We present TSD-CT (Truthfulness Stance Detection-Claim and Tweet), a benchmark dataset designed to advance research in truthfulness stance detection. While prior stance detection datasets focus primarily on political figures, topics, or events, TSD-CT targets truthfulness stance of social media posts toward factual claims. Truthfulness stance reflects whether a post endorses a claim as true, rejects it as false, or expresses no clear position. This focus is particularly valuable for tracking public reactions to misinformation and for enabling applications that analyze belief dynamics in online discourse. TSD-CT comprises 5,331 claim-tweet pairs, each annotated into one of five classes: positive, negative, neutral/no stance, topically different, or problematic. To ensure annotation quality, we introduce a strategy that uses gold-standard labels to compute error scores, evaluate annotator performance, and filter out low-quality contributions. The resulting dataset achieves strong inter-annotator agreement. An error analysis further highlights frequent sources of confusion, particularly between neutral/no stance and other classes. The dataset, along with the annotation interface and codebase, is publicly released to facilitate further research.

Factual claims and misinformation circulate widely on social media, shaping public opinion and decision-making. The concept of truthfulness stance refers to whether a text affirms a claim as true, rejects it as false, or takes no clear position. Capturing such stances is essential for understanding how the public engages with and propagates misinformation. We present TrustMap, an application that identifies and visualizes stances of tweets toward factual claims. Users may input factual claims or select claims from a curated set. For each claim, TrustMap retrieves relevant social media posts and applies a retrieval-augmented approach with fine-tuned language models to classify stance. Posts are classified as positive, negative, or neutral/no stance. These classifications are then aggregated by location to reveal regional variations in public opinion. To enhance interpretability, TrustMap uses large language models to generate stance explanations for individual posts and to produce regional stance summaries. By integrating retrieval-augmented truthfulness stance detection with geographical visualization, TrustMap provides the first tool of its kind for exploring how belief in factual claims varies across regions.

Learning an accurate value function for a given policy is a critical step in solving reinforcement learning (RL) problems. So far, however, the convergence speed and sample complexity performances of most existing policy evaluation algorithms remain unsatisfactory, particularly with non-linear function approximation. This challenge motivates us to develop a new path-integrated primal-dual stochastic gradient (PILOT) method, that is able to achieve a fast convergence speed for RL policy evaluation with nonlinear function approximation. To further alleviate the periodic full gradient evaluation requirement, we further propose an enhanced method with an adaptive-batch adjustment called PILOT$^+$. The main advantages of our methods include: i) PILOT allows the use of {\em{constant}} step sizes and achieves the $\mathcal{O}(1/K)$ convergence rate to first-order stationary points of non-convex policy evaluation problems; ii) PILOT is a generic {\em{single}}-timescale algorithm that is also applicable for solving a large class of non-convex strongly-concave minimax optimization problems; iii) By adaptively adjusting the batch size via historical stochastic gradient information, PILOT$^+$ is more sample-efficient empirically without loss of theoretical convergence rate. Our extensive numerical experiments verify our theoretical findings and showcase the high efficiency of the proposed PILOT and PILOT$^+$ algorithms compared with the state-of-the-art methods.

We present Wildfire, an innovative social sensing platform designed for laypersons. The goal is to support users in conducting social sensing tasks using Twitter data without programming and data analytics skills. Existing open-source and commercial social sensing tools only support data collection using simple keyword-based or account-based search. On the contrary, Wildfire employs a heuristic graph exploration method to selectively expand the collected tweet-account graph in order to further retrieve more task-relevant tweets and accounts. This approach allows for the collection of data to support complex social sensing tasks that cannot be met with a simple keyword search. In addition, Wildfire provides a range of analytic tools, such as text classification, topic generation, and entity recognition, which can be crucial for tasks such as trend analysis. The platform also provides a web-based user interface for creating and monitoring tasks, exploring collected data, and performing analytics.

In generating natural language descriptions for knowledge graph triples, prior works used either small-scale, human-annotated datasets or datasets with limited variety of graph shapes, e.g., those having mostly star graphs.Graphto-text models trained and evaluated on such datasets are largely not assessed for more realistic large-scale, open-domain settings.We introduce a new dataset, GraphNarrative, to fill this gap.Fine-tuning transformer-based pretrained language models has achieved state-ofthe-art performance among graph-to-text models.However, this method suffers from information hallucination-the generated text may contain fabricated facts not present in input graphs.We propose a novel approach that, given a graph-sentence pair in GraphNarrative, trims the sentence to eliminate portions that are not present in the corresponding graph, by utilizing the sentence's dependency parse tree.Our experiment results verify this approach using models trained on GraphNarrative and existing datasets.The dataset, source code, and trained models are released at https: //github.com/idirlab/graphnarrator.

Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. Also, with appropriate algorithmic designs, one could achieve the desirable linear speedup for convergence effect in FL. However, most existing works on FL are limited to systems with i.i.d. data and centralized parameter servers and results on decentralized FL with heterogeneous datasets remains limited. Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question. In this paper, we address these challenges by proposing a new algorithm, called NET-FLEET, for fully decentralized FL systems with data heterogeneity. The key idea of our algorithm is to enhance the local update scheme in FL (originally intended for communication efficiency) by incorporating a recursive gradient correction technique to handle heterogeneous datasets. We show that, under appropriate parameter settings, the proposed NET-FLEET algorithm achieves a linear speedup for convergence. We further conduct extensive numerical experiments to evaluate the performance of the proposed NET-FLEET algorithm and verify our theoretical findings.

Network-consensus-based decentralized learning optimization algorithms have attracted a significant amount of attention in recent years due to their rapidly growing applications. However, most of the existing decentralized learning algorithms could not achieve low sample and communication complexities simultaneously - two important metrics in evaluating the trade-off between computation and communication costs of decentralized learning. To overcome these limitations, in this paper, we propose a triple hybrid decentralized stochastic gradient descent (TH-DSGD) algorithm for efficiently solving non-convex network-consensus optimization problems for decentralized learning. We show that to reach an ϵ2-stationary solution, the total sample complexity of TH-DSGD is O(ϵ-3) and the communication complexity is O(ϵ-3), both of which are independent of dataset sizes and significantly improve the sample and communication complexities of the existing works. We conduct extensive experiments with a variety of learning models to verify our theoretical findings. We also show that our TH-DSGD algorithm is stable as the network topology gets sparse and enjoys better convergence in the large-system regime.

Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity. However, three fundamental challenges in designing decentralized optimization algorithms are how to reduce their sample, communication, and memory complexities. In this paper, we propose a gradient-tracking-based stochastic recursive momentum (GT-STORM) algorithm for efficiently solving nonconvex optimization problems. We show that to reach an ϵ2-stationary solution, the total number of sample evaluations of our algorithm is Õ(m1/2ϵ-3) and the number of communication rounds is Õ(m1/2ϵ-3), which improve the O(ϵ-4) costs of sample evaluations and communications for the existing decentralized stochastic gradient algorithms. We conduct extensive experiments with a variety of learning models, including non-convex logistical regression and convolutional neural networks, to verify our theoretical findings. Collectively, our results contribute to the state of the art of theories and algorithms for decentralized network optimization.

Cooperative multi-agent reinforcement learning (MARL) has received increasing attention in recent years and has found many scientific and engineering applications. However, a key challenge arising from many cooperative MARL algorithm designs (e.g., the actor-critic framework) is the policy evaluation problem, which can only be conducted in a {\em decentralized} fashion. In this paper, we focus on decentralized MARL policy evaluation with nonlinear function approximation, which is often seen in deep MARL. We first show that the empirical decentralized MARL policy evaluation problem can be reformulated as a decentralized nonconvex-strongly-concave minimax saddle point problem. We then develop a decentralized gradient-based descent ascent algorithm called GT-GDA that enjoys a convergence rate of $\mathcal{O}(1/T)$. To further reduce the sample complexity, we propose two decentralized stochastic optimization algorithms called GT-SRVR and GT-SRVRI, which enhance GT-GDA by variance reduction techniques. We show that all algorithms all enjoy an $\mathcal{O}(1/T)$ convergence rate to a stationary point of the reformulated minimax problem. Moreover, the fast convergence rates of GT-SRVR and GT-SRVRI imply $\mathcal{O}(\epsilon^{-2})$ communication complexity and $\mathcal{O}(m\sqrt{n}\epsilon^{-2})$ sample complexity, where $m$ is the number of agents and $n$ is the length of trajectories. To our knowledge, this paper is the first work that achieves both $\mathcal{O}(\epsilon^{-2})$ sample complexity and $\mathcal{O}(\epsilon^{-2})$ communication complexity in decentralized policy evaluation for cooperative MARL. Our extensive experiments also corroborate the theoretical performance of our proposed decentralized policy evaluation algorithms.

