OpenRaiser/Intern-Atlas huggingface dataset:
Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs
Venue: arXiv cs.LG 2023
Year: 2023

While physics-informed neural networks (PINNs) have been proven effective for low-dimensional partial differential equations (PDEs), the computational cost remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling Monte Carlo methods for derivative approximation, eliminating the need for costly auto-differentiation. Despite its computational efficiency in high dimensions, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD). We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss and the PDE nonlinearity. We propose tailored bias correction techniques based on the order of PDE nonlinearity. The unbiased RS-PINN allows for a detailed examination of its pros and cons compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, HJB, viscous Burgers', Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are provided for selecting biased, unbiased, or hybrid versions, depending on the dimensionality and nonlinearity of the specific PDE problem.
Spatial and Temporal Characteristics of Freight Tours: A Data-Driven Exploratory Analysis
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a modeling approach to infer scheduling and routing patterns from digital freight transport activity data for different freight markets. We provide a complete modeling framework including a new discrete-continuous decision tree approach for extracting rules from the freight transport data. We apply these models to collected tour data for the Netherlands to understand departure time patterns and tour strategies, also allowing us to evaluate the effectiveness of the proposed algorithm. We find that spatial and temporal characteristics are important to capture the types of tours and time-of-day patterns of freight activities. Also, the empirical evidence indicates that carriers in most of the transport markets are sensitive to the level of congestion. Many of them adjust the type of tour, departure time, and the number of stops per tour when facing a congested zone. The results can be used by practitioners to get more grip on transport markets and develop freight and traffic management measures.
Obj-NeRF: Extract Object NeRFs from Multi-view Images
Venue: arXiv cs.CV 2023
Year: 2023

Neural Radiance Fields (NeRFs) have demonstrated remarkable effectiveness in novel view synthesis within 3D environments. However, extracting a radiance field of one specific object from multi-view images encounters substantial challenges due to occlusion and background complexity, thereby presenting difficulties in downstream applications such as NeRF editing and 3D mesh extraction. To solve this problem, in this paper, we propose Obj-NeRF, a comprehensive pipeline that recovers the 3D geometry of a specific object from multi-view images using a single prompt. This method combines the 2D segmentation capabilities of the Segment Anything Model (SAM) in conjunction with the 3D reconstruction ability of NeRF. Specifically, we first obtain multi-view segmentation for the indicated object using SAM with a single prompt. Then, we use the segmentation images to supervise NeRF construction, integrating several effective techniques. Additionally, we construct a large object-level NeRF dataset containing diverse objects, which can be useful in various downstream tasks. To demonstrate the practicality of our method, we also apply Obj-NeRF to various applications, including object removal, rotation, replacement, and recoloring.
Student's Interests Related to Web and Mobile Technologies Study
Venue: arXiv cs.HC 2023
Year: 2023

We explore in this paper the interests and challenges of students regarding web and mobile technologies. Our study is based on a survey among undergraduate students, students that attend a Web Programming course. In particular, we study the challenges students have in following a successful career in web or mobile development and we have found that the most important one is the large effort required for keeping up to date with the fast changing web and mobile technologies. Overall, the attitude of the surveyed undergraduate students towards web development and mobile development is rather positive, as more than 60% of them said that they are interested in a career in web or mobile development. We also found out that most of them prefer working on back-end web technologies. As for the specific web technologies students are interested on, they are highly varied. Overall, our study provides valuable insights into the interests and challenges of students regarding web and mobile technologies, which can guide the development of effective teaching and learning approaches in this area.
Sketch Video Synthesis
Venue: arXiv cs.CV 2023
Year: 2023

Understanding semantic intricacies and high-level concepts is essential in image sketch generation, and this challenge becomes even more formidable when applied to the domain of videos. To address this, we propose a novel optimization-based framework for sketching videos represented by the frame-wise Bézier curve. In detail, we first propose a cross-frame stroke initialization approach to warm up the location and the width of each curve. Then, we optimize the locations of these curves by utilizing a semantic loss based on CLIP features and a newly designed consistency loss using the self-decomposed 2D atlas network. Built upon these design elements, the resulting sketch video showcases impressive visual abstraction and temporal coherence. Furthermore, by transforming a video into SVG lines through the sketching process, our method unlocks applications in sketch-based video editing and video doodling, enabled through video composition, as exemplified in the teaser.
Evaluating Search Systems through Field Codes and Classifications: The Case of the First Published Swedish Nursing Study
Venue: arXiv cs.DL 2023
Year: 2023

