OpenRaiser/Intern-Atlas huggingface dataset:
Fake News in Social Networks
Venue: arXiv cs.AI 2025
Year: 2025

We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in unaccustomed populations and in populations that have adapted to the presence of fake news. In particular the latter is challenging for existing methods. We find that a fake-news attack is more effective if it targets highly connected people and people with weaker private information. Attacks are more effective when the disinformation is spread across several agents than when the disinformation is concentrated with more intensity on fewer agents. Furthermore, fake news spread less well in balanced networks than in clustered networks. We test a part of our findings in a human-subject experiment. The experimental evidence provides support for the predictions from the model, suggesting that the model is suitable to analyze the spread of fake news in social networks.
GroundSLAM: A Robust Visual SLAM System for Warehouse Robots Using Ground Textures
Venue: arXiv cs.RO 2025
Year: 2025

A robust visual localization and mapping system is essential for warehouse robot navigation, as cameras offer a more cost-effective alternative to LiDAR sensors. However, existing forward-facing camera systems often encounter challenges in dynamic environments and open spaces, leading to significant performance degradation during deployment. To address these limitations, a localization system utilizing a single downward-facing camera to capture ground textures presents a promising solution. Nevertheless, existing feature-based ground-texture localization methods face difficulties when operating on surfaces with sparse features or repetitive patterns. To address this limitation, we propose GroundSLAM, a novel feature-free and ground-texture-based simultaneous localization and mapping (SLAM) system. GroundSLAM consists of three components: feature-free visual odometry, ground-texture-based loop detection and map optimization, and map reuse. Specifically, we introduce a kernel cross-correlator (KCC) for image-level pose tracking, loop detection, and map reuse to improve localization accuracy and robustness, and incorporate adaptive pruning strategies to enhance efficiency. Due to these specific designs, GroundSLAM is able to deliver efficient and stable localization across various ground surfaces such as those with sparse features and repetitive patterns. To advance research in this area, we introduce the first ground-texture dataset with precise ground-truth poses, consisting of 131k images collected from 10 kinds of indoor and outdoor ground surfaces. Extensive experimental results show that GroundSLAM outperforms state-of-the-art methods for both indoor and outdoor localization. We release our code and dataset at https://github.com/sair-lab/GroundSLAM.
There is a Hyper-Greedoid lurking behind every Graphical Accessible Computational Search Problem solvable in Polynomial Time: $P \not= NP$
Venue: arXiv cs.CC 2025
Year: 2025

Consider $G[X]$, where $G$ is a connected, isthmus-less and labelled graph, and $X$ is the edge-set or the vertex-set of the graph $G$. A Graphical Search Problem (GSP), denoted $Π(G[X],γ)$, consists of finding $Y$, where $Y \subseteq X$ and $Y$ satisfies the predicate $γ$ in $G$. The subset $Y$ is a solution of the problem $Π(G[X],γ)$. A sub-solution of $Π(G[X],γ)$ is a subset $Y'$ such that $Y'$ is not a solution of $Π(G[X],γ)$, but $Y'$ is a solution of the problem $Π(H[X'],γ)$, where $X' \subset X$ and $H[X']$ is a contraction-minor of $G[X]$. Solutions and sub-solutions are the feasible sets of $Π(G[X],γ)$. Let $\mathfrak{I}$ be the family of all the feasible sets of $Π(G[X],γ)$. A Hyper-greedoid is a set system $(X, \mathfrak{I})$ satisfying the following axioms. A1: Accessibility: if $I \in \mathfrak{I}$, there is an element $x \in I$ such that $I-x \in \mathfrak{I}$ A2: Augmentability: If $I$ is a sub-solution, there is a polynomial time function $κ: \mathfrak{I} \rightarrow \mathfrak{I}$ and there is a element $x \in X-κ(I)$ such that $κ(I) \cup x \in \mathfrak{I}$. That is, every sub-solution can be augmented using a polynomial time algorithm akin to Edmond Augmenting Path Algorithm. Given a graph $G$, the GSP MISP consists of finding an independent set of vertices of $G$. MISP satisfies axioms A1 and A2. Using the P-completeness of the Decision Problem associated to MISP, we prove that every GSP that satisfies A1 is solvable in Polynomial Time if and only if it satisfies A2. On the other hand, let HCP be the GSP that consists of finding a Hamiltonian cycle of the graph $G$. HCP satisfies A1, but does not satisfies A2. Since the Decision Problem associated with HCP is NP-Complete, we get $P \not = NP$.
The generalized stochastic preference choice model
Venue: arXiv cs.GT 2025
Year: 2025

