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40,778 | 28 | Title: Energy Savings under Performance Constraints via Carrier Shutdown with Bayesian Learning
Abstract: By shutting down frequency carriers, the power consumed by a base station can be considerably reduced. However, this typically comes with traffic performance degradation, as the congestion on the remaining active carriers is increased. We leverage a hysteresis carrier shutdown policy that attempts to keep the average traffic load on each sector within a certain min/max threshold pair. We propose a closed-loop Bayesian method optimizing such thresholds on a sector basis and aiming at minimizing the power consumed by the power amplifiers while maintaining the probability that KPI's are acceptable above a certain value. We tested our approach in a live customer 4G network. The power consumption at the base station was reduced by 11 % and the selected KPI's met the predefined targets. | [] | Train |
40,779 | 24 | Title: Understanding Progressive Training Through the Framework of Randomized Coordinate Descent
Abstract: We propose a Randomized Progressive Training algorithm (RPT) -- a stochastic proxy for the well-known Progressive Training method (PT) (Karras et al., 2017). Originally designed to train GANs (Goodfellow et al., 2014), PT was proposed as a heuristic, with no convergence analysis even for the simplest objective functions. On the contrary, to the best of our knowledge, RPT is the first PT-type algorithm with rigorous and sound theoretical guarantees for general smooth objective functions. We cast our method into the established framework of Randomized Coordinate Descent (RCD) (Nesterov, 2012; Richt\'arik&Tak\'a\v{c}, 2014), for which (as a by-product of our investigations) we also propose a novel, simple and general convergence analysis encapsulating strongly-convex, convex and nonconvex objectives. We then use this framework to establish a convergence theory for RPT. Finally, we validate the effectiveness of our method through extensive computational experiments. | [] | Train |
40,780 | 6 | Title: Subjective Vertical Conflict Model with Visual Vertical: Predicting Motion Sickness on Autonomous Personal Mobility Vehicles
Abstract: Passengers of level 3-5 autonomous personal mobility vehicles (APMV) can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness, especially when frequently avoiding pedestrians or obstacles in shared spaces. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Among them, models based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs v.s. those expected by the central nervous system, have been actively developed. However, no current computational model can integrate visual vertical information with vestibular sensations. We proposed a 6 DoF SVC-VV model which added a visually perceived vertical block into a conventional 6 DoF SVC model to predict visual vertical directions from image data simulating the visual input of a human. In a driving experiment, 27 participants experienced an APMV with two visual conditions: looking ahead (LAD) and working with a tablet device (WAD). We verified that passengers got motion sickness while riding the APMV, and the symptom were severer when especially working on it, by simulating the frequent pedestrian avoidance scenarios of the APMV in the experiment. In addition, the results of the experiment demonstrated that the proposed 6 DoF SVC-VV model could describe the increased motion sickness experienced when the visual vertical and gravitational acceleration directions were different. | [] | Train |
40,781 | 24 | Title: Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction
Abstract: Explainability in yield prediction helps us fully explore the potential of machine learning models that are already able to achieve high accuracy for a variety of yield prediction scenarios. The data included for the prediction of yields are intricate and the models are often difficult to understand. However, understanding the models can be simplified by using natural groupings of the input features. Grouping can be achieved, for example, by the time the features are captured or by the sensor used to do so. The state-of-the-art for interpreting machine learning models is currently defined by the game-theoretic approach of Shapley values. To handle groups of features, the calculated Shapley values are typically added together, ignoring the theoretical limitations of this approach. We explain the concept of Shapley values directly computed for predefined groups of features and introduce an algorithm to compute them efficiently on tree structures. We provide a blueprint for designing swarm plots that combine many local explanations for global understanding. Extensive evaluation of two different yield prediction problems shows the worth of our approach and demonstrates how we can enable a better understanding of yield prediction models in the future, ultimately leading to mutual enrichment of research and application. | [] | Validation |
40,782 | 33 | Title: Decomposing Finite Languages
Abstract: The paper completely characterizes the primality of acyclic DFAs, where a DFA $\mathcal{A}$ is prime if there do not exist DFAs $\mathcal{A}_1,\dots,\mathcal{A}_t$ with $\mathcal{L}(\mathcal{A}) = \bigcap_{i=1}^{t} \mathcal{L}({\mathcal{A}_i})$ such that each $\mathcal{A}_i$ has strictly less states than the minimal DFA recognizing the same language as $\mathcal{A}$. A regular language is prime if its minimal DFA is prime. Thus, this result also characterizes the primality of finite languages. Further, the $\mathsf{NL}$-completeness of the corresponding decision problem $\mathsf{PrimeDFA}_{\text{fin}}$ is proven. The paper also characterizes the primality of acyclic DFAs under two different notions of compositionality, union and union-intersection compositionality. Additionally, the paper introduces the notion of S-primality, where a DFA $\mathcal{A}$ is S-prime if there do not exist DFAs $\mathcal{A}_1,\dots,\mathcal{A}_t$ with $\mathcal{L}(\mathcal{A}) = \bigcap_{i=1}^{t} \mathcal{L}(\mathcal{A}_i)$ such that each $\mathcal{A}_i$ has strictly less states than $\mathcal{A}$ itself. It is proven that the problem of deciding S-primality for a given DFA is $\mathsf{NL}$-hard. To do so, the $\mathsf{NL}$-completeness of $\mathsf{2MinimalDFA}$, the basic problem of deciding minimality for a DFA with at most two letters, is proven. | [] | Train |
40,783 | 27 | Title: Freespace Optical Flow Modeling for Automated Driving
Abstract: Optical flow and disparity are two informative visual features for autonomous driving perception. They have been used for a variety of applications, such as obstacle and lane detection. The concept of"U-V-Disparity"has been widely explored in the literature, while its counterpart in optical flow has received relatively little attention. Traditional motion analysis algorithms estimate optical flow by matching correspondences between two successive video frames, which limits the full utilization of environmental information and geometric constraints. Therefore, we propose a novel strategy to model optical flow in the collision-free space (also referred to as drivable area or simply freespace) for intelligent vehicles, with the full utilization of geometry information in a 3D driving environment. We provide explicit representations of optical flow and deduce the quadratic relationship between the optical flow component and the vertical coordinate. Through extensive experiments on several public datasets, we demonstrate the high accuracy and robustness of our model. Additionally, our proposed freespace optical flow model boasts a diverse array of applications within the realm of automated driving, providing a geometric constraint in freespace detection, vehicle localization, and more. We have made our source code publicly available at https://mias.group/FSOF. | [
18251,
31155
] | Train |
40,784 | 24 | Title: Factorized Contrastive Learning: Going Beyond Multi-view Redundancy
Abstract: In a wide range of multimodal tasks, contrastive learning has become a particularly appealing approach since it can successfully learn representations from abundant unlabeled data with only pairing information (e.g., image-caption or video-audio pairs). Underpinning these approaches is the assumption of multi-view redundancy - that shared information between modalities is necessary and sufficient for downstream tasks. However, in many real-world settings, task-relevant information is also contained in modality-unique regions: information that is only present in one modality but still relevant to the task. How can we learn self-supervised multimodal representations to capture both shared and unique information relevant to downstream tasks? This paper proposes FactorCL, a new multimodal representation learning method to go beyond multi-view redundancy. FactorCL is built from three new contributions: (1) factorizing task-relevant information into shared and unique representations, (2) capturing task-relevant information via maximizing MI lower bounds and removing task-irrelevant information via minimizing MI upper bounds, and (3) multimodal data augmentations to approximate task relevance without labels. On large-scale real-world datasets, FactorCL captures both shared and unique information and achieves state-of-the-art results on six benchmarks. | [
45392,
15278,
4063
] | Validation |
40,785 | 16 | Title: Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations
Abstract: The rapid development and application of foundation models have revolutionized the field of artificial intelligence. Large diffusion models have gained significant attention for their ability to generate photorealistic images and support various tasks. On-device deployment of these models provides benefits such as lower server costs, offline functionality, and improved user privacy. However, common large diffusion models have over 1 billion parameters and pose challenges due to restricted computational and memory resources on devices. We present a series of implementation optimizations for large diffusion models that achieve the fastest reported inference latency to-date(under 12 seconds for Stable Diffusion 1.4 without INT8 quantization for a 512 × 512 image with 20 iterations) on GPU-equipped mobile devices. These enhancements broaden the applicability of generative AI and improve the overall user experience across a wide range of devices. | [
36736,
26145,
34074,
18223,
18516,
32410
] | Validation |
40,786 | 23 | Title: APIHarvest: Harvesting API Information from Various Online Sources
Abstract: Using APIs to develop software applications is the norm. APIs help developers to build applications faster as they do not need to reinvent the wheel. It is therefore important for developers to understand the APIs that they plan to use. Developers should also make themselves aware of relevant information updates about APIs. In order to do so, developers need to find and keep track of relevant information about the APIs that they are concerned with. Yet, the API information is scattered across various online sources, which makes it difficult to track by hand. Moreover, identifying content that is related to an API is not trivial. Motivated by these challenges, in this work, we introduce a tool named \tool that aims to ease the process of finding API information from various online sources. \tool is built on works that link APIs or libraries to various online sources. It supports finding API information on GitHub repositories, Stack Overflow's posts, tweets, YouTube videos, and common vulnerability and exposure (CVE) entries; and is extensible to support other sources. | [] | Train |
40,787 | 30 | Title: The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who
Abstract: Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation. Nevertheless, few papers thoroughly discuss how. We document this by analysing 100 highly-cited papers, and annotating epistemic elements related to intended use, i.e., means, ends, and stakeholders. We find that narratives leaving out some of these aspects are common, that many papers propose inconsistent means and ends, and that the feasibility of suggested strategies rarely has empirical backing. We argue that this vagueness actively hinders the technology from reaching its goals, as it encourages overclaiming, limits criticism, and prevents stakeholder feedback. Accordingly, we provide several recommendations for thinking and writing about the use of fact-checking artefacts. | [
39680,
42200
] | Train |
40,788 | 24 | Title: PADL: Language-Directed Physics-Based Character Control
Abstract: Developing systems that can synthesize natural and life-like motions for simulated characters has long been a focus for computer animation. But in order for these systems to be useful for downstream applications, they need not only produce high-quality motions, but must also provide an accessible and versatile interface through which users can direct a character’s behaviors. Natural language provides a simple-to-use and expressive medium for specifying a user’s intent. Recent breakthroughs in natural language processing (NLP) have demonstrated effective use of language-based interfaces for applications such as image generation and program synthesis. In this work, we present PADL, which leverages recent innovations in NLP in order to take steps towards developing language-directed controllers for physics-based character animation. PADL allows users to issue natural language commands for specifying both high-level tasks and low-level skills that a character should perform. We present an adversarial imitation learning approach for training policies to map high-level language commands to low-level controls that enable a character to perform the desired task and skill specified by a user’s commands. Furthermore, we propose a multi-task aggregation method that leverages a language-based multiple-choice question-answering approach to determine high-level task objectives from language commands. We show that our framework can be applied to effectively direct a simulated humanoid character to perform a diverse array of complex motor skills. | [
10529,
45122,
4803,
34579,
759,
5784,
35770,
4990
] | Test |
40,789 | 2 | Title: Beyond Logic Programming for Legal Reasoning
Abstract: Logic programming has long being advocated for legal reasoning, and several approaches have been put forward relying upon explicit representation of the law in logic programming terms. In this position paper we focus on the PROLEG logic-programming-based framework for formalizing and reasoning with Japanese presupposed ultimate fact theory. Specifically, we examine challenges and opportunities in leveraging deep learning techniques for improving legal reasoning using PROLEG identifying four distinct options ranging from enhancing fact extraction using deep learning to end-to-end solutions for reasoning with textual legal descriptions. We assess advantages and limitations of each option, considering their technical feasibility, interpretability, and alignment with the needs of legal practitioners and decision-makers. We believe that our analysis can serve as a guideline for developers aiming to build effective decision-support systems for the legal domain, while fostering a deeper understanding of challenges and potential advancements by neuro-symbolic approaches in legal applications. | [
37349
] | Validation |
40,790 | 16 | Title: DreamEditor: Text-Driven 3D Scene Editing with Neural Fields
Abstract: Neural fields have achieved impressive advancements in view synthesis and scene reconstruction. However, editing these neural fields remains challenging due to the implicit encoding of geometry and texture information. In this paper, we propose DreamEditor, a novel framework that enables users to perform controlled editing of neural fields using text prompts. By representing scenes as mesh-based neural fields, DreamEditor allows localized editing within specific regions. DreamEditor utilizes the text encoder of a pretrained text-to-Image diffusion model to automatically identify the regions to be edited based on the semantics of the text prompts. Subsequently, DreamEditor optimizes the editing region and aligns its geometry and texture with the text prompts through score distillation sampling [29]. Extensive experiments have demonstrated that DreamEditor can accurately edit neural fields of real-world scenes according to the given text prompts while ensuring consistency in irrelevant areas. DreamEditor generates highly realistic textures and geometry, significantly surpassing previous works in both quantitative and qualitative evaluations. | [
18720,
9599,
34199,
42937,
541,
34167
] | Train |
40,791 | 24 | Title: The Marginal Value of Momentum for Small Learning Rate SGD
Abstract: Momentum is known to accelerate the convergence of gradient descent in strongly convex settings without stochastic gradient noise. In stochastic optimization, such as training neural networks, folklore suggests that momentum may help deep learning optimization by reducing the variance of the stochastic gradient update, but previous theoretical analyses do not find momentum to offer any provable acceleration. Theoretical results in this paper clarify the role of momentum in stochastic settings where the learning rate is small and gradient noise is the dominant source of instability, suggesting that SGD with and without momentum behave similarly in the short and long time horizons. Experiments show that momentum indeed has limited benefits for both optimization and generalization in practical training regimes where the optimal learning rate is not very large, including small- to medium-batch training from scratch on ImageNet and fine-tuning language models on downstream tasks. | [
