Inflation is one of the most important macroeconomic indicators that have a great impact on the population of any country and region. Inflation is influenced by range of factors, one of which is inflation expectations. Many central banks take this factor into consideration while implementing monetary policy within the inflation targeting regime. Nowadays, a lot of people are active users of the Internet, especially social networks. There is a hypothesis that people search, read, and discuss mainly only those issues that are of particular interest to them. It is logical to assume that the dynamics of prices may also be in the focus of user discussions. So, such discussions could be regarded as an alternative source of more rapid information about inflation expectations. This study is based on unstructured data from Vkontakte social network to analyze upward and downward inflationary trends (on the example of the Omsk region). The sample of more than 8.5 million posts was collected between January 2010 and May 2022. The authors used BERT neural networks to solve the problem. These models demonstrated better results than the benchmarks (e.g., logistic regression, decision tree classifier, etc.). It makes possible to define pro-inflationary and disinflationary types of keywords in different contexts and get their visualization with SHAP method. This analysis provides additional operational information about inflationary processes at the regional level The proposed approach can be scaled for other regions. At the same time the limitation of the work is the time and power costs for the initial training of similar models for all regions of Russia.

This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs). This task seeks to adjust the models' responses to opinion-related questions on specified topics since an individual's personality often manifests in the form of their expressed opinions, thereby showcasing different personality traits. Specifically, we construct a new benchmark dataset PersonalityEdit to address this task. Drawing on the theory in Social Psychology, we isolate three representative traits, namely Neuroticism, Extraversion, and Agreeableness, as the foundation for our benchmark. We then gather data using GPT-4, generating responses that not only align with a specified topic but also embody the targeted personality trait. We conduct comprehensive experiments involving various baselines and discuss the representation of personality behavior in LLMs. Our intriguing findings uncover potential challenges of the proposed task, illustrating several remaining issues. We anticipate that our work can provide the NLP community with insights. Code and datasets will be released at https://github.com/zjunlp/EasyEdit.

This paper introduces a new transformer-based model for the problem of travel time estimation. The key feature of the proposed GCT-TTE architecture is the utilization of different data modalities capturing different properties of an input path. Along with the extensive study regarding the model configuration, we implemented and evaluated a sufficient number of actual baselines for path-aware and path-blind settings. The conducted computational experiments have confirmed the viability of our pipeline, which outperformed state-of-the-art models on both considered datasets. Additionally, GCT-TTE was deployed as a web service accessible for further experiments with user-defined routes.

Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. In several aspects, PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. The learning procedure of PLD solves the optimization problem related to the search for rules (called probabilistic laws), which have a minimal length and relatively high probability. At inference, ensembles of these rules are used for prediction. Probabilistic laws are human-readable and PLD based models are transparent and inherently interpretable. Applications of PLD include classification/clusterization/regression tasks, as well as time series analysis/anomaly detection and adaptive (robotic) control. In this paper, we outline the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.

A Russian teenager has been sentenced to five years in prison for allegedly planning to blow up a virtual FSB security service building in the video game Minecraft. The ruling falls into a broader pattern under President Vladimir Putin in which young Russians are put behind bars on controversial and preemptive terrorism charges. A military court in Siberia sentenced 16-year-old Nikita Uvarov to five years in a penal colony on charges of "training for terrorist activities", the rights lawyer Pavel Chikov said on the messaging service Telegram. Two other defendants were cleared of criminal charges and handed suspended sentences because they cooperated with investigators, Chikov added. The hearing was held behind closed doors. Uvarov and two other teenagers in the Siberian city of Kansk were detained in the summer of 2020 for spreading leaflets in support of a Moscow mathematician and anarchist activist who was on trial for vandalism.

