Miracle-dz/UniFAR-FTUs huggingface dataset:
A Close-Up Investigation of Halftone Color Prints

Modeling the color reproduction of halftone prints is difficult, because of light scattering, causing optical dot gain. Most available models are limited to macroscopic color measurements, averaging the reflectance over an area that is large relative the dot size. The aim of this study is to go beyond the macroscopic approach and study optical dot gain on a micro-scale level, using colorimetric images of printed halftones. An experimental imaging system, combining the accuracy of color measurement instruments with a high spatial resolution, opens up possibilities to better study the color reproduction in halftone color prints. The main focus is to study how the reflectance values of the printed dots and the paper between the dots, Ri(Fi) and Rp(Fi), vary with the dot area fraction. Micro-scale images, i.e. when the resolution of the images is high in relation to the resolution of the halftone, allow for measurements of the individual halftone dots, as well as the paper between them. To capture the characteristics of large populations of halftone dots, histograms are computed. From the histogram data it is possible to derive the mean reflectance, R, the reflectance for the dots, Ri(Fi), and the paper between the dots, Rp(Fi). The true dot area coverage, including the physical dot gain, is computed using histogram data, as well as using line scans in the micro-scale images. A previously proposed extension of the Murray-Davies equation, incorporating Ri(Fi) and Rp(Fi), is evaluated. The model is further extended to handle color prints, predicting tristimulus values, by using 3D histograms in CIEXYZ color space. To reduce the complexity, projection from XYZ coordinates into one dimensional color distributions are used. The prediction errors of the model were found to be equivalent, or better, to that of the Yule-Nielsen model using an optimal n-factor. However, unlike Yule-Nielsen, the extended Murray-Davies model preserves the linear additivity of reflectance, thus providing a better physical description of optical dot gain in halftone color prints.
Towards a Generalized Assessment of Computational Thinking for Introductory Physics Students

Computational thinking in physics has many different forms, definitions, and implementations depending on the level of physics, or the institution it is presented in. In order to better integrate computational thinking in introductory physics, we need to understand what physicists find important about computational thinking in introductory physics. We present a qualitative analysis of twenty-six interviews asking academic (N=18) and industrial (N=8) physicists about the teaching and learning of computational thinking in introductory physics courses. These interviews are part of a longer-term project towards developing an assessment protocol for computational thinking in introductory physics. We find that academic and industrial physicists value students' ability to read code and that Python (or VPython) and spreadsheets were the preferred computational language or environment used. Additionally, the interviewees mentioned that identifying the core physics concepts within a program, explaining code to others, and good program hygiene (i.e., commenting and using meaningful variable names) are important skills for introductory students to acquire. We also find that while a handful of interviewees note that the experience and skills gained from computation are quite useful for student's future careers, they also describe multiple limiting factors of teaching computation in introductory physics, such as curricular overhaul, not having"space"for computation, and student rejection. The interviews show that while adding computational thinking to physics students repertoire is important, the importance really comes from using computational thinking to learn and understand physics better. This informs us that the assessment we develop should only include the basics of computational thinking needed to assess introductory physics knowledge.
Cell-type specific regulator RBPMS switches alternative splicing via higher-order oligomerization and heterotypic interactions with other splicing regulators

Abstract Alternative pre-mRNA splicing decisions are regulated by RNA binding proteins (RBPs) that can activate or repress regulated splice sites. Repressive RBPs typically harness multivalent interactions to bind stably to target RNAs. Multivalency can be achieved by homomeric oligomerization and heteromeric interactions with other RBPs, often mediated by intrinsically disordered regions (IDRs), and by possessing multiple RNA binding domains. Cell-specific splicing decisions often involve the action of widely expressed RBPs, which are able to bind multivalently around target exons, but without effect in the absence of a cell-specific regulator. To address how cell-specific regulators can collaborate with constitutive RBPs in alternative splicing regulation, we used the smooth-muscle specific regulator RBPMS. Recombinant RBPMS is sufficient to confer smooth muscle cell specific alternative splicing of Tpm1 exon 3 in cell-free assays by preventing assembly of ATP-dependent splicing complexes. This activity depends upon a C-terminal IDR that facilitates dynamic higher-order self-assembly, cooperative binding to multivalent RNA and interactions with widely expressed splicing co-regulators, including MBNL1 and RBFOX2, allowing cooperative assembly of stable cell-specific regulatory complexes.
NeuroAiR: Deep Learning Framework for Airwriting Recognition from Scalp-recorded Neural Signals