The aim of the study was to assess and compare established search systems and approaches by using the search goal of identifying the first (as in oldest) nursing-related document, with reference to the first Swedish-affiliated document, in Scopus, Web of Science, and PubMed (Medline). In doing so, the objective was to provide concrete examples and illustrate how search systems and field code searches versus classification-based searches differ. The Scopus database, Web of Science database aggregator, and the PubMed (Medline) search engine were used for evaluations. Two different search strategies were compared in each database: one guided by field codes (i.e., text-based), and one guided by pre-existing categorizations within database infrastructures. Findings highlight several factors that are important to consider when formulating and executing a search strategy. The findings illustrate important aspects of search systems and search approaches, namely the publication year, delimitations related to a country or region, and features related to a subject or research area. The findings also highlight the importance of prioritizing between different ideals in retrieval. For example, is it more important to reach the oldest records (publication year), to identify as many records as possible (exhaustiveness), or to make sure they are as subject-relevant as possible (e.g., in terms of authors belonging to the same field)? Secondly, researchers using bibliometric methods to analyze research literature should be more transparent when reporting searches, as different search systems and search approaches yield varied numbers and quality of records. While most bibliometric researchers strive to combine different approaches, this is not always made clear in reporting.
Web-Based Dynamic Paintings: Real-Time Interactive Artworks in Web Using a 2.5D Pipeline
Venue: arXiv cs.GR 2023
Year: 2023

In this work, we present a 2.5D pipeline approach to creating dynamic paintings that can be re-rendered interactively in real-time on the Web. Using this 2.5D approach, any existing simple painting such as portraits can be turned into an interactive dynamic web-based artwork. Our interactive system provides most global illumination effects such as reflection, refraction, shadow, and subsurface scattering by processing images. In our system, the scene is defined only by a set of images. These include (1) a shape image, (2) two diffuse images, (3) a background image, (4) one foreground image, and (5) one transparency image. A shape image is either a normal map or a height. Two diffuse images are usually hand-painted. They are interpolated using illumination information. The transparency image is used to define the transparent and reflective regions that can reflect the foreground image and refract the background image, both of which are also hand-drawn. This framework, which mainly uses hand-drawn images, provides qualitatively convincing painterly global illumination effects such as reflection and refraction. We also include parameters to provide additional artistic controls. For instance, using our piecewise linear Fresnel function, it is possible to control the ratio of reflection and refraction. This system is the result of a long line of research contributions. On the other hand, the art-directed Fresnel function that provides physically plausible compositing of reflection and refraction with artistic control is completely new. Art-directed warping equations that provide qualitatively convincing refraction and reflection effects with linearized artistic control are also new. You can try our web-based system for interactive dynamic real-time paintings at http://mock3d.tamu.edu/.
Understanding the Utilization of Cryptocurrency in the Metaverse and Security Implications
Venue: arXiv cs.CR 2023
Year: 2023

We present our results on analyzing and understanding the behavior and security of various metaverse platforms incorporating cryptocurrencies. We obtained the top metaverse coins with a capitalization of at least 25 million US dollars and the top metaverse domains for the coins, and augmented our data with name registration information (via whois), including the hosting DNS IP addresses, registrant location, registrar URL, DNS service provider, expiry date and check each metaverse website for information on fiat currency for cryptocurrency. The result from virustotal.com includes the communication files, passive DNS, referrer files, and malicious detections for each metaverse domain. Among other insights, we discovered various incidents of malicious detection associated with metaverse websites. Our analysis highlights indicators of (in)security, in the correlation sense, with the files and other attributes that are potentially responsible for the malicious activities.
Untargeted Code Authorship Evasion with Seq2Seq Transformation
Venue: arXiv cs.CR 2023
Year: 2023

Code authorship attribution is the problem of identifying authors of programming language codes through the stylistic features in their codes, a topic that recently witnessed significant interest with outstanding performance. In this work, we present SCAE, a code authorship obfuscation technique that leverages a Seq2Seq code transformer called StructCoder. SCAE customizes StructCoder, a system designed initially for function-level code translation from one language to another (e.g., Java to C#), using transfer learning. SCAE improved the efficiency at a slight accuracy degradation compared to existing work. We also reduced the processing time by about 68% while maintaining an 85% transformation success rate and up to 95.77% evasion success rate in the untargeted setting.
Optimally Teaching a Linear Behavior Cloning Agent
Venue: arXiv cs.LG 2023
Year: 2023