5729,
16005
] | Train |
40,792 | 23 | Title: MUFIN: Improving Neural Repair Models with Back-Translation
Abstract: Automated program repair is the task of automatically repairing software bugs. A promising direction in this field is self-supervised learning, a learning paradigm in which repair models are trained without commits representing pairs of bug/fix. In self-supervised neural program repair, those bug/fix pairs are generated in some ways. The main problem is to generate interesting and diverse pairs that maximize the effectiveness of training. As a contribution to this problem, we propose to use back-translation, a technique coming from neural machine translation. We devise and implement MUFIN, a back-translation training technique for program repair, with specifically designed code critics to select high-quality training samples. Our results show that MUFIN's back-translation loop generates valuable training samples in a fully automated, self-supervised manner, generating more than half-a-million pairs of bug/fix. The code critic design is key because of a fundamental trade-off between how restrictive a critic is and how many samples are available for optimization during back-translation. | [
42523
] | Train |
40,793 | 16 | Title: Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow
Abstract: Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing methods typically adopt the domain adaptation to transfer the motion knowledge from clean to synthetic foggy domain. However, these methods unexpectedly neglect the synthetic-to-real domain gap, and thus are erroneous when applied to real-world scenes. To handle the practical optical flow under real foggy scenes, in this work, we propose a novel unsupervised cumulative domain adaptation optical flow (UCDA-Flow) framework: depth-association motion adaptation and correlation-alignment motion adaptation. Specifically, we discover that depth is a key ingredient to in-fluence the optical flow: the deeper depth, the inferior optical flow, which motivates us to design a depth-association motion adaptation module to bridge the clean-to-foggy domain gap. Moreover, we figure out that the cost volume correlation shares similar distribution of the synthetic and real foggy images, which enlightens us to devise a correlation-alignment motion adaptation module to distill motion knowledge of the synthetic foggy domain to the real foggy domain. Note that synthetic fog is designed as the intermediate domain. Under this unified framework, the proposed cumulative adaptation progressively transfers knowledge from clean scenes to real foggy scenes. Extensive experiments have been performed to verify the superiority of the proposed method. | [] | Test |
40,794 | 31 | Title: PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
Abstract: Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands. Many existing re-ranking methods directly take the initial ranking list as input, and generate the optimal permutation through a well-designed context-wise model, which brings the evaluation-before-reranking problem. Meanwhile, evaluating all candidate permutations brings unacceptable computational costs in practice. Thus, to better balance efficiency and effectiveness, online systems usually use a two-stage architecture which uses some heuristic methods such as beam-search to generate a suitable amount of candidate permutations firstly, which are then fed into the evaluation model to get the optimal permutation. However, existing methods in both stages can be improved through the following aspects. As for generation stage, heuristic methods only use point-wise prediction scores and lack an effective judgment. As for evaluation stage, most existing context-wise evaluation models only consider the item context and lack more fine-grained feature context modeling. This paper presents a novel end-to-end re-ranking framework named PIER to tackle the above challenges which still follows the two-stage architecture and contains two mainly modules named FPSM and OCPM. Inspired by long-time user behavior modeling methods, we apply SimHash in FPSM to select top-K candidates from the full permutation based on user's permutation-level interest in an efficient way. Then we design a novel omnidirectional attention mechanism in OCPM to better capture the context information in the permutation. Finally, we jointly train these two modules in an end-to-end way by introducing a comparative learning loss, which use the predict value of OCPM to guide the FPSM to generate better permutations. Offline experiment results demonstrate that PIER outperforms baseline models on both public and industrial datasets, and we have successfully deployed PIER on Meituan food delivery platform. | [
4297
] | Train |
40,795 | 24 | Title: MessageNet: Message Classification using Natural Language Processing and Meta-data
Abstract: In this paper we propose a new Deep Learning (DL) approach for message classification. Our method is based on the state-of-the-art Natural Language Processing (NLP) building blocks, combined with a novel technique for infusing the meta-data input that is typically available in messages such as the sender information, timestamps, attached image, audio, affiliations, and more. As we demonstrate throughout the paper, going beyond the mere text by leveraging all available channels in the message, could yield an improved representation and higher classification accuracy. To achieve message representation, each type of input is processed in a dedicated block in the neural network architecture that is suitable for the data type. Such an implementation enables training all blocks together simultaneously, and forming cross channels features in the network. We show in the Experiments Section that in some cases, message's meta-data holds an additional information that cannot be extracted just from the text, and when using this information we achieve better performance. Furthermore, we demonstrate that our multi-modality block approach outperforms other approaches for injecting the meta data to the the text classifier. | [] | Validation |
40,796 | 7 | Title: Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots
Abstract: Computational fluid dynamics is a common tool in cardiovascular science and engineering to simulate, predict and study hemodynamics in arteries. However, owing to the complexity and scale of cardiovascular flow problems, the evaluation of the model could be computationally expensive, especially in those cases where a large number of evaluations are required, such as uncertainty quantification and design optimisation. In such scenarios, the model may have to be repeatedly evaluated due to the changes or distinctions of simulation domains. In this work, a data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. The proposed surrogate model leverages surface registration to parameterise those similar but distinct shapes and formulate corresponding hemodynamics information into geometry-informed snapshots by the diffeomorphism constructed between the reference domain and target domain. A non-intrusive reduced-order model for geometrical parameters is subsequently constructed using proper orthogonal decomposition, and a radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model based on reduced coefficients of geometrical parameters of the shape. Two examples of blood flowing through a stenosis and a bifurcation are presented and analysed. The proposed surrogate model demonstrates its accuracy and efficiency in hemodynamics prediction and shows its potential application toward real-time simulation or uncertainty quantification for complex patient-specific scenarios. | [] | Train |
40,797 | 16 | Title: DeePoint: Visual Pointing Recognition and Direction Estimation
Abstract: In this paper, we realize automatic visual recognition and direction estimation of pointing. We introduce the first neural pointing understanding method based on two key contributions. The first is the introduction of a first-of-its-kind large-scale dataset for pointing recognition and direction estimation, which we refer to as the DP Dataset. DP Dataset consists of more than 2 million frames of 33 people pointing in various styles annotated for each frame with pointing timings and 3D directions. The second is DeePoint, a novel deep network model for joint recognition and 3D direction estimation of pointing. DeePoint is a Transformer-based network which fully leverages the spatio-temporal coordination of the body parts, not just the hands. Through extensive experiments, we demonstrate the accuracy and efficiency of DeePoint. We believe DP Dataset and DeePoint will serve as a sound foundation for visual human intention understanding. | [] | Train |
40,798 | 36 | Title: Proportionality in Approval-Based Participatory Budgeting
Abstract: The ability to measure the satisfaction of (groups of) voters is a crucial prerequisite for formulating proportionality axioms in approval-based participatory budgeting elections. Two common -- but very different -- ways to measure the satisfaction of a voter consider (i) the number of approved projects and (ii) the total cost of approved projects, respectively. In general, it is difficult to decide which measure of satisfaction best reflects the voters' true utilities. In this paper, we study proportionality axioms with respect to large classes of approval-based satisfaction functions. We establish logical implications among our axioms and related notions from the literature, and we ask whether outcomes can be achieved that are proportional with respect to more than one satisfaction function. We show that this is impossible for the two commonly used satisfaction functions when considering proportionality notions based on extended justified representation, but achievable for a notion based on proportional justified representation. For the latter result, we introduce a strengthening of priceability and show that it is satisfied by several polynomial-time computable rules, including the Method of Equal Shares and Phragmén's sequential rule. | [
30097,
12055
] | Test |
40,799 | 24 | Title: Reward Shaping via Diffusion Process in Reinforcement Learning
Abstract: Reinforcement Learning (RL) models have continually evolved to navigate the exploration - exploitation trade-off in uncertain Markov Decision Processes (MDPs). In this study, I leverage the principles of stochastic thermodynamics and system dynamics to explore reward shaping via diffusion processes. This provides an elegant framework as a way to think about exploration-exploitation trade-off. This article sheds light on relationships between information entropy, stochastic system dynamics, and their influences on entropy production. This exploration allows us to construct a dual-pronged framework that can be interpreted as either a maximum entropy program for deriving efficient policies or a modified cost optimization program accounting for informational costs and benefits. This work presents a novel perspective on the physical nature of information and its implications for online learning in MDPs, consequently providing a better understanding of information-oriented formulations in RL. | [] | Train |
40,800 | 16 | Title: STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition
Abstract: Existing methods of privacy-preserving action recognition (PPAR) mainly focus on frame-level (spatial) privacy removal through 2D CNNs. Unfortunately, they have two major drawbacks. First, they may compromise temporal dynamics in input videos, which are critical for accurate action recognition. Second, they are vulnerable to practical attacking scenarios where attackers probe for privacy from an entire video rather than individual frames. To address these issues, we propose a novel framework STPrivacy to perform video-level PPAR. For the first time, we introduce vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i.e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective. In specific, our privacy sparsification mechanism applies adaptive token selection to abandon action-irrelevant tubelets. Then, our anonymization mechanism implicitly manipulates the remaining action-tubelets to erase privacy in the embedding space through adversarial learning. These mechanisms provide significant advantages in terms of privacy preservation for human eyes and action-privacy trade-off adjustment during deployment. We additionally contribute the first two large-scale PPAR benchmarks, VP-HMDB51 and VP-UCF101, to the community. Extensive evaluations on them, as well as two other tasks, validate the effectiveness and generalization capability of our framework. | [] | Test |
40,801 | 30 | Title: mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
Abstract: Document understanding refers to automatically extract, analyze and comprehend information from various types of digital documents, such as a web page. Existing Multi-model Large Language Models (MLLMs), including mPLUG-Owl, have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition, indicating their potential for OCR-free document understanding. Nevertheless, without in-domain training, these models tend to ignore fine-grained OCR features, such as sophisticated tables or large blocks of text, which are essential for OCR-free document understanding. In this paper, we propose mPLUG-DocOwl based on mPLUG-Owl for OCR-free document understanding. Specifically, we first construct a instruction tuning dataset featuring a wide range of visual-text understanding tasks. Then, we strengthen the OCR-free document understanding ability by jointly train the model on language-only, general vision-and-language, and document instruction tuning dataset with our unified instruction tuning strategy. We also build an OCR-free document instruction understanding evaluation set LLMDoc to better compare models' capabilities on instruct compliance and document understanding. Experimental results show that our model outperforms existing multi-modal models, demonstrating its strong ability of document understanding. Besides, without specific fine-tuning, mPLUG-DocOwl generalizes well on various downstream tasks. Our code, models, training data and evaluation set are available at https://github.com/X-PLUG/mPLUG-DocOwl. | [
10978,
37987,
13700,
29559,
41104,
5361,
30900,
29527,
3609,
1854
] | Validation |
40,802 | 4 | Title: Measuring the Leakage and Exploitability of Authentication Secrets in Super-apps: The WeChat Case
Abstract: We conduct a large-scale measurement of developers' insecure practices leading to mini-app to super-app authentication bypass, among which hard-coding developer secrets for such authentication is a major contributor. We also analyze the exploitability and security consequences of developer secret leakage in mini-apps by examining individual super-app server-side APIs. We develop an analysis framework for measuring such secret leakage, and primarily analyze 110,993 WeChat mini-apps, and 10,000 Baidu mini-apps (two of the most prominent super-app platforms), along with a few more datasets to test the evolution of developer practices and platform security enforcement over time. We found a large number of WeChat mini-apps (36,425, 32.8%) and a few Baidu mini-apps (112) leak their developer secrets, which can cause severe security and privacy problems for the users and developers of mini-apps. A network attacker who does not even have an account on the super-app platform, can effectively take down a mini-app, send malicious and phishing links to users, and access sensitive information of the mini-app developer and its users. We responsibly disclosed our findings and also put forward potential directions that could be considered to alleviate/eliminate the root causes of developers hard-coding the app secrets in the mini-app's front-end code. | [
20787
] | Test |
40,803 | 30 | Title: Cluster-Level Contrastive Learning for Emotion Recognition in Conversations
Abstract: A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses categorical emotion labels as supervision signals and contrasts in high-dimensional semantic space. However, categorical labels fail to provide quantitative information between emotions. ERC is also not equally dependent on all embedded features in the semantic space, which makes the high-dimensional SCL inefficient. To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Learning (SCCL) method, which first reduces the high-dimensional SCL space to a three-dimensional affect representation space Valence-Arousal-Dominance (VAD), then performs cluster-level contrastive learning to incorporate measurable emotion prototypes. To help modelling the dialogue and enriching the context, we leverage the pre-trained knowledge adapters to infuse linguistic and factual knowledge. Experiments show that our method achieves new state-of-the-art results with 69.81% on IEMOCAP, 65.7% on MELD, and 62.51% on DailyDialog datasets. The analysis also proves that the VAD space is not only suitable for ERC but also interpretable, with VAD prototypes enhancing its performance and stabilising the training of SCCL. In addition, the pre-trained knowledge adapters benefit the performance of the utterance encoder and SCCL. Our code is available at: https://github.com/SteveKGYang/SCCL | [