Network-distributed optimization has attracted sig-nificant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (DGD) algorithm is communication-inefficient for large-scale and high-dimensional network-distributed optimization problems. To address this challenge, many compressed DGD-based algorithms have been proposed. However, most of the existing works have high complexity and assume compressors with bounded noise power. To overcome these limitations, in this paper, we propose a new differential-coded compressed DGD (DC-DGD) algorithm. The key features of DC-DGD include: i) DC-DGD works with general SNR-constrained compressors, relaxing the bounded noise power assumption; ii) The differential-coded design entails the same convergence rate as the original DGD algorithm; and iii) DC-DGD has the same low-complexity structure as the original DGD due to a self-noise-reduction effect. Moreover, the above features inspire us to develop a hybrid compression scheme that offers a systematic mechanism to minimize the communication cost. Finally, we conduct extensive experiments to verify the efficacy of the proposed DC-DGD and hybrid compressor.

With the rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing. Toward this end, we propose a new distributed stochastic gradient method with sparse differential Gaussian-masked stochastic gradients (SDM-DSGD) for non-convex distributed edge learning. Our main contributions are three-fold: i) We theoretically establish the privacy and communication efficiency performance guarantee for our SDM-DSGD method, which outperforms all existing works; ii) We propose a generalized differential-coded DSGD update, which enables a much lower transmit probability for gradient sparsification, and provides an [EQUATION] convergence rate; and iii) We reveal theoretical insights and offer practical design guidelines for the interactions between privacy preservation and communication efficiency - two conflicting performance goals. We conduct extensive experiments with a variety of learning models on MNIST and CIFAR-10 datasets to verify our theoretical findings.

Network consensus optimization has received increasing attention in recent years and has found important applications in many scientific and engineering fields. To solve network consensus optimization problems, one of the most well-known approaches is the distributed gradient descent method (DGD). However, in networks with slow communication rates, DGD's performance is unsatisfactory for solving high-dimensional network consensus problems due to the communication bottleneck. This motivates us to design a communication-efficient DGD-type algorithm based on compressed information exchanges. Our contributions in this paper are three-fold: i) We develop a communication-efficient algorithm called amplified-differential compression DGD (ADC-DGD) and show that it converges under any unbiased compression operator; ii) We rigorously prove the convergence performances of ADC-DGD and show that they match with those of DGD without compression; iii) We reveal an interesting phase transition phenomenon in the convergence speed of ADC-DGD. Collectively, our findings advance the state-of-the-art of network consensus optimization theory.

Self-consistency is a fundamental principle in statistics for retaining maximum amount of information in the data. In this paper this principle is applied to develop a new method for nonparametric spectrum estimation with missing data. One major advantage of the proposed method is that it can be coupled with any complete data nonparametric spectrum estimation procedure, including kernel smoothing, wavelet and spline estimators. The practical performance of the method is illustrated by a simulation study.

Spatial scan statistics are used to determine hotspots in spatial data, and are widely used in epidemiology and biosurveillance. In recent years, there has been much effort invested in designing efficient algorithms for finding such "high discrepancy" regions, with methods ranging from fast heuristics for special cases, to general grid-based methods, and to efficient approximation algorithms with provable guarantees on performance and quality.In this paper, we make a number of contributions to the computational study of spatial scan statistics. First, we describe a simple exact algorithm for finding the largest discrepancy region in a domain. Second, we propose a new approximation algorithm for a large class of discrepancy functions (including the Kulldorff scan statistic) that improves the approximation versus run time trade-off of prior methods. Third, we extend our simple exact and our approximation algorithms to data sets which lie naturally on a grid or are accumulated onto a grid. Fourth, we conduct a detailed experimental comparison of these methods with a number of known methods, demonstrating that our approximation algorithm has far superior performance in practice to prior methods, and exhibits a good performance-accuracy trade-off.All extant methods (including those in this paper) are suitable for data sets that are modestly sized; if data sets are of the order of millions of data points, none of these methods scale well. For such massive data settings, it is natural to examine whether small-space streaming algorithms might yield accurate answers. Here, we provide some negative results, showing that any streaming algorithms that even provide approximately optimal answers to the discrepancy maximization problem must use space linear in the input.