Purpose: The timespan over which exploratory searching can occur, as well as the scope and volume of the search activities undertaken, can make it difficult for searchers to remember key details about their search activities. These difficulties are present both in the midst of searching as well as when resuming a search that spans multiple sessions. In this paper, we present a search interface design and prototype implementation to support cross-session exploratory search in a public digital library context. Methods: Search Timelines provides a visualization of current and past search activities via a dynamic timeline of the search activity (queries and saved resources). This timeline is presented at two levels of detail. An Overview Timeline is provided alongside the search results in a typical search engine results page design. A Detailed Timeline is provided in the workspace, where searchers can review the history of their search activities and their saved resources. A controlled laboratory study (n=32) was conducted to compare this approach to a baseline interface modelled after a typical public digital library search/workspace interface. Results: Participants who used Search Timelines reported higher levels of user engagement, usability, and perceived knowledge gain, during an initial search session and when resuming the search after a 7-8 day interval. This came at the expense of the searchers taking more time to complete the search task, which we view as positive evidence of engagement in cross-session exploratory search processes. Conclusion: Search Timelines serves as an example of how lightweight visualization approaches can be used to enhance typical search interface designs to support exploratory search. The results highlight the value of providing persistent representations of past search activities within the search interface.}

We investigate the potential for detecting pion-like dark matter within the framework of a Strongly Interacting Massive Particle (SIMP) model using the MoEDAL Apparatus for Penetrating Particles (MAPP) at the LHC. The model, motivated by Chiral Perturbation Theory, features milli-charged dark pions that couple to Standard Model particles via kinetic mixing with a dark photon. Production channels considered include Drell--Yan pair production and photon fusion via the Wess--Zumino--Witten term, the latter providing a distinctive three-body final state signature. Cross-sections are computed using MadGraph and WHIZARD for a range of dark pion masses, decay constants, and dark $Z$ boson masses. Sensitivity projections for MAPP-1 during the High-Luminosity LHC phase are presented under a background-free assumption, showing competitive reach into previously unexplored parameter space for milli-charged dark pseudoscalars. These results highlight the complementary role of MoEDAL-MAPP in probing dark sector models and motivate further detector-level simulations to refine exclusion limits.

Persuasion is a powerful capability of large language models (LLMs) that both enables beneficial applications (e.g. helping people quit smoking) and raises significant risks (e.g. large-scale, targeted political manipulation). Prior work has found models possess a significant and growing persuasive capability, measured by belief changes in simulated or real users. However, these benchmarks overlook a crucial risk factor: the propensity of a model to attempt to persuade in harmful contexts. Understanding whether a model will blindly ``follow orders'' to persuade on harmful topics (e.g. glorifying joining a terrorist group) is key to understanding the efficacy of safety guardrails. Moreover, understanding if and when a model will engage in persuasive behavior in pursuit of some goal is essential to understanding the risks from agentic AI systems. We propose the Attempt to Persuade Eval (APE) benchmark, that shifts the focus from persuasion success to persuasion attempts, operationalized as a model's willingness to generate content aimed at shaping beliefs or behavior. Our evaluation framework probes frontier LLMs using a multi-turn conversational setup between simulated persuader and persuadee agents. APE explores a diverse spectrum of topics including conspiracies, controversial issues, and non-controversially harmful content. We introduce an automated evaluator model to identify willingness to persuade and measure the frequency and context of persuasive attempts. We find that many open and closed-weight models are frequently willing to attempt persuasion on harmful topics and that jailbreaking can increase willingness to engage in such behavior. Our results highlight gaps in current safety guardrails and underscore the importance of evaluating willingness to persuade as a key dimension of LLM risk. APE is available at this http URL

Although shill bidding is a common auction fraud, it is however very tough to detect. Due to the unavailability and lack of training data, in this study, we build a high-quality labeled shill bidding dataset based on recently collected auctions from eBay. Labeling shill biding instances with multidimensional features is a critical phase for the fraud classification task. For this purpose, we introduce a new approach to systematically label the fraud data with the help of the hierarchical clustering CURE that returns remarkable results as illustrated in the experiments.