30273,
1269,
44825
] | Train |
40,804 | 30 | Title: Statistical Rejection Sampling Improves Preference Optimization
Abstract: Improving the alignment of language models with human preferences remains an active research challenge. Previous approaches have primarily utilized Reinforcement Learning from Human Feedback (RLHF) via online RL methods such as Proximal Policy Optimization (PPO). Recently, offline methods such as Sequence Likelihood Calibration (SLiC) and Direct Preference Optimization (DPO) have emerged as attractive alternatives, offering improvements in stability and scalability while maintaining competitive performance. SLiC refines its loss function using sequence pairs sampled from a supervised fine-tuned (SFT) policy, while DPO directly optimizes language models based on preference data, foregoing the need for a separate reward model. However, the maximum likelihood estimator (MLE) of the target optimal policy requires labeled preference pairs sampled from that policy. DPO's lack of a reward model constrains its ability to sample preference pairs from the optimal policy, and SLiC is restricted to sampling preference pairs only from the SFT policy. To address these limitations, we introduce a novel approach called Statistical Rejection Sampling Optimization (RSO) that aims to source preference data from the target optimal policy using rejection sampling, enabling a more accurate estimation of the optimal policy. We also propose a unified framework that enhances the loss functions used in both SLiC and DPO from a preference modeling standpoint. Through extensive experiments across three diverse tasks, we demonstrate that RSO consistently outperforms both SLiC and DPO on evaluations from both Large Language Model (LLM) and human raters. | [
9280,
25772,
43641,
29396,
3289,
16890
] | Validation |
40,805 | 16 | Title: Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation
Abstract: The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to improve detection performance. Our method is evaluated on a real-world medical imaging dataset and outperforms relevant baselines that do not consider disagreements among annotators. | [] | Validation |
40,806 | 17 | Title: Boundary Value Caching for Walk on Spheres
Abstract: Grid-free Monte Carlo methods such as walk on spheres can be used to solve elliptic partial differential equations without mesh generation or global solves. However, such methods independently estimate the solution at every point, and hence do not take advantage of the high spatial regularity of solutions to elliptic problems. We propose a fast caching strategy which first estimates solution values and derivatives at randomly sampled points along the boundary of the domain (or a local region of interest). These cached values then provide cheap, output-sensitive evaluation of the solution (or its gradient) at interior points, via a boundary integral formulation. Unlike classic boundary integral methods, our caching scheme introduces zero statistical bias and does not require a dense global solve. Moreover we can handle imperfect geometry (e.g., with self-intersections) and detailed boundary/source terms without repairing or resampling the boundary representation. Overall, our scheme is similar in spirit to virtual point light methods from photorealistic rendering: it suppresses the typical salt-and-pepper noise characteristic of independent Monte Carlo estimates, while still retaining the many advantages of Monte Carlo solvers: progressive evaluation, trivial parallelization, geometric robustness, etc. We validate our approach using test problems from visual and geometric computing. | [
19569,
24181
] | Train |
40,807 | 24 | Title: Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
Abstract: Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stochastic optimizer lacks clear convergence criteria and requires tuning parameters. Moreover, ADVI inherits the poor posterior uncertainty estimates of mean-field variational Bayes (MFVB). We introduce"deterministic ADVI"(DADVI) to address these issues. DADVI replaces the intractable MFVB objective with a fixed Monte Carlo approximation, a technique known in the stochastic optimization literature as the"sample average approximation"(SAA). By optimizing an approximate but deterministic objective, DADVI can use off-the-shelf second-order optimization, and, unlike standard mean-field ADVI, is amenable to more accurate posterior covariances via linear response (LR). In contrast to existing worst-case theory, we show that, on certain classes of common statistical problems, DADVI and the SAA can perform well with relatively few samples even in very high dimensions, though we also show that such favorable results cannot extend to variational approximations that are too expressive relative to mean-field ADVI. We show on a variety of real-world problems that DADVI reliably finds good solutions with default settings (unlike ADVI) and, together with LR covariances, is typically faster and more accurate than standard ADVI. | [
27348,
2934
] | Train |
40,808 | 16 | Title: A Probabilistic Attention Model with Occlusion-aware Texture Regression for 3D Hand Reconstruction from a Single RGB Image
Abstract: Recently, deep learning based approaches have shown promising results in 3D hand reconstruction from a single RGB image. These approaches can be roughly divided into model-based approaches, which are heavily dependent on the model's parameter space, and model-free approaches, which require large numbers of 3D ground truths to reduce depth ambiguity and struggle in weakly-supervised scenarios. To overcome these issues, we propose a novel probabilistic model to achieve the robustness of model-based approaches and reduced dependence on the model's parameter space of model-free approaches. The proposed probabilistic model incorporates a model-based network as a prior-net to estimate the prior probability distribution of joints and vertices. An Attention-based Mesh Vertices Uncertainty Regression (AMVUR) model is proposed to capture dependencies among vertices and the correlation between joints and mesh vertices to improve their feature representation. We further propose a learning based occlusion-aware Hand Texture Regression model to achieve high-fidelity texture reconstruction. We demonstrate the flexibility of the proposed probabilistic model to be trained in both supervised and weakly-supervised scenarios. The experimental results demonstrate our probabilistic model's state-of-the-art accuracy in 3D hand and texture reconstruction from a single image in both training schemes, including in the presence of severe occlusions. | [] | Train |
40,809 | 30 | Title: PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
Abstract: The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers. In this paper, we introduce PrimeQA: a one-stop and open-source QA repository with an aim to democratize QA research and facilitate easy replication of state-of-the-art (SOTA) QA methods. PrimeQA supports core QA functionalities like retrieval and reading comprehension as well as auxiliary capabilities such as question generation. It has been designed as an end-to-end toolkit for various use cases: building front-end applications, replicating SOTA methods on public benchmarks, and expanding pre-existing methods. PrimeQA is available at: https://github.com/primeqa. | [] | Train |
40,810 | 30 | Title: Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Abstract: Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence. Nevertheless, existing CKGs are typically oriented to English, limiting the research in non-English languages. Meanwhile, the emergence of foundation models like ChatGPT and GPT-4 has shown promising intelligence with the help of reinforcement learning from human feedback. Under the background, in this paper, we utilize foundation models to construct a Chinese CKG, named Snowman. Specifically, we distill different types of commonsense head items from ChatGPT, and continue to use it to collect tail items with respect to the head items and pre-defined relations. Based on the preliminary analysis, we find the negative commonsense knowledge distilled by ChatGPT achieves lower human acceptance compared to other knowledge. Therefore, we design a simple yet effective self-instruct filtering strategy to filter out invalid negative commonsense. Overall, the constructed Snowman covers more than ten million Chinese commonsense triples, making it the largest Chinese CKG. Moreover, human studies show the acceptance of Snowman achieves 90.6\%, indicating the high-quality triples distilled by the cutting-edge foundation model. We also conduct experiments on commonsense knowledge models to show the usability and effectiveness of our Snowman. | [
33220,
13510,
38886,
6392,
35545,
33311
] | Train |
40,811 | 22 | Title: Formalizing Box Inference for Capture Calculus
Abstract: Capture calculus has recently been proposed as a solution to effect checking, achieved by tracking the captured references of terms in the types. Boxes, along with the box and unbox operations, are a crucial construct in capture calculus, which maintains the hygiene of types and improves the expressiveness of polymorphism over capturing types. Despite their usefulness in the formalism, boxes would soon become a heavy notational burden for users when the program grows. It thus necessitates the inference of boxes when integrating capture checking into a mainstream programming language. In this paper, we develop a formalisation of box inference for capture calculus. We begin by introducing a semi-algorithmic variant of the capture calculus, from which we derive an inference system where typed transformations are applied to complete missing box operations in programs. Then, we propose a type-level system that performs provably equivalent inference on the type level, without actually transforming the program. In the metatheory, we establish the relationships between these systems and capture calculus, thereby proving both the soundness and the completeness of box inference. | [
12351
] | Validation |
40,812 | 10 | Title: A Categorical Framework of General Intelligence
Abstract: Can machines think? Since Alan Turing asked this question in 1950, nobody is able to give a direct answer, due to the lack of solid mathematical foundations for general intelligence. In this paper, we introduce a categorical framework towards this goal, with two main results. First, we investigate object representation through presheaves, introducing the notion of self-state awareness as a categorical analogue to self-consciousness, along with corresponding algorithms for its enforcement and evaluation. Secondly, we extend object representation to scenario representation using diagrams and limits, which then become building blocks for mathematical modeling, interpretability and AI safety. As an ancillary result, our framework introduces various categorical invariance properties that can serve as the alignment signals for model training. | [
5777,
30243,
16153
] | Validation |
40,813 | 24 | Title: Robust Reinforcement Learning via Adversarial Kernel Approximation
Abstract: Robust Markov Decision Processes (RMDPs) provide a framework for sequential decision-making that is robust to perturbations on the transition kernel. However, robust reinforcement learning (RL) approaches in RMDPs do not scale well to realistic online settings with high-dimensional domains. By characterizing the adversarial kernel in RMDPs, we propose a novel approach for online robust RL that approximates the adversarial kernel and uses a standard (non-robust) RL algorithm to learn a robust policy. Notably, our approach can be applied on top of any underlying RL algorithm, enabling easy scaling to high-dimensional domains. Experiments in classic control tasks, MinAtar and DeepMind Control Suite demonstrate the effectiveness and the applicability of our method. | [
19540,
30807
] | Train |
40,814 | 27 | Title: Dynamic Modeling and Validation of Soft Robotic Snake Locomotion
Abstract: Soft robotic snakes made of compliant materials can continuously deform their bodies and, therefore, mimic the biological snakes' flexible and agile locomotion gaits better than their rigid-bodied counterparts. Without wheel support, to date, soft robotic snakes are limited to emulating planar locomotion gaits, which are derived via kinematic modeling and tested on robotic prototypes. Given that the snake locomotion results from the reaction forces due to the distributed contact between their skin and the ground, it is essential to investigate the locomotion gaits through efficient dynamic models capable of accommodating distributed contact forces. We present a complete spatial dynamic model that utilizes a floating-base kinematic model with distributed contact dynamics for a pneumatically powered soft robotic snake. We numerically evaluate the feasibility of the planar and spatial rolling gaits utilizing the proposed model and experimentally validate the corresponding locomotion gait trajectories on a soft robotic snake prototype. We qualitatively and quantitatively compare the numerical and experimental results which confirm the validity of the proposed dynamic model. | [
9352,
33022
] | Test |
40,815 | 24 | Title: Graph Neural Network-Based Anomaly Detection for River Network Systems
Abstract: Background: Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under typical conditions. This paper presents a solution to the challenging task of anomaly detection for river network sensor data, which is essential for accurate and continuous monitoring. Methods: We use a graph neural network model, the recently proposed Graph Deviation Network (GDN), which employs graph attention-based forecasting to capture the complex spatio-temporal relationships between sensors. We propose an alternate anomaly threshold criteria for the model, GDN+, based on the learned graph. To evaluate the model's efficacy, we introduce new benchmarking simulation experiments with highly-sophisticated dependency structures and subsequence anomalies of various types. We also introduce software called gnnad. Results: We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data. Conclusions: Findings suggest that GDN+ outperforms the baseline approach in high-dimensional data, while also providing improved interpretability. | [] | Train |
40,816 | 16 | Title: Inference Time Evidences of Adversarial Attacks for Forensic on Transformers
Abstract: Vision Transformers (ViTs) are becoming a very popular paradigm for vision tasks as they achieve state-of-the-art performance on image classification. However, although early works implied that this network structure had increased robustness against adversarial attacks, some works argue ViTs are still vulnerable. This paper presents our first attempt toward detecting adversarial attacks during inference time using the network's input and outputs as well as latent features. We design four quantifications (or derivatives) of input, output, and latent vectors of ViT-based models that provide a signature of the inference, which could be beneficial for the attack detection, and empirically study their behavior over clean samples and adversarial samples. The results demonstrate that the quantifications from input (images) and output (posterior probabilities) are promising for distinguishing clean and adversarial samples, while latent vectors offer less discriminative power, though they give some insights on how adversarial perturbations work. | [] | Test |
40,817 | 24 | Title: Towards Machine Learning-based Fish Stock Assessment
Abstract: The accurate assessment of fish stocks is crucial for sustainable fisheries management. However, existing statistical stock assessment models can have low forecast performance of relevant stock parameters like recruitment or spawning stock biomass, especially in ecosystems that are changing due to global warming and other anthropogenic stressors. In this paper, we investigate the use of machine learning models to improve the estimation and forecast of such stock parameters. We propose a hybrid model that combines classical statistical stock assessment models with supervised ML, specifically gradient boosted trees. Our hybrid model leverages the initial estimate provided by the classical model and uses the ML model to make a post-hoc correction to improve accuracy. We experiment with five different stocks and find that the forecast accuracy of recruitment and spawning stock biomass improves considerably in most cases. | [] | Test |
40,818 | 24 | Title: Shared Growth of Graph Neural Networks via Free-direction Knowledge Distillation
