Miracle-dz/UniFAR-FTUs huggingface dataset:
Morse theory of Euclidean distance functions and applications to real algebraic geometry

Given two closed subsets $X, Y$ in $\mathbb{R}^n$, we construct a version of Morse Theory for $\mathrm{dist}_Y|_X \colon X \to \mathbb{R}$, the restriction to $X$ of the Euclidean distance function from $Y$. We use the notion of critical points of Lipschitz functions introduced by Clarke and apply the more general Morse Theory of continuous selections, as presented by Agrachev, Pallaschke, and Scholtes. In this framework, nondegenerate critical points have two indices: a quadratic index as in classical Morse Theory, and a piecewise linear index that relates to the notion of bottlenecks. This framework is flexible enough to simultaneously treat two cases of interest for computational algebraic geometry: the Bottleneck Degree (BND) and the Euclidean Distance Degree (EDD). We provide bounds on the number of critical points of $\mathrm{dist}_Y|_X$ when $X$ and $Y$ are generic real algebraic hypersurfaces and relate these bounds to the BND and EDD. We also prove a duality formula relating the Euler Characteristics of $X$ and $Y$ with the number of critical points of $\mathrm{dist}_Y|_X$ and $\mathrm{dist}_X|_Y$, respectively. Moreover, we introduce a technical toolset of independent interest, which guarantees that our Morse Theory can be used in the generic algebraic case.
W F Libby and the Archaeologists, 1946-1948

Archaeologists began to participate in cross-disciplinary endeavors in the 1930's, albeit on a very limited basis. The passage of time found members of that discipline unprepared for collaboration with physical scientists when W F Libby announced the development of the radiocarbon dating method. Libby proposed to apply to archeologic and geologic samples techniques based on ideas that were completely foreign to archeology …. The initial reactions of archeologists were sometimes amusing but more often significant, for they led to the foundation and emergence of the radiocarbon chronology that has so profoundly affected our understanding of prehistory (Johnson, 1967, p 165). To date, our historical knowledge about the nature, function, and impact of the early (1946-1948) relations between Libby and American archaeologists has come to us in the form of published anecdotes, many of which contain inaccurate information. The author's access to W F Libby's private 14C correspondence, combined with data obtained from interviews with some of the principal participants throughout this period, offers many new or different insights into the nascent years of radiocarbon dating. When, and under what unexpected cricumstances, did Libby first encounter representatives of the achaeologic community? What strategies were employed to facilitate diffusion of knowledge about 14C dating across disciplinary boundaries? How did archaeologists respond to the introduction or “intrusion” into their field of Libby's radioactive age-measurement tool?
Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications

This paper explores the role and challenges of Artificial Intelligence (AI) algorithms, specifically AI-based software elements, in autonomous driving systems. These AI systems are fundamental in executing real-time critical functions in complex and high-dimensional environments. They handle vital tasks like multi-modal perception, cognition, and decision-making tasks such as motion planning, lane keeping, and emergency braking. A primary concern relates to the ability (and necessity) of AI models to generalize beyond their initial training data. This generalization issue becomes evident in real-time scenarios, where models frequently encounter inputs not represented in their training or validation data. In such cases, AI systems must still function effectively despite facing distributional or domain shifts. This paper investigates the risk associated with overconfident AI models in safety-critical applications like autonomous driving. To mitigate these risks, methods for training AI models that help maintain performance without overconfidence are proposed. This involves implementing certainty reporting architectures and ensuring diverse training data. While various distribution-based methods exist to provide safety mechanisms for AI models, there is a noted lack of systematic assessment of these methods, especially in the context of safety-critical automotive applications. Many methods in the literature do not adapt well to the quick response times required in safety-critical edge applications. This paper reviews these methods, discusses their suitability for safety-critical applications, and highlights their strengths and limitations. The paper also proposes potential improvements to enhance the safety and reliability of AI algorithms in autonomous vehicles in the context of rapid and accurate decision-making processes.
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning

Graph learning is crucial in the fields of bioinformatics, social networks, and chemicals. Although high-order graphlets, such as cycles, are critical to achieving an informative graph representation for node classification, edge prediction, and graph recognition, modeling high-order topological characteristics poses significant computational challenges, restricting its widespread applications in machine learning. To address this limitation, we introduce the concept of \textit{message detouring} to hierarchically characterize cycle representation throughout the entire graph, which capitalizes on the contrast between the shortest and longest pathways within a range of local topologies associated with each graph node. The topological feature representations derived from our message detouring landscape demonstrate comparable expressive power to high-order \textit{Weisfeiler-Lehman} (WL) tests but much less computational demands. In addition to the integration with graph kernel and message passing neural networks, we present a novel message detouring neural network, which uses Transformer backbone to integrate cycle representations across nodes and edges. Aside from theoretical results, experimental results on expressiveness, graph classification, and node classification show message detouring can significantly outperform current counterpart approaches on various benchmark datasets.
Sparsity via Sparse Group k-max Regularization

