OpenRaiser/Intern-Atlas huggingface dataset:
An algorithm for determining the state of a non-stationary dynamic system for assessing fire safety control in an enterprise by the method of integrated indicators
Venue: arXiv cs.CY 2024
Year: 2024

Analysis of the scientific literature showed that a lot of work is devoted to assessing the effectiveness of fire safety management in an enterprise. It is worth noting that today there is no universal method for the integrated assessment of fire safety management, taking into account the interconnectedness of all enterprise subsystems and the influence of environmental factors. One of the original approaches to assessing the effectiveness of the fire safety management system is the method of integral indicators. The method of integral indicators is used in the algorithm for analyzing the state of a dynamic non-stationary system for assessing fire safety management in an enterprise. The algorithm is implemented in the author's complex of programs described in the text of the article. In the simulation, an analysis of 1.2 million values is performed on a well-studied economic object with the spaces identified at each time step: actual data, control and environmental parameters. In the experiment, the basic mode of operation of the enterprise does not contain the implementation of a fire safety management strategy. The research showed significant changes in the values of the integral indicator characterizing the state of the enterprise during the implementation of the fire safety management system at the enterprise.
Towards an Environmental Ethics of Artificial Intelligence
Venue: arXiv cs.CY 2024
Year: 2024

In recent years, much research has been dedicated to uncovering the environmental impact of Artificial Intelligence (AI), showing that training and deploying AI systems require large amounts of energy and resources, and the outcomes of AI may lead to decisions and actions that may negatively impact the environment. This new knowledge raises new ethical questions, such as: When is it (un)justifiable to develop an AI system, and how to make design choices, considering its environmental impact? However, so far, the environmental impact of AI has largely escaped ethical scrutiny, as AI ethics tends to focus strongly on themes such as transparency, privacy, safety, responsibility, and bias. Considering the environmental impact of AI from an ethical perspective expands the scope of AI ethics beyond an anthropocentric focus towards including more-than-human actors such as animals and ecosystems. This paper explores the ethical implications of the environmental impact of AI for designing AI systems by drawing on environmental justice literature, in which three categories of justice are distinguished, referring to three elements that can be unjust: the distribution of benefits and burdens (distributive justice), decision-making procedures (procedural justice), and institutionalized social norms (justice as recognition). Based on these tenets of justice, we outline criteria for developing environmentally just AI systems, given their ecological impact.
Exploitation All the Way Down: Calling out the Root Cause of Bad Online Experiences for Users of the "Majority World"
Venue: arXiv cs.CY 2024
Year: 2024

Global Majority users are exposed to multitudes of harm when interacting with online platforms. This essay illuminates how exploitation in the advances of Artificial Intelligence is tied to historical exploitation and how the use of blanket terminology overshadows the layers of exploitation and harm ``Global Majority'' populations face. It first discusses the multitude of harm content moderators from the Global Majority face, arguing against the current trend of protection through exploitation, then it illustrates the nuances and differences within the Global Majority, and finally, it outlines actionable items to move away from such harm.
The Manhattan Trap: Why a Race to Artificial Superintelligence is Self-Defeating
Venue: arXiv cs.CY 2024
Year: 2024

This paper examines the strategic dynamics of international competition to develop Artificial Superintelligence (ASI). We argue that the same assumptions that might motivate the US to race to develop ASI also imply that such a race is extremely dangerous. These assumptions--that ASI would provide a decisive military advantage and that states are rational actors prioritizing survival--imply that a race would heighten three critical risks: great power conflict, loss of control of ASI systems, and the undermining of liberal democracy. Our analysis shows that ASI presents a trust dilemma rather than a prisoners dilemma, suggesting that international cooperation to control ASI development is both preferable and strategically sound. We conclude that cooperation is achievable.
Heat: Satellite's meat is GPU's poison
Venue: arXiv cs.DC 2024
Year: 2024

