Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction

Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game of telephone, where one person observes a stimulus and reproduces it for the next to form a chain of reproductions. Past serial reproduction experiments typically employ a single sensory modality, but humans often communicate abstractions of the world to each other through language. To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa. We ran unimodal and multimodal chains with both humans and GPT-4 and find that adding language as a modality has a larger effect on human reproductions than GPT-4's. This suggests human visual and linguistic representations are more dissociable than those of GPT-4.
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution

Guided image synthesis methods, like SDEdit based on the diffusion model, excel at creating realistic images from user inputs such as stroke paintings. However, existing efforts mainly focus on image quality, often overlooking a key point: the diffusion model represents a data distribution, not individual images. This introduces a low but critical chance of generating images that contradict user intentions, raising ethical concerns. For example, a user inputting a stroke painting with female characteristics might, with some probability, get male faces from SDEdit. To expose this potential vulnerability, we aim to build an adversarial attack forcing SDEdit to generate a specific data distribution aligned with a specified attribute (e.g., female), without changing the input's attribute characteristics. We propose the Targeted Attribute Generative Attack (TAGA), using an attribute-aware objective function and optimizing the adversarial noise added to the input stroke painting. Empirical studies reveal that traditional adversarial noise struggles with TAGA, while natural perturbations like exposure and motion blur easily alter generated images' attributes. To execute effective attacks, we introduce FoolSDEdit: We design a joint adversarial exposure and blur attack, adding exposure and motion blur to the stroke painting and optimizing them together. We optimize the execution strategy of various perturbations, framing it as a network architecture search problem. We create the SuperPert, a graph representing diverse execution strategies for different perturbations. After training, we obtain the optimized execution strategy for effective TAGA against SDEdit. Comprehensive experiments on two datasets show our method compelling SDEdit to generate a targeted attribute-aware data distribution, significantly outperforming baselines.
Estimating the Local Learning Coefficient at Scale

The \textit{local learning coefficient} (LLC) is a principled way of quantifying model complexity, originally derived in the context of Bayesian statistics using singular learning theory (SLT). Several methods are known for numerically estimating the local learning coefficient, but so far these methods have not been extended to the scale of modern deep learning architectures or data sets. Using a method developed in {\tt arXiv:2308.12108 [stat.ML]} we empirically show how the LLC may be measured accurately and self-consistently for deep linear networks (DLNs) up to 100M parameters. We also show that the estimated LLC has the rescaling invariance that holds for the theoretical quantity.
Perpetual Future Contracts in Centralized and Decentralized Exchanges: Mechanism and Traders' Behavior

This study presents a groundbreaking Systematization of Knowledge (SoK) initiative, focusing on an in-depth exploration of the dynamics and behavior of traders on perpetual future contracts across both centralized exchanges (CEXs) and decentralized exchanges (DEXs). We summarize the features of CEXs and DEXs and propose 4 exchange models that govern the operations of exchanges in different type, allowing for the classification of any exchange into one of these predefined models. Our research also includes an empirical analysis of traders' behavior in exchanges of the 4 types of models. On DEX of Virtual Automated Market Making (VAMM) Model, open interest on short and long positions exert effect on price volatility in opposite direction, attributable to VAMM's price formation mechanism. In the DEXs with Oracle Pricing Model, we observed a distinct asymmetry in trader behavior between buyers and sellers. Such asymmetry might stem from uninformed traders reacting more strongly to positive news than to negative, leading to a tendency to accumulate long positions. This study sheds light on the potential risks and advantages of using perpetual future contracts within the DeFi space while provides mathematical basis and empirical insights based on which future theoretical works can be configurated, offering crucial insights into the rapidly evolving world of blockchain-based financial instruments.
Retrospective Cost-based Extremum Seeking Control with Vanishing Perturbation for Online Output Minimization

Extremum seeking control (ESC) constitutes a powerful technique for online optimization with theoretical guarantees for convergence to the neighborhood of the optimizer under well-understood conditions. However, ESC requires a nonconstant perturbation signal to provide persistent excitation to the target system to yield convergent results, which usually results in steady state oscillations. While certain techniques have been proposed to eliminate perturbations once the neighborhood of the minimizer is reached, system modifications and environmental perturbations can suddenly change the minimizer and nonconstant perturbations would once more be required to convergence to the new minimizer. Hence, this paper develops a retrospective cost-based ESC(RC/ESC) technique for online output minimization with a vanishing perturbation, that is, a perturbation that becomes zero as time increases independently from the state of the controller or the controlled system. The performance of the proposed algorithm is illustrated via numerical examples.
3Doodle: Compact Abstraction of Objects with 3D Strokes