Abstract: Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper teacher GNN into a shallower student GNN. However, it is often quite challenging to train a satisfactory deeper GNN due to the well-known over-parametrized and over-smoothing issues, leading to invalid knowledge transfer in practical applications. In this paper, we propose the first Free-direction Knowledge Distillation framework via reinforcement learning for GNNs, called FreeKD, which is no longer required to provide a deeper well-optimized teacher GNN. Our core idea is to collaboratively learn two shallower GNNs in an effort to exchange knowledge between them via reinforcement learning in a hierarchical way. As we observe that one typical GNN model often exhibits better and worse performances at different nodes during training, we devise a dynamic and free-direction knowledge transfer strategy that involves two levels of actions: 1) node-level action determines the directions of knowledge transfer between the corresponding nodes of two networks; and then 2) structure-level action determines which of the local structures generated by the node-level actions to be propagated. Furthermore, considering the diverse knowledge present in different GNNs when dealing with multi-view inputs, we introduce FreeKD++ as a solution to enable free-direction knowledge transfer among multiple shallow GNNs operating on multi-view inputs. Extensive experiments on five benchmark datasets demonstrate our approaches outperform the base GNNs in a large margin, and shows their efficacy to various GNNs. More surprisingly, our FreeKD has comparable or even better performance than traditional KD algorithms that distill knowledge from a deeper and stronger teacher GNN. | [
10008,
7175
] | Train |
40,819 | 24 | Title: Mechanic: A Learning Rate Tuner
Abstract: We introduce a technique for tuning the learning rate scale factor of any base optimization algorithm and schedule automatically, which we call \textsc{mechanic}. Our method provides a practical realization of recent theoretical reductions for accomplishing a similar goal in online convex optimization. We rigorously evaluate \textsc{mechanic} on a range of large scale deep learning tasks with varying batch sizes, schedules, and base optimization algorithms. These experiments demonstrate that depending on the problem, \textsc{mechanic} either comes very close to, matches or even improves upon manual tuning of learning rates. | [
15816,
39628,
16094,
42572
] | Train |
40,820 | 3 | Title: Consumer's Behavior Analysis of Electric Vehicle using Cloud Computing in the State of New York
Abstract: Sales of Electric Vehicles (EVs) in the United States have grown fast in the past decade. We analyze the Electric Vehicle Drive Clean Rebate data from the New York State Energy Research and Development Authority (NYSERDA) to understand consumer behavior in EV purchasing and their potential environmental impact. Based on completed rebate applications since 2017, this dataset features the make and model of the EV that consumers purchased, the geographic location of EV consumers, transaction type to obtain the EV, projected environmental impact, and tax incentive issued. This analysis consists of a mapped and calculated statistical data analysis over an established period. Using the SAP Analytics Cloud (SAC), we first import and clean the data to generate statistical snapshots for some primary attributes. Next, different EV options were evaluated based on environmental carbon footprints and rebate amounts. Finally, visualization, geo, and time-series analysis presented further insights and recommendations. This analysis helps the reader to understand consumers' EV buying behavior, such as the change of most popular maker and model over time, acceptance of EVs in different regions in New York State, and funds required to support clean air initiatives. Conclusions from the current study will facilitate the use of renewable energy, reduce reliance on fossil fuels, and accelerate economic growth sustainably, in addition to analyzing the trend of rebate funding size over the years and predicting future funding. | [] | Train |
40,821 | 28 | Title: Channel Estimation and Signal Detection for NLOS Ultraviolet Scattering Communication with Space Division Multiple Access
Abstract: We design a receiver assembling several photomultipliers (PMTs) as an array to increase the field of view (FOV) of the receiver and adapt to multiuser situation over None-line-of-sight (NLOS) ultraviolet (UV) channels. Channel estimation and signal detection have been investigated according to the space division characteristics of the structure. Firstly, we adopt the balanced structure on the pilot matrix, analyze the channel estimation mean square error (MSE), and optimize the structure parameters. Then, with the estimated parameters, an analytical threshold detection rule is proposed as a preliminary work of multiuser detection. The detection rule can be optimized by analyzing the separability of two users based on the Gaussian approximation of Poisson weighted sum. To assess the effect of imperfect estimation, the sensitivity analysis of channel estimation error on two-user signal detection is performed. Moreover, we propose a successive elimination method for on-off keying (OOK) modulated multiuser symbol detection based on the previous threshold detection rule. A closed-form upper bound on the detection error rate is calculated, which turns out to be a good approximation of that of multiuser maximum-likelihood (ML) detection. The proposed successive elimination method is twenty times faster than the ML detection with negligible detection error rate degradation. | [] | Train |
40,822 | 10 | Title: Game-Theoretical Analysis of Reviewer Rewards in Peer-Review Journal Systems: Analysis and Experimental Evaluation using Deep Reinforcement Learning
Abstract: In this paper, we navigate the intricate domain of reviewer rewards in open-access academic publishing, leveraging the precision of mathematics and the strategic acumen of game theory. We conceptualize the prevailing voucher-based reviewer reward system as a two-player game, subsequently identifying potential shortcomings that may incline reviewers towards binary decisions. To address this issue, we propose and mathematically formalize an alternative reward system with the objective of mitigating this bias and promoting more comprehensive reviews. We engage in a detailed investigation of the properties and outcomes of both systems, employing rigorous game-theoretical analysis and deep reinforcement learning simulations. Our results underscore a noteworthy divergence between the two systems, with our proposed system demonstrating a more balanced decision distribution and enhanced stability. This research not only augments the mathematical understanding of reviewer reward systems, but it also provides valuable insights for the formulation of policies within journal review system. Our contribution to the mathematical community lies in providing a game-theoretical perspective to a real-world problem and in the application of deep reinforcement learning to simulate and understand this complex system. | [] | Train |
40,823 | 16 | Title: Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing
Abstract: — In recent years Convolutional neural networks (CNN) have made significant progress in computer vision. These advancements have been applied to other areas, such as remote sensing and have shown satisfactory results. However, the lack of large labeled datasets and the inherent complexity of remote sensing problems have made it difficult to train deep CNNs for dense prediction problems. To solve this issue, ImageNet pre-trained weights have been used as a starting point in various dense predictions tasks. Although this type of transfer learning has led to improvements, the domain difference between natural and remote sensing images has also limited the performance of deep CNNs. On the other hand, self-supervised learning methods for learning visual representations from large unlabeled images have grown substantially over the past two years. Accordingly, in this paper we have explored the effectiveness of in-domain representations in both supervised and self-supervised forms to solve the domain difference between remote sensing and the ImageNet dataset. The obtained weights from remote sensing images are utilized as initial weights for solving semantic segmentation and object detection tasks and state-of-the-art results are obtained. For self-supervised pre-training, we have utilized the SimSiam algorithm as it is simple and does not need huge computational resources. One of the most influential factors in acquiring general visual representations from remote sensing images is the pre-training dataset. To examine the effect of the pre-training dataset, equal-sized remote sensing datasets are used for pre-training. Our results have demonstrated that using datasets with a high spatial resolution for self-supervised representation learning leads to high performance in downstream tasks. | [] | Train |
40,824 | 24 | Title: Interpretable Graph Neural Networks for Tabular Data
Abstract: Data in tabular format is frequently occurring in real-world applications. Graph Neural Networks (GNNs) have recently been extended to effectively handle such data, allowing feature interactions to be captured through representation learning. However, these approaches essentially produce black-box models, in the form of deep neural networks, precluding users from following the logic behind the model predictions. We propose an approach, called IGNNet (Interpretable Graph Neural Network for tabular data), which constrains the learning algorithm to produce an interpretable model, where the model shows how the predictions are exactly computed from the original input features. A large-scale empirical investigation is presented, showing that IGNNet is performing on par with state-of-the-art machine-learning algorithms that target tabular data, including XGBoost, Random Forests, and TabNet. At the same time, the results show that the explanations obtained from IGNNet are aligned with the true Shapley values of the features without incurring any additional computational overhead. | [] | Validation |
40,825 | 27 | Title: Dynamic Open Vocabulary Enhanced Safe-landing with Intelligence (DOVESEI)
Abstract: This work targets what we consider to be the foundational step for urban airborne robots, a safe landing. Our attention is directed toward what we deem the most crucial aspect of the safe landing perception stack: segmentation. We present a streamlined reactive UAV system that employs visual servoing by harnessing the capabilities of open vocabulary image segmentation. This approach can adapt to various scenarios with minimal adjustments, bypassing the necessity for extensive data accumulation for refining internal models, thanks to its open vocabulary methodology. Given the limitations imposed by local authorities, our primary focus centers on operations originating from altitudes of 100 meters. This choice is deliberate, as numerous preceding works have dealt with altitudes up to 30 meters, aligning with the capabilities of small stereo cameras. Consequently, we leave the remaining 20m to be navigated using conventional 3D path planning methods. Utilizing monocular cameras and image segmentation, our findings demonstrate the system's capability to successfully execute landing maneuvers at altitudes as low as 20 meters. However, this approach is vulnerable to intermittent and occasionally abrupt fluctuations in the segmentation between frames in a video stream. To address this challenge, we enhance the image segmentation output by introducing what we call a dynamic focus: a masking mechanism that self adjusts according to the current landing stage. This dynamic focus guides the control system to avoid regions beyond the drone's safety radius projected onto the ground, thus mitigating the problems with fluctuations. Through the implementation of this supplementary layer, our experiments have reached improvements in the landing success rate of almost tenfold when compared to global segmentation. All the source code is open source and available online (github.com/MISTLab/DOVESEI). | [] | Train |
40,826 | 34 | Title: On 2-strong connectivity orientations of mixed graphs and related problems
Abstract: A mixed graph $G$ is a graph that consists of both undirected and directed edges. An orientation of $G$ is formed by orienting all the undirected edges of $G$, i.e., converting each undirected edge $\{u,v\}$ into a directed edge that is either $(u,v)$ or $(v,u)$. The problem of finding an orientation of a mixed graph that makes it strongly connected is well understood and can be solved in linear time. Here we introduce the following orientation problem in mixed graphs. Given a mixed graph $G$, we wish to compute its maximal sets of vertices $C_1,C_2,\ldots,C_k$ with the property that by removing any edge $e$ from $G$ (directed or undirected), there is an orientation $R_i$ of $G\setminus{e}$ such that all vertices in $C_i$ are strongly connected in $R_i$. We discuss properties of those sets, and we show how to solve this problem in linear time by reducing it to the computation of the $2$-edge twinless strongly connected components of a directed graph. A directed graph $G=(V,E)$ is twinless strongly connected if it contains a strongly connected spanning subgraph without any pair of antiparallel (or twin) edges. The twinless strongly connected components (TSCCs) of a directed graph $G$ are its maximal twinless strongly connected subgraphs. A $2$-edge twinless strongly connected component (2eTSCC) of $G$ is a maximal subset of vertices $C$ such that any two vertices $u, v \in C$ are in the same twinless strongly connected component of $G \setminus e$, for any edge $e$. These concepts are motivated by several diverse applications, such as the design of road and telecommunication networks, and the structural stability of buildings. | [] | Train |
40,827 | 23 | Title: Beware of the Unexpected: Bimodal Taint Analysis
Abstract: Static analysis is a powerful tool for detecting security vulnerabilities and other programming problems. Global taint tracking, in particular, can spot vulnerabilities arising from complicated data flow across multiple functions. However, precisely identifying which flows are problematic is challenging, and sometimes depends on factors beyond the reach of pure program analysis, such as conventions and informal knowledge. For example, learning that a parameter name of an API function locale ends up in a file path is surprising and potentially problematic. In contrast, it would be completely unsurprising to find that a parameter command passed to an API function execaCommand is eventually interpreted as part of an operating-system command. This paper presents Fluffy, a bimodal taint analysis that combines static analysis, which reasons about data flow, with machine learning, which probabilistically determines which flows are potentially problematic. The key idea is to let machine learning models predict from natural language information involved in a taint flow, such as API names, whether the flow is expected or unexpected, and to inform developers only about the latter. We present a general framework and instantiate it with four learned models, which offer different trade-offs between the need to annotate training data and the accuracy of predictions. We implement Fluffy on top of the CodeQL analysis framework and apply it to 250K JavaScript projects. Evaluating on five common vulnerability types, we find that Fluffy achieves an F1 score of 0.85 or more on four of them across a variety of datasets. | [] | Train |
40,828 | 16 | Title: Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images
Abstract: Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm. | [
21828,
37098,
40943,
29456,
20949
] | Test |
40,829 | 25 | Title: TST: Time-Sparse Transducer for Automatic Speech Recognition
Abstract: End-to-end model, especially Recurrent Neural Network Transducer (RNN-T), has achieved great success in speech recognition. However, transducer requires a great memory footprint and computing time when processing a long decoding sequence. To solve this problem, we propose a model named time-sparse transducer, which introduces a time-sparse mechanism into transducer. In this mechanism, we obtain the intermediate representations by reducing the time resolution of the hidden states. Then the weighted average algorithm is used to combine these representations into sparse hidden states followed by the decoder. All the experiments are conducted on a Mandarin dataset AISHELL-1. Compared with RNN-T, the character error rate of the time-sparse transducer is close to RNN-T and the real-time factor is 50.00% of the original. By adjusting the time resolution, the time-sparse transducer can also reduce the real-time factor to 16.54% of the original at the expense of a 4.94% loss of precision. | [] | Test |
40,830 | 16 | Title: Looking at words and points with attention: a benchmark for text-to-shape coherence
Abstract: While text-conditional 3D object generation and manipulation have seen rapid progress, the evaluation of coherence between generated 3D shapes and input textual descriptions lacks a clear benchmark. The reason is twofold: a) the low quality of the textual descriptions in the only publicly available dataset of text-shape pairs; b) the limited effectiveness of the metrics used to quantitatively assess such coherence. In this paper, we propose a comprehensive solution that addresses both weaknesses. Firstly, we employ large language models to automatically refine textual descriptions associated with shapes. Secondly, we propose a quantitative metric to assess text-to-shape coherence, through cross-attention mechanisms. To validate our approach, we conduct a user study and compare quantitatively our metric with existing ones. The refined dataset, the new metric and a set of text-shape pairs validated by the user study comprise a novel, fine-grained benchmark that we publicly release to foster research on text-to-shape coherence of text-conditioned 3D generative models. Benchmark available at https://cvlab-unibo.github.io/CrossCoherence-Web/. | [