In satellite applications, managing thermal conditions is a significant challenge due to the extreme fluctuations in temperature during orbital cycles. One of the solutions is to heat the satellite when it is not exposed to sunlight, which could protect the satellites from extremely low temperatures. However, heat dissipation is necessary for Graphics Processing Units (GPUs) to operate properly and efficiently. In this way, this paper investigates the use of GPU as a means of passive heating in low-earth orbit (LEO) satellites. Our approach uses GPUs to generate heat during the eclipse phase of satellite orbits, substituting traditional heating systems, while the GPUs are also cooled down during this process. The results highlight the potential advantages and limitations of this method, including the cost implications, operational restrictions, and the technical complexity involved. Also, this paper explores the thermal behavior of GPUs under different computational loads, specifically focusing on execution-dominated and FLOP-dominated workloads. Moreover, this paper discusses future directions for improving GPU-based heating solutions, including further cost analysis, system optimization, and practical testing in real satellite missions.
Equation discovery framework EPDE: Towards a better equation discovery
Venue: arXiv cs.NE 2024
Year: 2024

Equation discovery methods hold promise for extracting knowledge from physics-related data. However, existing approaches often require substantial prior information that significantly reduces the amount of knowledge extracted. In this paper, we enhance the EPDE algorithm -- an evolutionary optimization-based discovery framework. In contrast to methods like SINDy, which rely on pre-defined libraries of terms and linearities, our approach generates terms using fundamental building blocks such as elementary functions and individual differentials. Within evolutionary optimization, we may improve the computation of the fitness function as is done in gradient methods and enhance the optimization algorithm itself. By incorporating multi-objective optimization, we effectively explore the search space, yielding more robust equation extraction, even when dealing with complex experimental data. We validate our algorithm's noise resilience and overall performance by comparing its results with those from the state-of-the-art equation discovery framework SINDy.
DropMicroFluidAgents (DMFAs): Autonomous Droplet Microfluidic Research Framework Through Large Language Model Agents
Venue: arXiv cs.CY 2024
Year: 2024

Applying Large language models (LLMs) within specific domains requires substantial adaptation to account for the unique terminologies, nuances, and context-specific challenges inherent to those areas. Here, we introduce DropMicroFluidAgents (DMFAs), an advanced language-driven framework leveraging state-of-the-art pre-trained LLMs. DMFAs employs LLM agents to perform two key functions: (1) delivering focused guidance, answers, and suggestions specific to droplet microfluidics and (2) generating machine learning models to optimise and automate the design of droplet microfluidic devices, including the creation of code-based computer-aided design (CAD) scripts to enable rapid and precise design execution. Experimental evaluations demonstrated that the integration of DMFAs with the LLAMA3.1 model yielded the highest accuracy of 76.15%, underscoring the significant performance enhancement provided by agent integration. This effect was particularly pronounced when DMFAs were paired with the GEMMA2 model, resulting in a 34.47% improvement in accuracy compared to the standalone GEMMA2 configuration. This study demonstrates the effective use of LLM agents in droplet microfluidics research as powerful tools for automating workflows, synthesising knowledge, optimising designs, and interacting with external systems. These capabilities enable their application across education and industrial support, driving greater efficiency in scientific discovery and innovation.
Knowledge Graphs: The Future of Data Integration and Insightful Discovery
Venue: arXiv cs.AI 2024
Year: 2024

Knowledge graphs are an efficient method for representing and connecting information across various concepts, useful in reasoning, question answering, and knowledge base completion tasks. They organize data by linking points, enabling researchers to combine diverse information sources into a single database. This interdisciplinary approach helps uncover new research questions and ideas. Knowledge graphs create a web of data points (nodes) and their connections (edges), which enhances navigation, comprehension, and utilization of data for multiple purposes. They capture complex relationships inherent in unstructured data sources, offering a semantic framework for diverse entities and their attributes. Strategies for developing knowledge graphs include using seed data, named entity recognition, and relationship extraction. These graphs enhance chatbot accuracy and include multimedia data for richer information. Creating high-quality knowledge graphs involves both automated methods and human oversight, essential for accurate and comprehensive data representation.
Level-Navi Agent: A Framework and benchmark for Chinese Web Search Agents
Venue: arXiv cs.IR 2024
Year: 2024