For the linear inverse problem with sparsity constraints, the $l_0$ regularized problem is NP-hard, and existing approaches either utilize greedy algorithms to find almost-optimal solutions or to approximate the $l_0$ regularization with its convex counterparts. In this paper, we propose a novel and concise regularization, namely the sparse group $k$-max regularization, which can not only simultaneously enhance the group-wise and in-group sparsity, but also casts no additional restraints on the magnitude of variables in each group, which is especially important for variables at different scales, so that it approximate the $l_0$ norm more closely. We also establish an iterative soft thresholding algorithm with local optimality conditions and complexity analysis provided. Through numerical experiments on both synthetic and real-world datasets, we verify the effectiveness and flexibility of the proposed method.
JeFaPaTo - A joint toolbox for blinking analysis and facial features extraction

Analyzing facial features and expressions is a complex task in computer vision. The human face is intricate, with significant shape, texture, and appearance variations. In medical contexts, facial structures that differ from the norm, such as those affected by paralysis, are particularly important to study and require precise analysis. One area of interest is the subtle movements involved in blinking, a process that is not yet fully understood and needs high-resolution, time-specific analysis for detailed understanding. However, a significant challenge is that many advanced computer vision techniques demand programming skills, making them less accessible to medical professionals who may not have these skills. The Jena Facial Palsy Toolbox (JeFaPaTo) has been developed to bridge this gap. It utilizes cutting-edge computer vision algorithms and offers a user-friendly interface for those without programming expertise. This toolbox is designed to make advanced facial analysis more accessible to medical experts, simplifying integration into their workflow. The state of the eye closure is of high interest to medical experts, e.g., in the context of facial palsy or Parkinson's disease. Due to facial nerve damage, the eye-closing process might be impaired and could lead to many undesirable side effects. Hence, more than a simple distinction between open and closed eyes is required for a detailed analysis. Factors such as duration, synchronicity, velocity, complete closure, the time between blinks, and frequency over time are highly relevant. Such detailed analysis could help medical experts better understand the blinking process, its deviations, and possible treatments for better eye care.
MetaTra: Meta-Learning for Generalized Trajectory Prediction in Unseen Domain

Trajectory prediction has garnered widespread attention in different fields, such as autonomous driving and robotic navigation. However, due to the significant variations in trajectory patterns across different scenarios, models trained in known environments often falter in unseen ones. To learn a generalized model that can directly handle unseen domains without requiring any model updating, we propose a novel meta-learning-based trajectory prediction method called MetaTra. This approach incorporates a Dual Trajectory Transformer (Dual-TT), which enables a thorough exploration of the individual intention and the interactions within group motion patterns in diverse scenarios. Building on this, we propose a meta-learning framework to simulate the generalization process between source and target domains. Furthermore, to enhance the stability of our prediction outcomes, we propose a Serial and Parallel Training (SPT) strategy along with a feature augmentation method named MetaMix. Experimental results on several real-world datasets confirm that MetaTra not only surpasses other state-of-the-art methods but also exhibits plug-and-play capabilities, particularly in the realm of domain generalization.
Diffeomorphic Measure Matching with Kernels for Generative Modeling

This article presents a general framework for the transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs), inspired by ideas from diffeomorphic matching and image registration. A theoretical analysis of the proposed method is presented, giving a priori error bounds in terms of the complexity of the model, the number of samples in the training set, and model misspecification. An extensive suite of numerical experiments further highlights the properties, strengths, and weaknesses of the method and extends its applicability to other tasks, such as conditional simulation and inference.
LLMs Among Us: Generative AI Participating in Digital Discourse

The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of many social media platforms. While this can bring promising opportunities, it also raises many threats, such as biases and privacy concerns, and may contribute to the spread of propaganda by malicious actors. We developed the"LLMs Among Us"experimental framework on top of the Mastodon social media platform for bot and human participants to communicate without knowing the ratio or nature of bot and human participants. We built 10 personas with three different LLMs, GPT-4, LLama 2 Chat, and Claude. We conducted three rounds of the experiment and surveyed participants after each round to measure the ability of LLMs to pose as human participants without human detection. We found that participants correctly identified the nature of other users in the experiment only 42% of the time despite knowing the presence of both bots and humans. We also found that the choice of persona had substantially more impact on human perception than the choice of mainstream LLMs.
Multi-Blade detector with VMM3a-ASIC-based readout: installation and commissioning at the reflectometer Amor at PSI