32148,
42666,
30500,
17583
] | Train |
40,831 | 30 | Title: Interactive Molecular Discovery with Natural Language
Abstract: Natural language is expected to be a key medium for various human-machine interactions in the era of large language models. When it comes to the biochemistry field, a series of tasks around molecules (e.g., property prediction, molecule mining, etc.) are of great significance while having a high technical threshold. Bridging the molecule expressions in natural language and chemical language can not only hugely improve the interpretability and reduce the operation difficulty of these tasks, but also fuse the chemical knowledge scattered in complementary materials for a deeper comprehension of molecules. Based on these benefits, we propose the conversational molecular design, a novel task adopting natural language for describing and editing target molecules. To better accomplish this task, we design ChatMol, a knowledgeable and versatile generative pre-trained model, enhanced by injecting experimental property information, molecular spatial knowledge, and the associations between natural and chemical languages into it. Several typical solutions including large language models (e.g., ChatGPT) are evaluated, proving the challenge of conversational molecular design and the effectiveness of our knowledge enhancement method. Case observations and analysis are conducted to provide directions for further exploration of natural-language interaction in molecular discovery. | [] | Test |
40,832 | 38 | Title: OpenCitations Meta
Abstract: OpenCitations Meta is a new database that contains bibliographic metadata of scholarly publications involved in citations indexed by the OpenCitations infrastructure. It adheres to Open Science principles and provides data under a CC0 license for maximum reuse. The data can be accessed through a SPARQL endpoint, REST APIs, and dumps. OpenCitations Meta serves three important purposes. Firstly, it enables disambiguation of citations between publications described using different identifiers from various sources. For example, it can link publications identified by DOIs in Crossref and PMIDs in PubMed. Secondly, it assigns new globally persistent identifiers (PIDs), known as OpenCitations Meta Identifiers (OMIDs), to bibliographic resources without existing external persistent identifiers like DOIs. Lastly, by hosting the bibliographic metadata internally, OpenCitations Meta improves the speed of metadata retrieval for citing and cited documents. The database is populated through automated data curation, including deduplication, error correction, and metadata enrichment. The data is stored in RDF format following the OpenCitations Data Model, and changes and provenance information are tracked. OpenCitations Meta and its production. OpenCitations Meta currently incorporates data from Crossref, DataCite, and the NIH Open Citation Collection. In terms of semantic publishing datasets, it is currently the first in data volume. | [] | Train |
40,833 | 24 | Title: Beta Diffusion
Abstract: We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges. Using scaled and shifted beta distributions, beta diffusion utilizes multiplicative transitions over time to create both forward and reverse diffusion processes, maintaining beta distributions in both the forward marginals and the reverse conditionals, given the data at any point in time. Unlike traditional diffusion-based generative models relying on additive Gaussian noise and reweighted evidence lower bounds (ELBOs), beta diffusion is multiplicative and optimized with KL-divergence upper bounds (KLUBs) derived from the convexity of the KL divergence. We demonstrate that the proposed KLUBs are more effective for optimizing beta diffusion compared to negative ELBOs, which can also be derived as the KLUBs of the same KL divergence with its two arguments swapped. The loss function of beta diffusion, expressed in terms of Bregman divergence, further supports the efficacy of KLUBs for optimization. Experimental results on both synthetic data and natural images demonstrate the unique capabilities of beta diffusion in generative modeling of range-bounded data and validate the effectiveness of KLUBs in optimizing diffusion models, thereby making them valuable additions to the family of diffusion-based generative models and the optimization techniques used to train them. | [
20353,
31969,
12515,
18022,
103,
30312,
34215,
20174,
21014,
11607,
5278
] | Validation |
40,834 | 16 | Title: Beyond Learned Metadata-based Raw Image Reconstruction
Abstract: While raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels, they are not widely adopted by general users due to their substantial storage requirements. Very recent studies propose to compress raw images by designing sampling masks within the pixel space of the raw image. However, these approaches often leave space for pursuing more effective image representations and compact metadata. In this work, we propose a novel framework that learns a compact representation in the latent space, serving as metadata, in an end-to-end manner. Compared with lossy image compression, we analyze the intrinsic difference of the raw image reconstruction task caused by rich information from the sRGB image. Based on the analysis, a novel backbone design with asymmetric and hybrid spatial feature resolutions is proposed, which significantly improves the rate-distortion performance. Besides, we propose a novel design of the context model, which can better predict the order masks of encoding/decoding based on both the sRGB image and the masks of already processed features. Benefited from the better modeling of the correlation between order masks, the already processed information can be better utilized. Moreover, a novel sRGB-guided adaptive quantization precision strategy, which dynamically assigns varying levels of quantization precision to different regions, further enhances the representation ability of the model. Finally, based on the iterative properties of the proposed context model, we propose a novel strategy to achieve variable bit rates using a single model. This strategy allows for the continuous convergence of a wide range of bit rates. Extensive experimental results demonstrate that the proposed method can achieve better reconstruction quality with a smaller metadata size. | [
38336
] | Train |
40,835 | 30 | Title: Solving Dialogue Grounding Embodied Task in a Simulated Environment using Further Masked Language Modeling
Abstract: Enhancing AI systems with efficient communication skills that align with human understanding is crucial for their effective assistance to human users. Proactive initiatives from the system side are needed to discern specific circumstances and interact aptly with users to solve these scenarios. In this research, we opt for a collective building assignment taken from the Minecraft dataset. Our proposed method employs language modeling to enhance task understanding through state-of-the-art (SOTA) methods using language models. These models focus on grounding multi-modal understandinging and task-oriented dialogue comprehension tasks. This focus aids in gaining insights into how well these models interpret and respond to a variety of inputs and tasks. Our experimental results provide compelling evidence of the superiority of our proposed method. This showcases a substantial improvement and points towards a promising direction for future research in this domain. | [
39992,
6875,
27309,
37463
] | Train |
40,836 | 30 | Title: Unsupervised extraction of local and global keywords from a single text
Abstract: We propose an unsupervised, corpus-independent method to extract keywords from a single text. It is based on the spatial distribution of words and the response of this distribution to a random permutation of words. As compared to existing methods (such as e.g. YAKE) our method has three advantages. First, it is significantly more effective at extracting keywords from long texts. Second, it allows inference of two types of keywords: local and global. Third, it uncovers basic themes in texts. Additionally, our method is language-independent and applies to short texts. The results are obtained via human annotators with previous knowledge of texts from our database of classical literary works (the agreement between annotators is from moderate to substantial). Our results are supported via human-independent arguments based on the average length of extracted content words and on the average number of nouns in extracted words. We discuss relations of keywords with higher-order textual features and reveal a connection between keywords and chapter divisions. | [] | Test |
40,837 | 16 | Title: 3D Human Keypoints Estimation from Point Clouds in the Wild without Human Labels
Abstract: Training a 3D human keypoint detector from point clouds in a supervised manner requires large volumes of high quality labels. While it is relatively easy to capture large amounts of human point clouds, annotating 3D key-points is expensive, subjective, error prone and especially difficult for long-tail cases (pedestrians with rare poses, scooterists, etc.). In this work, we propose GC-KPL - Geometry Consistency inspired Key Point Leaning, an approach for learning 3D human joint locations from point clouds without human labels. We achieve this by our novel unsupervised loss formulations that account for the structure and movement of the human body. We show that by training on a large training set from Waymo Open Dataset [21] without any human annotated keypoints, we are able to achieve reasonable performance as compared to the fully supervised approach. Further, the backbone benefits from the unsupervised training and is useful in downstream few-shot learning of keypoints, where fine-tuning on only 10 percent of the labeled training data gives comparable performance to fine-tuning on the entire set. We demonstrated that GC-KPL outperforms by a large margin over SoTA when trained on entire dataset and efficiently leverages large volumes of unlabeled data. | [] | Validation |
40,838 | 25 | Title: Reef Elegy: An Auditory Display of Hawaii's 2019 Coral Bleaching Data
Abstract: This paper describes an auditory display of Hawaii's 2019 coral bleaching data via means of spatial audio and parameter mapping methods. Selected data fields spanning 78 days are mapped to sound surrogates of coral reefs' natural soundscapes, which are progressively altered in their constituent elements as the corresponding coral locations undergo bleaching. For some of these elements, this process outlines a trajectory from a dense to a sparser, reduced soundscape, while for others it translates moving away from harmonic tones and towards complex spectra. This experiment is accompanied by a short evaluation study to contextualize it in an established aesthetic perspective space and to probe its potential for public engagement in the discourse around climate change. | [] | Train |
40,839 | 26 | Title: Understanding Differences in News Article Interaction Patterns on Facebook: Public vs. Private Sharing with Varying Bias and Reliability
Abstract: The proliferation of news on social media platforms has led to concerns about the impact of biased and unreliable information on public discourse. This study examines differences in interaction patterns between public and private sharing of news articles on Facebook, focusing on articles with varying bias and reliability, as well as the depth of interactions. To analyze these patterns, we employed two complementary data collection methods using the CrowdTangle browser extension. We collected interaction data across all Facebook posts (private + public) referencing a manually labeled collection of over 30K news articles, as well as interaction data on public posts posted in the forums tracked by CrowdTangle. Our empirical findings, backed by rigorous statistical analysis, reveal significant differences in interaction patterns between public and private sharing across different classes of news in terms of bias and reliability, highlighting the role of user preferences and privacy settings in shaping the spread of news articles. Notably, we find that irrespective of news class, users tend to engage more deeply in private discussions compared to public ones. Additionally, Facebook users engage more deeply with content from the Right-biased class, and exhibit higher deep interaction ratio levels with content from the Most-unreliable class. This study is the first to directly compare the dynamics of public and private sharing of news articles on Facebook, specifically examining the interactions and depth of engagement with articles of varying bias and reliability. By providing new insights and shedding light on these aspects, our findings have significant implications for understanding the influence of social media on shaping public discourse. | [] | Validation |
40,840 | 24 | Title: On the Learnability of Multilabel Ranking
Abstract: Multilabel ranking is a central task in machine learning. However, the most fundamental question of learnability in a multilabel ranking setting with relevance-score feedback remains unanswered. In this work, we characterize the learnability of multilabel ranking problems in both batch and online settings for a large family of ranking losses. Along the way, we give two equivalence classes of ranking losses based on learnability that capture most, if not all, losses used in practice. | [
35812,
9703
] | Validation |
40,841 | 28 | Title: Quantized Phase Alignment by Discrete Phase Shifts for Reconfigurable Intelligent Surface-Assisted Communication Systems
Abstract: Reconfigurable intelligent surface (RIS) has aroused a surge of interest in recent years. In this paper, we investigate the joint phase alignment and phase quantization on discrete phase shift designs for RIS-assisted single-input single-output (SISO) system. Firstly, the phenomena of phase distribution in far field and near field are respectively unveiled, paving the way for discretization of phase shift for RIS. Then, aiming at aligning phases, the phase distribution law and its underlying degree-of-freedom (DoF) are characterized, serving as the guideline of phase quantization strategies. Subsequently, two phase quantization methods, dynamic threshold phase quantization (DTPQ) and equal interval phase quantization (EIPQ), are proposed to strengthen the beamforming effect of RIS. DTPQ is capable of calculating the optimal discrete phase shifts with linear complexity in the number of unit cells on RIS, whilst EIPQ is a simplified method with a constant complexity yielding sub-optimal solution. Simulation results demonstrate that both methods achieve substantial improvements on power gain, stability, and robustness over traditional quantization methods. The path loss (PL) scaling law under discrete phase shift of RIS is unveiled for the first time, with the phase shifts designed by DTPQ due to its optimality. Additionally, the field trials conducted at 2.6 GHz and 35 GHz validate the favourable performance of the proposed methods in practical communication environment. | [
31367
] | Test |
40,842 | 27 | Title: Path Planning for Air-Ground Robot Considering Modal Switching Point Optimization
Abstract: An innovative sort of mobility platform that can both drive and fly is the air-ground robot. The need for an agile flight cannot be satisfied by traditional path planning techniques for air-ground robots. Prior studies had mostly focused on improving the energy efficiency of paths, seldom taking the seeking speed and optimizing take-off and landing places into account. A robot for the field application environment was proposed, and a lightweight global spatial planning technique for the robot based on the graph-search algorithm taking mode switching point optimization into account, with an emphasis on energy efficiency, searching speed, and the viability of real deployment. The fundamental concept is to lower the computational burden by employing an interchangeable search approach that combines planar and spatial search. Furthermore, to safeguard the health of the power battery and the integrity of the mission execution, a trap escape approach was also provided. Simulations are run to test the effectiveness of the suggested model based on the field DEM map. The simulation results show that our technology is capable of producing finished, plausible 3D paths with a high degree of believability. Additionally, the mode-switching point optimization method efficiently identifies additional acceptable places for mode switching, and the improved paths use less time and energy. | [] | Train |
40,843 | 16 | Title: Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness
Abstract: The CLIP (Contrastive Language-Image Pre-training) model and its variants are becoming the de facto backbone in many applications. However, training a CLIP model from hundreds of millions of image-text pairs can be prohibitively expensive. Furthermore, the conventional CLIP model doesn't differentiate between the visual semantics and meaning of text regions embedded in images. This can lead to non-robustness when the text in the embedded region doesn't match the image's visual appearance. In this paper, we discuss two effective approaches to improve the efficiency and robustness of CLIP training: (1) augmenting the training dataset while maintaining the same number of optimization steps, and (2) filtering out samples that contain text regions in the image. By doing so, we significantly improve the classification and retrieval accuracy on public benchmarks like ImageNet and CoCo. Filtering out images with text regions also protects the model from typographic attacks. To verify this, we build a new dataset named ImageNet with Adversarial Text Regions (ImageNet-Attr). Our filter-based CLIP model demonstrates a top-1 accuracy of 68.78\%, outperforming previous models whose accuracy was all below 50\%. | [
35,
23493,
44746,
39628,
7124
] | Test |
40,844 | 6 | Title: Cinematic Techniques in Narrative Visualization
Abstract: The many genres of narrative visualization (e.g. data comics, data videos) each offer a unique set of affordances and constraints. To better understand a genre that we call cinematic visualizations-3D visualizations that make highly deliberate use of a camera to convey a narrative-we gathered 50 examples and analyzed their traditional cinematic aspects to identify the benefits and limitations of the form. While the cinematic visualization approach can violate traditional rules of visualization, we find that through careful control of the camera, cinematic visualizations enable immersion in data-driven, anthropocentric environments, and can naturally incorporate in-situ narrators, concrete scales, and visual analogies. Our analysis guides our design of a series of cinematic visualizations, created for NASA's Earth Science Communications team. We present one as a case study to convey design guidelines covering cinematography, lighting, set design, and sound, and discuss challenges in creating cinematic visualizations. | [] | Validation |
40,845 | 10 | Title: A Decision Making Framework for Recommended Maintenance of Road Segments
Abstract: With the rapid development of global road transportation, countries worldwide have completed the construction of road networks. However, the ensuing challenge lies in the maintenance of existing roads. It is well-known that countries allocate limited budgets to road maintenance projects, and road management departments face difficulties in making scientifically informed maintenance decisions. Therefore, integrating various artificial intelligence decision-making techniques to thoroughly explore historical maintenance data and adapt them to the context of road maintenance scientific decision-making has become an urgent issue. This integration aims to provide road management departments with more scientific tools and evidence for decision-making. The framework proposed in this paper primarily addresses the following four issues: 1) predicting the pavement performance of various routes, 2) determining the prioritization of maintenance routes, 3) making maintenance decisions based on the evaluation of the effects of past maintenance, and considering comprehensive technical and management indicators, and 4) determining the prioritization of maintenance sections based on the maintenance effectiveness and recommended maintenance effectiveness. By tackling these four problems, the framework enables intelligent decision-making for the optimal maintenance plan and maintenance sections, taking into account limited funding and historical maintenance management experience. | [] | Train |
40,846 | 23 | Title: Impact of Large Language Models on Generating Software Specifications
Abstract: Software specifications are essential for ensuring the reliability of software systems. Existing specification extraction approaches, however, suffer from limited generalizability and require manual efforts. We study the effectiveness of Large Language Models (LLMs) in generating software specifications from software documentation, utilizing Few-Shot Learning (FSL) to enable LLMs to generalize from a small number of examples. We compare the performance of LLMs with FSL to that of state-of-the-art specification extraction techniques and study the impact of prompt construction strategies on LLM performance. In addition, we conduct a comprehensive analysis of their symptoms and root causes of the failures to understand the pros and cons of LLMs and existing approaches. We also compare 11 LLMs' performance, cost, and response time for generating software specifications. Our findings include that (1) the best performing LLM outperforms existing approaches by 9.1--13.7% with a few similar examples, (2) the two dominant root causes combined (ineffective prompts and missing domain knowledge) result in 57--60% of LLM failures, and (3) most of the 11 LLMs achieve better or comparable performance compared to traditional techniques. Our study offers valuable insights for future research to improve specification generation. | [
43769
] | Validation |
40,847 | 24 | Title: Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks
Abstract: Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under AC voltage is well-established, the analysis of PDs under DC voltage remains an active research field. A key focus of these investigations is the classification of different PD sources to enable subsequent sophisticated analysis. In this paper, we propose and analyze a neural network-based approach for classifying PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS, without relying on pulse sequence analysis features. In contrast to previous approaches, our proposed model can discriminate the studied PD signals obtained at negative and positive potentials, while also generalizing to unseen operating voltage multiples. Additionally, we compare the performance of time- and frequency-domain input signals and explore the impact of different normalization schemes to mitigate the influence of free-space path loss between the sensor and defect location. | [] | Train |
40,848 | 4 | Title: On Cross-Layer Interactions of QUIC, Encrypted DNS and HTTP/3: Design, Evaluation and Dataset
Abstract: Every Web session involves a DNS resolution. While, in the last decade, we witnessed a promising trend towards an encrypted Web in general, DNS encryption has only recently gained traction with the standardisation of DNS over TLS (DoT) and DNS over HTTPS (DoH). Meanwhile, the rapid rise of QUIC deployment has now opened up an exciting opportunity to utilise the same protocol to not only encrypt Web communications, but also DNS. In this paper, we evaluate this benefit of using QUIC to coalesce name resolution via DNS over QUIC (DoQ), and Web content delivery via HTTP/3 (H3) with 0-RTT. We compare this scenario using several possible combinations where H3 is used in conjunction with DoH and DoQ, as well as the unencrypted DNS over UDP (DoUDP). We observe, that when using H3 1-RTT, page load times with DoH can get inflated by $>$30\% over fixed-line and by $>$50\% over mobile when compared to unencrypted DNS with DoUDP. However, this cost of encryption can be drastically reduced when encrypted connections are coalesced (DoQ + H3 0-RTT), thereby reducing the page load times by 1/3 over fixed-line and 1/2 over mobile, overall making connection coalescing with QUIC the best option for encrypted communication on the Internet. | [
45700
] | Test |
40,849 | 4 | Title: That Doesn't Go There: Attacks on Shared State in Multi-User Augmented Reality Applications
Abstract: Augmented Reality (AR) is expected to become a pervasive component in enabling shared virtual experiences. In order to facilitate collaboration among multiple users, it is crucial for multi-user AR applications to establish a consensus on the"shared state"of the virtual world and its augmentations, through which they interact within augmented reality spaces. Current methods to create and access shared state collect sensor data from devices (e.g., camera images), process them, and integrate them into the shared state. However, this process introduces new vulnerabilities and opportunities for attacks. Maliciously writing false data to"poison"the shared state is a major concern for the security of the downstream victims that depend on it. Another type of vulnerability arises when reading the shared state; by providing false inputs, an attacker can view hologram augmentations at locations they are not allowed to access. In this work, we demonstrate a series of novel attacks on multiple AR frameworks with shared states, focusing on three publicly-accessible frameworks. We show that these frameworks, while using different underlying implementations, scopes, and mechanisms to read from and write to the shared state, have shared vulnerability to a unified threat model. Our evaluation of these state-of-art AR applications demonstrates reliable attacks both on updating and accessing shared state across the different systems. To defend against such threats, we discuss a number of potential mitigation strategies that can help enhance the security of multi-user AR applications. | [] | Validation |
40,850 | 4 | Title: A Framework to Allow a Third Party to Watermark Numerical Data in an Encrypted Domain while Preserving its Statistical Properties
Abstract: Watermarking data for source tracking applications by its owner can be unfair for recipients because the data owner may redistribute the same watermarked data to many users. Hence, each data recipient should know the watermark embedded in their data; however, this may enable them to remove it, which violates the watermark security. To overcome this problem, this research develops a framework that allows the cloud to watermark numerical data taking into consideration: the correctness of the results of selected statistics, data privacy, the recipient's right to know the watermark that is embedded in their data, and the security of the watermark against passive attacks. The proposed framework contains two irreversible watermarking algorithms, each can preserve the correctness of the results for certain statistical operations. To preserve data privacy, the framework allows the cloud to watermark data while it is encrypted. Furthermore, the framework robustifies the security of the chosen algorithms to nominate the cloud as the only neutral judge able to verify the data ownership even if other users know the watermark. The security is enhanced in a way that does not affect the data usability. The time complexity to find the watermark is $\mathcal{O}(\frac{n!}{r!(n-r)!})$. | [] | Train |
40,851 | 22 | Title: Inferring Needless Write Memory Accesses on Ethereum Bytecode (Extended Version)
Abstract: Efficiency is a fundamental property of any type of program, but it is even more so in the context of the programs executing on the blockchain (known as smart contracts). This is because optimizing smart contracts has direct consequences on reducing the costs of deploying and executing the contracts, as there are fees to pay related to their bytes-size and to their resource consumption (called gas). Optimizing memory usage is considered a challenging problem that, among other things, requires a precise inference of the memory locations being accessed. This is also the case for the Ethereum Virtual Machine (EVM) bytecode generated by the most-widely used compiler, \texttt{solc}, whose rather unconventional and low-level memory usage challenges automated reasoning. This paper presents a static analysis, developed at the level of the EVM bytecode generated by \texttt{solc}, that infers write memory accesses that are needless and thus can be safely removed. The application of our implementation on more than 19,000 real smart contracts has detected about 6,200 needless write accesses in less than 4 hours. Interestingly, many of these writes were involved in memory usage patterns generated by \texttt{solc} that can be greatly optimized by removing entire blocks of bytecodes. To the best of our knowledge, existing optimization tools cannot infer such needless write accesses, and hence cannot detect these inefficiencies that affect both the deployment and the execution costs of Ethereum smart contracts. | [] | Train |
40,852 | 16 | Title: Generating an interactive online map of future sea level rise along the North Shore of Vancouver: methods and insights on enabling geovisualisation for coastal communities
Abstract: Contemporary sea level rise (SLR) research seldom considers enabling effective geovisualisation for the communities. This lack of knowledge transfer impedes raising awareness on climate change and its impacts. The goal of this study is to produce an online SLR map accessible to the public that allows them to interact with evolving high-resolution geospatial data and techniques. The study area was the North Shore of Vancouver, British Columbia, Canada. While typically coarser resolution (10m+/pixel) Digital Elevation Models have been used by previous studies, we explored an open access airborne 1 metre LiDAR which has a higher resolution and vertical accuracy and can penetrate tree cover at a higher degree than most satellite imagery. A bathtub method model with hydrologic connectivity was used to delineate the inundation zones for various SLR scenarios which allows for a not overly complex model and process using standard tools such as ArcGIS and QGIS with similar levels of accuracy as more complex models, especially with the high-resolution data. Deep Learning and 3D visualizations were used to create past, present, and modelled future Land Use/Land Cover and 3D flyovers. Analysis of the possible impacts of 1m, 2m, 3m, and 4m SLR over the unique coastline, terrain and land use was detailed. The generated interactive online map helps local communities visualise and understand the future of their coastlines. We have provided a detailed methodology and the methods and results are easily reproducible for other regions. Such initiatives can help popularise community-focused geovisualisation to raise awareness about SLR. | [] | Validation |
40,853 | 16 | Title: Surgical Action Triplet Detection by Mixed Supervised Learning of Instrument-Tissue Interactions
Abstract: Surgical action triplets describe instrument-tissue interactions as (instrument, verb, target) combinations, thereby supporting a detailed analysis of surgical scene activities and workflow. This work focuses on surgical action triplet detection, which is challenging but more precise than the traditional triplet recognition task as it consists of joint (1) localization of surgical instruments and (2) recognition of the surgical action triplet associated with every localized instrument. Triplet detection is highly complex due to the lack of spatial triplet annotation. We analyze how the amount of instrument spatial annotations affects triplet detection and observe that accurate instrument localization does not guarantee better triplet detection due to the risk of erroneous associations with the verbs and targets. To solve the two tasks, we propose MCIT-IG, a two-stage network, that stands for Multi-Class Instrument-aware Transformer-Interaction Graph. The MCIT stage of our network models per class embedding of the targets as additional features to reduce the risk of misassociating triplets. Furthermore, the IG stage constructs a bipartite dynamic graph to model the interaction between the instruments and targets, cast as the verbs. We utilize a mixed-supervised learning strategy that combines weak target presence labels for MCIT and pseudo triplet labels for IG to train our network. We observed that complementing minimal instrument spatial annotations with target embeddings results in better triplet detection. We evaluate our model on the CholecT50 dataset and show improved performance on both instrument localization and triplet detection, topping the leaderboard of the CholecTriplet challenge in MICCAI 2022. | [] | Train |
40,854 | 30 | Title: Good, but not always Fair: An Evaluation of Gender Bias for three commercial Machine Translation Systems
Abstract: Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale. Consequently, analyses have been redirected to more nuanced aspects, intricate phenomena, as well as potential risks that may arise from the widespread use of MT tools. Along this line, this paper offers a meticulous assessment of three commercial MT systems - Google Translate, DeepL, and Modern MT - with a specific focus on gender translation and bias. For three language pairs (English/Spanish, English/Italian, and English/French), we scrutinize the behavior of such systems at several levels of granularity and on a variety of naturally occurring gender phenomena in translation. Our study takes stock of the current state of online MT tools, by revealing significant discrepancies in the gender translation of the three systems, with each system displaying varying degrees of bias despite their overall translation quality. | [
37419
] | Train |
40,855 | 16 | Title: Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
