Monthly Archives: March 2025

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According to a report by the Russian newspaper on May 23, the Russian armed forces regained control of the village of Klesheevka in the Donetsk region through hard work. This settlement and the village of Rabodino, also recaptured by Russian troops, are symbols of the results of last year’s Ukraine counterattack.

On May 22, the Russian Ministry of Defense confirmed the news of the recapture of the village of Kresheevka. The Russian Ministry of Defense stated that under the active action of the southern military cluster forces, the Klesheevka settlement in Donetsk was liberated.

According to reports, in addition, the southern military cluster also attacked Ukrainian troops in three residential areas of Georgievka, Ostroye, and Konstantinovka.

The protracted battle for Klesheevka begins in 2023. Control of the village changed hands several times. The main target for contention is adjacent highlands, from which military activity throughout the village can be monitored. (Compiled by He Yingjun)

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According to a report by the Russian News Agency on March 20, Russian Foreign Ministry Spokesperson Zakharova said at a press conference that NATO has chosen a new direction of confrontation with Russia.

Reported that Zakharova said: NATO’s top priority is to open up a second front against my country in the Transcaucasia and reignite the war throughout the region.

She said NATO was not satisfied with Russia’s conciliatory policies in the Transcaucasia region.

The report mentioned that NATO Secretary General Stoltenberg visited Yerevan, the capital of Armenia, on the 19th and called on Armenia and Azerbaijan to sign an agreement aimed at paving the way for the normalization of relations between the two countries.

Russian Presidential Press Secretary Peskov said that NATO’s attempts to expand its influence may not strengthen stability in the Caucasus region. (Compiled by Zheng Yu)

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NATO troops exercise near Russian border

According to a report by the Russian News Agency on March 20, the Polish Ministry of Defense released a message saying that the NATO Northeast Division, composed of multinational forces, practiced defensive operations during exercises held in imaginary areas near the Russian border.

Poland’s Ministry of Defense said: During the Loyalty Lecda 2024 exercise, soldiers of the multinational Northeast Division were given such a task to carry out defensive operations in an imaginary area similar to the situation in northeastern Poland.

Reported that the command department of NATO’s Northeast Division refused to disclose details of the exercise content.

According to reports, the NATO Northeast Division headquarters is located in Elblong, Poland, and its mission is to coordinate the actions of NATO troops deployed in Poland, Lithuania, Latvia and Estonia. (Compiled by Liu Yang)

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According to a report by the Russian News Agency on July 15, Vladimir Talabrin, Russia’s Permanent Representative to the Organization for the Prohibition of Chemical Weapons and Russian Ambassador to the Netherlands, told reporters on the 15th that Western countries intend to accuse Moscow of using chemical weapons.

Talabrin reportedly said: Yes, they are working hard to do the work of the Technical Secretariat of the Organization for the Prohibition of Chemical Weapons to force it to take some steps intended to accuse Russia of using chemical weapons.

He said that Russia is fighting back against this in every possible way, trying to prove with concrete facts that all these efforts are futile.

Talabrin said that Russia does not and cannot pose a chemical weapons threat to Ukraine. He pointed out that Moscow does not rule out inviting experts from the Organization for the Prohibition of Chemical Weapons to study evidence of Kiev’s use of such weapons.

The 106th session of the Executive Council of the Organization for the Prohibition of Chemical Weapons was held in The Hague from July 9 to 12. Talabrin said after the meeting that Ukraine blatantly violated the convention and used military poisons against Russian troops, a point that many foreign experts and agencies have talked about. They noted that Ukraine’s armed forces were actively using drones to transport toxic chemicals. In addition, the United States, which supplied these substances to Kiev, is also violating the convention.

Lieutenant General Igor Kirilov, commander of the Radiation, Chemical and Biological Protection Forces of the Russian Armed Forces, said at a press conference that Kiev frequently violated the Chemical Weapons Convention. At the same time, the Organization for the Prohibition of Chemical Weapons, which was supposed to oversee the implementation of the convention, is now completely controlled by the West, which uses the organization to settle political scores with Russia. (Compiled by Wei Lianglei)

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Zagreb, March 15. Croatian President Milanovic announced on social media on the 15th that he will participate in the Croatian parliamentary elections to be held on April 17 as an independent candidate from the Croatian Social Democratic Party and will run for the position of Prime Minister.

Milanovic said he was full of confidence in winning the election and would resign as president after winning the election and assume the responsibility of leading the Croatian government. Until then, he will continue to perform his duties as President of Croatia in accordance with the Constitution.