While free-hand sketching has long served as an efficient representation to convey characteristics of an object, they are often subjective, deviating significantly from realistic representations. Moreover, sketches are not consistent for arbitrary viewpoints, making it hard to catch 3D shapes. We propose 3Dooole, generating descriptive and view-consistent sketch images given multi-view images of the target object. Our method is based on the idea that a set of 3D strokes can efficiently represent 3D structural information and render view-consistent 2D sketches. We express 2D sketches as a union of view-independent and view-dependent components. 3D cubic B ezier curves indicate view-independent 3D feature lines, while contours of superquadrics express a smooth outline of the volume of varying viewpoints. Our pipeline directly optimizes the parameters of 3D stroke primitives to minimize perceptual losses in a fully differentiable manner. The resulting sparse set of 3D strokes can be rendered as abstract sketches containing essential 3D characteristic shapes of various objects. We demonstrate that 3Doodle can faithfully express concepts of the original images compared with recent sketch generation approaches.
Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine

Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a classical nature and the search for practical, real-world applications of QML remains active. In this work, we apply the well-studied quantum support vector machine (QSVM), a powerful QML model, to a binary classification task which classifies peptides as either hemolytic or non-hemolytic. Using three peptide datasets, we apply and contrast the performance of the QSVM, numerous classical SVMs, and the best published results on the same peptide classification task, out of which the QSVM performs best. The contributions of this work include (i) the first application of the QSVM to this specific peptide classification task, (ii) an explicit demonstration of QSVMs outperforming the best published results attained with classical machine learning models on this classification task and (iii) empirical results showing that the QSVM is capable of outperforming many (and possibly all) classical SVMs on this classification task. This foundational work paves the way to verifiable quantum advantages in the field of computational biology and facilitates safer therapeutic development.
A unified approach to fluctuations of spectral statistics of generalized patterned random matrices

An $N \times N$ generalized patterned random matrix is a symmetric matrix defined as $A=(x_{L(i,j)}\mathbf{1}_{\Delta}(i,j))$, where $\{x_k; k \in \mathbb{Z}^d\}$ is known as the input sequence, $L:\{1,2,\ldots, N\}^2 \rightarrow \mathbb{Z}^d$ is known as the link function and $\Delta \subseteq \{1,2,\ldots , N\}^2$. In 2008, Bose and Sen showed that under some restrictions on the link function $L$ and for $\Delta = \{1,2,\ldots , N\}^2$, the corresponding patterned matrices always have a sub-Gaussian limiting spectral distribution. Let $N: \mathbb{N} \rightarrow \mathbb{N}$ be a strictly increasing function and $A_{n}=\left(x_{L(i, j)} \mathbf{1}_{\Delta}(i,j) \right)_{i,j=1}^{N(n)}$ be a sequence of generalized patterned matrices. We consider the LES of $A_n/\sqrt{N(n)}$ for the test function $\phi(x)=x^p$, given by $$ \frac{1}{\sqrt{N(n)}} \sum_{i=1}^{N(n)} \lambda_i^p=\eta_p, \mbox{ say}, $$ where $p$ is a positive integer and $\lambda_i$'s are the eigenvalues of $A_n/\sqrt{N(n)}$. Under some restrictions on $L$ and some moment assumptions on input entries, we show that when $p$ is even, $(\eta_p - \mathbb{E} [\eta_p])$ converges in distribution either to a normal distribution or to the degenerate distribution at zero. We show that under further assumptions on $L$, the limit is always a normal distribution. For odd degree monomial test functions, we derive the limiting moments of $\eta_p$ and show that the LES may not converge to a Gaussian distribution. We also study the LES for independent Brownian motion entries. We show that under some assumption on link function, and that if the limiting moment sequence of the LES of the generalized patterned random matrix with independent standard Gaussian entries is M-determinate, then the LES for Brownian motion entries converge to a deterministic real-valued process.
Compound Returns Reduce Variance in Reinforcement Learning

Multistep returns, such as $n$-step returns and $\lambda$-returns, are commonly used to improve the sample efficiency of reinforcement learning (RL) methods. The variance of the multistep returns becomes the limiting factor in their length; looking too far into the future increases variance and reverses the benefits of multistep learning. In our work, we demonstrate the ability of compound returns -- weighted averages of $n$-step returns -- to reduce variance. We prove for the first time that any compound return with the same contraction modulus as a given $n$-step return has strictly lower variance. We additionally prove that this variance-reduction property improves the finite-sample complexity of temporal-difference learning under linear function approximation. Because general compound returns can be expensive to implement, we introduce two-bootstrap returns which reduce variance while remaining efficient, even when using minibatched experience replay. We conduct experiments showing that two-bootstrap returns can improve the sample efficiency of $n$-step deep RL agents, with little additional computational cost.
VRMM: A Volumetric Relightable Morphable Head Model