Airwriting recognition is a task that involves identifying letters written in free space using finger movement. It is a special case of gesture recognition, where gestures correspond to letters in a specific language. Electroencephalography (EEG) is a non-invasive technique for recording brain activity and has been widely used in brain-computer interface applications. Leveraging EEG signals for airwriting recognition offers a promising alternative input method for Human-Computer Interaction. One key advantage of airwriting recognition is that users don't need to learn new gestures. By concatenating recognized letters, a wide range of words can be formed, making it applicable to a broader population. However, there has been limited research in the recognition of airwriting using EEG signals, which forms the core focus of this study. The NeuroAiR dataset comprising EEG signals recorded during writing English uppercase alphabets is first constructed. Various features are then explored in conjunction with different deep learning models to achieve accurate airwriting recognition. These features include processed EEG data, Independent Component Analysis components, source-domain-based scout time series, and spherical and head harmonic decomposition-based features. Furthermore, the impact of different EEG frequency bands on system performance is comprehensively investigated. The highest accuracy achieved in this study is 44.04% using Independent Component Analysis components and the EEGNet classification model. The results highlight the potential of EEG-based airwriting recognition as a user-friendly modality for alternative input methods in Human-Computer Interaction applications. This research sets a strong baseline for future advancements and demonstrates the viability and utility of EEG-based airwriting recognition.
Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation

Near-infrared (NIR) image spectrum translation is a challenging problem with many promising applications. Existing methods struggle with the mapping ambiguity between the NIR and the RGB domains, and generalize poorly due to the limitations of models' learning capabilities and the unavailability of sufficient NIR-RGB image pairs for training. To address these challenges, we propose a cooperative learning paradigm that colorizes NIR images in parallel with another proxy grayscale colorization task by exploring latent cross-domain priors (i.e., latent spectrum context priors and task domain priors), dubbed CoColor. The complementary statistical and semantic spectrum information from these two task domains -- in the forms of pre-trained colorization networks -- are brought in as task domain priors. A bilateral domain translation module is subsequently designed, in which intermittent NIR images are generated from grayscale and colorized in parallel with authentic NIR images; and vice versa for the grayscale images. These intermittent transformations act as latent spectrum context priors for efficient domain knowledge exchange. We progressively fine-tune and fuse these modules with a series of pixel-level and feature-level consistency constraints. Experiments show that our proposed cooperative learning framework produces satisfactory spectrum translation outputs with diverse colors and rich textures, and outperforms state-of-the-art counterparts by 3.95dB and 4.66dB in terms of PNSR for the NIR and grayscale colorization tasks, respectively.
Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever

Few-shot and zero-shot entity linking focus on the tail and emerging entities, which are more challenging but closer to real-world scenarios. The mainstream method is the ''retrieve and rerank'' two-stage framework. In this paper, we propose a coarse-to-fine lexicon-based retriever to retrieve entity candidates in an effective manner, which operates in two layers. The first layer retrieves coarse-grained candidates by leveraging entity names, while the second layer narrows down the search to fine-grained candidates within the coarse-grained ones. In addition, this second layer utilizes entity descriptions to effectively disambiguate tail or new entities that share names with existing popular entities. Experimental results indicate that our approach can obtain superior performance without requiring extensive finetuning in the retrieval stage. Notably, our approach ranks the 1st in NLPCC 2023 Shared Task 6 on Chinese Few-shot and Zero-shot Entity Linking.
The horizontal magnetic primitive equations approximation of the anisotropic MHD equations in a thin 3D domain

