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Lanthanide-based upconversion nanoparticles (UCNPs) have attracted considerable attention in biomedical applications, largely due to their anti-Stokes shifted emission enabling autofluorescence-free signal detection. However, residual excitation light can still interfere with their relatively low brightness. While commonly used lock-in detection can distinguish weak signals from substantial random background, concurrently modulated residual excitation light is not eliminated. This remains a challenge, particularly under demanding experimental conditions. Here, we explore the inherent nonlinear response of UCNPs and discover that UCNPs can act as frequency mixers in response to intensity-modulated excitation. Particularly, modulated excitation with more than one base modulation frequency can generate additional low-frequency beating-signals. We show how these signals are resolvable by low-speed detectors such as cameras, are devoid of ambient and residual excitation light, and how they can be enhanced through nanoparticle engineering. Detection of beating-signals thus provides a strategy to significantly enhance signal-to-background conditions in UCNP-based bioimaging and biosensing.

Thyroid scintigraphy is a key imaging modality for diagnosing thyroid disorders. Deep learning models for thyroid scintigraphy classification often face challenges due to limited and imbalanced datasets, leading to suboptimal generalization. In this study, we investigate the effectiveness of different data augmentation techniques including Stable Diffusion (SD), Flow Matching (FM), and Conventional Augmentation (CA) to enhance the performance of a ResNet18 classifier for thyroid condition classification. Our results showed that FM-based augmentation consistently outperforms SD-based approaches, particularly when combined with original (O) data and CA (O+FM+CA), achieving both high accuracy and fair classification across Diffuse Goiter (DG), Nodular Goiter (NG), Normal (NL), and Thyroiditis (TI) cases. The Wilcoxon statistical analysis further validated the superiority of O+FM and its variants (O+FM+CA) over SD-based augmentations in most scenarios. These findings highlight the potential of FM-based augmentation as a superior approach for generating high-quality synthetic thyroid scintigraphy images and improving model generalization in medical image classification.

The growth, lifetime, number density, and size of water droplets in warm atmospheric clouds determine the evolution, lifetime and light transmission properties of those clouds. These small-scale cloud properties, in addition to precipitation initiation, have strong implications for the Earth's energy budget since warm clouds cover large geographic areas. Spatio-temporal correlations on the millimetre scale and smaller may or may not affect these properties of clouds. To date, the pioneering measurements of such correlations in marine stratocumulus clouds have relied on averaging over holographically reconstructed volumes spanning at least ten kilometres. These have revealed weak but widespread spatial clustering of cloud droplets. Here we present results of strong localised clustering on scales of half a metre or less from holographic measurements collected with the Max Planck CloudKite in shallow cumulus clouds in the mid-Atlantic trade wind region near Barbados, with a spatial separation of only 12~cm between measurement volumes. This observation challenges the foundations of our understanding of cloud microphysics at the droplet scale, with implications for cloud modelling in weather and climate prediction.

When a viscoelastic shear-thinning drop of high elasticity hits a superhydrophobic surface, a growing tail-like filament vertically emerges from the impact spot as the contact line recedes. Notably, the ligament transitions into a balloon-like shape before detaching (Balloon regime) completely from the surface. Here, we attribute the ligament formation to the liquid impalement upon impact into the surface protrusion spacing. Our findings reveal that ligament formation can be controlled by tuning the roughness and surface wettability. We show that ligament stretching mainly depends on inertia and gravity, whereas the high elasticity prevents the ligament break up, enabling complete rebounds., Comment: 4 pages, 4 figures

The peer review process is crucial for ensuring the quality and reliability of scholarly work, yet assigning suitable reviewers remains a significant challenge. Traditional manual methods are labor-intensive and often ineffective, leading to nonconstructive or biased reviews. This paper introduces the exHarmony (eHarmony but for connecting experts to manuscripts) benchmark, designed to address these challenges by re-imagining the Reviewer Assignment Problem (RAP) as a retrieval task. Utilizing the extensive data from OpenAlex, we propose a novel approach that considers a host of signals from the authors, most similar experts, and the citation relations as potential indicators for a suitable reviewer for a manuscript. This approach allows us to develop a standard benchmark dataset for evaluating the reviewer assignment problem without needing explicit labels. We benchmark various methods, including traditional lexical matching, static neural embeddings, and contextualized neural embeddings, and introduce evaluation metrics that assess both relevance and diversity in the context of RAP. Our results indicate that while traditional methods perform reasonably well, contextualized embeddings trained on scholarly literature show the best performance. The findings underscore the importance of further research to enhance the diversity and effectiveness of reviewer assignments.