We study optimal teaching of Linear Behavior Cloning (LBC) learners. In this setup, the teacher can select which states to demonstrate to an LBC learner. The learner maintains a version space of infinite linear hypotheses consistent with the demonstration. The goal of the teacher is to teach a realizable target policy to the learner using minimum number of state demonstrations. This number is known as the Teaching Dimension(TD). We present a teaching algorithm called ``Teach using Iterative Elimination(TIE)" that achieves instance optimal TD. However, we also show that finding optimal teaching set computationally is NP-hard. We further provide an approximation algorithm that guarantees an approximation ratio of $\log(|A|-1)$ on the teaching dimension. Finally, we provide experimental results to validate the efficiency and effectiveness of our algorithm.
Uncertainty-aware Language Modeling for Selective Question Answering
Venue: arXiv cs.CL 2023
Year: 2023

We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs capable of estimating uncertainty with every prediction. Our approach is model- and data-agnostic, is computationally-efficient, and does not rely on external models or systems. We evaluate converted models on the selective question answering setting -- to answer as many questions as possible while maintaining a given accuracy, forgoing providing predictions when necessary. As part of our results, we test BERT and Llama 2 model variants on the SQuAD extractive QA task and the TruthfulQA generative QA task. We show that using the uncertainty estimates provided by our approach to selectively answer questions leads to significantly higher accuracy over directly using model probabilities.
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

In medical image analysis, Federated Learning (FL) stands out as a key technology that enables privacy-preserved, decentralized data processing, crucial for handling sensitive medical data. Currently, most FL models employ random initialization, which has been proven effective in various instances. However, given the unique challenges posed by non-IID (independently and identically distributed) data in FL, we propose a novel perspective: exploring the impact of using the foundation model with enormous pre-trained knowledge, such as the Segment Anything Model (SAM), as an instructive teacher for FL model initialization in medical image segmentation task. This work for the first time attempts to utilize the foundation model as an instructive teacher for initialization in FL, assessing its impact on the performance of FL models, especially in non-IID data scenarios. Our empirical evaluation on chest x-ray lung segmentation showcases that FL with foundation model instructed initialization not only achieves faster convergence but also improves performance in complex data contexts. These findings offer a new perspective for model initialization in FL.
MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers
Venue: arXiv cs.CV 2023
Year: 2023

We introduce MeshGPT, a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes, in contrast to dense triangle meshes extracted by iso-surfacing methods from neural fields. Inspired by recent advances in powerful large language models, we adopt a sequence-based approach to autoregressively generate triangle meshes as sequences of triangles. We first learn a vocabulary of latent quantized embeddings, using graph convolutions, which inform these embeddings of the local mesh geometry and topology. These embeddings are sequenced and decoded into triangles by a decoder, ensuring that they can effectively reconstruct the mesh. A transformer is then trained on this learned vocabulary to predict the index of the next embedding given previous embeddings. Once trained, our model can be autoregressively sampled to generate new triangle meshes, directly generating compact meshes with sharp edges, more closely imitating the efficient triangulation patterns of human-crafted meshes. MeshGPT demonstrates a notable improvement over state of the art mesh generation methods, with a 9% increase in shape coverage and a 30-point enhancement in FID scores across various categories.
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
Venue: arXiv cs.IR 2023
Year: 2023

