Miracle-dz/UniFAR-FTUs huggingface dataset:
An obstruction bundle relating Gromov-Witten invariants of curves and Kahler surfaces

In [LP] the authors defined symplectic "Local Gromov-Witten invariants" associated to spin curves and showed that the GW invariants of a Kahler surface X with p_g>0 are a sum of such local GW invariants. This paper describes how the local GW invariants arise from an obstruction bundle (in the sense of Taubes) over the space of stable maps into curves. Together with the results of [LP], this reduces the calculation of the GW invariants of complex surfaces to computations in the GW theory of curves.
Terrain Matching Positioning Method Based on Node Multi-information Fusion

Measurement bias and lack of terrain features often cause false peaks during underwater terrain matching positioning, that is, there is more than one peak near the real position. Previous methods to address this problem have increased the number of measurement beams, but this also increases the data processing time and energy consumption. At the same time, the ratio of measured information that is used does not increase. In other words, we should increase the ratio of measured information that is used, not simply increase the amount of information that is measured. Conventional matching algorithms only use the height of nodes without considering surface information, which is composed of height and the position of multiple nodes in three-dimensional space. Multi-beam sonar can obtain the three-dimensional distribution of terrain nodes. This node information is not just a height sequence, as it is used in previous methods. If we consider the nodes as a three-dimensional distribution of points with height and position information, this increases the matching position information and more of the terrain features can be extracted from the same measured data. Hence, in this paper, a terrain positioning method called the Node Multi-information Fusion (NMIF) is presented. This method focuses on improving the stability and accuracy degraded by bias in the Digital Elevation Map (DEM), terrain repeatability, and other factors. First, the concept of a Single Node Data Packet (SNDP) is introduced. The SNDP includes elevation and surface information surrounding the node, such as roughness, gradient, and slope. This additional topographic feature information improves the robustness and accuracy of the system. A computer simulation using actual ocean bottom topography verifies the advantages of the proposed NMIF algorithm.
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. However, a good prompt is not solely defined by its wording, but also binds to the nature of the LLM in question. In this work, we first quantitatively demonstrate that different prompts should be adapted to different LLMs to enhance their capabilities across various down-stream tasks in NLP. Then we novelly propose a model-adaptive prompt optimizer (MAPO) method that optimizes the original prompts for each specific LLM in downstream tasks. Extensive experiments indicate that the proposed method can effectively refine prompts for an LLM, leading to significant improvements over various downstream tasks.
HEAR: Hearing Enhanced Audio Response for Video-grounded Dialogue

Video-grounded Dialogue (VGD) aims to answer questions regarding a given multi-modal input comprising video, audio, and dialogue history. Although there have been numerous efforts in developing VGD systems to improve the quality of their responses, existing systems are competent only to incorporate the information in the video and text and tend to struggle in extracting the necessary information from the audio when generating appropriate responses to the question. The VGD system seems to be deaf, and thus, we coin this symptom of current systems' ignoring audio data as a deaf response. To overcome the deaf response problem, Hearing Enhanced Audio Response (HEAR) framework is proposed to perform sensible listening by selectively attending to audio whenever the question requires it. The HEAR framework enhances the accuracy and audibility of VGD systems in a model-agnostic manner. HEAR is validated on VGD datasets (i.e., AVSD@DSTC7 and AVSD@DSTC8) and shows effectiveness with various VGD systems.
SFLSH: Shape-Dependent Soft-Flesh Avatars

We present a multi-person soft-tissue avatar model. This model maps a body shape descriptor to heterogeneous geometric and mechanical parameters of a soft-tissue model across the body, effectively producing a shape-dependent parametric soft avatar model. The design of the model overcomes two major challenges, the potential redundancy of geometric and mechanical parameters, and the complexity to obtain abundant subject data, which together induce major risk of overfitting the resulting model. To overcome these challenges, we introduce a local shape-dependent regularization of the model. We demonstrate accurate results, on par with independent per-subject estimation, accurate interpolation within the range of body shapes of the training subjects, and good generalization to unseen body shapes. As a result, we obtain a parametric soft-flesh avatar model easy to integrate in many existing applications.
Holographic Near-eye Display with Real-time Embedded Rendering

We present a wearable full-color holographic augmented reality headset with binocular vision support and real-time embedded hologram calculation. Contrarily to most previously proposed prototypes, our headset employs high-speed amplitude-only microdisplays and embeds a compact and lightweight electronic board to drive and synchronize the microdisplays and light source engines. In addition, to enable a standalone usage of the headset, we developed a real-time hologram rendering engine capable of computing full-color binocular holograms at over 35 frames per second on a NVIDIA Jetson AGX Orin embedded platform. Finally, we provide a comparison of the efficiency of laser diodes and superluminescent diodes for the reduction of speckle noise, which greatly affects the reconstructed image’s quality. Experimental results show that our prototype enables full-color holographic images to be reconstructed with accurate focus cues and reduced speckle noise in real-time.
Neural Motion Graph