Large language models (LLMs), adopted to understand human language, drive the development of artificial intelligence (AI) web search agents. Compared to traditional search engines, LLM-powered AI search agents are capable of understanding and responding to complex queries with greater depth, enabling more accurate operations and better context recognition. However, little attention and effort has been paid to the Chinese web search, which results in that the capabilities of open-source models have not been uniformly and fairly evaluated. The difficulty lies in lacking three aspects: an unified agent framework, an accurately labeled dataset, and a suitable evaluation metric. To address these issues, we propose a general-purpose and training-free web search agent by level-aware navigation, Level-Navi Agent, accompanied by a well-annotated dataset (Web24) and a suitable evaluation metric. Level-Navi Agent can think through complex user questions and conduct searches across various levels on the internet to gather information for questions. Meanwhile, we provide a comprehensive evaluation of state-of-the-art LLMs under fair settings. To further facilitate future research, source code is available at Github.
Leveraging Large Language Models For Optimized Item Categorization using UNSPSC Taxonomy
Venue: arXiv cs.CL 2024
Year: 2024

Effective item categorization is vital for businesses, enabling the transformation of unstructured datasets into organized categories that streamline inventory management. Despite its importance, item categorization remains highly subjective and lacks a uniform standard across industries and businesses. The United Nations Standard Products and Services Code (UNSPSC) provides a standardized system for cataloguing inventory, yet employing UNSPSC categorizations often demands significant manual effort. This paper investigates the deployment of Large Language Models (LLMs) to automate the classification of inventory data into UNSPSC codes based on Item Descriptions. We evaluate the accuracy and efficiency of LLMs in categorizing diverse datasets, exploring their language processing capabilities and their potential as a tool for standardizing inventory classification. Our findings reveal that LLMs can substantially diminish the manual labor involved in item categorization while maintaining high accuracy, offering a scalable solution for businesses striving to enhance their inventory management practices.
Extended UCB Policies for Multi-armed Bandit Problems
Venue: arXiv cs.LG 2025
Year: 2025

The multi-armed bandit (MAB) problems are widely studied in fields of operations research, stochastic optimization, and reinforcement learning. In this paper, we consider the classical MAB model with heavy-tailed reward distributions and introduce the extended robust UCB policy, which is an extension of the results of Bubeck et al. [5] and Lattimore [22] that are further based on the pioneering idea of UCB policies [e.g. Auer et al. 3]. The previous UCB policies require some strict conditions on reward distributions, which can be difficult to guarantee in practical scenarios. Our extended robust UCB generalizes Lattimore's seminary work (for moments of orders $p=4$ and $q=2$) to arbitrarily chosen $p>q>1$ as long as the two moments have a known controlled relationship, while still achieving the optimal regret growth order $O(log T)$, thus providing a broadened application area of UCB policies for heavy-tailed reward distributions. Furthermore, we achieve a near-optimal regret order without any knowledge of the reward distributions as long as their $p$-th moments exist for some $p>1$. Finally, we briefly present our earlier work on light-tailed reward distributions for a complete illustration of the amazing simplicity and power of UCB policies.
Algebraic Data Integration
Venue: arXiv cs.DB 2025
Year: 2025