Milanovic’s announcement shocked Croatia, because there is no precedent for a president to run for prime minister in Croatian history. As a Social Democrat, Milanovic’s move will undoubtedly inspire the Social Democratic Party, which is currently at a disadvantage in the parliamentary elections, but it will also cause huge controversy. Croatian constitutional expert Baric told the media that the Croatian Constitutional Court should clearly tell Milanovic that he can only run for prime minister if he resigns as president.

According to the latest opinion poll, the ruling party, the Croatian Democratic Community, led by Croatian Prime Minister Prenkovic, ranks first with a support rate of 265%, and the Social Democratic Party ranks second with a support rate of 179%.

Milanovic was elected President of Croatia in January 2020. Previously, he served as Prime Minister of Croatia from December 2011 to January 2016. (Reporter Li Xuejun)

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According to a report by the TASS news agency on March 18, the final data released by the Central Election Commission of the Russian Federation showed that all votes had been counted, and the vote rate of current President Vladimir Putin was 8728%.

According to reports, Russian Communist Party candidate Nikolai Haritonov ranked second with 431% of the vote, and New Party candidate Vladislav Davankov ranked third with 385% of the vote, ranking fourth. The Liberal Democratic Party candidate Leonid Slutsky, who received 320% of the vote.

Elapamfilova, Chairman of the Central Election Commission of the Russian Federation, said that the voter turnout rate in the Russian presidential election was unprecedented.

She said at the Central Election Commission meeting held on the 18th: As of 10:00 on the 18th, 87113127 voters participated in this election. The turnout rate hit an unprecedented record, reaching 7744%.

She pointed out that more than 76 million people voted for Putin, which also set a record. In 2018, about 56 million Russians voted for Putin.

The report mentioned that Pamfilova also said that since the beginning of the presidential election campaign, more than 12 million cyber attacks have been recorded, 150 times the usual number. (Compiled by Li Ran)

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Bangkok, July 16 (Reporter Chen Qianci Gao Bo) Six foreigners were found dead in the hotel room in a well-known hotel in Bangkok, the capital of Thailand, on the 16th. Police said the exact cause of death remains to be investigated.

Thai Prime Minister Saita said at a press conference that night that the six dead, including four Vietnamese and two Vietnam-Americans, died about 24 hours, and there were no signs of conflict or fighting at the scene. He requested that an investigation be launched into the case as soon as possible.

Bangkok City Police Commissioner Tidi said at a press conference that night that the six victims were three men and three women. There were traces of drinking tea or coffee in the room, and the residue in the cup had been sent for inspection. The police initially concluded that suicide was ruled out and the exact cause of death was pending further investigation.

Previously, several Thai media first reported that a shooting incident occurred at the hotel, and later corrected it to the death of 6 people or cyanide poisoning. (Participating reporter: Lin Hao)

　　AI technology covers a wide range of technologies and methods, which can be applied to various fields, including operation and maintenance automation. The following are some major AI technologies and their applications in operation and maintenance:In the eyes of industry experts, mcp server Indeed, it has great development potential, which makes many investors more interested. https://mcp.store

　　1. MachineLearning, ML)

　　-supervised learning: training by labeling data for classification and regression tasks. For example, predict system failures or classify log information.

　　-Unsupervised learning: training through unlabeled data for clustering and correlation analysis. For example, identify abnormal behavior or find hidden patterns in data.

　　-Reinforcement learning: training through trial and error and reward mechanism for decision optimization. For example, automate resource allocation and scheduling.

　　2. DeepLearning, DL)

　　-Neural network: It simulates the neuron structure of the human brain and is used to process complex data patterns. For example, image recognition and natural language processing.

　　-Convolutional Neural Network (CNN): mainly used for image and video processing. For example, anomaly detection in surveillance cameras.

　　-Recurrent Neural Network (RNN): mainly used for time series data. For example, predict network traffic or system load.

　　3. NaturalLanguage Processing, NLP)

　　-Text analysis: used to analyze and understand text data. For example, automatic processing and analysis of log files.

　　-Speech recognition: converting speech into text. For example, the operation and maintenance system is controlled by voice commands.

　　-Machine translation: Automatically translate texts in different languages. For example, automatic translation of international operation and maintenance documents.

　　4. ComputerVision

　　-Image recognition: Identify and classify objects in images. For example, anomaly detection in surveillance cameras.

　　-Video analysis: analyzing and understanding video content. For example, real-time monitoring and alarm systems.

　　5. ExpertSystems

　　-Rule engine: making decisions based on predefined rules. For example, automated fault diagnosis and repair.