Deep learning techniques have been employed to design a controllable human motion synthesizer. Despite their potential, however, designing a neural network-based motion synthesis that enables flexible user interaction, fine-grained controllability, and the support of new types of motions at reduced time and space consumption costs remains a challenge. In this paper, we propose a novel approach, a neural motion graph, that addresses the challenge by enabling scalability to new motions while using compact neural networks. Our approach represents each type of motion with a separate neural node to reduce the cost of adding new motion types. In addition, designing a separate neural node for each motion type enables task-specific control strategies and has greater potential to achieve a high-quality synthesis of complex motions, such as the Mongolian dance. Furthermore, a single transition network, which acts as neural edges, is used to model the transition between two motion nodes. The transition network is designed with a lightweight control module to achieve a fine-grained response to user control signals. Overall, the design choice makes the neural motion graph highly controllable and scalable. In addition to being fully flexible to user interaction through high-level and fine-grained user-control signals, our experimental and subjective evaluation results demonstrate that our proposed approach, neural motion graph, outperforms state-of-the-art human motion synthesis methods in terms of the quality of controlled motion generation.
Breaking the Small Cluster Barrier of Graph Clustering Supplementary Material Nir Ailon

In this supplementary material, we present proof details. 1 Notation and Conventions We use the following notation and conventions throughout the supplement. For a real n× n matrix M , we use the unadorned norm ‖M‖ to denote its spectral norm. The notation ‖M‖F refers to the Frobenius norm, ‖M‖1 is ∑ i,j |M(i, j)| and ‖M‖∞ is maxij |M(i, j)|. We will also study operators on the space of matrices. To distinguish them from the matrices studied in this work, we will simply call these objects “operators”, and will denote them using a calligraphic font, e.g. P. The norm ‖P‖ of an operator is defined as ‖P‖ = sup M :‖M‖F =1 ‖PM‖F , where the supremum is over matrices M . For a fixed, real n× n matrix M , we define the matrix linear subspace T (M) as follows: T (M) := {YM +MX : X,Y ∈ Rn×n} . In words, this subspace is the set of matrices spanned by matrices each row of which is in the row space of M , and matrices each column of which is in the column space of M . For any given subspace of matrices S ⊆ Rn×n, we let PS denote the orthogonal projection onto S with respect to the the inner product 〈X,Y 〉 = ∑n i,j=1X(i, j)Y (i, j) = trX Y . This means that for any matrix M , PSM = argminX∈S ‖M −X‖F . For a matrix M , we let Γ(M) denote the set of matrices supported on a subset of the support of M . Note that for any matrix X, (PΓ(X)M)(i, j) = { M(i, j) X(i, j) 6= 0 0 otherwise . It is a well known fact that PT (X) is given as follows: PT (X)M = PC(X)M +MPR(X) − PC(X)MPR(X) ,
SIURU: A Framework for Machine Learning Based Anomaly Detection in IoT Network Traffic

With the increasing adoption of Internet of Things (IoT), research into anomaly detection (AD) in IoT network traffic is gaining importance. Malicious disturbances (e.g. malware and cyber attacks) and operational issues (e.g. software/sensor failures and physical damage) can seriously disrupt network operations. These problems can be detected with traffic monitoring and responded to with mitigation systems. In recent years, machine learning (ML) models have been successfully applied for AD, with state-of-the-art solutions reaching detection rates of . However, the heterogeneity of devices and anomalies in IoT networks poses a challenge for existing solutions. High AD accuracy is usually limited to the types of anomalies present in the training data, falling off significantly in the face of new anomalies. Many IoT-AD solutions are additionally limited to certain types of devices or require additional hardware setup. To tackle these challenges, we present SIURU, a flexible framework for AD research and development capable of applying various feature extraction and ML algorithms. SIURU’s intended deployment environment is broker-based publish-subscribe IoT systems, posing no additional constraints on the IoT device capabilities or network setup. SIURU supports the creation of custom pipelines with data loading, preprocessing, and encoding components, followed by training and testing of ML-based AD models and reporting of prediction results. To validate our proposed framework, we implement the architecture and evaluate its runtime performance. Finally, we demonstrate the effectiveness of the proposed architecture by a case example that tests the performance of eXplainable Artificial Intelligence (XAI) algorithms with multiple ADs and datasets.
The American College of St Maurice at Münster, 1867–1879: the formation of Catholic clergy for the United States between seminary education and academic studies