In this paper, we introduce the Volumetric Relightable Morphable Model (VRMM), a novel volumetric and parametric facial prior for 3D face modeling. While recent volumetric prior models offer improvements over traditional methods like 3D Morphable Models (3DMMs), they face challenges in model learning and personalized reconstructions. Our VRMM overcomes these by employing a novel training framework that efficiently disentangles and encodes latent spaces of identity, expression, and lighting into low-dimensional representations. This framework, designed with self-supervised learning, significantly reduces the constraints for training data, making it more feasible in practice. The learned VRMM offers relighting capabilities and encompasses a comprehensive range of expressions. We demonstrate the versatility and effectiveness of VRMM through various applications like avatar generation, facial reconstruction, and animation. Additionally, we address the common issue of overfitting in generative volumetric models with a novel prior-preserving personalization framework based on VRMM. Such an approach enables accurate 3D face reconstruction from even a single portrait input. Our experiments showcase the potential of VRMM to significantly enhance the field of 3D face modeling.
Artificial Intelligence in Image-based Cardiovascular Disease Analysis: A Comprehensive Survey and Future Outlook

Recent advancements in Artificial Intelligence (AI) have significantly influenced the field of Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper presents an extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential. We systematically categorize the literature based on the primary anatomical structures related to CVD, dividing them into non-vessel structures (such as ventricles and atria) and vessel structures (including the aorta and coronary arteries). This categorization provides a structured approach to explore various imaging modalities like Magnetic Resonance Imaging (MRI), which are commonly used in CVD research. Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis. Additionally, we compile a list of publicly accessible cardiac image datasets and code repositories, intending to support research reproducibility and facilitate data and algorithm sharing within the community. We conclude with an examination of the challenges and limitations inherent in current AI-based CVD analysis methods and suggest directions for future research to overcome these hurdles.
Low-Distortion Clustering with Ordinal and Limited Cardinal Information

Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of n agents located in an underlying metric space, our goal is to partition them into k clusters, optimizing some social cost objective. The metric space is defined by a distance function d between the agent locations. Information about d is available only implicitly via n rankings, through which each agent ranks all other agents in terms of their distance from her. Still, even though no cardinal information (i.e., the exact distance values) is available, we would like to evaluate clustering algorithms in terms of social cost objectives that are defined using d. This is done using the notion of distortion, which measures how far from optimality a clustering can be, taking into account all underlying metrics that are consistent with the ordinal information available. Unfortunately, the most important clustering objectives (e.g., those used in the well-known k-median and k-center problems) do not admit algorithms with finite distortion. To sidestep this disappointing fact, we follow two alternative approaches: We first explore whether resource augmentation can be beneficial. We consider algorithms that use more than k clusters but compare their social cost to that of the optimal k-clusterings. We show that using exponentially (in terms of k) many clusters, we can get low (constant or logarithmic) distortion for the k-center and k-median objectives. Interestingly, such an exponential blowup is shown to be necessary. More importantly, we explore whether limited cardinal information can be used to obtain better results. Somewhat surprisingly, for k-median and k-center, we show that a number of queries that is polynomial in k and only logarithmic in n (i.e., only sublinear in the number of agents for the most relevant scenarios in practice) is enough to get constant distortion.
Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context

Large Language Models (LLMs) have achieved remarkable success in code completion, as evidenced by their essential roles in developing code assistant services such as Copilot. Being trained on in-file contexts, current LLMs are quite effective in completing code for single source files. However, it is challenging for them to conduct repository-level code completion for large software projects that require cross-file information. Existing research on LLM-based repository-level code completion identifies and integrates cross-file contexts, but it suffers from low accuracy and limited context length of LLMs. In this paper, we argue that Integrated Development Environments (IDEs) can provide direct, accurate and real-time cross-file information for repository-level code completion. We propose IDECoder, a practical framework that leverages IDE native static contexts for cross-context construction and diagnosis results for self-refinement. IDECoder utilizes the rich cross-context information available in IDEs to enhance the capabilities of LLMs of repository-level code completion. We conducted preliminary experiments to validate the performance of IDECoder and observed that this synergy represents a promising trend for future exploration.
YOLOPoint Joint Keypoint and Object Detection

Intelligent vehicles of the future must be capable of understanding and navigating safely through their surroundings. Camera-based vehicle systems can use keypoints as well as objects as low- and high-level landmarks for GNSS-independent SLAM and visual odometry. To this end we propose YOLOPoint, a convolutional neural network model that simultaneously detects keypoints and objects in an image by combining YOLOv5 and SuperPoint to create a single forward-pass network that is both real-time capable and accurate. By using a shared backbone and a light-weight network structure, YOLOPoint is able to perform competitively on both the HPatches and KITTI benchmarks.
Vanishing in fractal space: Thermal melting and hydrodynamic collapse