In this paper we develop an algebraic approach to data integration by combining techniques from functional programming, category theory, and database theory. In our formalism, database schemas and instances are algebraic (multi-sorted equational) theories of a certain form. Schemas denote categories, and instances denote their initial (term) algebras. The instances on a schema S form a category, S-Inst, and a morphism of schemas F : S -> T induces three adjoint data migration functors: Sigma_F : S-Inst -> T-Inst, defined by substitution along F, which has a right adjoint Delta_F : T-Inst -> S-Inst, which in turn has a right adjoint Pi_F : S-Inst -> T-Inst. We present a query language based on for/where/return syntax where each query denotes a sequence of data migration functors; a pushout-based design pattern for performing data integration using our formalism; and describe the implementation of our formalism in a tool we call CQL.
Characterizing a Set of Popular Matchings Defined by Preference Lists with Ties
Venue: arXiv cs.DS 2025
Year: 2025

In this paper, we give a simple characterization of a set of popular matchings defined by preference lists with ties. By employing our characterization, we propose a polynomial time algorithm for finding a minimum cost popular matching.
Solutions of Word Equations over Partially Commutative Structures
Venue: arXiv cs.FL 2025
Year: 2025

Let $M(A,I)$ be a free partially commutative monoid with involution and $G(A,I)$ be its quotient group, e.g. a right-angled Artin or Coxeter group. Given a system of word equations over $M(A,I)$ with recognizable constraints with input size $n$ we show the structural result about the solution set of the system: the set of all solutions in $M(A,I)$ or in the group $G(A,I)$ is an EDT0L language. That is, it is given by an NFA $\mathcal{A}$ recognizing endomorphisms over some extended monoid. Moreover, $\mathcal{A}$ is effectively constructible by an NSPACE($n \log n$)-transducer. This implies that Satisfiability: `Is the system is solvable?' and Finiteness: `Are there infinitely many solutions?' can be decided in NSPACE($n \log n$). In the uniform version, these problems are PSPACE-complete, but for a suitable subclass of constraints we have more precise complexities and we conjecture that the decision problems above are NP-complete in this setting. Our results apply also to word equation over free monoids in the classical case where the involution is reading words right-to-left. This allows to specify that solutions are restricted to be palindromes.
Min cost flow on unit capacity networks and convex cost K-flow are as easy as the assignment problem with All-Min-Cuts algorithm
Venue: arXiv cs.DS 2025
Year: 2025

We explore here surprising links between the time-cost-tradeoff problem and the minimum cost flow problem that lead to fast, strongly polynomial, algorithms for both problems. One of the main results is a new algorithm for the unit capacity min cost flow that matches the complexity of the fastest strongly polynomial algorithm known for the assignment problem. The time cost tradeoff (TCT) problem in project management is to expedite the durations of activities, subject to precedence constraints, in order to achieve a target project completion time at minimum expediting costs, or, to maximize the net benefit from a reward associated with project completion time reduction. Each activity is associated with integer normal duration, minimum duration, and expediting cost per unit reduction in duration. We devise here the {\em all-min-cuts} procedure, which for a given maximum flow, is capable of generating all minimum cuts (equivalent to minimum cost expediting) of equal value very efficiently. Equivalently, the procedure identifies all solutions that reside on the TCT curve between consecutive breakpoints in average $O(m+n \log n)$ time, where $m$ and $n$ are the numbers of arcs and nodes in the network. The all-min-cuts procedure implies faster algorithms for certain TCT problems and for certain min cost flow problem with a significantly different approach from the ones known.
Linear-Time Safe-Alternating DFS and SCCs
Venue: arXiv cs.DS 2025
Year: 2025

