OpenRaiser/Intern-Atlas huggingface dataset:
Surface Reconstruction Using Rotation Systems
Venue: arXiv cs.CG 2024
Year: 2024

Inspired by the seminal result that a graph and an associated rotation system uniquely determine the topology of a closed manifold, we propose a combinatorial method for reconstruction of surfaces from points. Our method constructs a spanning tree and a rotation system. Since the tree is trivially a planar graph, its rotation system determines a genus zero surface with a single face which we proceed to incrementally refine by inserting edges to split faces and thus merging them. In order to raise the genus, special handles are added by inserting edges between different faces and thus merging them. We apply our method to a wide range of input point clouds in order to investigate its effectiveness, and we compare our method to several other surface reconstruction methods. We find that our method offers better control over outlier classification, i.e. which points to include in the reconstructed surface, and also more control over the topology of the reconstructed surface.
Measuring productivity in networks: A game-theoretic approach
Venue: arXiv cs.GT 2024
Year: 2024

Measuring individual productivity (or equivalently distributing the overall productivity) in a network structure of workers displaying peer effects has been a subject of ongoing interest in many areas ranging from academia to industry. In this paper, we propose a novel approach based on cooperative game theory that takes into account the peer effects of worker productivity represented by a complete bipartite network of interactions. More specifically, we construct a series of cooperative games where the characteristic function of each coalition of workers is equal to the sum of each worker intrinsic productivity as well as the productivity of other workers within a distance discounted by an attenuation factor. We show that these (truncated) games are balanced and converge to a balanced game when the distance of influence grows large. We then provide an explicit formula for the Shapley value and propose an alternative coalitionally stable distribution of productivity which is computationally much more tractable than the Shapley value. Lastly, we characterize this alternative distribution based on three sensible properties of a logistic network. This analysis enhances our understanding of game-theoretic analysis within logistics networks, offering valuable insights into the peer effects' impact when assessing the overall productivity and its distribution among workers.
CoLe and LYS at BioASQ MESINESP8 Task: similarity based descriptor assignment in Spanish
Venue: arXiv cs.IR 2024
Year: 2024

In this paper, we describe our participation in the MESINESP Task of the BioASQ biomedical semantic indexing challenge. The participating system follows an approach based solely on conventional information retrieval tools. We have evaluated various alternatives for extracting index terms from IBECS/LILACS documents in order to be stored in an Apache Lucene index. Those indexed representations are queried using the contents of the article to be annotated and a ranked list of candidate labels is created from the retrieved documents. We also have evaluated a sort of limited Label Powerset approach which creates meta-labels joining pairs of DeCS labels with high co-occurrence scores, and an alternative method based on label profile matching. Results obtained in official runs seem to confirm the suitability of this approach for languages like Spanish.
Open-Loop and Feedback Nash Trajectories for Competitive Racing with iLQGames
Venue: arXiv cs.RO 2024
Year: 2024

Interaction-aware trajectory planning is crucial for closing the gap between autonomous racing cars and human racing drivers. Prior work has applied game theory as it provides equilibrium concepts for non-cooperative dynamic problems. With this contribution, we formulate racing as a dynamic game and employ a variant of iLQR, called iLQGames, to solve the game. iLQGames finds trajectories for all players that satisfy the equilibrium conditions for a linear-quadratic approximation of the game and has been previously applied in traffic scenarios. We analyze the algorithm's applicability for trajectory planning in racing scenarios and evaluate it based on interaction awareness, competitiveness, and safety. With the ability of iLQGames to solve for open-loop and feedback Nash equilibria, we compare the behavioral outcomes of the two equilibrium concepts in simple scenarios on a straight track section.
FuzzSlice: Pruning False Positives in Static Analysis Warnings Through Function-Level Fuzzing
Venue: arXiv cs.SE 2024
Year: 2024

Manual confirmation of static analysis reports is a daunting task. This is due to both the large number of warnings and the high density of false positives among them. Fuzzing techniques have been proposed to verify static analysis warnings. However, a major limitation is that fuzzing the whole project to reach all static analysis warnings is not feasible. This can take several days and exponential machine time to increase code coverage linearly. Therefore, we propose FuzzSlice, a novel framework that automatically prunes possible false positives among static analysis warnings. Unlike prior work that mostly focuses on confirming true positives among static analysis warnings, which requires end-to-end fuzzing, FuzzSlice focuses on ruling out potential false positives, which are the majority in static analysis reports. The key insight that we base our work on is that a warning that does not yield a crash when fuzzed at the function level in a given time budget is a possible false positive. To achieve this, FuzzSlice first aims to generate compilable code slices at the function level and then fuzzes these code slices instead of the entire binary. FuzzSlice is also unlikely to misclassify a true bug as a false positive because the crashing input can be reproduced by a fuzzer at the function level as well. We evaluate FuzzSlice on the Juliet synthetic dataset and real-world complex C projects. Our evaluation shows that the ground truth in the Juliet dataset had 864 false positives which were all detected by FuzzSlice. For the open-source repositories, we were able to get the developers from two of these open-source repositories to independently label these warnings. FuzzSlice automatically identifies 33 out of 53 false positives confirmed by developers in these two repositories. Thus FuzzSlice reduces false positives by 62.26% in the open-source repositories and by 100% in the Juliet dataset.
Reducing Optimism Bias in Incomplete Cooperative Games
Venue: arXiv cs.GT 2024
Year: 2024