Abstract: Current arbitrary style transfer models are limited to either image or video domains. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training processes on image and video domains, respectively. In this paper, we show that this can be precluded by introducing UniST, a Unified Style Transfer framework for both images and videos. At the core of UniST is a domain interaction transformer (DIT), which first explores context information within the specific domain and then interacts contextualized domain information for joint learning. In particular, DIT enables exploration of temporal information from videos for the image style transfer task and meanwhile allows rich appearance texture from images for video style transfer, thus leading to mutual benefits. Considering heavy computation of traditional multi-head self-attention, we present a simple yet effective axial multi-head self-attention (AMSA) for DIT, which improves computational efficiency while maintains style transfer performance. To verify the effectiveness of UniST, we conduct extensive experiments on both image and video style transfer tasks and show that UniST performs favorably against state-of-the-art approaches on both tasks. Code is available at https://github.com/NevSNev/UniST. | [] | Train |
40,856 | 26 | Title: To be a pro-vax or not, the COVID-19 vaccine conundrum on Twitter
Abstract: The most surprising observation reported by the study in (arXiv:2208.13523), involving stance detection of COVID-19 vaccine related tweets during the first year of pandemic, is the presence of a significant number of users (~2 million) who posted tweets with both anti-vax and pro-vax stances. This is a sizable cohort even when the stance detection noise is considered. In this paper, we tried to get deeper understanding of this 'dual-stance' group. Out of this group, 60% of users have more pro-vax tweets than anti-vax tweets and 17% have the same number of tweets in both classes. The rest have more anti-vax tweets, and they were highly active in expressing concerns about mandate and safety of a fast-tracked vaccine, while also tweeted some updates about vaccine development. The leaning pro-vax group have opposite composition: more vaccine updates and some posts about concerns. It is important to note that vaccine concerns were not always genuine and had a large dose of misinformation. 43% of the balanced group have only tweeted one tweet of each type during our study period and are the less active participants in the vaccine discourse. Our temporal study also shows that the change-of-stance behaviour became really significant once the trial results of COVID-19 vaccine were announced to the public, and it appears as the change of stance towards pro-vax is a reaction to people changing their opinion towards anti-vax. Our study finished at Mar 23, 2021 when the conundrum was still going strong. The dilemma might be a reflection of the uncertain and stressful times, but it also highlights the importance of building public trust to combat prevalent misinformation. | [] | Train |
40,857 | 24 | Title: NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
Abstract: The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs. However, when faced with real-world physical data, which are often highly non-uniformly distributed, it is challenging to use mesh-based techniques such as the FFT. To address this, we introduce the Non-Uniform Neural Operator (NUNO), a comprehensive framework designed for efficient operator learning with non-uniform data. Leveraging a K-D tree-based domain decomposition, we transform non-uniform data into uniform grids while effectively controlling interpolation error, thereby paralleling the speed and accuracy of learning from non-uniform data. We conduct extensive experiments on 2D elasticity, (2+1)D channel flow, and a 3D multi-physics heatsink, which, to our knowledge, marks a novel exploration into 3D PDE problems with complex geometries. Our framework has reduced error rates by up to 60% and enhanced training speeds by 2x to 30x. The code is now available at https://github.com/thu-ml/NUNO. | [
36735
] | Test |
40,858 | 16 | Title: Fast Inference in Denoising Diffusion Models via MMD Finetuning
Abstract: Denoising Diffusion Models (DDMs) have become a popular tool for generating high-quality samples from complex data distributions. These models are able to capture sophisticated patterns and structures in the data, and can generate samples that are highly diverse and representative of the underlying distribution. However, one of the main limitations of diffusion models is the complexity of sample generation, since a large number of inference timesteps is required to faithfully capture the data distribution. In this paper, we present MMD-DDM, a novel method for fast sampling of diffusion models. Our approach is based on the idea of using the Maximum Mean Discrepancy (MMD) to finetune the learned distribution with a given budget of timesteps. This allows the finetuned model to significantly improve the speed-quality trade-off, by substantially increasing fidelity in inference regimes with few steps or, equivalently, by reducing the required number of steps to reach a target fidelity, thus paving the way for a more practical adoption of diffusion models in a wide range of applications. We evaluate our approach on unconditional image generation with extensive experiments across the CIFAR-10, CelebA, ImageNet and LSUN-Church datasets. Our findings show that the proposed method is able to produce high-quality samples in a fraction of the time required by widely-used diffusion models, and outperforms state-of-the-art techniques for accelerated sampling. Code is available at: https://github.com/diegovalsesia/MMD-DDM. | [
43881,
18022
] | Train |
40,859 | 30 | Title: Technical Report on Token Position Bias in Transformers
Abstract: Language Models (LMs) have shown state-of-the-art performance in Natural Language Processing (NLP) tasks. Downstream tasks such as Named Entity Recognition (NER) or Part-of-Speech (POS) tagging are known to suffer from data imbalance issues, specifically in terms of the ratio of positive to negative examples, and class imbalance. In this paper, we investigate an additional specific issue for language models, namely the position bias of positive examples in token classification tasks. Therefore, we conduct an in-depth evaluation of the impact of position bias on the performance of LMs when fine-tuned on Token Classification benchmarks. Our study includes CoNLL03 and OntoNote5.0 for NER, English Tree Bank UD_en and TweeBank for POS tagging. We propose an evaluation approach to investigate position bias in Transformer models. We show that encoders like BERT, ERNIE, ELECTRA, and decoders such as GPT2 and BLOOM can suffer from this bias with an average drop of 3\% and 9\% in their performance. To mitigate this effect, we propose two methods: Random Position Shifting and Context Perturbation, that we apply on batches during the training process. The results show an improvement of $\approx$ 2\% in the performance of the model on CoNLL03, UD_en, and TweeBank. | [] | Test |
40,860 | 24 | Title: On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples
Abstract: Offline reinforcement learning (offline RL) considers problems where learning is performed using only previously collected samples and is helpful for the settings in which collecting new data is costly or risky. In model-based offline RL, the learner performs estimation (or optimization) using a model constructed according to the empirical transition frequencies. We analyze the sample complexity of vanilla model-based offline RL with dependent samples in the infinite-horizon discounted-reward setting. In our setting, the samples obey the dynamics of the Markov decision process and, consequently, may have interdependencies. Under no assumption of independent samples, we provide a high-probability, polynomial sample complexity bound for vanilla model-based off-policy evaluation that requires partial or uniform coverage. We extend this result to the off-policy optimization under uniform coverage. As a comparison to the model-based approach, we analyze the sample complexity of off-policy evaluation with vanilla importance sampling in the infinite-horizon setting. Finally, we provide an estimator that outperforms the sample-mean estimator for almost deterministic dynamics that are prevalent in reinforcement learning. | [] | Train |
40,861 | 24 | Title: Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
Abstract: It is becoming increasingly imperative to design robust ML defenses. However, recent work has found that many defenses that initially resist state-of-the-art attacks can be broken by an adaptive adversary. In this work we take steps to simplify the design of defenses and argue that white-box defenses should eschew randomness when possible. We begin by illustrating a new issue with the deployment of randomized defenses that reduces their security compared to their deterministic counterparts. We then provide evidence that making defenses deterministic simplifies robustness evaluation, without reducing the effectiveness of a truly robust defense. Finally, we introduce a new defense evaluation framework that leverages a defense's deterministic nature to better evaluate its adversarial robustness. | [
41328,
26623
] | Test |
40,862 | 16 | Title: Q-Diffusion: Quantizing Diffusion Models
Abstract: Diffusion models have achieved great success in image synthesis through iterative noise estimation using deep neural networks. However, the slow inference, high memory consumption, and computation intensity of the noise estimation model hinder the efficient adoption of diffusion models. Although post-training quantization (PTQ) is considered a go-to compression method for other tasks, it does not work out-of-the-box on diffusion models. We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture of the diffusion models, which compresses the noise estimation network to accelerate the generation process. We identify the key difficulty of diffusion model quantization as the changing output distributions of noise estimation networks over multiple time steps and the bimodal activation distribution of the shortcut layers within the noise estimation network. We tackle these challenges with timestep-aware calibration and split shortcut quantization in this work. Experimental results show that our proposed method is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance (small FID change of at most 2.34 compared to>100 for traditional PTQ) in a training-free manner. Our approach can also be applied to text-guided image generation, where we can run stable diffusion in 4-bit weights with high generation quality for the first time. | [
19948,
500,
1141,
18516,
9880
] | Train |
40,863 | 16 | Title: Strong Baselines for Parameter Efficient Few-Shot Fine-tuning
Abstract: Few-shot classification (FSC) entails learning novel classes given only a few examples per class after a pre-training (or meta-training) phase on a set of base classes. Recent works have shown that simply fine-tuning a pre-trained Vision Transformer (ViT) on new test classes is a strong approach for FSC. Fine-tuning ViTs, however, is expensive in time, compute and storage. This has motivated the design of parameter efficient fine-tuning (PEFT) methods which fine-tune only a fraction of the Transformer's parameters. While these methods have shown promise, inconsistencies in experimental conditions make it difficult to disentangle their advantage from other experimental factors including the feature extractor architecture, pre-trained initialization and fine-tuning algorithm, amongst others. In our paper, we conduct a large-scale, experimentally consistent, empirical analysis to study PEFTs for few-shot image classification. Through a battery of over 1.8k controlled experiments on large-scale few-shot benchmarks including Meta-Dataset (MD) and ORBIT, we uncover novel insights on PEFTs that cast light on their efficacy in fine-tuning ViTs for few-shot classification. Through our controlled empirical study, we have two main findings: (i) Fine-tuning just the LayerNorm parameters (which we call LN-Tune) during few-shot adaptation is an extremely strong baseline across ViTs pre-trained with both self-supervised and supervised objectives, (ii) For self-supervised ViTs, we find that simply learning a set of scaling parameters for each attention matrix (which we call AttnScale) along with a domain-residual adapter (DRA) module leads to state-of-the-art performance (while being $\sim\!$ 9$\times$ more parameter-efficient) on MD. Our extensive empirical findings set strong baselines and call for rethinking the current design of PEFT methods for FSC. | [
45080,
38066,
46134
] | Train |
40,864 | 30 | Title: L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models
Abstract: The exploration of sentiment analysis in low-resource languages, such as Marathi, has been limited due to the availability of suitable datasets. In this work, we present L3Cube-MahaSent-MD, a multi-domain Marathi sentiment analysis dataset, with four different domains - movie reviews, general tweets, TV show subtitles, and political tweets. The dataset consists of around 60,000 manually tagged samples covering 3 distinct sentiments - positive, negative, and neutral. We create a sub-dataset for each domain comprising 15k samples. The MahaSent-MD is the first comprehensive multi-domain sentiment analysis dataset within the Indic sentiment landscape. We fine-tune different monolingual and multilingual BERT models on these datasets and report the best accuracy with the MahaBERT model. We also present an extensive in-domain and cross-domain analysis thus highlighting the need for low-resource multi-domain datasets. The data and models are available at https://github.com/l3cube-pune/MarathiNLP . | [] | Validation |
40,865 | 28 | Title: An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures
Abstract: The information bottleneck (IB) approach, initially introduced by Tishby et al. to assess the"compression--relevance"tradeoff for a remote source coding problem in communications, gains popularity recently in its application to modern machine learning (ML). Despite its seemingly simple form, the solution to IB problem remains largely unknown, and can only be assessed numerically even in the simple setting of Gaussian mixture model that is of fundamental significance in ML. In this paper, by combining ideas of hard quantization and soft nonlinear transformation, we derive closed-form achievable bounds for the IB problem under the above setting. The derived bounds establish surprisingly close behavior to the (numerically) optimal IB solution obtained by Blahut--Arimoto (BA) algorithm, on both synthetic and real-world (so non-Gaussian mixture) datasets, suggesting possibly wider applicability of our results. | [] | Validation |
40,866 | 16 | Title: Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations
Abstract: Deep neural networks have proven to be vulnerable to adversarial attacks in the form of adding specific perturbations on images to make wrong outputs. Designing stronger adversarial attack methods can help more reliably evaluate the robustness of DNN models. To release the harbor burden and improve the attack performance, auto machine learning (AutoML) has recently emerged as one successful technique to help automatically find the near-optimal adversarial attack strategy. However, existing works about AutoML for adversarial attacks only focus on $L_{\infty}$-norm-based perturbations. In fact, semantic perturbations attract increasing attention due to their naturalnesses and physical realizability. To bridge the gap between AutoML and semantic adversarial attacks, we propose a novel method called multi-objective evolutionary search of variable-length composite semantic perturbations (MES-VCSP). Specifically, we construct the mathematical model of variable-length composite semantic perturbations, which provides five gradient-based semantic attack methods. The same type of perturbation in an attack sequence is allowed to be performed multiple times. Besides, we introduce the multi-objective evolutionary search consisting of NSGA-II and neighborhood search to find near-optimal variable-length attack sequences. Experimental results on CIFAR10 and ImageNet datasets show that compared with existing methods, MES-VCSP can obtain adversarial examples with a higher attack success rate, more naturalness, and less time cost. | [] | Train |
40,867 | 16 | Title: High-Fidelity Clothed Avatar Reconstruction from a Single Image
Abstract: This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit model to learn the general shape in the canonical space of a person in a learning-based way, and at the second stage, we refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way. A hyper-network is utilized to generate a good initialization so that the convergence of the optimization process is greatly accelerated. Extensive experiments on various datasets show that the proposed CAR successfully produces high-fidelity avatars for arbitrarily clothed humans in real scenes. The codes will be released in https://github.com/TingtingLiao/CAR. | [
32384,
6739,
39340,
20350
] | Validation |
40,868 | 16 | Title: Does Visual Pretraining Help End-to-End Reasoning?
