This paper systematically examines nation-level biases exhibited by Large Language Models (LLMs) within the domain of International Relations (IR). Leveraging historical records from the United Nations Security Council (UNSC), we developed a bias evaluation framework comprising three distinct tests to explore nation-level bias in various LLMs, with a particular focus on the five permanent members of the UNSC. Experimental results show that, even with the general bias patterns across models (e.g., favorable biases toward the western nations, and unfavorable biases toward Russia), these still vary based on the LLM. Notably, even within the same LLM, the direction and magnitude of bias for a nation change depending on the evaluation context. This observation suggests that LLM biases are fundamentally multidimensional, varying across models and tasks. We also observe that models with stronger reasoning abilities show reduced bias and better performance. Building on this finding, we introduce a debiasing framework that improves LLMs' factual reasoning combining Retrieval-Augmented Generation with Reflexion-based self-reflection techniques. Experiments show it effectively reduces nation-level bias, and improves performance, particularly in GPT-4o-mini and LLama-3.3-70B. Our findings emphasize the need to assess nation-level bias alongside performance when applying LLMs in the IR domain.

Moldova has handed a note of protest to the Russian ambassador to Chisinau over alleged interference in its recent elections. The foreign ministry in Chisinau said in a statement on Tuesday that it turned over the "note of firm protest" in relation to the "illegal and deliberate interference" to envoy Oleg Ozerov during a meeting at its offices. Moldova has accused Russia of seeking to influence its recent presidential election and referendum on joining the European Union. Russia sought to affect results and delegitimise the democratic process, the ministry complained. Chisinau accused Russia of organising ineligible voting, bribery, and security threats in a bid to influence the votes.

Diffusion models have demonstrated significant potential in achieving state-of-the-art performance across various text generation tasks. In this systematic study, we investigate their application to the table-to-text problem by adapting the diffusion model to the task and conducting an in-depth analysis. Our experiments cover multiple aspects of diffusion models training. We explore sampling strategy influence by inducing recent diffusion model accelerator DPM-Solver++ into our core model. We have tested different prediction aggregation methods, like ROVER and Minimum Bayes-Risk (MBR). Our studies cover the impact of the pre-training phase in diffusion models and the generation length constraints influence. We also have compared diffusion model generation with auto-regressive text-to-text models with different temperature settings for diversity evaluation. Our key observation is that diffusion models demonstrate the balance between quality and diversity while auto-regressive text-to-text models are not successful at handling both at the same time. Furthermore, we found out that to achieve the highest quality possible, it is preferable to use a regular sampler with the strictest length constraint to create multiple samples, and then use MBR to aggregate the predictions. However, if you are prepared to give up high level of diversity and to accelerate the process, you can also utilize a fast sampler DPM-Solver++. Our findings reveal that diffusion models achieve comparable results in the table-to-text domain, highlighting their viability in the table-to-text challenge as a promising research direction.

Colorectal cancer is the third-most common cancer in the Western Hemisphere. The segmentation of colorectal and colorectal cancer by computed tomography is an urgent problem in medicine. Indeed, a system capable of solving this problem will enable the detection of colorectal cancer at early stages of the disease, facilitate the search for pathology by the radiologist, and significantly accelerate the process of diagnosing the disease. However, scientific publications on medical image processing mostly use closed, non-public data. This paper presents an extension of the Medical Decathlon dataset with colorectal markups in order to improve the quality of segmentation algorithms. An experienced radiologist validated the data, categorized it into subsets by quality, and published it in the public domain. Based on the obtained results, we trained neural network models of the UNet architecture with 5-part cross-validation and achieved a Dice metric quality of $0.6988 \pm 0.3$. The published markups will improve the quality of colorectal cancer detection and simplify the radiologist's job for study description.

Arkady Volozh, the billionaire cofounder of Russia's biggest internet company, was removed from the EU sanctions list today, clearing the way for his return to the world of international tech. On Tuesday a spokesperson for the European Council confirmed to WIRED that the Yandex cofounder was among three people whose sanctions were lifted this week. Volozh, 60, was initially included on the EU sanctions list in June 2023, following Russia's full-scale invasion of Ukraine in February 2022. "Volozh is a leading businessperson involved in economic sectors providing a substantial source of revenue to the Government of the Russian Federation," the bloc said last year to justify its decision. "As founder and CEO of Yandex, he is supporting, materially or financially, the Government of the Russian Federation."

