Event extraction is a complex information extraction task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-world scenarios. In this study, we consider a more realistic setting of this task, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given without any oracle information, including event type, event ontology and trigger word. To solve this task, we propose a new framework, called COFFEE, which extracts the events solely based on the document context without referring to any oracle information. In particular, a contrastive selection model is introduced in COFFEE to rectify the generated triggers and handle multi-event instances. The proposed COFFEE outperforms state-of-the-art approaches under the oracle-free setting of the event extraction task, as evaluated on a public event extraction benchmark ACE05.

Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which is distinct and complementary to existing works: Instead of using external OOD data, we fully exploit the internal in-distribution (ID) training set by utilizing generative models to produce additional synthetic ID images. The classifier is then trained using a novel objective that computes weighted loss on real and synthetic ID samples together. Our training framework, which is termed SIO, serves as a "plug-and-play" technique that is designed to be compatible with existing and future OOD detection algorithms, including the ones that leverage available OOD training data. Our experiments on CIFAR-10, CIFAR-100, and ImageNet variants demonstrate that SIO consistently improves the performance of nearly all state-of-the-art (SOTA) OOD detection algorithms. For instance, on the challenging CIFAR-10 v.s. CIFAR-100 detection problem, SIO improves the average OOD detection AUROC of 18 existing methods from 86.25\% to 89.04\% and achieves a new SOTA of 92.94\% according to the OpenOOD benchmark. Code is available at https://github.com/zjysteven/SIO.

Sentence simplification tends to focus on the generic simplification of sentences by making them more readable and easier to understand. This paper provides a dataset aimed at training models that perform subject aware sentence simplifications rather than simplifying sentences as a whole. We also test models on that dataset which are inspired by model architecture used in abstractive summarization. We hand generated portions of the data and augment the dataset by further manipulating those hand written simplifications. Our results show that data-augmentation, data-masking, and model architecture choices used in summarization provide a solid baseline for comparison on subject aware simplification.

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one `best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a consensus matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name.

A trial in remote Australia is using artificial intelligence to improve diagnosis and treatment of heart disease.

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America's credibility among its adversaries has dwindled under President Biden, with some experts arguing a line can be drawn from the disastrous U.S. withdrawal from Afghanistan to more recent events such as the Chinese spy balloon and the downing of a U.S. drone by Russian forces. "I think the Biden administration's disastrous withdrawal from Afghanistan was a key catalyst for multiple trends that have undermined U.S. influence and deterrence," James Phillips, the senior research fellow for foreign policy at the Heritage Foundation, told Fox News Digital. "U.S. allies were shocked by the naive assumptions behind the withdrawal, the speed with which Washington abandoned longtime allies, and the incompetence of the policymakers that supervised the withdrawal." Phillips argues that it was not just American allies who took note of the administration's hastily executed exit from Afghanistan, but also adversaries such as China and Russia, who no longer fear U.S. deterrence. "U.S. adversaries perceived the withdrawal from Afghanistan as a manifestation of U.S. weakness and a desire to rapidly exit the Middle East," Phillips said.

CXOToday has engaged in an exclusive interview with Dr. Abhijit Dasgupta, SP Jain Global school of Management I have had experience as a Visiting Faculty at IIT Bombay, NIFT New Delhi, SPJIMR etc. during the last 25 years while I was having Leadership roles in Corporates in India / overseas. Since 2018, I am a full-time academic. Youthfulness and excitement to learn new things of students and the requirement to stay updated on the topics that I am teaching (among others) keeps me motivated – these are a couple of things that keeps me connected to the education sector. Till date it has been an intellectually satisfying experience for me. My first engagement with Analytics started way back in 2003, when as a CIO, the organization that I was working with during that time, invested in SAS suite of products to generate effective business intelligence.

It's early autumn in central China, and the streets of Ding Qingzi's village are turning into gold. Thousands of husked corncobs lie in orderly rectangles in front of homes, their kernels drying in the sun. The harvest is one of the heartbeats of rural life in Anhui Province, a constant that Ding, 35, has known since childhood. Yet few other rhythms remain. Except for the corn, the streets are almost empty. The sounds of children have faded. And for years, Ding struggled to find a wife. Few young women still live in the village. Fewer still would marry a welder unable to buy a house or pay a bride-price. "My family is not rich," Ding says.

SensOre Ltd (ASX:S3N) is pleased to announce that SensOre and Gold Road (ASX: GOR) have reached agreement to restructure arrangements surrounding the Yilgarn Exploration Ventures (YEV) portfolio. SensOre has agreed to acquire Gold Road Resources' 40% minority interest in YEV for 800,000 SensOre shares. Yilgarn Exploration Ventures holds a portfolio of prospective gold assets in the Eastern Goldfields of Western Australia. SensOre aims to become the top performing minerals targeting company in the world through the deployment of AI and machine learning (ML) technologies, specifically its Discriminant Predictive Targeting (DPT) workflow. SensOre collects all available geological information in a terrane and places it in a multidimensional hypercube or data cube.