In this paper, we give a rigorous justification of the deviation of the primitive equations with only horizontal viscosity and magnetic diffusivity (PEHM) as the small aspect ratio limit of the incompressible three-dimensional scaled horizontal viscous MHD (SHMHD) equations. Choosing an aspect ratio parameter $\varepsilon \in(0,\infty)$, we consider the case that if the horizontal and vertical viscous coefficients are of $\mu = O(1)$ and $\nu = O({\varepsilon ^\alpha })$, and the orders of magnetic diffusion coefficients $k$ and $\sigma$ are $k = O(1)$ and $\sigma = O({\varepsilon ^\alpha })$, with $\alpha>2$, then the limiting system is the PEHM as $\varepsilon$ goes to zero. For ${H^1}$-initial data, we prove that the global weak solutions of the SHMHD equations converge strongly to the local-in-time strong solutions of the PEHM, as $\varepsilon$ tends to zero. For ${H^1}$-initial data with additional regularity $({\partial _z}{\tilde A_0},{\partial _z}{\tilde B_0}) \in {L^p}(\Omega )(2<p<\infty)$, we slightly improve the well-posed result in \cite{2017-Cao-Li-Titi-Global} to extend the local-in-time strong convergences to the global-in-time one. For ${H^2}$-initial data, we show that the local-in-time strong solutions of the SHMHD equations converge strongly to the global-in-time strong solutions of the PEHM, as $\varepsilon$ goes to zero. Moreover, the rate of convergence is of the order $O({\varepsilon ^{\gamma /2}})$, where $\gamma = \min \{ 2,\alpha - 2\}$ with $\alpha \in (2,\infty )$. It should be noted that in contrast to the case $\alpha>2$, the case $\alpha =2$ has been investigated by Du and Li in \cite{2023-Du-Li}, in which they consider the PEM and the rate of global-in-time convergences is of the order $O(\varepsilon)$.
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing

Implicit Graph Neural Networks (GNNs) have achieved significant success in addressing graph learning problems recently. However, poorly designed implicit GNN layers may have limited adaptability to learn graph metrics, experience over-smoothing issues, or exhibit suboptimal convergence and generalization properties, potentially hindering their practical performance. To tackle these issues, we introduce a geometric framework for designing implicit graph diffusion layers based on a parameterized graph Laplacian operator. Our framework allows learning the metrics of vertex and edge spaces, as well as the graph diffusion strength from data. We show how implicit GNN layers can be viewed as the fixed-point equation of a Dirichlet energy minimization problem and give conditions under which it may suffer from over-smoothing during training (OST) and inference (OSI). We further propose a new implicit GNN model to avoid OST and OSI. We establish that with an appropriately chosen hyperparameter greater than the largest eigenvalue of the parameterized graph Laplacian, DIGNN guarantees a unique equilibrium, quick convergence, and strong generalization bounds. Our models demonstrate better performance than most implicit and explicit GNN baselines on benchmark datasets for both node and graph classification tasks.
Depression, anxiety, and the risk of cancer: An individual participant data meta‐analysis

Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol‐related, and smoking‐related cancers), individual participant data (IPD) meta‐analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY‐CA) consortium.
SEM-GAT: Explainable Semantic Pose Estimation Using Learned Graph Attention

This paper proposes a Graph Neural Network (GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accurate lidar-based pose estimation. Our novel lightweight static graph structure informs our attention-based node aggregation network by identifying semantic-instance relationships, acting as an inductive bias to significantly reduce the computational burden of pointcloud registration. By connecting candidate nodes and exploiting cross-graph attention, we identify confidence scores for all potential registration correspondences and estimate the displacement between pointcloud scans. Our pipeline enables introspective analysis of the model's performance by correlating it with the individual contributions of local structures in the environment, providing valuable insights into the system's behaviour. We test our method on the KITTI odometry dataset, achieving competitive accuracy compared to benchmark methods and a higher track smoothness while relying on significantly fewer network parameters.
Using Overlapping Methods to Counter Adversaries in Community Detection

When dealing with large graphs, community detection is a useful data triage tool that can identify subsets of the network that a data analyst should investigate. In an adversarial scenario, the graph may be manipulated to avoid scrutiny of certain nodes by the analyst. Robustness to such behavior is an important consideration for data analysts in high-stakes scenarios such as cyber defense and counterterrorism. In this paper, we evaluate the use of overlapping community detection methods in the presence of adversarial attacks aimed at lowering the priority of a specific vertex. We formulate the data analyst's choice as a Stackelberg game in which the analyst chooses a community detection method and the attacker chooses an attack strategy in response. Applying various attacks from the literature to seven real network datasets, we find that, when the attacker has a sufficient budget, overlapping community detection methods outperform non-overlapping methods, often overwhelmingly so. This is the case when the attacker can only add edges that connect to the target and when the capability is added to add edges between neighbors of the target. We also analyze the tradeoff between robustness in the presence of an attack and performance when there is no attack. Our extensible analytic framework enables network data analysts to take these considerations into account and incorporate new attacks and community detection methods as they are developed.
Implementing Immune Repertoire Models Using Weighted Finite State Machines

