Likewise, independently withamodified named JP2-WSI15].

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.

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.

As more content generated by large language models (LLMs) floods into the Internet, information retrieval (IR) systems now face the challenge of distinguishing and handling a blend of human-authored and machine-generated texts. Recent studies suggest that neural retrievers may exhibit a preferential inclination toward LLM-generated content, while classic term-based retrievers like BM25 tend to favor human-written documents. This paper investigates the influence of LLM-generated content on term-based retrieval models, which are valued for their efficiency and robust generalization across domains. Our linguistic analysis reveals that LLM-generated texts exhibit smoother high-frequency and steeper low-frequency Zipf slopes, higher term specificity, and greater document-level diversity. These traits are aligned with LLMs being trained to optimize reader experience through diverse and precise expressions. Our study further explores whether term-based retrieval models demonstrate source bias, concluding that these models prioritize documents whose term distributions closely correspond to those of the queries, rather than displaying an inherent source bias. This work provides a foundation for understanding and addressing potential biases in term-based IR systems managing mixed-source content.

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.