Cooperative game theory has diverse applications in contemporary artificial intelligence, including domains like interpretable machine learning, resource allocation, and collaborative decision-making. However, specifying a cooperative game entails assigning values to exponentially many coalitions, and obtaining even a single value can be resource-intensive in practice. Yet simply leaving certain coalition values undisclosed introduces ambiguity regarding individual contributions to the collective grand coalition. This ambiguity often leads to players holding overly optimistic expectations, stemming from either inherent biases or strategic considerations, frequently resulting in collective claims exceeding the actual grand coalition value. In this paper, we present a framework aimed at optimizing the sequence for revealing coalition values, with the overarching goal of efficiently closing the gap between players' expectations and achievable outcomes in cooperative games. Our contributions are threefold: (i) we study the individual players' optimistic completions of games with missing coalition values along with the arising gap, and investigate its analytical characteristics that facilitate more efficient optimization; (ii) we develop methods to minimize this gap over classes of games with a known prior by disclosing values of additional coalitions in both offline and online fashion; and (iii) we empirically demonstrate the algorithms' performance in practical scenarios, together with an investigation into the typical order of revealing coalition values.
Digits micro-model for accurate and secure transactions
Venue: arXiv cs.LG 2024
Year: 2024

Automatic Speech Recognition (ASR) systems are used in the financial domain to enhance the caller experience by enabling natural language understanding and facilitating efficient and intuitive interactions. Increasing use of ASR systems requires that such systems exhibit very low error rates. The predominant ASR models to collect numeric data are large, general-purpose commercial models -- Google Speech-to-text (STT), or Amazon Transcribe -- or open source (OpenAI's Whisper). Such ASR models are trained on hundreds of thousands of hours of audio data and require considerable resources to run. Despite recent progress large speech recognition models, we highlight the potential of smaller, specialized "micro" models. Such light models can be trained perform well on number recognition specific tasks, competing with general models like Whisper or Google STT while using less than 80 minutes of training time and occupying at least an order of less memory resources. Also, unlike larger speech recognition models, micro-models are trained on carefully selected and curated datasets, which makes them highly accurate, agile, and easy to retrain, while using low compute resources. We present our work on creating micro models for multi-digit number recognition that handle diverse speaking styles reflecting real-world pronunciation patterns. Our work contributes to domain-specific ASR models, improving digit recognition accuracy, and privacy of data. An added advantage, their low resource consumption allows them to be hosted on-premise, keeping private data local instead uploading to an external cloud. Our results indicate that our micro-model makes less errors than the best-of-breed commercial or open-source ASRs in recognizing digits (1.8% error rate of our best micro-model versus 5.8% error rate of Whisper), and has a low memory footprint (0.66 GB VRAM for our model versus 11 GB VRAM for Whisper).
Fundamental Challenges in Cybersecurity and a Philosophy of Vulnerability-Guided Hardening
Venue: arXiv cs.CR 2024
Year: 2024

Research in cybersecurity may seem reactive, specific, ephemeral, and indeed ineffective. Despite decades of innovation in defense, even the most critical software systems turn out to be vulnerable to attacks. Time and again. Offense and defense forever on repeat. Even provable security, meant to provide an indubitable guarantee of security, does not stop attackers from finding security flaws. As we reflect on our achievements, we are left wondering: Can security be solved once and for all? In this paper, we take a philosophical perspective and develop the first theory of cybersecurity that explains what precisely and *fundamentally* prevents us from making reliable statements about the security of a software system. We substantiate each argument by demonstrating how the corresponding challenge is routinely exploited to attack a system despite credible assurances about the absence of security flaws. To make meaningful progress in the presence of these challenges, we introduce a philosophy of cybersecurity.
Online Transfer Learning for RSV Case Detection
Venue: arXiv cs.LG 2024
Year: 2024

Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications. However, utilizing this technique for classification tasks with sequential data often faces challenges, primarily attributed to the scarcity of class labels. To address this challenge, we introduce Multi-Source Adaptive Weighting (MSAW), an online multi-source transfer learning method. MSAW integrates a dynamic weighting mechanism into an ensemble framework, enabling automatic adjustment of weights based on the relevance and contribution of each source (representing historical knowledge) and target model (learning from newly acquired data). We demonstrate the effectiveness of MSAW by applying it to detect Respiratory Syncytial Virus cases within Emergency Department visits, utilizing multiple years of electronic health records from the University of Pittsburgh Medical Center. Our method demonstrates performance improvements over many baselines, including refining pre-trained models with online learning as well as three static weighting approaches, showing MSAW's capacity to integrate historical knowledge with progressively accumulated new data. This study indicates the potential of online transfer learning in healthcare, particularly for developing machine learning models that dynamically adapt to evolving situations where new data is incrementally accumulated.
GenFormer: A Deep-Learning-Based Approach for Generating Multivariate Stochastic Processes
Venue: arXiv cs.LG 2024
Year: 2024

Stochastic generators are essential to produce synthetic realizations that preserve target statistical properties. We propose GenFormer, a stochastic generator for spatio-temporal multivariate stochastic processes. It is constructed using a Transformer-based deep learning model that learns a mapping between a Markov state sequence and time series values. The synthetic data generated by the GenFormer model preserves the target marginal distributions and approximately captures other desired statistical properties even in challenging applications involving a large number of spatial locations and a long simulation horizon. The GenFormer model is applied to simulate synthetic wind speed data at various stations in Florida to calculate exceedance probabilities for risk management.
NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense SLAM
Venue: arXiv cs.CV 2024
Year: 2024

We introduce NeuV-SLAM, a novel dense simultaneous localization and mapping pipeline based on neural multiresolution voxels, characterized by ultra-fast convergence and incremental expansion capabilities. This pipeline utilizes RGBD images as input to construct multiresolution neural voxels, achieving rapid convergence while maintaining robust incremental scene reconstruction and camera tracking. Central to our methodology is to propose a novel implicit representation, termed VDF that combines the implementation of neural signed distance field (SDF) voxels with an SDF activation strategy. This approach entails the direct optimization of color features and SDF values anchored within the voxels, substantially enhancing the rate of scene convergence. To ensure the acquisition of clear edge delineation, SDF activation is designed, which maintains exemplary scene representation fidelity even under constraints of voxel resolution. Furthermore, in pursuit of advancing rapid incremental expansion with low computational overhead, we developed hashMV, a novel hash-based multiresolution voxel management structure. This architecture is complemented by a strategically designed voxel generation technique that synergizes with a two-dimensional scene prior. Our empirical evaluations, conducted on the Replica and ScanNet Datasets, substantiate NeuV-SLAM's exceptional efficacy in terms of convergence speed, tracking accuracy, scene reconstruction, and rendering quality.
RIDERS: Radar-Infrared Depth Estimation for Robust Sensing
Venue: arXiv cs.CV 2024
Year: 2024

Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning. Adverse weather conditions, including haze, dust, rain, snow, and darkness, introduce significant challenges to accurate dense depth estimation, thereby posing substantial safety risks in autonomous driving. These challenges are particularly pronounced for traditional depth estimation methods that rely on short electromagnetic wave sensors, such as visible spectrum cameras and near-infrared LiDAR, due to their susceptibility to diffraction noise and occlusion in such environments. To fundamentally overcome this issue, we present a novel approach for robust metric depth estimation by fusing a millimeter-wave Radar and a monocular infrared thermal camera, which are capable of penetrating atmospheric particles and unaffected by lighting conditions. Our proposed Radar-Infrared fusion method achieves highly accurate and finely detailed dense depth estimation through three stages, including monocular depth prediction with global scale alignment, quasi-dense Radar augmentation by learning Radar-pixels correspondences, and local scale refinement of dense depth using a scale map learner. Our method achieves exceptional visual quality and accurate metric estimation by addressing the challenges of ambiguity and misalignment that arise from directly fusing multi-modal long-wave features. We evaluate the performance of our approach on the NTU4DRadLM dataset and our self-collected challenging ZJU-Multispectrum dataset. Especially noteworthy is the unprecedented robustness demonstrated by our proposed method in smoky scenarios. Our code will be released at \url{https://github.com/MMOCKING/RIDERS}.
All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark
Venue: arXiv cs.CV 2024
Year: 2024