Accurate pain assessment in patients with limited ability to communicate, such as older adults with dementia, represents a critical healthcare challenge. Robust automated systems of pain detection may facilitate such assessments. Existing pain detection datasets, however, suffer from limited ethnic/racial diversity, privacy constraints, and underrepresentation of older adults who are the primary target population for clinical deployment. We present SynPAIN, a large-scale synthetic dataset containing 10,710 facial expression images (5,355 neutral/expressive pairs) across five ethnicities/races, two age groups (young: 20-35, old: 75+), and two genders. Using commercial generative AI tools, we created demographically balanced synthetic identities with clinically meaningful pain expressions. Our validation demonstrates that synthetic pain expressions exhibit expected pain patterns, scoring significantly higher than neutral and non-pain expressions using clinically validated pain assessment tools based on facial action unit analysis. We experimentally demonstrate SynPAIN's utility in identifying algorithmic bias in existing pain detection models. Through comprehensive bias evaluation, we reveal substantial performance disparities across demographic characteristics. These performance disparities were previously undetectable with smaller, less diverse datasets. Furthermore, we demonstrate that age-matched synthetic data augmentation improves pain detection performance on real clinical data, achieving a 7.0% improvement in average precision. SynPAIN addresses critical gaps in pain assessment research by providing the first publicly available, demographically diverse synthetic dataset specifically designed for older adult pain detection, while establishing a framework for measuring and mitigating algorithmic bias. The dataset is available at this https URL

We present limits on sterile neutrino mixing using 4,438 live-days of atmospheric neutrino data from the Super-Kamiokande experiment. We search for fast oscillations driven by an eV$^2$-scale mass splitting and for oscillations into sterile neutrinos instead of tau neutrinos at the atmospheric mass splitting. When performing both these searches we assume that the sterile mass splitting is large, allowing $\sin^2(\Delta m^2 L/4E)$ to be approximated as $0.5$, and we assume that there is no mixing between electron neutrinos and sterile neutrinos ($|U_{e4}|^2 = 0$). No evidence of sterile oscillations is seen and we limit $|U_{\mu4}|^2$ to less than 0.041 and $|U_{\tau4}|^2$ to less than 0.18 for $\Delta m^2 > 0.8$ eV$^2$ at the 90% C.L. in a 3+1 framework. The approximations that can be made with atmospheric neutrinos allow these limits to be easily applied to 3+N models, and we provide our results in a generic format to allow comparisons with other sterile neutrino models.

Modern computational systems have an unprecedented ability to detect, leverage and influence human attention. Prior work identified user engagement and dwell time as two key metrics of attention in digital environments, but these metrics have yet to be integrated into a unified model that can advance the theory andpractice of digital attention. We draw on work from cognitive science, digital advertising, and AI to propose a two-stage model of attention for social media environments that disentangles engagement and dwell. In an online experiment, we show that attention operates differently in these two stages and find clear evidence of dissociation: when dwelling on posts (Stage 1), users attend more to sensational than credible content, but when deciding whether to engage with content (Stage 2), users attend more to credible than sensational content. These findings have implications for the design and development of computational systems that measure and model human attention, such as newsfeed algorithms on social media.

Model averaging has demonstrated superior performance for ensemble forecasting in high-dimensional framework, its extension to incomplete datasets remains a critical but underexplored challenge. Moreover, identifying the parsimonious model through averaging procedure in quantile regression demands urgent methodological innovation. In this paper, we propose a novel model averaging method for high-dimensional quantile regression with nonignorable missingness. The idea is to relax the parametric constraint on the conditional distribution of respondents, which is constructed through the two-phase scheme: (i) a semiparametric likelihood-based estimation for the missing mechanism, and (ii) a semiparametric weighting procedure to combine candidate models. One of pivotal advantages is our SMA estimator can asymptotically concentrate on the optimally correct model when the candidate set involves at least one correct model. Theoretical results show that the estimator achieves asymptotic optimality even under complex missingness conditions. Empirical conclusions illustrate the efficiency of the method.

In recent years, street view imagery has grown to become one of the most important sources of geospatial data collection and urban analytics, which facilitates generating meaningful insights and assisting in decision-making. Synthesizing a street-view image from its corresponding satellite image is a challenging task due to the significant differences in appearance and viewpoint between the two domains. In this study, we screened 20 recent research papers to provide a thorough review of the state-of-the-art of how street-view images are synthesized from their corresponding satellite counterparts. The main findings are: (i) novel deep learning techniques are required for synthesizing more realistic and accurate street-view images; (ii) more datasets need to be collected for public usage; and (iii) more specific evaluation metrics need to be investigated for evaluating the generated images appropriately. We conclude that, due to applying outdated deep learning techniques, the recent literature failed to generate detailed and diverse street-view images.