An alternating graph is a directed graph whose vertex set is partitioned into two classes, existential and universal. This forms the basic arena for a plethora of infinite duration two-player games where Player~$\square$ and~$\ocircle$ alternate in a turn-based sliding of a pebble along the arcs they control. We study alternating strongly-connectedness as a generalization of strongly-connectedness in directed graphs, aiming at providing a linear time decomposition and a sound structural graph characterization. For this a refined notion of alternating reachability is introduced: Player~$\square$ attempts to reach vertices without leaving a prescribed subset of the vertices, while Player~$\ocircle$ works against. This is named \emph{safe alternating reachability}. It is shown that every arena uniquely decomposes into safe alternating strongly-connected components where Player~$\square$ can visit each vertex within a given component infinitely often, without having to ever leave out the component itself. Our main result is a linear time algorithm for computing this alternating graph decomposition. Both the underlying graph structures and the algorithm generalize the classical decomposition of a directed graph into strongly-connected components. The algorithm builds on a linear time generalization of the depth-first search on alternation, taking inspiration from Tarjan 1972 machinery. Our theory has direct applications in solving well-known infinite duration pebble games faster. Dinneen and Khoussainov showed in 1999 that deciding a given Update Game costs $O(mn)$ time, where $n$ is the number of vertices and $m$ is that of arcs. We solve the task in $Θ(m+n)$ linear~time. The complexity of Explicit McNaughton-Müller Games also improves from cubic to quadratic.
Recognizing and Eliciting Weakly Single Crossing Profiles on Trees
Venue: arXiv cs.MA 2025
Year: 2025

We introduce and study the weakly single-crossing domain on trees which is a generalization of the well-studied single-crossing domain in social choice theory. We design a polynomial-time algorithm for recognizing preference profiles which belong to this domain. We then develop an efficient elicitation algorithm for this domain which works even if the preferences can be accessed only sequentially and the underlying single-crossing tree structure is not known beforehand. We also prove matching lower bound on the query complexity of our elicitation algorithm when the number of voters is large compared to the number of candidates. We also prove a lower bound of $Ω(m^2\log n)$ on the number of queries that any algorithm needs to ask to elicit single crossing profile when random queries are allowed. This resolves an open question in an earlier paper and proves optimality of their preference elicitation algorithm when random queries are allowed.
On Distance Preserving and Sequentially Distance Preserving Graphs
Venue: arXiv cs.DM 2025
Year: 2025

A graph $H$ is an \emph{isometric} subgraph of $G$ if $d_H(u,v)= d_G(u,v)$, for every pair~$u,v\in V(H)$. A graph is \emph{distance preserving} if it has an isometric subgraph of every possible order. A graph is \emph{sequentially distance preserving} if its vertices can be ordered such that deleting the first $i$ vertices results in an isometric subgraph, for all $i\ge1$. We give an equivalent condition to sequentially distance preserving based upon simplicial orderings. Using this condition, we prove that if a graph does not contain any induced cycles of length~$5$ or greater, then it is sequentially distance preserving and thus distance preserving. Next we consider the distance preserving property on graphs with a cut vertex. Finally, we define a family of non-distance preserving graphs constructed from cycles.
The Third Evolution Equation for Optimal Control Computation
Venue: arXiv cs.SY 2025
Year: 2025

The Variation Evolving Method (VEM) that originates from the continuous-time dynamics stability theory seeks the optimal solutions with variation evolution principle. After establishing the first and the second evolution equations within its frame, the third evolution equation is developed. This equation only solves the control variables along the variation time to get the optimal solution, and its definite conditions may be arbitrary since the equation can eliminate possible infeasibilities. With this equation, the dimension of the resulting Initial-value Problem (IVP), transformed via the semi-discrete method, is greatly reduced. Therefore it might relieve the computation burden in seeking solutions. Illustrative examples are solved and it is shown that the proposed equation may produce more precise numerical solutions than the second evolution equation, and its computation time may be shorter for the dense discretization.
The Schulze Method of Voting
Venue: arXiv cs.GT 2025
Year: 2025

We propose a new single-winner election method ("Schulze method") and prove that it satisfies many academic criteria (e.g. monotonicity, reversal symmetry, resolvability, independence of clones, Condorcet criterion, k-consistency, polynomial runtime). We then generalize this method to proportional representation by the single transferable vote ("Schulze STV") and to methods to calculate a proportional ranking ("Schulze proportional ranking"). Furthermore, we propose a generalization of the Condorcet criterion to multi-winner elections. This paper contains a large number of examples to illustrate the proposed methods.