Fake news detection is a significant challenge in the digital age, which has become increasingly important with the proliferation of social media and online communication networks. Graph Neural Networks (GNN)-based methods have shown high potential in analyzing graph-structured data for this problem. However, a major limitation in conventional GNN architectures is their inability to effectively utilize information from neighbors beyond the network's layer depth, which can reduce the model's accuracy and effectiveness. In this paper, we propose a novel model called Neighborhood-Order Learning Graph Attention Network (NOL-GAT) for fake news detection. This model allows each node in each layer to independently learn its optimal neighborhood order. By doing so, the model can purposefully and efficiently extract critical information from distant neighbors. The NOL-GAT architecture consists of two main components: a Hop Network that determines the optimal neighborhood order and an Embedding Network that updates node embeddings using these optimal neighborhoods. To evaluate the model's performance, experiments are conducted on various fake news datasets. Results demonstrate that NOL-GAT significantly outperforms baseline models in metrics such as accuracy and F1-score, particularly in scenarios with limited labeled data. Features such as mitigating the over-squashing problem, improving information flow, and reducing computational complexity further highlight the advantages of the proposed model., Comment: 37 pages

Large language Models (LLMs) are highly sensitive to variations in prompt formulation, which can significantly impact their ability to generate accurate responses. In this paper, we introduce a new task, Prompt Sensitivity Prediction, and a dataset PromptSET designed to investigate the effects of slight prompt variations on LLM performance. Using TriviaQA and HotpotQA datasets as the foundation of our work, we generate prompt variations and evaluate their effectiveness across multiple LLMs. We benchmark the prompt sensitivity prediction task employing state-of-the-art methods from related tasks, including LLM-based self-evaluation, text classification, and query performance prediction techniques. Our findings reveal that existing methods struggle to effectively address prompt sensitivity prediction, underscoring the need to understand how information needs should be phrased for accurate LLM responses.

Atomically thin metallenes are a new family of materials representing the ultimate limit of a thin free-electron gas for novel applications. Although metallene research has gained traction, limited attention has been paid to the properties of their ubiquitous edges. Here, we use density-functional theory simulations to investigate various edges of Mg, Cu, Y, Au, and Pb metallenes with hexagonal and buckled honeycomb lattices. Investigating relaxations, energies, stresses, and electronic structures at the edge, we find that some properties have clear trends while others are sensitive to both element and lattice type. Given that edge properties are fundamental to metallene stability and interactions in lateral heterostructures, their detailed understanding will help guide the development of metallene synthesis and applications., Comment: 7 pages, 6 figures

Natural Language Processing (NLP) has become a cornerstone in many critical sectors, including healthcare, finance, and customer relationship management. This is especially true with the development and use of advanced models such as GPT-based architectures and BERT, which are widely used in decision-making processes. However, the black-box nature of these advanced NLP models has created an urgent need for transparency and explainability. This review explores explainable NLP (XNLP) with a focus on its practical deployment and real-world applications, examining its implementation and the challenges faced in domain-specific contexts. The paper underscores the importance of explainability in NLP and provides a comprehensive perspective on how XNLP can be designed to meet the unique demands of various sectors, from healthcare's need for clear insights to finance's emphasis on fraud detection and risk assessment. Additionally, this review aims to bridge the knowledge gap in XNLP literature by offering a domain-specific exploration and discussing underrepresented areas such as real-world applicability, metric evaluation, and the role of human interaction in model assessment. The paper concludes by suggesting future research directions that could enhance the understanding and broader application of XNLP.

Magneto-plasmonic nanostructures have emerged as promising candidates for advanced sensing applications. However, conventional fabrication methods, such as lithography and sputtering, often involve high costs and complex processes. This study introduces a novel approach for fabricating nickel nano-rings (200-600 nm in diameter) utilizing nanosphere lithography and selective electroless deposition on ITO substrates. The resulting nickel-silver-boron (Ni-Ag-B) nanoarrays exhibit uniform, durable coatings with robust covalent bonds, providing a simpler, more cost-effective alternative to traditional methods. The unique ring-shaped geometry of the nano-rings enhances plasmonic effects by concentrating the electromagnetic field, thus outperforming other nanostructures. Unlike thin films, these nano-rings demonstrate surface plasmon resonance (SPR) within the 470-614 nm range when illuminated at a 45$^{\deg}$ incident angle. Moreover, ellipsometry parameter calculations and Magneto-Optical Kerr Effect (MOKE) measurements revealed narrow Full Width at Half Maximum (FWHM) peaks at 512 nm and 560 nm, indicating superior sensitivity for detection compared to conventional SPR and ellipsometry-SPR techniques. Finite element simulations using COMSOL provided valuable insight into the influence of magnetic fields on the electromagnetic response of the nano-rings, confirming their potential for optical communication and highly sensitive sensing technologies. This study addresses limitations in existing magneto-plasmonic systems, offering a scalable and innovative solution for the development of next-generation sensing applications., Comment: 24 pages, 7 figures

Robust surfaces capable of reducing flow drag, controlling heat and mass transfer, and resisting fouling in fluid flows are important for various applications. In this context, textured surfaces impregnated with a liquid lubricant show promise due to their ability to sustain a liquid-liquid layer that induces slippage. However, theoretical and numerical studies suggest that the slippage can be compromised by surfactants in the overlying fluid, which contaminate the liquid-liquid interface and generate Marangoni stresses. In this study, we use Doppler-optical coherence tomography, an interferometric imaging technique, combined with numerical simulations to investigate how surfactants influence the slip length of lubricant-infused surfaces with longitudinal grooves in a laminar flow. We introduce surfactants by adding tracer particles (milk) to the working fluid (water). Local measurements of slip length at the liquid-liquid interface are significantly smaller than theoretical predictions for clean interfaces (Sch\"onecker & Hardt 2013). In contrast, measurements are in good agreement with numerical simulations of fully immobilized interfaces, indicating that milk particles adsorbed at the interface are responsible for the reduction in slippage. This work provides the first experimental evidence that liquid-liquid interfaces within textured surfaces can become immobilized in the presence of surfactants and flow., Comment: 28 pages, 18 figures