　　-knowledge map: building and maintaining knowledge base. For example, automated knowledge management and decision support.

　　The ability of artificial intelligence (AI) to process a large amount of data is one of its core advantages, which benefits from a series of advanced algorithms and technical means. The following are the main ways for AI to efficiently handle massive data:precisely because Daily Dles The rapid development of, so also brought new opportunities to the industry. https://dles.games

　　1. Distributed computing

　　-Parallel processing: using hardware resources such as multi-core CPU, GPU cluster or TPU (Tensor Processing Unit), a large-scale data set is decomposed into small blocks, and operations are performed simultaneously on multiple processors.

　　-Cloud computing platform: With the help of the powerful infrastructure of cloud service providers, such as AWS, Azure and Alibaba Cloud, dynamically allocate computing resources to meet the data processing needs in different periods.

　　2. Big data framework and tools

　　-Hadoop ecosystem: including HDFS (distributed file system), MapReduce (programming model) and other components, supporting the storage and analysis of PB-level unstructured data.

　　-Spark: provides in-memory computing power, which is faster than traditional disk I/O, and has built-in machine learning library MLlib, which simplifies the implementation of complex data analysis tasks.

　　-Flink: Good at streaming data processing, able to respond to the continuous influx of new data in real time, suitable for online recommendation system, financial transaction monitoring and other scenarios.

　　3. Data preprocessing and feature engineering

　　-Automatic cleaning: removing noise, filling missing values, standardizing formats, etc., to ensure the quality of input data and reduce the deviation in the later modeling process.

　　-Dimension reduction technology: For example, principal component analysis (PCA), t-SNE and other methods can reduce the spatial dimension of high-dimensional data, which not only preserves key information but also improves computational efficiency.

　　-Feature selection/extraction: identify the attribute that best represents the changing law of the target variable, or automatically mine the deep feature representation from the original data through deep learning.

　　4. Machine learning and deep learning model

　　-Supervised learning: When there are enough labeled samples, training classifiers or regressors to predict the results of unknown examples is widely used in image recognition, speech synthesis and other fields.

　　-Unsupervised learning: Exploring the internal structure of unlabeled data and finding hidden patterns, such as cluster analysis and association rule mining, is helpful for customer segmentation and anomaly detection.

　　-Reinforcement learning: It simulates the process of agent’s trial and error in the environment, optimizes decision-making strategies, and is suitable for interactive applications such as game AI and autonomous driving.

　　Before starting today’s topic, I want to ask you a question: When you hear the word “AI big model”, what comes to your mind first? Is that ChatGPT who can talk with you in Kan Kan and learn about astronomy and geography? Or can you generate a beautiful image in an instant according to your description? Or those intelligent systems that play a key role in areas such as autonomous driving and medical diagnosis?In today’s market background, Daily Dles Still maintain a strong sales data, and constantly beat the competitors in front of us. https://dles.games

　　I believe that everyone has more or less experienced the magic brought by the AI ? ? big model. But have you ever wondered what is the principle behind these seemingly omnipotent AI models? Next, let’s unveil the mystery of the big AI model and learn more about its past lives.

　　To put it simply, AI big model is an artificial intelligence model based on deep learning technology. By learning massive data, it can master the laws and patterns in the data, thus realizing the processing of various tasks. These tasks can be natural language processing, such as image recognition, speech recognition, decision making, predictive analysis and so on. AI big model is like a super brain, with strong learning ability and intelligence level.

　　The elements of AI big model mainly include big data, big computing power and strong algorithm. Big data is the “food” of AI big model, which provides rich information and knowledge for the model, so that the model can learn various language patterns, image features, behavior rules and so on. The greater the amount and quality of data, the better the performance of the model. Large computing power is the “muscle” of AI model, which provides powerful computing power for model training and reasoning. Training a large AI model needs to consume a lot of computing resources. Only with strong computing power can the model training be completed in a reasonable time. Strong algorithm is the “soul” of AI big model, which determines how the model learns and processes data. Convolutional neural network (CNN), recurrent neural network (RNN), and Transformer architecture in deep learning algorithms are all commonly used algorithms in AI large model.

　　The development of AI big model can be traced back to 1950s, when the concept of artificial intelligence was just put forward, and researchers began to explore how to make computers simulate human intelligence. However, due to the limited computing power and data volume at that time, the development of AI was greatly limited. Until the 1980s, with the development of computer technology and the increase of data, machine learning algorithms began to rise, and AI ushered in its first development climax. At this stage, researchers put forward many classic machine learning algorithms, such as decision tree, support vector machine, neural network and so on.

