

Product descriptions play an important role in the e-commerce ecosystem, conveying to buyers information about a merchandise they may purchase. Yet, on leading websites, with high volumes of new items offered for sale every day, product are often lacking or missing altogether. Moreover, many include that holds little value and sometimes even disrupts buyers, attempt draw attention purchases. In this work, we suggest mitigate these issues by generating short crowd-based from user reviews . We...

The challenge in computational biology and drug discovery lies creating comprehensive representations of proteins molecules that capture their intrinsic properties interactions. Traditional methods often focus on unimodal data, such as protein sequences or molecular structures, limiting ability to complex biochemical relationships. This work enhances these by integrating reactions encompassing interactions between proteins. By leveraging reaction data alongside pre-trained embeddings from...

Computational prediction of enzymatic reactions represents a crucial challenge in sustainable chemical synthesis across various scientific domains, ranging from drug discovery to materials science and green chemistry. These syntheses rely on proteins that selectively catalyze complex molecular transformations. protein catalysts exhibit remarkable substrate adaptability, with the same often catalyzing different transformations depending its partners. Current approaches representation reaction...

Given a current news event, we tackle the problem of generating plausible predictions future events it might cause. We present new methodology for modeling and predicting such using machine learning data mining techniques. Our Pundit algorithm generalizes examples causality pairs to infer predictor. To obtain precisely labeled examples, mine 150 years articles apply semantic natural language techniques headlines containing certain predefined patterns. For generalization, model uses vast...

Abstract Background Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility machine learning in creating a prediction model and algorithm for early OUD. Subjects methods analyzed data gathered commercial claim database from January 1, 2006, December 31, 2018 10 medical insurance claims 550 000 patient records. compiled 436 predictor candidates, divided six feature groups ‐...

Understanding the dynamics of complex biological and physiological systems has been explored for many years in form physically-based mathematical simulators. The behavior a physical system is often described via ordinary differential equations (ODE), referred to as dynamics. In standard case, are derived from purely considerations. By contrast, this work we study how can be learned by generative adversarial network which combines both data As use focus on heart signal electrocardiogram...

Search systems are often focused on providing relevant results for the "now", assuming both corpora and user needs that focus present. However, many today reflect significant longitudinal collections ranging from 20 years of Web to hundreds digitized newspapers books. Understanding temporal intent retrieving most historical content has become a challenge. Common search features, such as query expansion, leverage relationship between terms but cannot function well across all times when...