Disruptive technology is the technology that affects the normal operation of a market or an industry. Digital disruption entails established companies and start-ups alike enlisting new technologies in the fight to dislodge incumbents, protect entrenched positions, or to re-invent entire industries and business activities. And to remain disruptive in the market, it is really important to keep innovating. This is crucial because, innovations occur now and then in every industry, however, to be truly disruptive, and innovation must entirely transform a product or solution that historically was so complicated only a few could access it. On a minimum level, digital transformation enables an organization to address the needs of its customers more simply and directly. But through disruptive innovation, companies can offer a far better way to users of doing things that current incumbents simply cannot compete with. Artificial intelligence (AI), E-Commerce, cloud, social networking, Internet of Things, 5G, blockchain and other emerging technologies are being leveraged to blur the lines between industries, creating new business models and converging sectors. A company that disrupts its market is in a great position to take advantage of new opportunities. Sometimes offering something different can change the whole market for the better. Most of the top disruptive companies get this label by offering highly innovative products and services and here are 100 such top disruptive companies listed below. The company provides innovative, managed cloud services to help its customers succeed. With best-in-class service and technology, 403Tech protects companies against cybercrimes while enabling greater efficiency and productivity. Some of its popular services include desktop support, server support, wired and wireless networking, virus removal, data recovery, and backup and hosted cloud services. Aegeus Technologies aims to design and develop robotic technologies and solutions.

Russia: Cognitive Agro Pilot, an autonomous AI-based driving system for farming equipment which was designed by Sber and its ecosystem member Cognitive Pilot – has succeeded in industrial use across 35 regions of Russia when reaping the 2020 harvest. From June to October 2020, over 350 New Holland, John Deere and CLAAS autonomous combines equipped with Cognitive Agro Pilot system farmed over 160,000 hectares of field and harvested more than 720,000 tonnes of crops. With the help of Cognitive Agro Pilot as many as 590,000 metric tonnes of grain crops such as wheat, soybeans, barley, oats, sorghum, buckwheat, among others, were harvested over 130,000 hectares, and some 130,000 metric tonnes of row crops and roll crops (corn, sunflower, etc.) were harvested over 30,000 hectares in Kaliningrad, Kaluga, Kursk, Belgorod, Tambov, Penza, Rostov, Tomsk, Kurgan, Krasnodar, Krasnoyarsk and Stavropol regions. Thanks to the use of Cognitive Agro Pilot, this harvesting season stakeholders were able to save – on fuel and other related materials, shorter harvesting time (machine hours), equipment depreciation, extended active use of equipment before capital expenditures, fewer human errors, optimisation of business processes, and other parameters. According to the estimates of project members, in the next three years, every 10th harvester in Russia may become autonomous.

The article considers the problem of the optimal control of a wind power plant based on fuzzy control and automation of generating the fuzzy rule base. Fuzzy rules by experts do not always provide a maximum power output of the wind plant and fuzzy rule bases require an adjustment in the case of changing the parameters of the wind power plant or the environment. This research proposes the method for optimizing the fuzzy rules base compiled by various experts. The method is based on balancing weights of fuzzy rules into the base by the Particle Swarm Optimization algorithm. The experiment has shown that the proposed method allows forming the fuzzy rule base as an exemplary optimal base from a non-optimized set of fuzzy rules. The optimal fuzzy rule base has been taken under consideration for the concrete control loop of wind power plant and the concrete fuzzy model of the wind.

Scientists at the TSU Laboratory of Biophotonics, working with Tomsk National Research Medical Center (TNIMC) oncologists, have developed a new approach to the diagnosis of adenocarcinoma, a malignant tumor of the prostate gland, that uses artificial intelligence to identify oncopathology and determine the stage of the disease. Using machine learning, a computer model was taught to distinguish between healthy tissues and pathology with 100 percent accuracy. The gold standard for the diagnosis of cancer is histology, during which tissue from a patient is examined for malignant changes. So that the samples can be stored for a long time, they are dehydrated and packed in paraffin. Then experts make thin sections and examine these slides under a microscope.

Researchers at Tomsk Polytechnic University jointly with the University of Chemistry and Technology (Prague) conducted a series of experiments which proved that artificial neural networks can accurately identify DNA damage caused by UV radiation. In the future, this approach can be used in modern medical diagnostics. An article, dedicated to those studies, was published in the Biosensors and Bioelectronics journal. According to the authors, the ways UV could affect the DNA structure, especially with short-term irradiation, remain practically unstudied. UV radiation is also known to cause cancer.