We propose a new discrete choice model, called the generalized stochastic preference (GSP) model, that incorporates non-rationality into the stochastic preference (SP) choice model, also known as the rank-based model. Our model can capture several context-dependent choice behaviors that cannot be represented by any SP model, such as the well-documented compromise and attraction effects, while still including the SP model as a special case. The GSP model is defined as a distribution over consumer types, where each type extends the choice behavior of rational types in the SP model. We build on existing methods for estimating the SP model and propose an iterative estimation algorithm for the GSP model that finds new types by solving an integer linear program in each iteration. We further show that our proposed notion of non-rationality can be incorporated into other choice models, like the random utility maximization (RUM) model class as well as any of its subclasses. As a concrete example, we introduce the non-rational extension of the classical MNL model, which we term the generalized MNL (GMNL) model and present an efficient expectation-maximization (EM) algorithm for estimating it. \av{For the GSP model, we demonstrate that the worst-case performance guarantee of revenue-ordered assortments is significantly worse than for the SP model. For the GMNL model, we establish that assortment optimization with totally unimodular constraints is NP-hard to approximate to within a factor of $O(n^{1-ε})$ for any $ε> 0$, where $n$ is the number of products.} Finally, numerical evaluation on synthetic and real choice data shows that the GMNL and GSP models can outperform their rational counterparts in out-of-sample prediction accuracy.
Average Case Analysis of Leaf-Centric Binary Tree Sources
Venue: arXiv cs.DM 2025
Year: 2025

We study the average number of distinct fringe subtrees in random trees generated by leaf-centric binary tree sources as introduced by Zhang, Yang and Kieffer. A leaf-centric binary tree source induces for every $n \geq 2$ a probability distribution on the set of binary trees with $n$ leaves. We generalize a result by Flajolet, Gourdon, Martinez and Devroye, according to which the average number of distinct fringe subtrees in a random binary search tree of size $n$ is in $Θ(n/\log n)$, as well as a result by Flajolet, Sipala and Steayert, according to which the number of distinct fringe subtrees in a uniformly random binary tree of size $n$ is in $Θ(n/\sqrt{\log n})$.
Real-time Air Pollution prediction model based on Spatiotemporal Big data
Venue: arXiv cs.CY 2025
Year: 2025

Air pollution is one of the most concerns for urban areas. Many countries have constructed monitoring stations to hourly collect pollution values. Recently, there is a research in Daegu city, Korea for real-time air quality monitoring via sensors installed on taxis running across the whole city. The collected data is huge (1-second interval) and in both Spatial and Temporal format. In this paper, based on this spatiotemporal Big data, we propose a real-time air pollution prediction model based on Convolutional Neural Network (CNN) algorithm for image-like Spatial distribution of air pollution. Regarding to Temporal information in the data, we introduce a combination of a Long Short-Term Memory (LSTM) unit for time series data and a Neural Network model for other air pollution impact factors such as weather conditions to build a hybrid prediction model. This model is simple in architecture but still brings good prediction ability.
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Venue: arXiv cs.IR 2025
Year: 2025

Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time. In this work, we propose a neural networkbased approach for dynamic entity relatedness, leveraging the collective attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.
Identification of Wearable Devices with Bluetooth
Venue: arXiv cs.CR 2025
Year: 2025

With wearable devices such as smartwatches on the rise in the consumer electronics market, securing these wearables is vital. However, the current security mechanisms only focus on validating the user not the device itself. Indeed, wearables can be (1) unauthorized wearable devices with correct credentials accessing valuable systems and networks, (2) passive insiders or outsider wearable devices, or (3) information-leaking wearables devices. Fingerprinting via machine learning can provide necessary cyber threat intelligence to address all these cyber attacks. In this work, we introduce a wearable fingerprinting technique focusing on Bluetooth classic protocol, which is a common protocol used by the wearables and other IoT devices. Specifically, we propose a non-intrusive wearable device identification framework which utilizes 20 different Machine Learning (ML) algorithms in the training phase of the classification process and selects the best performing algorithm for the testing phase. Furthermore, we evaluate the performance of proposed wearable fingerprinting technique on real wearable devices, including various off-the-shelf smartwatches. Our evaluation demonstrates the feasibility of the proposed technique to provide reliable cyber threat intelligence. Specifically, our detailed accuracy results show on average 98.5%, 98.3% precision and recall for identifying wearables using the Bluetooth classic protocol.
A Formal Separation Between Strategic and Nonstrategic Behavior
Venue: arXiv cs.GT 2025
Year: 2025