Fractals emerge everywhere in nature, exhibiting intricate geometric complexities through the self-organizing patterns that span across multiple scales. Here, we investigate beyond steady-states the interplay between this geometry and the vanishing dynamics, through phase-transitional thermal melting and hydrodynamic void collapse, within fractional continuous models. We present general analytical expressions for estimating vanishing times with their applicability contingent on the fractality of space. We apply our findings on the fractal environments crucial for plant growth: natural soils. We focus on the transport phenomenon of cavity shrinkage in incompressible fluid, conducting a numerical study beyond the inviscid limit. We reveal how a minimal collapsing time can emerge through a non-trivial coupling between the fluid viscosity and the surface fractal dimension.
Spatial Assisted Human-Drone Collaborative Navigation and Interaction through Immersive Mixed Reality

Aerial robots have the potential to play a crucial role in assisting humans with complex and dangerous tasks. Nevertheless, the future industry demands innovative solutions to streamline the interaction process between humans and drones to enable seamless collaboration and efficient co-working. In this paper, we present a novel tele-immersive framework that promotes cognitive and physical collaboration between humans and robots through Mixed Reality (MR). This framework incorporates a novel bi-directional spatial awareness and a multi-modal virtual-physical interaction approaches. The former seamlessly integrates the physical and virtual worlds, offering bidirectional egocentric and exocentric environmental representations. The latter, leveraging the proposed spatial representation, further enhances the collaboration combining a robot planning algorithm for obstacle avoidance with a variable admittance control. This allows users to issue commands based on virtual forces while maintaining compatibility with the environment map. We validate the proposed approach by performing several collaborative planning and exploration tasks involving a drone and an user equipped with a MR headset.
Incivility in Open Source Projects: A Comprehensive Annotated Dataset of Locked GitHub Issue Threads

In the dynamic landscape of open source software (OSS) development, understanding and addressing incivility within issue discussions is crucial for fostering healthy and productive collaborations. This paper presents a curated dataset of 404 locked GitHub issue discussion threads and 5961 individual comments, collected from 213 OSS projects. We annotated the comments with various categories of incivility using Tone Bearing Discussion Features (TBDFs), and, for each issue thread, we annotated the triggers, targets, and consequences of incivility. We observed that Bitter frustration, Impatience, and Mocking are the most prevalent TBDFs exhibited in our dataset. The most common triggers, targets, and consequences of incivility include Failed use of tool/code or error messages, People, and Discontinued further discussion, respectively. This dataset can serve as a valuable resource for analyzing incivility in OSS and improving automated tools to detect and mitigate such behavior.
Spatially Consistent Air-to-Ground Channel Modeling via Generative Neural Networks

This article proposes a generative neural network architecture for spatially consistent air-to-ground channel modeling. The approach considers the trajectories of uncrewed aerial vehicles along typical urban paths, capturing spatial dependencies within received signal strength (RSS) sequences from multiple cellular base stations (gNBs). Through the incorporation of conditioning data, the model accurately discriminates between gNBs and drives the correlation matrix distance between real and generated sequences to minimal values. This enables evaluating performance and mobility management metrics with spatially (and by extension temporally) consistent RSS values, rather than independent snapshots. For some tasks underpinned by these metrics, say handovers, consistency is essential.
Comparison of the Efficacy of Cryoballoon Ablation for Paroxysmal and Persistent Atrial Fibrillation

Background: Similar clinical outcomes have been demonstrated following pulmonary vein (PV) isolation (I) by radiofrequency catheter and cryoballoon ablation (CBA) in patients with paroxysmal atrial fibrillation (PAF). However, no comparison of the clinical outcome in patients with PAF and persistent AF (PerAF) by CBA has been reported to date. The purpose of this study was to compare the efficacy of PVI in patients with PAF and PerAF. Methods: CBA based PVI using a second-generation 28-mm balloon was performed in 58 patients with PAF and 32 with PerAF. Follow-up (FU) was based on outpatient clinic visits at 1, 3, 6, and 12 months, which included Holter electrocardiograms and ambulatory event electrocardiograms. Results: The freedom from atrial tachyarrhythmia recurrences following a single cryoballoon ablation in patients with PAF and PerAF did not differ significantly between the PAF and PerAF patients over a relatively shorter follow-up period, as estimated by the Kaplan-Meir method. Conclusion: CB-based PVI had similar efficacy for both PAF and PerAF.