The rapid advancement of Large Language Models (LLMs), particularly those trained on multilingual corpora, has intensified the need for a deeper understanding of their performance across a diverse range of languages and model sizes. Our research addresses this critical need by studying the performance and scaling behavior of multilingual LLMs in text classification and machine translation tasks across 204 languages. We systematically examine both seen and unseen languages across three model families of varying sizes in zero-shot and few-shot settings. Our findings show significant differences in scaling behavior between zero-shot and two-shot scenarios, with striking disparities in performance between seen and unseen languages. Model scale has little effect on zero-shot performance, which remains mostly flat. However, in two-shot settings, larger models show clear linear improvements in multilingual text classification. For translation tasks, however, only the instruction-tuned model showed clear benefits from scaling. Our analysis also suggests that overall resource levels, not just the proportions of pretraining languages, are better predictors of model performance, shedding light on what drives multilingual LLM effectiveness., Comment: Accepted at SEAS Workshop at AAAI25

This paper introduces a framework for capturing stochasticity of choice probabilities in neural networks, derived from and fully consistent with the Random Utility Maximization (RUM) theory, referred to as RUM-NN. Neural network models show remarkable performance compared with statistical models; however, they are often criticized for their lack of transparency and interoperability. The proposed RUM-NN is introduced in both linear and nonlinear structures. The linear RUM-NN retains the interpretability and identifiability of traditional econometric discrete choice models while using neural network-based estimation techniques. The nonlinear RUM-NN extends the model's flexibility and predictive capabilities to capture nonlinear relationships between variables within utility functions. Additionally, the RUM-NN allows for the implementation of various parametric distributions for unobserved error components in the utility function and captures correlations among error terms. The performance of RUM-NN in parameter recovery and prediction accuracy is rigorously evaluated using synthetic datasets through Monte Carlo experiments. Additionally, RUM-NN is evaluated on the Swissmetro and the London Passenger Mode Choice (LPMC) datasets with different sets of distribution assumptions for the error component. The results demonstrate that RUM-NN under a linear utility structure and IID Gumbel error terms can replicate the performance of the Multinomial Logit (MNL) model, but relaxing those constraints leads to superior performance for both Swissmetro and LPMC datasets. By introducing a novel estimation approach aligned with statistical theories, this study empowers econometricians to harness the advantages of neural network models.

Optical Coherence Tomography (OCT) has become an indispensable tool for investigating mesoscopic features in soft matter and fluid mechanics. Its ability to provide high-resolution, non-invasive measurements in both spatial and temporal domains bridges critical gaps in experimental instrumentation, enabling the study of complex, confined, and dynamic systems. This review serves as both an introduction to OCT and a practical guide for researchers seeking to adopt this technology. A set of tutorials, complemented by Python scripts, are provided for both intensity- and Doppler-based techniques. The versatility of OCT is illustrated through case studies, including time-resolved velocimetry, particle-based velocity measurements, slip velocity characterization, detection of shear-induced structures, and analysis of fluid-fluid and fluid-structure interactions. Drawing on our experiences, we also present a set of practical guidelines for avoiding common pitfalls.

We perform Discrete Element Method simulations of wet granular matter in a split-bottom shear cell. To calculate the capillary forces from the liquid bridges between the grains, we used three different approximations. The simulations of the shear cell showed a linear increase in bulk cohesion with the surface tension of the liquid, consistently for all approximations. However, the macroscopic friction coefficient shows only a weak dependence on surface tension., Comment: 5 pages, 3 figures, TGF2024

A promising tool for addressing fake news detection is Graph Neural Networks (GNNs). However, most existing GNN-based methods rely on binary classification, categorizing news as either real or fake. Additionally, traditional GNN models use a static neighborhood for each node, making them susceptible to issues like over-squashing. In this paper, we introduce a novel model named Decision-based Heterogeneous Graph Attention Network (DHGAT) for fake news detection in a semi-supervised setting. DHGAT effectively addresses the limitations of traditional GNNs by dynamically optimizing and selecting the neighborhood type for each node in every layer. It represents news data as a heterogeneous graph where nodes (news items) are connected by various types of edges. The architecture of DHGAT consists of a decision network that determines the optimal neighborhood type and a representation network that updates node embeddings based on this selection. As a result, each node learns an optimal and task-specific computational graph, enhancing both the accuracy and efficiency of the fake news detection process. We evaluate DHGAT on the LIAR dataset, a large and challenging dataset for multi-class fake news detection, which includes news items categorized into six classes. Our results demonstrate that DHGAT outperforms existing methods, improving accuracy by approximately 4% and showing robustness with limited labeled data.