It is common to make a distinction between "strategic" behavior and other forms of intentional but "nonstrategic" behavior: typically, that strategic agents model other agents while nonstrategic agents do not. However, a crisp boundary between these concepts has proven elusive. This problem is pervasive throughout the game theoretic literature on bounded rationality and particularly critical in parts of the behavioral game theory literature that make an explicit distinction between the behavior of "nonstrategic" level-0 agents and "strategic" higher-level agents (e.g., the level-k and cognitive hierarchy models). Overall, work discussing bounded rationality rarely gives clear guidance on how the rationality of nonstrategic agents must be bounded, instead typically just singling out specific decision rules (e.g., randomizing uniformly, playing toward the best case, optimizing the worst case) and informally asserting that they are nonstrategic. In this work, we propose a new, formal characterization of nonstrategic behavior. Our main contribution is to show that it satisfies two properties: (1) it is general enough to capture all purportedly "nonstrategic" decision rules of which we are aware in the behavioral game theory literature; (2) behavior that obeys our characterization is distinct from strategic behavior in a precise sense.
The Optimal Approximation Factor in Density Estimation
Venue: arXiv cs.LG 2025
Year: 2025

Consider the following problem: given two arbitrary densities $q_1,q_2$ and a sample-access to an unknown target density $p$, find which of the $q_i$'s is closer to $p$ in total variation. A remarkable result due to Yatracos shows that this problem is tractable in the following sense: there exists an algorithm that uses $O(ε^{-2})$ samples from $p$ and outputs~$q_i$ such that with high probability, $TV(q_i,p) \leq 3\cdot\mathsf{opt} + ε$, where $\mathsf{opt}= \min\{TV(q_1,p),TV(q_2,p)\}$. Moreover, this result extends to any finite class of densities $\mathcal{Q}$: there exists an algorithm that outputs the best density in $\mathcal{Q}$ up to a multiplicative approximation factor of 3. We complement and extend this result by showing that: (i) the factor 3 can not be improved if one restricts the algorithm to output a density from $\mathcal{Q}$, and (ii) if one allows the algorithm to output arbitrary densities (e.g.\ a mixture of densities from $\mathcal{Q}$), then the approximation factor can be reduced to 2, which is optimal. In particular this demonstrates an advantage of improper learning over proper in this setup. We develop two approaches to achieve the optimal approximation factor of 2: an adaptive one and a static one. Both approaches are based on a geometric point of view of the problem and rely on estimating surrogate metrics to the total variation. Our sample complexity bounds exploit techniques from {\it Adaptive Data Analysis}.
Predicting human decisions with behavioral theories and machine learning
Venue: arXiv cs.AI 2025
Year: 2025

Predicting human decisions under risk and uncertainty remains a fundamental challenge across disciplines. Existing models often struggle even in highly stylized tasks like choice between lotteries. We introduce BEAST Gradient Boosting (BEAST-GB), a hybrid model integrating behavioral theory (BEAST) with machine learning. We first present CPC18, a competition for predicting risky choice, in which BEAST-GB won. Then, using two large datasets, we demonstrate BEAST-GB predicts more accurately than neural networks trained on extensive data and dozens of existing behavioral models. BEAST-GB also generalizes robustly across unseen experimental contexts, surpassing direct empirical generalization, and helps refine and improve the behavioral theory itself. Our analyses highlight the potential of anchoring predictions on behavioral theory even in data-rich settings and even when the theory alone falters. Our results underscore how integrating machine learning with theoretical frameworks, especially those-like BEAST-designed for prediction, can improve our ability to predict and understand human behavior.
Controller Design and Implementation of a New Quadrotor Manipulation System
Venue: arXiv cs.RO 2025
Year: 2025

The previously introduced aerial manipulation systems suffer from either limited end-effector DOF or small payload capacity. In this dissertation, a quadrotor with a 2-DOF manipulator is investigated that has a unique topology to enable the end-effector to track 6-DOF trajectory with the minimum possible number of actuators/links and hence, maximize the payload and/or mission time. The proposed system is designed, modeled, and constructed. An identification process is carried out to find the system parameters. An experimental setup is proposed with a 6-DOF state measurement and estimation scheme. The system feasibility is validated via numerical and experimental results. The inverse kinematics require a solution of complicated algebraic-differential equations. Therefore, an algorithm is developed to get an approximate solution of these equations.
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability.We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking attack with two constraints: 1) the classification of the other images should be unchanged and 2) the parameter modifications should be minimized. Specifically, the first constraint requires us not only to inject designated faults (misclassifications), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. The second constraint requires us to minimize the parameter modifications (using L0 norm to measure the number of modifications and L2 norm to measure the magnitude of modifications). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance.
The UN Security Council debates 1992-2023
Venue: arXiv cs.DL 2025
Year: 2025

