Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model. Flow-based models are attractive in this setting because they admit exact likelihood optimization, which is equivalent to minimizing the expected number of bits per message. However, conventional flows assume continuous data, which may lead to reconstruction errors when quantized for compression. For that reason, we introduce a flow-based generative model for ordinal discrete data called Integer Discrete Flow (IDF): a bijective integer map that can learn rich transformations on high-dimensional data. As building blocks for IDFs, we introduce a flexible transformation layer called integer discrete coupling. Our experiments show that IDFs are competitive with other flow-based generative models. Furthermore, we demonstrate that IDF based compression achieves state-of-the-art lossless compression rates on CIFAR10, ImageNet32, and ImageNet64. To the best of our knowledge, this is the first lossless compression method that uses invertible neural networks.

Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model.

In this paper, we capture the dependence among the labels using a learned tree-structured hierarchy.

Israeli strike on Yemen's Houthis reportedly kills eight The Israeli military says its air force has carried out its most powerful strike in Yemen in response to the Houthi movement's repeated drone and missile attacks on Israel. The Israel Defense Forces (IDF) said dozens of its aircraft bombed targets belonging to the Houthis' security and intelligence services, and military in the capital Sanaa. The Houthi-run government's health ministry denounced what it called Israel's brutal crime, saying civilian facilities and residential buildings were hit and that eight people were killed. It comes a day after 22 people were injured, two of them seriously, in a Houthi drone attack in the Israeli Red Sea resort of Eilat. The Houthis have controlled much of north-western Yemen since they ousted the country's internationally recognised government from there 10 years ago, sparking a civil war.

In this study, book summaries and categories taken from book sites were classified using word embedding methods, natural language processing techniques and machine learning algorithms. In addition, one hot encoding, Word2Vec and Term Frequency - Inverse Document Frequency (TF - IDF) methods, which are frequently used word embedding methods were used in this study and their success was compared. Additionally, the combination table of the pre - processing methods used is shown and added to the table. Looking at the results, it was observed that Support Vector Machine, Naive Bayes and Logistic Regression Models and TF - IDF and One - Hot Encoder word embedding techniques gave more successful results for Turkish texts. Using word2vec to process big text data.

The IDF said it had killed Izadi in a strike on an apartment in Qom, south of Tehran, in the early hours of Saturday. He had been in charge of the Palestine Corps of the Iranian Revolutionary Guards Corps's (IRGC) Quds Force, responsible for handling ties with the Palestinian armed groups. He was reportedly instrumental in arming and financing Hamas, and had been responsible for military co-ordination between senior IRGC commanders and Hamas leaders, the IDF said. In April 2024, Izadi narrowly survived an Israeli air strike targeting the Iranian consulate in Damascus, Syria - an attack that killed several high-ranking Quds Force commanders. Israel later on Saturday also claimed to have killed another Quds Force commander, Behnam Shahriyari in a drone strike as he was travelling in a car through western Iran.

Israel activated a new aerial defense system – dubbed "Barak Magen" – for the first time on Sunday night, saying it intercepted and destroyed multiple Iranian drones. Israel activated a new aerial defense system – dubbed "Barak Magen," meaning "lightning shield" – for the first time on Sunday night, saying it intercepted and destroyed multiple Iranian drones. The Israeli Navy intercepted eight Iranian drones using the "Barak Magen" and its long-range air defense (LRAD) interceptor, which were launched from an Israeli navy Sa'ar 6 missile ship, the Israel Defense Forces (IDF) said in a statement. John Hannah, senior fellow at the National Security of America and the co-author of a report published earlier this month on Israel's defense against two massive Iranian missile attacks in 2024, told Fox News Digital on Monday that the air defense system "significantly enhances" the air and missile defense architecture of Israel's navy. "The Barak Magen is simply another arrow in the expanding quiver of Israel's highly sophisticated and increasingly diverse multi-tiered missile defense architecture – which was already, by leaps and bounds, the most advanced and experienced air defense system fielded by any country in the world," Hannah said.

Before Ibtihal Aboussad was fired by Microsoft for protesting the company's work with the Israeli military during a celebration of the firm's 50th anniversary, she sent two emails. The first went to all of her colleagues. She appealed to their universal humanity and urged them to stand against Microsoft's contracts to provide cloud computing software and artificial intelligence products to the Israeli Defense Forces (IDF). She sent the second to the "Muslims at Microsoft" email list. With her email, Aboussad told the Guardian, she wanted Muslim staff of companies such as Microsoft, Google and Amazon to stop regarding the question of whether they organize against their employer's work with the IDF as an issue of secular or professional ethics. It was a question of Islam, of their faith, she argued.

Background: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of data collection is often compromised. It is well established that the quality of a dataset significantly impacts the performance of machine learning models. Methods: A rudimentary error rate metric was developed to evaluate textual dataset quality at the token level. Mixtral Large Language Model (LLM) was used to quantify and correct errors in low quality datasets. The study analyzed two healthcare datasets: the high-quality MIMIC-III public hospital dataset and a lower-quality private dataset from Australian aged care homes. Errors were systematically introduced into MIMIC at varying rates, while the ACH dataset quality was improved using the LLM. Results: For the sampled 35,774 and 6,336 patients from the MIMIC and ACH datasets respectively, we used Mixtral to introduce errors in MIMIC and correct errors in ACH. Mixtral correctly detected errors in 63% of progress notes, with 17% containing a single token misclassified due to medical terminology. LLMs demonstrated potential for improving progress note quality by addressing various errors. Under varying error rates, feature representation performance was tolerant to lower error rates (<10%) but declined significantly at higher rates. Conclusions: The study revealed that models performed relatively well on datasets with lower error rates (<10%), but their performance declined significantly as error rates increased (>=10%). Therefore, it is crucial to evaluate the quality of a dataset before utilizing it for machine learning tasks. For datasets with higher error rates, implementing corrective measures is essential to ensure the reliability and effectiveness of machine learning models.