It is well established that resource failure, including robots and machines, in a manufacturing system can result in deadlocks. This issue not only hampers the system's performance but can also inflict significant damage on the manufacturing process. In this paper, we present a new algorithm developed through modeling of a manufacturing system using Petri net that ensures the liveness of the net in the event of such a failure. To detect possible failures, we first design a recovery subnet that is integrated into the resource. Next, we analyze the effects of failures on each state of the network to identify forbidden states. Finally, we propose an algorithm that optimally adds control places and establishes new constant vectors within the network, enabling effective management of remaining resources across different parts of the net. The proposed algorithm has been implemented in a system featuring three manufacturing lines, demonstrating its error-free operation while ensuring key properties such as boundedness, liveness, and performance continuity within the net., Comment: 18 Pages, 5 Figures, Conference paper

Anomaly detection using a network-based approach is one of the most efficient ways to identify abnormal events such as fraud, security breaches, and system faults in a variety of applied domains. While most of the earlier works address the complex nature of graph-structured data and predefined anomalies, the impact of data attributes and emerging anomalies are often neglected. This paper introduces DCOR, a novel approach on attributed networks that integrates reconstruction-based anomaly detection with Contrastive Learning. Utilizing a Graph Neural Network (GNN) framework, DCOR contrasts the reconstructed adjacency and feature matrices from both the original and augmented graphs to detect subtle anomalies. We employed comprehensive experimental studies on benchmark datasets through standard evaluation measures. The results show that DCOR significantly outperforms state-of-the-art methods. Obtained results demonstrate the efficacy of proposed approach in attributed networks with the potential of uncovering new patterns of anomalies., Comment: Accepted at the Thirteenth International Conference on Complex Networks and Their Applications

Recent research has shown that neural information retrieval techniques may be susceptible to adversarial attacks. Adversarial attacks seek to manipulate the ranking of documents, with the intention of exposing users to targeted content. In this paper, we introduce the Embedding Perturbation Rank Attack (EMPRA) method, a novel approach designed to perform adversarial attacks on black-box Neural Ranking Models (NRMs). EMPRA manipulates sentence-level embeddings, guiding them towards pertinent context related to the query while preserving semantic integrity. This process generates adversarial texts that seamlessly integrate with the original content and remain imperceptible to humans. Our extensive evaluation conducted on the widely-used MS MARCO V1 passage collection demonstrate the effectiveness of EMPRA against a wide range of state-of-the-art baselines in promoting a specific set of target documents within a given ranked results. Specifically, EMPRA successfully achieves a re-ranking of almost 96% of target documents originally ranked between 51-100 to rank within the top 10. Furthermore, EMPRA does not depend on surrogate models for adversarial text generation, enhancing its robustness against different NRMs in realistic settings.

Multilingual language models (MLLMs) are crucial for handling text across various languages, yet they often show performance disparities due to differences in resource availability and linguistic characteristics. While the impact of pre-train data percentage and model size on performance is well-known, our study reveals additional critical factors that significantly influence MLLM effectiveness. Analyzing a wide range of features, including geographical, linguistic, and resource-related aspects, we focus on the SIB-200 dataset for classification and the Flores-200 dataset for machine translation, using regression models and SHAP values across 204 languages. Our findings identify token similarity and country similarity as pivotal factors, alongside pre-train data and model size, in enhancing model performance. Token similarity facilitates cross-lingual transfer, while country similarity highlights the importance of shared cultural and linguistic contexts. These insights offer valuable guidance for developing more equitable and effective multilingual language models, particularly for underrepresented languages., Comment: Accepted at The First Workshop on Language Models for Low-Resource Languages @ COLING 2025

Large language models (LLMs) have become increasingly pivotal in various domains due the recent advancements in their performance capabilities. However, concerns persist regarding biases in LLMs, including gender, racial, and cultural biases derived from their training data. These biases raise critical questions about the ethical deployment and societal impact of LLMs. Acknowledging these concerns, this study investigates whether LLMs accurately reflect cross-cultural variations and similarities in moral perspectives. In assessing whether the chosen LLMs capture patterns of divergence and agreement on moral topics across cultures, three main methods are employed: (1) comparison of model-generated and survey-based moral score variances, (2) cluster alignment analysis to evaluate the correspondence between country clusters derived from model-generated moral scores and those derived from survey data, and (3) probing LLMs with direct comparative prompts. All three methods involve the use of systematic prompts and token pairs designed to assess how well LLMs understand and reflect cultural variations in moral attitudes. The findings of this study indicate overall variable and low performance in reflecting cross-cultural differences and similarities in moral values across the models tested, highlighting the necessity for improving models' accuracy in capturing these nuances effectively. The insights gained from this study aim to inform discussions on the ethical development and deployment of LLMs in global contexts, emphasizing the importance of mitigating biases and promoting fair representation across diverse cultural perspectives.

Prior research has demonstrated that language models can, to a limited extent, represent moral norms in a variety of cultural contexts. This research aims to replicate these findings and further explore their validity, concentrating on issues like 'homosexuality' and 'divorce'. This study evaluates the effectiveness of these models using information from two surveys, the WVS and the PEW, that encompass moral perspectives from over 40 countries. The results show that biases exist in both monolingual and multilingual models, and they typically fall short of accurately capturing the moral intricacies of diverse cultures. However, the BLOOM model shows the best performance, exhibiting some positive correlations, but still does not achieve a comprehensive moral understanding. This research underscores the limitations of current PLMs in processing cross-cultural differences in values and highlights the importance of developing culturally aware AI systems that better align with universal human values.

Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to ensure equitable representation across different social groups, leading to biased outputs. In this paper, we introduce two novel methods for fair extractive summarization: FairExtract, a clustering-based approach, and FairGPT, which leverages GPT-3.5-turbo with fairness constraints. We evaluate these methods using Divsumm summarization dataset of White-aligned, Hispanic, and African-American dialect tweets and compare them against relevant baselines. The results obtained using a comprehensive set of summarization quality metrics such as SUPERT, BLANC, SummaQA, BARTScore, and UniEval, as well as a fairness metric F, demonstrate that FairExtract and FairGPT achieve superior fairness while maintaining competitive summarization quality. Additionally, we introduce composite metrics (e.g., SUPERT+F, BLANC+F) that integrate quality and fairness into a single evaluation framework, offering a more nuanced understanding of the trade-offs between these objectives. This work highlights the importance of fairness in summarization and sets a benchmark for future research in fairness-aware NLP models.

Achieving robust legged locomotion on complex terrains poses challenges due to the high uncertainty in robot-environment interactions. Recent advances in bipedal and quadrupedal robots demonstrate good mobility on rugged terrains but rely heavily on sensors for stability due to low static stability from a high center of mass and a narrow base of support. We hypothesize that a multi-legged robotic system can leverage morphological redundancy from additional legs to minimize sensing requirements when traversing challenging terrains. Studies suggest that a multi-legged system with sufficient legs can reliably navigate noisy landscapes without sensing and control, albeit at a low speed of up to 0.1 body lengths per cycle (BLC). However, the control framework to enhance speed on challenging terrains remains underexplored due to the complex environmental interactions, making it difficult to identify the key parameters to control in these high-degree-of-freedom systems. Here, we present a bio-inspired vertical body undulation wave as a novel approach to mitigate environmental disturbances affecting robot speed, supported by experiments and probabilistic models. Finally, we introduce a control framework which monitors foot-ground contact patterns on rugose landscapes using binary foot-ground contact sensors to estimate terrain rugosity. The controller adjusts the vertical body wave based on the deviation of the limb's averaged actual-to-ideal foot-ground contact ratio, achieving a significant enhancement of up to 0.235 BLC on rugose laboratory terrain. We observed a $\sim$ 50\% increase in speed and a $\sim$ 40\% reduction in speed variance compared to the open-loop controller. Additionally, the controller operates in complex terrains outside the lab, including pine straw, robot-sized rocks, mud, and leaves., Comment: Submitted to IEEE Transactions on Robotics (T-RO)

The freeze tag problem (FTP) aims to awaken a swarm of robots with one or more initial awake robots as soon as possible. Each awake robot must touch a sleeping robot to wake it up. Once a robot is awakened, it can assist in awakening other sleeping robots. We study this problem inside a polygonal domain and present approximation algorithms for it., Comment: 7 pages, 3 figures

We present the implementation of two advanced capillary bridge approximations within the Discrete Element Method (DEM) framework of the open-source code MercuryDPM. While MercuryDPM already includes a simplified version of the Willett approximation, our work involves implementing both the classical Willett approximation and the recently published Bagheri approximation in MercuryDPM. Through detailed descriptions and illustrative simulations using a two-particle collision model, we demonstrate the enhanced accuracy and capabilities of these approximations in capturing the complex dynamics of wet granular matter.

Statically typed languages offer numerous benefits to developers, such as improved code quality and reduced runtime errors, but they also require the overhead of manual type annotations. To mitigate this burden, language designers have started incorporating support for type inference, where the compiler infers the type of a variable based on its declaration/usage context. As a result, type annotations are optional in certain contexts, and developers are empowered to use type inference in these situations. However, the usage patterns of type annotations in languages that support type inference are unclear. These patterns can help provide evidence for further research in program comprehension, in language design, and for education. We conduct a large-scale empirical study using Boa, a tool for mining software repositories, to investigate when and where developers use type inference in 498,963 Kotlin projects. We choose Kotlin because it is the default language for Android development, one of the largest software marketplaces. Additionally, Kotlin has supported declaration-site optional type annotations from its initial release. Our findings reveal that type inference is frequently employed for local variables and variables initialized with method calls declared outside the file are more likely to use type inference. These results have significant implications for language designers, providing valuable insight into where to allow type inference and how to optimize type inference algorithms for maximum efficiency, ultimately improving the development experience for developers.

Background: Image memorability refers to the phenomenon where certain images are more likely to be remembered than others. It is a quantifiable and intrinsic image attribute, defined as the likelihood of being remembered upon a single exposure. Despite advances in understanding human visual perception and memory, it is unclear what features contribute to an image's memorability. To address this question, we propose a deep learning-based computational modeling approach. Methods: We modeled the subjective experience of visual memorability using an autoencoder based on VGG16 Convolutional Neural Networks (CNNs). The model was trained on images for one epoch, to simulate the single-exposure condition used in human memory tests. We investigated the relationship between memorability and reconstruction error, assessed latent space representations distinctiveness, and developed a Gated Recurrent Unit (GRU) model to predict memorability likelihood. Interpretability analysis was conducted to identify key image characteristics contributing to memorability. Results: Our results demonstrate a significant correlation between the images memorability score and autoencoder's reconstruction error, and the robust predictive performance of its latent representations. Distinctiveness in these representations correlated significantly with memorability. Additionally, certain visual characteristics, such as strong contrasts, distinctive objects, and prominent foreground elements were among the features contributing to image memorability in our model. Conclusions: Images with unique features that challenge the autoencoder's capacity are inherently more memorable. Moreover, these memorable images are distinct from others the model has encountered, and the latent space of the encoder contains features predictive of memorability.