After recapturing Kherson city, Ukraine kept Russian forces guessing about their next move, pinning down occupying troops in defensive positions and rendering them unavailable for offensive operations. Some 30,000 Russian troops that withdrew from the west bank of the Dnieper river earlier this month were entrenching themselves in the Zaporizhia and Kherson regions during the 39th week of the war, deputy head of Ukrainian military intelligence Major-General Vadym Skibitskyi, told the Kyiv Post. "[The Russians] are waiting for our liberation offensive, that's why they have created a defensive line in Kherson, another on the administrative border of [Kherson and] Crimea, and another in the northern Crimea region," Skibitskiy said. "The enemy is on the defensive in the Zaporizhzhia direction," said Ukraine's general staff. "In the Kryvyi Rih and Kherson directions, the enemy is creating an echeloned defence system, improving fortification equipment and logistical support of advanced units, and not stopping artillery fire at the positions of our troops and settlements on the right bank of the Dnipro River."

Power transformers are an important component of a nuclear power plant (NPP). Currently, the NPP operates a lot of power transformers with extended service life, which exceeds the designated 25 years. Due to the extension of the service life, the task of monitoring the technical condition of power transformers becomes urgent. An important method for monitoring power transformers is Chromatographic Analysis of Dissolved Gas. It is based on the principle of controlling the concentration of gases dissolved in transformer oil. The appearance of almost any type of defect in equipment is accompanied by the formation of gases that dissolve in oil, and specific types of defects generate their gases in different quantities. At present, at NPPs, the monitoring systems for transformer equipment use predefined control limits for the concentration of dissolved gases in the oil. This study describes the stages of developing an algorithm to detect defects and faults in transformers automatically using machine learning and data analysis methods. Among machine learning models, we trained Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks. The best of them were then combined into an ensemble (StackingClassifier) showing F1-score of 0.974 on a test sample. To develop mathematical models, we used data on the state of transformers, containing time series with values of gas concentrations (H2, CO, C2H4, C2H2). The datasets were labeled and contained four operating modes: normal mode, partial discharge, low energy discharge, low-temperature overheating.

The document contains the following terms with appropriate definitions: Medical dataset (reference data) is a collection of high-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate AI algorithms. Intellectual technologies are information technologies created based on "artificial intelligence" . " Artificial Intelligence" (AI) refers to systems that display intelligent behavior by analyzing their environment and taking actions - with some degree of autonomy - to achieve specific goals. AIbased systems can be purely software-based, acting in the virtual world (e.g., voice assistants, image analysis software, search engines, speech and face recognition systems) or AI can be embedded in hardware devices (e.g., advanced robots, autonomous cars, drones, or Internet of Things applications) [6]. Mathematical model is an abstract mathematical representation of a process, device or concept; it uses a number of variables to represent inputs, outputs and internal states, and sets of equations and inequalities to describe their interaction.

As companies building the technologies in Artificial Intelligence and Robotics that may be repurposed to develop autonomous weapons, we feel especially responsible in raising this alarm. We warmly welcome the decision of the UN's Conference of the Convention on Certain Conventional Weapons (CCW) to establish a Group of Governmental Experts (GGE) on Lethal Autonomous Weapon Systems. We entreat the High Contracting Parties participating in the GGE to work hard at finding means to prevent an arms race in these weapons, to protect civilians from their misuse, and to avoid the destabilizing effects of these technologies. We urge the High Contracting Parties therefore to double their efforts at the first meeting of the GGE now planned for November.

Package Name: International Stocks Forecast Length: 7 Days (08/28/2016 – 09/04/2016) I Know First Average: 6.85% The highest returning stock for the 7 days period was. The package's overall average return was 6.85%, providing investors with a 6.35% premium, over the SP500's return of 0.50%, during the same period. Mechel Sp ADR (MTL) engages in mining and steel businesses in Asia,the Russian Federation, the Commonwealth of Independent States, Europe, the Middle East, the United States, and internationally. Mechel PAO works in three different sectors,Mining, Steel, and Power.