The adaptive immune system's T and B cells can be viewed as large populations of simple, diverse classifiers. Artificial immune systems (AIS) $\unicode{x2013}$ algorithmic models of T or B cell repertoires $\unicode{x2013}$ are used in both computational biology and natural computing to investigate how the immune system adapts to its changing environments. However, researchers have struggled to build such systems at scale. For string-based AISs, finite state machines (FSMs) can store cell repertoires in compressed representations that are orders of magnitude smaller than explicitly stored receptor sets. This strategy allows AISs with billions of receptors to be generated in a matter of seconds. However, to date, these FSM-based AISs have been unable to deal with multiplicity in input data. Here, we show how weighted FSMs can be used to represent cell repertoires and model immunological processes like negative and positive selection, while also taking into account the multiplicity of input data. We use our method to build simple immune-inspired classifier systems that solve various toy problems in anomaly detection, showing how weights can be crucial for both performance and robustness to parameters. Our approach can potentially be extended to increase the scale of other population-based machine learning algorithms such as learning classifier systems.
Comparison of Screening Guidelines for Cardiovascular disease Prevention and Early Detection: a scoping review

Objective: Globally, cardiovascular disease (CVD) has a significant role in morbidity and early death. This review's objective is to provide a summary of the strengths and weaknesses in the variety of screening guidelines made by several international organizations for the early identification and prevention of CVD. Methods: As the reporting guideline for this review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). We performed a scoping review using a few guideline-specific databases. We tabulated the main key differences between the included screening guidelines for CVD prevention and early detection from different perspectives. Results: We included a total of 20 unique guidelines that were developed by various organizations throughout the world and focused on screening for CVD prevention and early detection out of the 2,466 guidelines discovered by our search based on our eligibility criteria. We concluded that the target populations, CVD risk assessment techniques, outcomes, and categories of the chosen CVD prevention guidelines widely varied. Additionally, some guidelines adopted no grading scheme for their evidence, while others did not advocate the use of any imaging screening tools in their evaluation of the CVD risk. Conclusions: This scoping review highlights the areas of each guideline's strengths and weaknesses and conducts a systematic comparison of a number of worldwide guidelines for CVD prevention and early diagnosis.
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation

In recent years, 3D representation learning has turned to 2D vision-language pre-trained models to overcome data scarcity challenges. However, existing methods simply transfer 2D alignment strategies, aligning 3D representations with single-view 2D images and coarse-grained parent category text. These approaches introduce information degradation and insufficient synergy issues, leading to performance loss. Information degradation arises from overlooking the fact that a 3D representation should be equivalent to a series of multi-view images and more fine-grained subcategory text. Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality. In this paper, we propose a multi-view joint modality modeling approach, termed JM3D, to obtain a unified representation for point cloud, text, and image. Specifically, a novel Structured Multimodal Organizer (SMO) is proposed to address the information degradation issue, which introduces contiguous multi-view images and hierarchical text to enrich the representation of vision and language modalities. A Joint Multi-modal Alignment (JMA) is designed to tackle the insufficient synergy problem, which models the joint modality by incorporating language knowledge into the visual modality. Extensive experiments on ModelNet40 and ScanObjectNN demonstrate the effectiveness of our proposed method, JM3D, which achieves state-of-the-art performance in zero-shot 3D classification. JM3D outperforms ULIP by approximately 4.3% on PointMLP and achieves an improvement of up to 6.5% accuracy on PointNet++ in top-1 accuracy for zero-shot 3D classification on ModelNet40. The source code and trained models for all our experiments are publicly available at https://github.com/Mr-Neko/JM3D.
Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation

Sequential recommender systems have achieved state-of-the-art recommendation performance by modeling the sequential dynamics of user activities. However, in most recommendation scenarios, the popular items comprise the major part of the previous user actions. Therefore, the learned models are biased towards the popular items irrespective of the user's real interests. In this paper, we propose a structural causal model-based method to address the popularity bias issue for sequential recommendation model learning. For more generalizable modeling, we disentangle the popularity and interest representations at both the item side and user context side. Based on the disentangled representation, we identify a more effective structural causal graph for general recommendation applications. Then, we design delicate sequential models to apply the aforementioned causal graph to the sequential recommendation scenario for unbiased prediction with counterfactual reasoning. Furthermore, we conduct extensive offline experiments and online A/B tests to verify the proposed \textbf{DCR} (Disentangled Counterfactual Reasoning) method's superior overall performance and understand the effectiveness of the various introduced components. Based on our knowledge, this is the first structural causal model specifically designed for the popularity bias correction of sequential recommendation models, which achieves significant performance gains over the existing methods.