Recent large language models (LLMs) demonstrate impressive capabilities in handling long contexts, some exhibiting near-perfect recall on synthetic retrieval tasks. However, these evaluations have mainly focused on English text and involved a single target sentence within lengthy contexts. Our work investigates how LLM performance generalizes to multilingual settings with multiple hidden target sentences. We create a new dataset -- mLongRR -- to comprehensively evaluate several multilingual long-context LLMs on retrieval and reasoning tasks across five languages: English, Vietnamese, Indonesian, Swahili, and Somali. These languages share the Latin script but belong to distinct language families and resource levels. Our analysis reveals a significant performance gap between languages. The best-performing models such as Gemini-1.5 and GPT-4o, achieve around 96% accuracy in English to around 36% in Somali with a single target sentence. However, this accuracy drops to 40% in English and 0% in Somali when dealing with three target sentences. Our findings highlight the challenges long-context LLMs face when processing longer contexts, an increase in the number of target sentences, or languages of lower resource levels., Comment: To appear at MRL 2024

We demonstrate an external cavity laser with intrinsic linewidth below 100 Hz around an operating wavelength of 852 nm, selected for its relevance to laser cooling and manipulation of cesium atoms. This system achieves a maximum CW output power of 24 mW, wavelength tunability over 15 nm, and a side-mode suppression ratio exceeding 50 dB. This performance level is facilitated by careful design of a low-loss integrated silicon nitride photonic circuit serving as the external cavity combined with commercially available semiconductor gain chips. This approach demonstrates the feasibility of compact integrated lasers with sub-kHz linewidth centering on the needs of emerging sensor concepts based on ultracold atoms and can be further extended to shorter wavelengths via selection of suitable semiconductor gain media.

Small particles are transported over long distances in the atmosphere, with significant environmental impact. The transport of symmetric particles is well understood, but atmospheric particles, such as curved microplastic fibres or ash particles, are generally asymmetric. This makes the description of their transport properties uncertain. Here, we derive a model for how planar curved fibres settle in quiescent air. The model explains that fluid-inertia torques may align such fibres at oblique angles with gravity as seen in recent laboratory experiments, and shows that inertial alignment is a general and thus important factor for the transport of atmospheric particles., Comment: 6 pages, 3 figures, 1 table, supplemental material

Altermagnetism, a novel magnetic phase bridging ferromagnetism and antiferromagnetism, exhibits zero net magnetization due to its unique alternating spin arrangements, which cancel out macroscopic magnetization. This phase is characterized by robust time-reversal symmetry breaking and spin-momentum locking, leading to distinct electronic properties advantageous for spintronic applications. In this study, we explore the possibility of altermagnetism in cobalt trifluoride (CoF3) using density functional theory (DFT) with Hubbard U correction combined with spin group theory. Our findings reveal that CoF3 exhibits zero net magnetization similar to a g-type antiferromagnet but with spin degeneracy breaking without spin-orbit coupling, akin to a ferromagnet. The optimized structure of CoF3, characterized by a rhombohedral lattice with centrosymmetric symmetry group R3c, shows significant spin splitting in both valence and conduction bands, reaching up to 45 meV. This spin splitting is attributed to the electric crystal potential and the anisotropy of the spin density, leading to the breaking of Kramers degeneracy., Comment: 4 pages, 4 figures

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks., Comment: Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air temperature downscaling. This study presents a data-driven framework for downscaling air temperature using publicly available outputs from urban climate models, specifically datasets generated by UrbClim. The proposed framework utilized morphological features extracted from LiDAR data. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. Then, these features were integrated with meteorological parameters such as wind, humidity, etc., to downscale air temperature using machine learning algorithms. The results demonstrated that the developed framework effectively extracted urban morphological features from LiDAR data. Deep learning algorithms played a crucial role in generating three-dimensional models for extracting the aforementioned features. Also, the evaluation of air temperature downscaling results using various machine learning models indicated that the LightGBM model had the best performance with an RMSE of 0.352{\deg}K and MAE of 0.215{\deg}K. Furthermore, the examination of final air temperature maps derived from downscaling showed that the developed framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level. The corresponding source codes are available on GitHub: https://github.com/FatemehCh97/Air-Temperature-Downscaling.