We perform a search for light sterile neutrinos using the data from the T2K far detector at a baseline of 295 km, with an exposure of 14.7 (7.6)$\times 10^{20}$ protons on target in neutrino (antineutrino) mode. A selection of neutral current interaction samples are also used to enhance the sensitivity to sterile mixing. No evidence of sterile neutrino mixing in the 3+1 model was found from a simultaneous fit to the charged-current muon, electron and neutral current neutrino samples. We set the most stringent limit on the sterile oscillation amplitude $\sin^2\theta_{24}$ for the sterile neutrino mass splitting \Delta m^2_{41}<3\times 10^{-3} eV$^2/c^4$.

Camouflaged object detection is an emerging and challenging computer vision task that requires identifying and segmenting objects that blend seamlessly into their environments due to high similarity in color, texture, and size. This task is further complicated by low-light conditions, partial occlusion, small object size, intricate background patterns, and multiple objects. While many sophisticated methods have been proposed for this task, current methods still struggle to precisely detect camouflaged objects in complex scenarios, especially with small and multiple objects, indicating room for improvement. We propose a Multi-Scale Recursive Network that extracts multi-scale features via a Pyramid Vision Transformer backbone and combines them via specialized Attention-Based Scale Integration Units, enabling selective feature merging. For more precise object detection, our decoder recursively refines features by incorporating Multi-Granularity Fusion Units. A novel recursive-feedback decoding strategy is developed to enhance global context understanding, helping the model overcome the challenges in this task. By jointly leveraging multi-scale learning and recursive feature optimization, our proposed method achieves performance gains, successfully detecting small and multiple camouflaged objects. Our model achieves state-of-the-art results on two benchmark datasets for camouflaged object detection and ranks second on the remaining two. Our codes, model weights, and results are available at \href{this https URL}{this https URL}.

A Constraint Satisfaction Problem (CSP) is a framework used for modeling and solving constrained problems. Tree-search algorithms like backtracking try to construct a solution to a CSP by selecting the variables of the problem one after another. The order in which these algorithm select the variables potentially have significant impact on the search performance. Various heuristics have been proposed for choosing good variable ordering. Many powerful variable ordering heuristics weigh the constraints first and then utilize the weights for selecting good order of the variables. Constraint weighting are basically employed to identify global bottlenecks in a CSP. In this paper, we propose a new approach for learning weights for the constraints using competitive coevolutionary Genetic Algorithm (GA). Weights learned by the coevolutionary GA later help to make better choices for the first few variables in a search. In the competitive coevolutionary GA, constraints and candidate solutions for a CSP evolve together through an inverse fitness interaction process. We have conducted experiments on several random, quasi-random and patterned instances to measure the efficiency of the proposed approach. The results and analysis show that the proposed approach is good at learning weights to distinguish the hard constraints for quasi-random instances and forced satisfiable random instances generated with the Model RB. For other type of instances, RNDI still seems to be the best approach as our experiments show.

This paper presents a novel approach for signal reconstruction using Spiking Neural Networks (SNN) based on the principles of Cognitive Informatics and Cognitive Computing. The proposed SNN leverages the Discrete Fourier Transform (DFT) to represent and reconstruct arbitrary time series signals. By employing N spiking neurons, the SNN captures the frequency components of the input signal, with each neuron assigned a unique frequency. The relationship between the magnitude and phase of the spiking neurons and the DFT coefficients is explored, enabling the reconstruction of the original signal. Additionally, the paper discusses the encoding of impulse delays and the phase differences between adjacent frequency components. This research contributes to the field of signal processing and provides insights into the application of SNN for cognitive signal analysis and reconstruction.

Measurements of SIDIS multiplicities for $\pi^+$ and $\pi^-$ from proton and deuteron targets are reported on a grid of hadron kinematic variables $z$, $P_{T}$, and $\phi^{*}$ for leptonic kinematic variables in the range $0.3

We employ the SMASH transport model to provide event-by-event initial conditions for the energy-momentum tensor and conserved charge currents in hydrodynamic simulations of relativistic heavy-ion collisions. We study the fluctuations and dynamical evolution of three conserved charge currents (net baryon, net electric charges, and net strangeness) with a 4D lattice-QCD-based equation of state, NEOS-4D, in the hydrodynamic phase. Out-of-equilibrium corrections at the particlization are generalized to finite densities to ensure the conservation of energy, momentum, and the three types of charges. These theoretical developments are integrated within X-SCAPE as a unified framework for studying the nuclear matter properties in the Beam Energy Scan program.