Multi-modality image fusion (MMIF) combines complementary information from different image modalities to provide a more comprehensive and objective interpretation of scenes. However, existing MMIF methods lack the ability to resist different weather interferences in real-world scenes, preventing them from being useful in practical applications such as autonomous driving. To bridge this research gap, we proposed an all-weather MMIF model. Achieving effective multi-tasking in this context is particularly challenging due to the complex and diverse nature of weather conditions. A key obstacle lies in the 'black box' nature of current deep learning architectures, which restricts their multi-tasking capabilities. To overcome this, we decompose the network into two modules: a fusion module and a restoration module. For the fusion module, we introduce a learnable low-rank representation model to decompose images into low-rank and sparse components. This interpretable feature separation allows us to better observe and understand images. For the restoration module, we propose a physically-aware clear feature prediction module based on an atmospheric scattering model that can deduce variations in light transmittance from both scene illumination and reflectance. We also construct a large-scale multi-modality dataset with 100,000 image pairs across rain, haze, and snow conditions, covering various degradation levels and diverse scenes to thoroughly evaluate image fusion methods in adverse weather. Experimental results in both real-world and synthetic scenes show that the proposed algorithm excels in detail recovery and multi-modality feature extraction. The code is available at https://github.com/ixilai/AWFusion.
Do Moral Judgment and Reasoning Capability of LLMs Change with Language? A Study using the Multilingual Defining Issues Test
Venue: arXiv cs.CL 2024
Year: 2024

This paper explores the moral judgment and moral reasoning abilities exhibited by Large Language Models (LLMs) across languages through the Defining Issues Test. It is a well known fact that moral judgment depends on the language in which the question is asked. We extend the work of beyond English, to 5 new languages (Chinese, Hindi, Russian, Spanish and Swahili), and probe three LLMs -- ChatGPT, GPT-4 and Llama2Chat-70B -- that shows substantial multilingual text processing and generation abilities. Our study shows that the moral reasoning ability for all models, as indicated by the post-conventional score, is substantially inferior for Hindi and Swahili, compared to Spanish, Russian, Chinese and English, while there is no clear trend for the performance of the latter four languages. The moral judgments too vary considerably by the language.
Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran
Venue: arXiv cs.LG 2024
Year: 2024

High resolution mapping of PM2.5 concentration over Tehran city is challenging because of the complicated behavior of numerous sources of pollution and the insufficient number of ground air quality monitoring stations. Alternatively, high resolution satellite Aerosol Optical Depth (AOD) data can be employed for high resolution mapping of PM2.5. For this purpose, different data-driven methods have been used in the literature. Recently, deep learning methods have demonstrated their ability to estimate PM2.5 from AOD data. However, these methods have several weaknesses in solving the problem of estimating PM2.5 from satellite AOD data. In this paper, the potential of the deep ensemble forest method for estimating the PM2.5 concentration from AOD data was evaluated. The results showed that the deep ensemble forest method with R2 = 0.74 gives a higher accuracy of PM2.5 estimation than deep learning methods (R2 = 0.67) as well as classic data-driven methods such as random forest (R2 = 0.68). Additionally, the estimated values of PM2.5 using the deep ensemble forest algorithm were used along with ground data to generate a high resolution map of PM2.5. Evaluation of the produced PM2.5 map revealed the good performance of the deep ensemble forest for modeling the variation of PM2.5 in the city of Tehran.
Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning
Venue: arXiv cs.AI 2024
Year: 2024

In achieving effective emergency response, the timely acquisition of environmental information, seamless command data transmission, and prompt decision-making are crucial. This necessitates the establishment of a resilient emergency communication dedicated network, capable of providing communication and sensing services even in the absence of basic infrastructure. In this paper, we propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I). The framework incorporates mechanisms for emergency computing, caching, integrated communication and sensing, and intelligence empowerment. E-SC3I ensures rapid access to a large user base, reliable data transmission over unstable links, and dynamic network deployment in a changing environment. However, these advantages come at the cost of significant computation overhead. Therefore, we specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning. Experimental results demonstrate our method's ability to achieve rapid inference of AI models with constrained computational and communication resources.
Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representation
Venue: arXiv cs.AI 2024
Year: 2024

Language models demonstrate fundamental abilities in syntax, semantics, and reasoning, though their performance often depends significantly on the inputs they process. This study introduces TSIS (Simplified TSID) and its variants:TSISD (TSIS with Depth-First Search), TSISO (TSIS in Order), and TSISR (TSIS in Random), as integral components of the t-SMILES framework. These additions complete the framework's design, providing diverse approaches to molecular representation. Through comprehensive analysis and experiments employing deep generative models, including GPT, diffusion models, and reinforcement learning, the findings reveal that the hierarchical structure of t-SMILES is more straightforward to parse than initially anticipated. Furthermore, t-SMILES consistently outperforms other linear representations such as SMILES, SELFIES, and SAFE, demonstrating superior convergence speed and enhanced generalization capabilities.