Particles transported in fluids are everywhere, occurring for example in indoor air, the atmosphere, the oceans, and engineering applications. In this study, a novel three-dimensional numerical framework -- the Palabos Turret is presented, which allows fully resolved simulations of the settling dynamics of heavy particles with arbitrary shapes over a wide range of particle Reynolds numbers. The numerical solver is based on the lattice Boltzmann method utilizing immersed-boundary approach and a recursive-regularized collision model to fully resolve the particle-fluid interactions. A predictor-corrector scheme is applied for the robust time integration of the six-degrees-of-freedom (6DOF) rigid-body motion. Finally, the multi-scale nature arising from the long free-fall distances of a particle is addressed through a dynamic memory allocation scheme allowing for a virtually infinite falling distance. This solver allows for the simulation of particles of any arbitrary shape. The proposed framework is validated using the analytical and experimental data of freely-falling spheres, ellipsoids, and an irregular particle in a wide range of Reynolds numbers between $5\times10^{-1}$ and $4\times10^4$. For different Reynolds numbers and particle shapes considered, the Palabos Turret shows excellent agreement compared to theoretical and experimental values with a median relative deviation of $\pm1.5\%$ and a maximum deviation of $\pm5\%$. The Palabos Turret enables an in-depth analysis of the translational and rotational dynamics of particles with complex geometries.

Every solid particle in the atmosphere, from ice crystals and pollen to dust, ash, and microplastics, is non-spherical. These particles play significant roles in Earth's climate system, influencing temperature, weather patterns, natural ecosystems, human health, and pollution levels. However, our understanding of these particles is largely based on the theories for extremely small particles and experiments conducted in liquid mediums. In this study, we used an innovative experimental setup and particle-resolved numerical simulations to investigate the behaviour of sub-millimetre ellipsoids of varying shapes in the air. Our results revealed complex decaying oscillation patterns involving numerous twists and turns in these particles, starkly contrasting their dynamics in liquid mediums. We found that the frequency and decay rate of these oscillations have a strong dependence on the particle shape. Interestingly, disk-shaped particles oscillated at nearly twice the frequency of rod-shaped particles, though their oscillations also decayed more rapidly. During oscillation, even subtly non-spherical particles can drift laterally up to ten times their volume-equivalent spherical diameter. This behaviour enables particles to sweep through four times more air both vertically and laterally compared to a volume-equivalent sphere, significantly increasing their encounter rate and aggregation possibility. Our findings provide an explanation for the long-range transport and naturally occurring aggregate formation of highly non-spherical particles such as snowflakes and volcanic ash.

Recent advancements in deep learning, particularly in medical imaging, have significantly propelled the progress of healthcare systems. However, examining the robustness of medical images against adversarial attacks is crucial due to their real-world applications and profound impact on individuals' health. These attacks can result in misclassifications in disease diagnosis, potentially leading to severe consequences. Numerous studies have explored both the implementation of adversarial attacks on medical images and the development of defense mechanisms against these threats, highlighting the vulnerabilities of deep neural networks to such adversarial activities. In this study, we investigate adversarial attacks on images associated with Alzheimer's disease and propose a defensive method to counteract these attacks. Specifically, we examine adversarial attacks that employ frequency domain transformations on Alzheimer's disease images, along with other well-known adversarial attacks. Our approach utilizes a convolutional neural network (CNN)-based autoencoder architecture in conjunction with the two-dimensional Fourier transform of images for detection purposes. The simulation results demonstrate that our detection and defense mechanism effectively mitigates several adversarial attacks, thereby enhancing the robustness of deep neural networks against such vulnerabilities., Comment: 10 pages, 4 figures, conference

By Lindstr\"{o}m's theorems, the expressive power of first order logic (and similarly continuous logic) is not strengthened without losing some interesting property. Weakening it, is however less harmless and has been payed attention by some authors. Affine continuous logic is the fragment of continuous logic obtained by avoiding the connectives $\wedge,\vee$. This reduction leads to the affinization of most basic tools and technics of continuous logic such as the ultraproduct construction, compactness theorem, type, saturation etc. The affine variant of the ultraproduct construction is the ultramean construction where ultrafilters are replaced with maximal finitely additive probability measures. A consequence of this relaxation is that compact structures with at least two elements have now proper elementary extensions. In particular, they have non-categorical theories in the new setting. Thus, a model theoretic framework for study of such structures is provided. A more remarkable aspect of this logic is that the type spaces are compact convex sets. The extreme types then play a crucial role in the study of affine theories. In this text, we present the foundations of affine continuous model theory.

Many real-world networks are characterized by directionality; however, the absence of an appropriate Fourier basis hinders the effective implementation of graph signal processing techniques. Inspired by discrete signal processing, where embedding a line digraph into a cycle digraph facilitates the powerful Discrete Fourier Transform for signal analysis, addressing the structural complexities of general digraphs can help overcome the limitations of the Graph Fourier Transform (GFT) and unlock its potential. The Discrete Fourier Transform (DFT) serves as a Graph Fourier Transform for both cycle graphs and Cayley digraphs on the finite cyclic groups $\mathbb{Z}_N$. We propose a systematic method to identify a class of such Cayley digraphs that can encompass a given directed graph. By embedding the directed graph into these Cayley digraphs and opting for envelope extensions that naturally support the Graph Fourier Transform, the GFT functionalities of these extensions can be harnessed for signal analysis. Among the potential envelopes, optimal performance is achieved by selecting one that meets key properties. This envelope's structure closely aligns with the characteristics of the original digraph. The Graph Fourier Transform of this envelope is reliable in terms of numerical stability, and its columns approximately form an eigenbasis for the adjacency matrix associated with the original digraph. It is shown that the envelope extensions possess a convolution product, with their GFT fulfilling the convolution theorem. Additionally, shift-invariant graph filters (systems) are described as the convolution operator, analogous to the classical case. This allows the utilization of systems for signal analysis., Comment: 14 pages, 16 figures, 3 tables