Abstract: We aim to investigate whether end-to-end learning of visual reasoning can be achieved with general-purpose neural networks, with the help of visual pretraining. A positive result would refute the common belief that explicit visual abstraction (e.g. object detection) is essential for compositional generalization on visual reasoning, and confirm the feasibility of a neural network"generalist"to solve visual recognition and reasoning tasks. We propose a simple and general self-supervised framework which"compresses"each video frame into a small set of tokens with a transformer network, and reconstructs the remaining frames based on the compressed temporal context. To minimize the reconstruction loss, the network must learn a compact representation for each image, as well as capture temporal dynamics and object permanence from temporal context. We perform evaluation on two visual reasoning benchmarks, CATER and ACRE. We observe that pretraining is essential to achieve compositional generalization for end-to-end visual reasoning. Our proposed framework outperforms traditional supervised pretraining, including image classification and explicit object detection, by large margins. | [
19787
] | Train |
40,869 | 24 | Title: QCRS: Improve Randomized Smoothing using Quasi-Concave Optimization
Abstract: Randomized smoothing is currently the state-of-the-art method that provides certified robustness for deep neural networks. However, it often cannot achieve an adequate certified region on real-world datasets. One way to obtain a larger certified region is to use an input-specific algorithm instead of using a fixed Gaussian filter for all data points. Several methods based on this idea have been proposed, but they either suffer from high computational costs or gain marginal improvement in certified radius. In this work, we show that by exploiting the quasiconvex problem structure, we can find the optimal certified radii for most data points with slight computational overhead. This observation leads to an efficient and effective input-specific randomized smoothing algorithm. We conduct extensive experiments and empirical analysis on Cifar10 and ImageNet. The results show that the proposed method significantly enhances the certified radii with low computational overhead. | [] | Validation |
40,870 | 16 | Title: Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field
Abstract: This work explores the use of 3D generative models to synthesize training data for 3D vision tasks. The key requirements of the generative models are that the generated data should be photorealistic to match the real-world scenarios, and the corresponding 3D attributes should be aligned with given sampling labels. However, we find that the recent NeRF-based 3D GANs hardly meet the above requirements due to their designed generation pipeline and the lack of explicit 3D supervision. In this work, we propose Lift3D, an inverted 2D-to-3D generation framework to achieve the data generation objectives. Lift3D has several merits compared to prior methods: (1) Unlike previous 3D GANs that the output resolution is fixed after training, Lift3D can generalize to any camera intrinsic with higher resolution and photorealistic output. (2) By lifting well-disentangled 2D GAN to 3D object NeRF, Lift3D provides explicit 3D information of generated objects, thus offering accurate 3D annotations for downstream tasks. We evaluate the effectiveness of our framework by augmenting autonomous driving datasets. Experimental results demonstrate that our data generation framework can effectively improve the performance of 3D object detectors. Code: len-li.github.io/lift3d-web | [
41680
] | Train |
40,871 | 23 | Title: On the Security Blind Spots of Software Composition Analysis
Abstract: Modern software heavily relies on the use of components. Those components are usually published in central repositories, and managed by build systems via dependencies. Due to issues around vulnerabilities, licenses and the propagation of bugs, the study of those dependencies is of utmost importance, and numerous software composition analysis tools have emerged to address those issues. A particular challenge are hidden dependencies that are the result of cloning or shading where code from a component is"inlined", and, in the case of shading, moved to different namespaces. We present an approach to detect cloned and shaded artifacts in the Maven repository. Our approach is lightweight in that it does not require the creation and maintenance of an index, and uses a custom AST-based clone detection. Our analysis focuses on the detection of vulnerabilities in artifacts which use cloning or shading. Starting with eight vulnerabilities with assigned CVEs (four of those classified as critical) and proof-of-vulnerability projects demonstrating the presence of a vulnerability in an artifact, we query the Maven repository and retrieve over 16k potential clones of the vulnerable artifacts. After running our analysis on this set, we detect 554 artifacts with the respective vulnerabilities (49 if versions are ignored). We synthesize a testable proof-of-vulnerability project for each of those. We demonstrate that existing SCA tools often miss these exposures. | [
19834
] | Train |
40,872 | 24 | Title: Certified Robust Control under Adversarial Perturbations
Abstract: Autonomous systems increasingly rely on machine learning techniques to transform high-dimensional raw inputs into predictions that are then used for decision-making and control. However, it is often easy to maliciously manipulate such inputs and, as a result, predictions. While effective techniques have been proposed to certify the robustness of predictions to adversarial input perturbations, such techniques have been disembodied from control systems that make downstream use of the predictions. We propose the first approach for composing robustness certification of predictions with respect to raw input perturbations with robust control to obtain certified robustness of control to adversarial input perturbations. We use a case study of adaptive vehicle control to illustrate our approach and show the value of the resulting end-to-end certificates through extensive experiments. | [] | Train |
40,873 | 16 | Title: Exploring External Knowledge for Accurate modeling of Visual and Language Problems
Abstract: The interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. The success can be partly attributed to the advancements of deep neural networks made in the sub-fields of AI such as Computer Vision (CV) and Natural Language Processing (NLP). The promising research area that this dissertation focuses on is visual and language understanding which involves many challenging tasks, i.e., classification, detection, segmentation, machine translation and captioning, etc. The state-of-the-art methods for solving these problems usually involves only two parts: source data and target labels, which is rather insufficient especially when the dataset is small. Meanwhile, many external tools or sources can provide extra useful information (external knowledge) that can help improve the performance of these methods. For example, a detection model has been applied to provide better object features than state-of-the-art ResNet for image captioning models. Inspired by this observation, we developed a methodology that we can first extract external knowledge and then integrate it with the original models. The external knowledge has to be extracted from the dataset, or can directly come from external, e.g., grammar rules or scene graphs. We apply this methodology to different AI tasks, including machine translation and image captioning and improve the original state-of-the-art models by a large margin. | [] | Validation |
40,874 | 30 | Title: Targeted Data Generation: Finding and Fixing Model Weaknesses
Abstract: Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust. Additional data collection may not help in addressing these weaknesses, as such challenging subgroups may be unknown to users, and underrepresented in the existing and new data. We propose Targeted Data Generation (TDG), a framework that automatically identifies challenging subgroups, and generates new data for those subgroups using large language models (LLMs) with a human in the loop. TDG estimates the expected benefit and potential harm of data augmentation for each subgroup, and selects the ones most likely to improve within-group performance without hurting overall performance. In our experiments, TDG significantly improves the accuracy on challenging subgroups for state-of-the-art sentiment analysis and natural language inference models, while also improving overall test accuracy. | [
7904,
27885
] | Test |
40,875 | 1 | Title: Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Prediction
Abstract: Volumetric video emerges as a new attractive video paradigm in recent years since it provides an immersive and interactive 3D viewing experience with six degree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetric videos require dense point clouds, voxels, meshes, or huge neural models to depict volumetric scenes, which results in a prohibitively high bandwidth burden for video delivery. Users' behavior analysis, especially the viewport and gaze analysis, then plays a significant role in prioritizing the content streaming within users' viewport and degrading the remaining content to maximize user QoE with limited bandwidth. Although understanding user behavior is crucial, to the best of our best knowledge, there are no available 3D volumetric video viewing datasets containing fine-grained user interactivity features, not to mention further analysis and behavior prediction. In this paper, we for the first time release a volumetric video viewing behavior dataset, with a large scale, multiple dimensions, and diverse conditions. We conduct an in-depth analysis to understand user behaviors when viewing volumetric videos. Interesting findings on user viewport, gaze, and motion preference related to different videos and users are revealed. We finally design a transformer-based viewport prediction model that fuses the features of both gaze and motion, which is able to achieve high accuracy at various conditions. Our prediction model is expected to further benefit volumetric video streaming optimization. Our dataset, along with the corresponding visualization tools is accessible at https://cuhksz-inml.github.io/user-behavior-in-vv-watching/ | [
30306
] | Train |
40,876 | 36 | Title: Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing
Abstract: This paper investigates the strategic decision-making capabilities of three Large Language Models (LLMs): GPT-3.5, GPT-4, and LLaMa-2, within the framework of game theory. Utilizing four canonical two-player games -- Prisoner's Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight -- we explore how these models navigate social dilemmas, situations where players can either cooperate for a collective benefit or defect for individual gain. Crucially, we extend our analysis to examine the role of contextual framing, such as diplomatic relations or casual friendships, in shaping the models' decisions. Our findings reveal a complex landscape: while GPT-3.5 is highly sensitive to contextual framing, it shows limited ability to engage in abstract strategic reasoning. Both GPT-4 and LLaMa-2 adjust their strategies based on game structure and context, but LLaMa-2 exhibits a more nuanced understanding of the games' underlying mechanics. These results highlight the current limitations and varied proficiencies of LLMs in strategic decision-making, cautioning against their unqualified use in tasks requiring complex strategic reasoning. | [
13510,
4071,
1292,
2833,
32921,
4634,
21724
] | Test |
40,877 | 22 | Title: Proving Logical Atomicity using Lock Invariants
Abstract: Logical atomicity has been widely accepted as a specification format for data structures in concurrent separation logic. While both lock-free and lock-based data structures have been verified against logically atomic specifications, most of the latter start with atomic specifications for the locks as well. In this paper, we compare this approach with one based on older lock-invariant-based specifications for locks. We show that we can still prove logically atomic specifications for data structures with fine-grained locking using these older specs, but the proofs are significantly more complicated than those that use atomic lock specifications. Our proof technique is implemented in the Verified Software Toolchain, which relies on older lock specifications for its soundness proof, and applied to C implementations of lock-based concurrent data structures. | [] | Validation |
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