Click-Through Rate (CTR) prediction, which aims to estimate the probability of a user clicking on an item, is a key task in online advertising. Numerous existing CTR models concentrate on modeling the feature interactions within a solitary domain, thereby rendering them inadequate for fulfilling the requisites of multi-domain recommendations in real industrial scenarios. Some recent approaches propose intricate architectures to enhance knowledge sharing and augment model training across multiple domains. However, these approaches encounter difficulties when being transferred to new recommendation domains, owing to their reliance on the modeling of ID features (e.g., item id). To address the above issue, we propose the Universal Feature Interaction Network (UFIN) approach for CTR prediction. UFIN exploits textual data to learn universal feature interactions that can be effectively transferred across diverse domains. For learning universal feature representations, we regard the text and feature as two different modalities and propose an encoder-decoder network founded on a Large Language Model (LLM) to enforce the transfer of data from the text modality to the feature modality. Building upon the above foundation, we further develop a mixtureof-experts (MoE) enhanced adaptive feature interaction model to learn transferable collaborative patterns across multiple domains. Furthermore, we propose a multi-domain knowledge distillation framework to enhance feature interaction learning. Based on the above methods, UFIN can effectively bridge the semantic gap to learn common knowledge across various domains, surpassing the constraints of ID-based models. Extensive experiments conducted on eight datasets show the effectiveness of UFIN, in both multidomain and cross-platform settings. Our code is available at https://github.com/RUCAIBox/UFIN.
Critiquing Self-report Practices for Human Mental and Wellbeing Computing at Ubicomp
Venue: arXiv cs.HC 2023
Year: 2023

Computing human mental and wellbeing is crucial to various domains, including health, education, and entertainment. However, the reliance on self-reporting in traditional research to establish ground truth often leads to methodological inconsistencies and susceptibility to response biases, thus hindering the effectiveness of modelling. This paper presents the first systematic methodological review of self-reporting practices in Ubicomp within the context of human mental and wellbeing computing. Drawing from existing survey research, we establish guidelines for self-reporting in human wellbeing studies and identify shortcomings in current practices at Ubicomp community. Furthermore, we explore the reliability of self-report as a means of ground truth and propose directions for improving ground truth measurement in this field. Ultimately, we emphasize the urgent need for methodological advancements to enhance human mental and wellbeing computing.
Function-constrained Program Synthesis
Venue: arXiv cs.LG 2023
Year: 2023

This work introduces (1) a technique that allows large language models (LLMs) to leverage user-provided code when solving programming tasks and (2) a method to iteratively generate modular sub-functions that can aid future code generation attempts when the initial code generated by the LLM is inadequate. Generating computer programs in general-purpose programming languages like Python poses a challenge for LLMs when instructed to use code provided in the prompt. Code-specific LLMs (e.g., GitHub Copilot, CodeLlama2) can generate code completions in real-time by drawing on all code available in a development environment. However, restricting code-specific LLMs to use only in-context code is not straightforward, as the model is not explicitly instructed to use the user-provided code and users cannot highlight precisely which snippets of code the model should incorporate into its context. Moreover, current systems lack effective recovery methods, forcing users to iteratively re-prompt the model with modified prompts until a sufficient solution is reached. Our method differs from traditional LLM-powered code-generation by constraining code-generation to an explicit function set and enabling recovery from failed attempts through automatically generated sub-functions. When the LLM cannot produce working code, we generate modular sub-functions to aid subsequent attempts at generating functional code. A by-product of our method is a library of reusable sub-functions that can solve related tasks, imitating a software team where efficiency scales with experience. We also introduce a new "half-shot" evaluation paradigm that provides tighter estimates of LLMs' coding abilities compared to traditional zero-shot evaluation. Our proposed evaluation method encourages models to output solutions in a structured format, decreasing syntax errors that can be mistaken for poor coding ability.
Exploring Trust and Risk during Online Bartering Interactions
Venue: arXiv cs.GT 2023
Year: 2023

This paper investigates how risk influences the way people barter. We used Minecraft to create an experimental environment in which people bartered to earn a monetary bonus. Our findings reveal that subjects exhibit risk-aversion to competitive bartering environments and deliberate over their trades longer when compared to cooperative environments. These initial experiments lay groundwork for development of agents capable of strategically trading with human counterparts in different environments.
Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context
Venue: arXiv cs.CL 2023
Year: 2023

Lexical ambiguity is a challenging and pervasive problem in machine translation (\mt). We introduce a simple and scalable approach to resolve translation ambiguity by incorporating a small amount of extra-sentential context in neural \mt. Our approach requires no sense annotation and no change to standard model architectures. Since actual document context is not available for the vast majority of \mt training data, we collect related sentences for each input to construct pseudo-documents. Salient words from pseudo-documents are then encoded as a prefix to each source sentence to condition the generation of the translation. To evaluate, we release \docmucow, a challenge set for translation disambiguation based on the English-German \mucow \cite{raganato-etal-2020-evaluation} augmented with document IDs. Extensive experiments show that our method translates ambiguous source words better than strong sentence-level baselines and comparable document-level baselines while reducing training costs.