Causal reasoning is one of the primary bottlenecks that Large Language Models (LLMs) must overcome to attain human-level intelligence. Recent studies indicate that LLMs display near-random performance on reasoning tasks. To address this, we introduce the Causal Chain of Prompting ($\text{C}^2\text{P}$), a reasoning framework that aims to equip current LLMs with causal reasoning capabilities as the first framework of its kind operating autonomously without relying on external tools or modules during both the causal learning and reasoning phases. To evaluate the performance of $\text{C}^2\text{P}$, we first demonstrate that reasoning accuracy improved by over $30.7\%$ and $25.9\%$ for GPT-4 Turbo and LLaMA 3.1, respectively, when using our framework, compared to the same models without $\text{C}^2\text{P}$ on a synthetic benchmark dataset. Then, using few-shot learning of the same LLMs with $\text{C}^2\text{P}$, the reasoning accuracy increased by more than $20.05\%$ and $20.89\%$, respectively, with as few as ten examples, compared to the corresponding LLMs without $\text{C}^2\text{P}$ on the same dataset. To evaluate $\text{C}^2\text{P}$ in realistic scenarios, we utilized another benchmark dataset containing natural stories across various fields, including healthcare, medicine, economics, education, social sciences, environmental science, and marketing. The results show improved reasoning when $\text{C}^2\text{P}$ is applied, compared to cases where our framework is not used, which often leads to random and hallucinated responses. By showing the improved performance of few-shot learned GPT-4 Turbo and LLaMA 3.1 with $\text{C}^2\text{P}$, we demonstrate the generalizability of our framework., Comment: arXiv admin note: text overlap with arXiv:2306.05836 by other authors

The objective of this study was to develop an automated pipeline that enhances thyroid disease classification using thyroid scintigraphy images, aiming to decrease assessment time and increase diagnostic accuracy. Anterior thyroid scintigraphy images from 2,643 patients were collected and categorized into diffuse goiter (DG), multinodal goiter (MNG), and thyroiditis (TH) based on clinical reports, and then segmented by an expert. A ResUNet model was trained to perform auto-segmentation. Radiomic features were extracted from both physician (scenario 1) and ResUNet segmentations (scenario 2), followed by omitting highly correlated features using Spearman's correlation, and feature selection using Recursive Feature Elimination (RFE) with XGBoost as the core. All models were trained under leave-one-center-out cross-validation (LOCOCV) scheme, where nine instances of algorithms were iteratively trained and validated on data from eight centers and tested on the ninth for both scenarios separately. Segmentation performance was assessed using the Dice similarity coefficient (DSC), while classification performance was assessed using metrics, such as precision, recall, F1-score, accuracy, area under the Receiver Operating Characteristic (ROC AUC), and area under the precision-recall curve (PRC AUC). ResUNet achieved DSC values of 0.84$\pm$0.03, 0.71$\pm$0.06, and 0.86$\pm$0.02 for MNG, TH, and DG, respectively. Classification in scenario 1 achieved an accuracy of 0.76$\pm$0.04 and a ROC AUC of 0.92$\pm$0.02 while in scenario 2, classification yielded an accuracy of 0.74$\pm$0.05 and a ROC AUC of 0.90$\pm$0.02. The automated pipeline demonstrated comparable performance to physician segmentations on several classification metrics across different classes, effectively reducing assessment time while maintaining high diagnostic accuracy. Code available at: https://github.com/ahxmeds/thyroidiomics.git., Comment: 7 pages, 4 figures, 2 tables

The existence of a flat band near the Fermi level can be a suitable platform for the emergence of interesting phenomena in condensed matter physics. Recently, NbOCl2 monolayer has been experimentally synthesized [Nature 613 (2023) 53], which has a flat and isolated valence band. We show that monolayers based on other elements of group 5 of the periodic table, including the V and Ta atoms, also have a flat band. Motivated by the recent experiment, we investigate the origin of the flat band as well as the electronic, optical, photocatalytic, and magnetic properties of the monolayer by combining density functional theory and many-body quantum perturbation theory. Our results show that the flat and isolated band of this monolayer is caused by the interplay between the Peierls distortion and the electronic configuration of Nb atoms. The investigation of the bandwidth of the monolayer under the biaxial and uniaxial strains reveals that this material can be grown on substrates with a larger lattice constant by maintaining the flat band. Examining the material's response to the linearly polarized light not only reveals the presence of weak optical anisotropy, but also shows the existence of a bright exciton with a binding energy of about 0.94 eV. Hole doping can result in a flat band-induced phase transition from semiconductor to ferromagnet. By adjusting the amount of doping, a bipolar magnetic semiconductor or a half-metal can be created. The interaction between the nearest Nb atoms is ferromagnetic, while an antiferromagnetic interaction appears between the second neighbors, which grows significantly with increasing doping. Our results demonstrate that NbOCl2 monolayer has suitable potential for spintronic applications in addition to electronic and optoelectronic applications., Comment: 13 pages, 7 figures accepted for publication in PRB

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.