Effective response to pandemics requires coordinated adoption of mitigation measures, like masking and quarantines, to curb a virus's spread. However, as the COVID-19 pandemic demonstrated, political divisions can hinder consensus on the appropriate response. To better understand these divisions, our study examines a vast collection of COVID-19-related tweets. We focus on five contentious issues: coronavirus origins, lockdowns, masking, education, and vaccines. We describe a weakly supervised method to identify issue-relevant tweets and employ state-of-the-art computational methods to analyze moral language and infer political ideology. We explore how partisanship and moral language shape conversations about these issues. Our findings reveal ideological differences in issue salience and moral language used by different groups. We find that conservatives use more negatively-valenced moral language than liberals and that political elites use moral rhetoric to a greater extent than non-elites across most issues. Examining the evolution and moralization on divisive issues can provide valuable insights into the dynamics of COVID-19 discussions and assist policymakers in better understanding the emergence of ideological divisions.

The density ratio model (DRM) is a semiparametric model that relates the distributions from multiple samples to a nonparametrically defined reference distribution via exponential tilting, with finite-dimensional parameters governing their differences in shape. When multiple types of partially observed (censored/truncated) failure time data are collected in an observational study, the DRM can be utilized to conduct a single unified analysis of the combined data. In this paper, we extend the methodology for censored length-biased/truncated data to the DRM framework and formulate the inference using empirical likelihood. We develop an EM algorithm to compute the DRM-based maximum empirical likelihood estimators of the model parameters and survival function, and assess its performance through extensive simulations under correct model specification, overspecification, and misspecification, across a range of failure-time distributions and censoring proportions. We also illustrate the efficacy of our method by analyzing the duration of time spent from admission to discharge in a Montreal-area hospital in Canada. The R code that implements our method is available on GitHub at \href{this https URL}{DRM-combined-survival}.

We report on a search for magnetic monopoles (MMs) produced in ultraperipheral Pb--Pb collisions during Run-1 of the LHC. The beam pipe surrounding the interaction region of the CMS experiment was exposed to 184.07 \textmu b$^{-1}$ of Pb--Pb collisions at 2.76 TeV center-of-mass energy per collision in December 2011, before being removed in 2013. It was scanned by the MoEDAL experiment using a SQUID magnetometer to search for trapped MMs. No MM signal was observed. The two distinctive features of this search are the use of a trapping volume very close to the collision point and ultra-high magnetic fields generated during the heavy-ion run that could produce MMs via the Schwinger effect. These two advantages allowed setting the first reliable, world-leading mass limits on MMs with high magnetic charge. In particular, the established limits are the strongest available in the range between 2 and 45 Dirac units, excluding MMs with masses of up to 80 GeV at 95\% confidence level.

Recent developments in Artificial Intelligence (AI) and their applications in critical industries such as healthcare, fin-tech and cybersecurity have led to a surge in research in explainability in AI. Innovative research methods are being explored to extract meaningful insight from blackbox AI systems to make the decision-making technology transparent and interpretable. Explainability becomes all the more critical when AI is used in decision making in domains like fintech, healthcare and safety critical systems such as cybersecurity and autonomous vehicles. However, there is still ambiguity lingering on the reliable evaluations for the users and nature of transparency in the explanations provided for the decisions made by black-boxed AI. To solve the blackbox nature of Machine Learning based Intrusion Detection Systems, a framework is proposed in this paper to give an explanation for IDSs decision making. This framework uses Local Interpretable Model-Agnostic Explanations (LIME) coupled with Explain Like I'm five (ELI5) and Decision Tree algorithms to provide local and global explanations and improve the interpretation of IDSs. The local explanations provide the justification for the decision made on a specific input. Whereas, the global explanations provides the list of significant features and their relationship with attack traffic. In addition, this framework brings transparency in the field of ML driven IDS that might be highly significant for wide scale adoption of eXplainable AI in cyber-critical systems. Our framework is able to achieve 85 percent accuracy in classifying attack behaviour on UNSW-NB15 dataset, while at the same time displaying the feature significance ranking of the top 10 features used in the classification.