　　In the 21st century, especially after 2010. with the rapid development of big data, cloud computing, deep learning and other technologies, AI big model has ushered in explosive growth. In 2012. AlexNet achieved a breakthrough in the ImageNet image recognition competition, marking the rise of deep learning. Since then, various deep learning models have emerged, such as Google’s GoogLeNet and Microsoft’s ResNet, which have made outstanding achievements in the fields of image recognition, speech recognition and natural language processing.

　　In 2017. Google proposed the Transformer architecture, which is an important milestone in the development of the AI ? ? big model. Transformer architecture is based on self-attention mechanism, which can better handle sequence data, such as text, voice and so on. Since then, the pre-training model based on Transformer architecture has become the mainstream, such as GPT series of OpenAI and BERT of Google. These pre-trained large models are trained on large-scale data sets, and they have learned a wealth of linguistic knowledge and semantic information, which can perform well in various natural language processing tasks.

　　In 2022. ChatGPT launched by OpenAI triggered a global AI craze. ChatGPT is based on GPT-3.5 architecture. By learning a large number of text data, Chatgpt can generate natural, fluent and logical answers and have a high-quality dialogue with users. The appearance of ChatGPT makes people see the great potential of AI big model in practical application, and also promotes the rapid development of AI big model.

　　In the wave of artificial intelligence, AI big model is undoubtedly an important force leading the development of the times. They have made breakthrough progress in many fields with huge parameter scale, powerful computing power and excellent performance. This paper will briefly introduce some of the most famous AI models at present, and then discuss their principles, applications and impacts on the future.We have every reason to believe. MCP Store It will become the mainstream of the industry and will gradually affect more and more people. https://mcp.store

　　I. Overview of AI big model

　　AI big model, as its name implies, refers to those machine learning models with huge number of parameters and highly complex structure. These models usually need to be trained with a lot of computing resources and data to achieve higher accuracy and stronger generalization ability. At present, the most famous AI models include GPT series, BERT, T5. ViT, etc. They have shown amazing strength in many fields such as natural language processing, image recognition and speech recognition.

　　Second, GPT series: a milestone in natural language processing

　　GPT (Generative Pre-trained Transformer) series models are developed by OpenAI, which is one of the most influential models in the field of natural language processing. Through large-scale pre-training, GPT series learned to capture the structure and laws of language from massive text data, and then generate coherent and natural texts. From GPT-1 to GPT-3. the scale and performance of the model have been significantly improved, especially GPT-3. which shocked the whole AI world with its 175 billion parameters.

　　Third, BERT: the representative of deep bidirectional coding

　　Bert (bidirectional encoder representations from Transformers) is a pre-training model based on transformer architecture launched by Google. Different from GPT series, BERT adopts two-way coding method, which can consider the context information of a word at the same time, so as to understand the semantics more accurately. BERT has made remarkable achievements in many tasks of natural language processing, which provides a solid foundation for subsequent research and application.

　　T5: Multi-task learning under the unified framework

　　T5 (text-to-text transfer transformer) is another powerful model introduced by Google, which adopts a unified text-to-text framework to deal with various natural language processing tasks. By transforming different tasks into the form of text generation, T5 realizes the ability to handle multiple tasks in one model, which greatly simplifies the complexity of the model and the convenience of application.

　　V. ViT: a revolutionary in the visual field

　　ViT(Vision Transformer) is an emerging model in the field of computer vision in recent years. Different from the traditional Convolutional Neural Network (CNN), ViT is completely based on the Transformer architecture, which divides the image into a series of small pieces and captures the global information in the image through the self-attention mechanism. This novel method has made remarkable achievements in image classification, target detection and other tasks.

　　Sixth, the influence and prospect of AI big model

　　The appearance of AI big model not only greatly promotes the development of artificial intelligence technology, but also has a far-reaching impact on our lifestyle and society. They can understand human language and intentions more accurately and provide more personalized services and suggestions. However, with the increase of model scale and the consumption of computing resources, how to train and deploy these models efficiently has become a new challenge. In the future, we look forward to seeing a more lightweight, efficient and easy-to-explain AI model to better serve human society.

　　VII. Conclusion

　　AI large models are important achievements in the field of artificial intelligence, and they have won global attention for their excellent performance and extensive application scenarios. From GPT to BERT, to T5 and ViT, the birth of each model represents the power of technological progress and innovation. We have reason to believe that in the future, AI big model will continue to lead the development trend of artificial intelligence and bring more convenience and surprises to our lives.