The Multi-Blade (MB) Boron-10-based neutron detector is the chosen technology for three instruments at the European Spallation Source (ESS): the two ESS reflectometers, ESTIA and FREIA, and the Test Beam Line. A fourth MB detector has been built, installed and commissioned for the user operation of the reflectometer Amor at PSI (Switzerland). Amor can be considered a downscaled version of the ESS reflectometer ESTIA. They are based on the same Selene guide concept, optimized for performing focusing reflectometry on small samples. The experience gained at Amor is invaluable for the future deployment of the MB detector at the ESS. This manuscript describes the MB detector construction and installation at Amor along with the readout electronics chain based on the VMM3a ASIC. The readout chain deployed at Amor is equivalent of that of the ESS, including the readout master module (RMM), event-formation-units (EFUs), Kafka, FileWriter and live visualisation tools.
Lumos : Empowering Multimodal LLMs with Scene Text Recognition

We introduce Lumos, the first end-to-end multimodal question-answering system with text understanding capabilities. At the core of Lumos is a Scene Text Recognition (STR) component that extracts text from first person point-of-view images, the output of which is used to augment input to a Multimodal Large Language Model (MM-LLM). While building Lumos, we encountered numerous challenges related to STR quality, overall latency, and model inference. In this paper, we delve into those challenges, and discuss the system architecture, design choices, and modeling techniques employed to overcome these obstacles. We also provide a comprehensive evaluation for each component, showcasing high quality and efficiency.
Towards the Human Digital Twin: Definition and Design - A survey

Human Digital Twins (HDTs) are a fast-emerging technology with significant potential in fields ranging from healthcare to sports. HDTs extend the traditional understanding of Digital Twins by representing humans as the underlying physical entity. This has introduced several significant challenges, including ambiguity in the definition of HDTs and a lack of guidance for their design. This survey brings together the recent advances in the field of HDTs to guide future developers by proposing a first cross-domain definition of HDTs based on their characteristics, as well as eleven key design considerations that emerge from the associated challenges.
Dumviri: Detecting Trackers and Mixed Trackers with a Breakage Detector

Previous automatic tracker detection work lacks features to recognize web page breakage and often resort to manual analysis to assess the breakage caused by blocking trackers. We introduce Dumviri, which incorporates a breakage detector that can automatically detect web page breakage caused by erroneously blocking a resource that is needed by the page to function properly. This addition allows Dumviri to prevent functional resources from being misclassified as trackers and increases overall detection accuracy. We designed Dumviri to take differential features. We further find that these features are agnostic to analysis granularity and enable Dumviri to predict tracking resources at the request field granularity, allowing Dumviri to handle some mixed trackers. Evaluating Dumviri on 15K pages shows its ability to replicate the labels of human-generated filter lists with an accuracy of 97.44%. Through a manual analysis, we found that Dumviri identified previously unreported trackers and its breakage detector can identify rules that cause web page breakage in commonly used filter lists like EasyPrivacy. In the case of mixed trackers, Dumviri, being the first automated mixed tracker detector, achieves a 79.09% accuracy. We have confirmed 22 previously unreported unique trackers and 26 unique mixed trackers. We promptly reported these findings to privacy developers, and we will publish our filter lists in uBlock Origin's extended syntax.
Camera Calibration through Geometric Constraints from Rotation and Projection Matrices

The process of camera calibration involves estimating the intrinsic and extrinsic parameters, which are essential for accurately performing tasks such as 3D reconstruction, object tracking and augmented reality. In this work, we propose a novel constraints-based loss for measuring the intrinsic (focal length: $(f_x, f_y)$ and principal point: $(p_x, p_y)$) and extrinsic (baseline: ($b$), disparity: ($d$), translation: $(t_x, t_y, t_z)$, and rotation specifically pitch: $(\theta_p)$) camera parameters. Our novel constraints are based on geometric properties inherent in the camera model, including the anatomy of the projection matrix (vanishing points, image of world origin, axis planes) and the orthonormality of the rotation matrix. Thus we proposed a novel Unsupervised Geometric Constraint Loss (UGCL) via a multitask learning framework. Our methodology is a hybrid approach that employs the learning power of a neural network to estimate the desired parameters along with the underlying mathematical properties inherent in the camera projection matrix. This distinctive approach not only enhances the interpretability of the model but also facilitates a more informed learning process. Additionally, we introduce a new CVGL Camera Calibration dataset, featuring over 900 configurations of camera parameters, incorporating 63,600 image pairs that closely mirror real-world conditions. By training and testing on both synthetic and real-world datasets, our proposed approach demonstrates improvements across all parameters when compared to the state-of-the-art (SOTA) benchmarks. The code and the updated dataset can be found here: https://github.com/CVLABLUMS/CVGL-Camera-Calibration
Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems

Efficient inference in high-dimensional models remains a central challenge in machine learning. This paper introduces the Gaussian Ensemble Belief Propagation (GEnBP) algorithm, a fusion of the Ensemble Kalman filter and Gaussian belief propagation (GaBP) methods. GEnBP updates ensembles by passing low-rank local messages in a graphical model structure. This combination inherits favourable qualities from each method. Ensemble techniques allow GEnBP to handle high-dimensional states, parameters and intricate, noisy, black-box generation processes. The use of local messages in a graphical model structure ensures that the approach is suited to distributed computing and can efficiently handle complex dependence structures. GEnBP is particularly advantageous when the ensemble size is considerably smaller than the inference dimension. This scenario often arises in fields such as spatiotemporal modelling, image processing and physical model inversion. GEnBP can be applied to general problem structures, including jointly learning system parameters, observation parameters, and latent state variables.
Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs

The knowledge concept recommendation in Massive Open Online Courses (MOOCs) is a significant issue that has garnered widespread attention. Existing methods primarily rely on the explicit relations between users and knowledge concepts on the MOOC platforms for recommendation. However, there are numerous implicit relations (e.g., shared interests or same knowledge levels between users) generated within the users' learning activities on the MOOC platforms. Existing methods fail to consider these implicit relations, and these relations themselves are difficult to learn and represent, causing poor performance in knowledge concept recommendation and an inability to meet users' personalized needs. To address this issue, we propose a novel framework based on contrastive learning, which can represent and balance the explicit and implicit relations for knowledge concept recommendation in MOOCs (CL-KCRec). Specifically, we first construct a MOOCs heterogeneous information network (HIN) by modeling the data from the MOOC platforms. Then, we utilize a relation-updated graph convolutional network and stacked multi-channel graph neural network to represent the explicit and implicit relations in the HIN, respectively. Considering that the quantity of explicit relations is relatively fewer compared to implicit relations in MOOCs, we propose a contrastive learning with prototypical graph to enhance the representations of both relations to capture their fruitful inherent relational knowledge, which can guide the propagation of students' preferences within the HIN. Based on these enhanced representations, to ensure the balanced contribution of both towards the final recommendation, we propose a dual-head attention mechanism for balanced fusion. Experimental results demonstrate that CL-KCRec outperforms several state-of-the-art baselines on real-world datasets in terms of HR, NDCG and MRR.
H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields

Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D indoor scene reconstruction. We introduce a novel two-phase learning approach, H2O-SDF, that discriminates between object and non-object regions within indoor environments. This method achieves a nuanced balance, carefully preserving the geometric integrity of room layouts while also capturing intricate surface details of specific objects. A cornerstone of our two-phase learning framework is the introduction of the Object Surface Field (OSF), a novel concept designed to mitigate the persistent vanishing gradient problem that has previously hindered the capture of high-frequency details in other methods. Our proposed approach is validated through several experiments that include ablation studies.
Vehicle Behavior Prediction by Episodic-Memory Implanted NDT

In autonomous driving, predicting the behavior (turning left, stopping, etc.) of target vehicles is crucial for the self-driving vehicle to make safe decisions and avoid accidents. Existing deep learning-based methods have shown excellent and accurate performance, but the black-box nature makes it untrustworthy to apply them in practical use. In this work, we explore the interpretability of behavior prediction of target vehicles by an Episodic Memory implanted Neural Decision Tree (abbrev. eMem-NDT). The structure of eMem-NDT is constructed by hierarchically clustering the text embedding of vehicle behavior descriptions. eMem-NDT is a neural-backed part of a pre-trained deep learning model by changing the soft-max layer of the deep model to eMem-NDT, for grouping and aligning the memory prototypes of the historical vehicle behavior features in training data on a neural decision tree. Each leaf node of eMem-NDT is modeled by a neural network for aligning the behavior memory prototypes. By eMem-NDT, we infer each instance in behavior prediction of vehicles by bottom-up Memory Prototype Matching (MPM) (searching the appropriate leaf node and the links to the root node) and top-down Leaf Link Aggregation (LLA) (obtaining the probability of future behaviors of vehicles for certain instances). We validate eMem-NDT on BLVD and LOKI datasets, and the results show that our model can obtain a superior performance to other methods with clear explainability. The code is available at https://github.com/JWFangit/eMem-NDT.