ABSTRACT The American College of St Maurice at Münster in Westphalia was founded in 1867 to train priests for the Catholic missions in the United States. This paper outlines the history of this short-lived educational institutions (1867–1879), with particular focus on the 68 seminarians, who attended this theological seminary for their pastoral formation, accompanied by scholarly studies in theology and philosophy at the Academy of Münster. This educational concept and history of the American College is considered against the background of the scholarly dispute about the dogma of papal infallibility and the Prussian Kulturkampf, that eventually led to the closure of the American College in 1879. Finally, attention is paid to the pastoral ministry of the alumni and the situation of the German Catholics in the United States. The alumni of the American College became a particularly sought-after group of priests, not only due to their religio-cultural background, but also with regards to their broader pastoral and academic formation that helped to meet the challenges ahead.
Curl Noise Jittering

We propose a method for implicitly generating blue noise point sets. Our method is based on the observations that curl noise vector fields are volume-preserving and that jittering can be construed as moving points along the streamlines of a vector field. We demonstrate that the volume preservation keeps the points well separated when jittered using a curl noise vector field. At the same time, the anisotropy that stems from regular lattices is significantly reduced by such jittering. In combination, these properties entail that jittering by curl noise effectively transforms a regular lattice into a point set with blue noise properties. Our implicit method does not require computing the point set in advance. This makes our technique valuable when an arbitrarily large set of points with blue noise properties is needed. We compare our method to several other methods based on jittering as well as other methods for blue noise point set generation. Finally, we show several applications of curl noise jittering in two and three dimensions.
Shadow Harmonization for Realistic Compositing

Compositing virtual objects into real background images requires one to carefully match the scene’s camera parameters, surface geometry, textures, and lighting to obtain plausible renderings. Recent learning approaches have shown many scene properties can be estimated from images, resulting in robust automatic single-image compositing systems, but many challenges remain. In particular, interactions between real and synthetic shadows are not handled gracefully by existing methods, which typically assume a shadow-free background. As a result, they tend to generate double shadows when the synthetic object’s cast shadow overlaps a background shadow, and ignore shadows from the background that should be cast onto the synthetic object. In this paper, we present a compositing method for outdoor scenes that addresses these issues and produces realistic cast shadows. This requires identifying existing shadows, including soft shadow boundaries, then reasoning about the ambiguity of unknown ground albedo and scene lighting to match the color and intensity of shaded areas. Using supervision from shadow removal and detection datasets, we propose a generative adversarial pipeline and improved composition equations that simultaneously handle both shadow interaction scenarios. We evaluate our method on challenging, real outdoor images from multiple distributions and datasets. Quantitative and qualitative comparisons show our approach produces more realistic results than existing alternatives. Our code, datasets, and trained models are publicly available at https://lvsn.github. io/shadowcompositing.
Reproducing popularity bias in recommendation: the effect of evaluation strategies

The extent to which popularity bias is propagated by media recommender systems is a current topic within the community, as is the uneven propagation among users with varying interests for niche items. Recent work focused on exactly this topic, with movies being the domain of interest. Later on, two different research teams reproduced the methodology in the domains of music and books respectively. The results across the different domains diverge. In this paper, we reproduce the three studies and identify four aspects that are relevant in investigating the differences in results: data, algorithms, division of users in groups and evaluation strategy. We run a set of experiments in which we measure general popularity bias propagation and unfair treatment of certain users with various combinations of these aspects. We conclude that all aspects account to some degree for the divergence in results, and should be carefully considered in future studies. Further, we find that the divergence in findings can be in large part attributed to the choice of evaluation strategy.
Best-Effort Scan of Twitch’s CDN in Europe

Twitch is a live video service that is a leader in game streaming with 20 billion annual viewing hours and an annual viewer growth rate of +26%. To sustain this load and growth rate, Twitch maintains a content distribution network (CDN) to ensure high service quality and to distribute load. Its CDN is a critical online infrastructure that should be better understood. To supplement recent works attempting to measure Twitch’s CDN that crawled few active channels (~2%) and focused on mapping the CDN, we aimed to (1) scan the channels as complete as possible, which we refer to as a best-effort scan and (2) infer the server allocation scheme behind. To contain the crawling traffic, we scope the scan within Europe. We observed 1045 servers in 24 server clusters which are located in 17 cities across 13 countries in Europe. We found that Twitch’s server allocation scheme has two phases: In the first phase, a viewer’s location (i.e., a vantage point or VP) is determined from their IP prefix and a pool of nearby server cluster(s) is selected. In the second phase, when a new channel is requested, a server cluster is selected and a server in the cluster is randomly selected to handle the load.
Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions

With the power of parallel processing, large datasets,and fast computational resources, deep neural networks (DNNs) have outperformed highly trained and experienced human experts in medical applications. However, the large global community of healthcare professionals, many of whom routinely face potentially life-or-death outcomes with complex medicolegal consequences, have yet to embrace this powerful technology. The major problem is that most current AI solutions function as a metaphorical black-box positioned between input data and output decisions without a rigorous explanation for their internal processes. With the goal of enhancing trust and improving acceptance of AI-based technology in clinical medicine, there is a large and growing effort to address this challenge using eXplainable AI (XAI), a set of techniques, strategies, and algorithms with an explicit focus on explaining the “hows and whys” of DNNs. Here, we provide a comprehensive review of the state-of-the-art XAI techniques concerning healthcare applications and discuss current challenges and future directions. We emphasize the strengths and limitations of each category, including image, tabular, and textual explanations, and explore a range of evaluation metrics for assessing the effectiveness of XAI solutions. Finally, we highlight promising opportunities for XAI research to enhance the acceptance of DNNs by the healthcare community.
Subspace-Preconditioned GPU Projective Dynamics with Contact for Cloth Simulation

We propose an efficient cloth simulation method that combines the merits of two drastically different numerical procedures, namely the subspace integration and parallelizable iterative relaxation. We show those two methods can be organically coupled within the framework of projective dynamics (PD), where both low- and high-frequency cloth motions are effectively and efficiently computed. Our method works seamlessly with the state-of-the-art contact handling algorithm, the incremental potential contact (IPC), to offer the non-penetration guarantee of the resulting animation. Our core ingredient centers around the utilization of subspace for the expedited convergence of Jacobi-PD. This involves solving the reduced global system and smartly employing its precomputed factorization. In addition, we incorporate a time-splitting strategy to handle the frictional self-contacts. Specifically, during the PD solve, we employ a quadratic proxy to approximate the contact barrier. The prefactorized subspace system matrix is exploited in a reduced-space LBFGS. The LBFGS method starts with the reduced system matrix of the rest shape as the initial Hessian approximation, incorporating contact information into the reduced system progressively, while the full-space Jacobi iteration captures high-frequency details. Furthermore, we address penetration issues through a penetration correction step. It minimizes an incremental potential without elasticity using Newton-PCG. Our method can be efficiently executed on modern GPUs. Experiments show significant performance improvements over existing GPU solvers for high-resolution cloth simulation.
ViCMA: Visual Control of Multibody Animations

Motion control of large-scale, multibody physics animations with contact is difficult. Existing approaches, such as those based on optimization, are computationally daunting, and, as the number of interacting objects increases, can fail to find satisfactory solutions. We present a new, complementary method for the visual control of multibody animations that exploits object motion and visibility, and has overall cost comparable to a single simulation. Our method is highly practical, and is demonstrated on numerous large-scale, contact-rich examples involving both rigid and deformable bodies.
Does Foreign Exchange Reserve Decumulation Lead to Currency Appreciation?

Many developing countries have increased their foreign reserve stocks dramatically in recent years, often motivated by the desire for precautionary self-insurance. One of the negative consequences of large accumulations for these countries is the risk of valuation losses. In this paper we examine the implications of systematic reserve decumulation by the Czech authorities aimed at mitigating valuation losses on euro-denominated assets. The policy was explicitly not intended to influence the value of the koruna relative to the euro. Initially the timing and size of reserve sales was not predictable, eventually sales occurred on a daily basis (in three equal installments within the day). This project examines whether these reserve sales, both during the regime of discretionary timing as well as when sales occurred every day, had unintended consequences for the domestic currency. Our findings using intraday exchange rate data and time-stamped reserve sales indicate that when decumulation occurred every day these sales led to significant appreciation of the koruna. Overall, our results suggest that the manner in which reserve sales are carried out matters for whether reserve decumulation influences the relative value of the domestic currency.
Making Do with Less: Working Harder During Recessions

There are two obvious possibilities that can account for the rise in productivity during recent recessions. The first is that the decline in the workforce was not random, and that the average worker was of higher quality during the recession than in the preceding period. The second is that each worker produced more while holding worker quality constant. We call the second effect, "making do with less," that is, getting more effort from fewer workers. Using data spanning June 2006 to May 2010 on individual worker productivity from a large firm, it is possible to measure the increase in productivity due to effort and sorting. For this firm, the second effect--that workers' effort increases--dominates the first effect--that the composition of the workforce differs over the business cycle.