This paper presents an updated dataset containing 106,302 speeches held in the public meetings of the UN Security Council (UNSC) between 1992 and 2023. The dataset is based on publicly available meeting transcripts with the S/PV document symbol and includes the full substance of individual speeches as well as automatically extracted and manually corrected metadata on the speaker, the position of the speech in the sequence of speeches of a meeting, and the date of the speech. After contextualizing the dataset in recent research on the UNSC, the paper presents descriptive statistics on UNSC meetings and speeches that characterize the period covered by the dataset. Data highlight the extensive presence of the UN bureaucracy in UNSC meetings as well as an emerging trend towards more lengthy open UNSC debates. These open debates cover key issues that have emerged only during the period that is covered by the dataset, for example the debates relating to Women, Peace and Security or Climate-related Disasters. The corpus is available online: https://doi.org/10.7910/DVN/KGVSYH
A robust graph-based approach to observational equivalence
Venue: arXiv cs.PL 2025
Year: 2025

We propose a new step-wise approach to proving observational equivalence, and in particular reasoning about fragility of observational equivalence. Our approach is based on what we call local reasoning. The local reasoning exploits the graphical concept of neighbourhood, and it extracts a new, formal, concept of robustness as a key sufficient condition of observational equivalence. Moreover, our proof methodology is capable of proving a generalised notion of observational equivalence. The generalised notion can be quantified over syntactically restricted contexts instead of all contexts, and also quantitatively constrained in terms of the number of reduction steps. The operational machinery we use is given by a hypergraph-rewriting abstract machine inspired by Girard's Geometry of Interaction. The behaviour of language features, including function abstraction and application, is provided by hypergraph-rewriting rules. We demonstrate our proof methodology using the call-by-value lambda-calculus equipped with (higher-order) state.
The Use of Binary Choice Forests to Model and Estimate Discrete Choices
Venue: arXiv cs.LG 2025
Year: 2025

Problem definition. In retailing, discrete choice models (DCMs) are commonly used to capture the choice behavior of customers when offered an assortment of products. When estimating DCMs using transaction data, flexible models (such as machine learning models or nonparametric models) are typically not interpretable and hard to estimate, while tractable models (such as the multinomial logit model) tend to misspecify the complex behavior represeted in the data. Methodology/results. In this study, we use a forest of binary decision trees to represent DCMs. This approach is based on random forests, a popular machine learning algorithm. The resulting model is interpretable: the decision trees can explain the decision-making process of customers during the purchase. We show that our approach can predict the choice probability of any DCM consistently and thus never suffers from misspecification. Moreover, our algorithm predicts assortments unseen in the training data. The mechanism and errors can be theoretically analyzed. We also prove that the random forest can recover preference rankings of customers thanks to the splitting criterion such as the Gini index and information gain ratio. Managerial implications. The framework has unique practical advantages. It can capture customers' behavioral patterns such as irrationality or sequential searches when purchasing a product. It handles nonstandard formats of training data that result from aggregation. It can measure product importance based on how frequently a random customer would make decisions depending on the presence of the product. It can also incorporate price information and customer features. Our numerical experiments using synthetic and real data show that using random forests to estimate customer choices can outperform existing methods.
Loss Patterns of Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

We present multi-point optimization: an optimization technique that allows to train several models simultaneously without the need to keep the parameters of each one individually. The proposed method is used for a thorough empirical analysis of the loss landscape of neural networks. By extensive experiments on FashionMNIST and CIFAR10 datasets we demonstrate two things: 1) loss surface is surprisingly diverse and intricate in terms of landscape patterns it contains, and 2) adding batch normalization makes it more smooth. Source code to reproduce all the reported results is available on GitHub: https://github.com/universome/loss-patterns.
Blind Network Revenue Management and Bandits with Knapsacks under Limited Switches
Venue: arXiv cs.LG 2025
Year: 2025

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributionally unknown demand, and must allocate finite initial inventory across multiple resources over time. In addition to standard resource constraints, we impose a switching constraint that limits the number of action changes over the time horizon. We establish matching upper and lower bounds on the optimal regret and develop computationally efficient limited-switch algorithms that achieve it. We show that the optimal regret rate is fully characterized by a piecewise-constant function of the switching budget, which further depends on the number of resource constraints. Our results highlight the fundamental role of resource constraints in shaping the statistical complexity of online learning under limited switches. Extensive simulations demonstrate that our algorithms maintain strong cumulative reward performance while significantly reducing the number of switches.
Optimal Single-Choice Prophet Inequalities from Samples
Venue: arXiv cs.DS 2025
Year: 2025

We study the single-choice Prophet Inequality problem when the gambler is given access to samples. We show that the optimal competitive ratio of $1/2$ can be achieved with a single sample from each distribution. When the distributions are identical, we show that for any constant $\varepsilon > 0$, $O(n)$ samples from the distribution suffice to achieve the optimal competitive ratio ($\approx 0.745$) within $(1+\varepsilon)$, resolving an open problem of Correa, Dütting, Fischer, and Schewior.