OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Pricing Algorithms for Online Set Cover
Venue: arXiv cs.DS 2024
Year: 2024

We consider dynamic pricing algorithms as applied to the online set cover problem. In the dynamic pricing framework, we assume the standard client server model with the additional constraint that the server can only place prices over the resources they maintain, rather than authoritatively assign them. In response, incoming clients choose the resource which minimizes their disutility when taking into account these additional prices. Our main contributions are the categorization of online algorithms which can be mimicked via dynamic pricing algorithms and the identification of a strongly competitive deterministic algorithm with respect to the frequency parameter of the online set cover input.
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
Venue: arXiv cs.CV 2024
Year: 2024

We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, which reflects the model's accuracy and calibration when exposed to various perturbations; and (2) OOD reliability, which measures the model's ability to detect object classes that are unknown during training. The challenge attracted nearly 100 submissions from international teams representing notable research institutions. The results reveal interesting insights into the importance of large-scale pre-training and minimal architectural design in developing robust and reliable semantic segmentation models.
CAMAL: Optimizing LSM-trees via Active Learning
Venue: arXiv cs.DB 2024
Year: 2024

We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations. We introduce a new approach Camal, which boasts the following features: (1) ML-Aided: Camal is the first attempt to apply active learning to tune LSM-tree based key-value stores. The learning process is coupled with traditional cost models to improve the training process; (2) Decoupled Active Learning: backed by rigorous analysis, Camal adopts active learning paradigm based on a decoupled tuning of each parameter, which further accelerates the learning process; (3) Easy Extrapolation: Camal adopts an effective mechanism to incrementally update the model with the growth of the data size; (4) Dynamic Mode: Camal is able to tune LSM-tree online under dynamically changing workloads; (5) Significant System Improvement: By integrating Camal into a full system RocksDB, the system performance improves by 28% on average and up to 8x compared to a state-of-the-art RocksDB design.
The Complexity of Counting Turns in the Line-Based Dial-a-Ride Problem
Venue: arXiv cs.CC 2024
Year: 2024

Dial-a-Ride problems have been proposed to model the challenge to consolidate passenger transportation requests with a fleet of shared vehicles. The line-based Dial-a-Ride problem (LiDARP) is a variant where the passengers are transported along a fixed sequence of stops, with the option of taking shortcuts. In this paper we consider the LiDARP with the objective function to maximize the number of transported requests. We investigate the complexity of two optimization problems: the LiDARP, and the problem to determine the minimum number of turns needed in an optimal LiDARP solution, called the MinTurn problem. Based on a number of instance parameters and characteristics, we are able to state the boundary between polynomially solvable and NP-hard instances for both problems. Furthermore, we provide parameterized algorithms that are able to solve both the LiDARP and MinTurn problem.
Fast and Accurate Triangle Counting in Graph Streams Using Predictions
Venue: arXiv cs.DS 2024
Year: 2024

In this work, we present the first efficient and practical algorithm for estimating the number of triangles in a graph stream using predictions. Our algorithm combines waiting room sampling and reservoir sampling with a predictor for the heaviness of edges, that is, the number of triangles in which an edge is involved. As a result, our algorithm is fast, provides guarantees on the amount of memory used, and exploits the additional information provided by the predictor to produce highly accurate estimates. We also propose a simple and domain-independent predictor, based on the degree of nodes, that can be easily computed with one pass on a stream of edges when the stream is available beforehand. Our analytical results show that, when the predictor provides useful information on the heaviness of edges, it leads to estimates with reduced variance compared to the state-of-the-art, even when the predictions are far from perfect. Our experimental results show that, when analyzing a single graph stream, our algorithm is faster than the state-of-the-art for a given memory budget, while providing significantly more accurate estimates. Even more interestingly, when sequences of hundreds of graph streams are analyzed, our algorithm significantly outperforms the state-of-the-art using our simple degree-based predictor built by analyzing only the first graph of the sequence.
HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning
Venue: arXiv cs.CV 2024
Year: 2024

Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences the measurements at other stations. However, the dynamic and implicit nature of water flow paths makes it challenging to extract a priori knowledge of the connectivity structure. We hypothesize that terrain elevation significantly affects flow and connectivity. To incorporate this, we use LiDAR terrain elevation data encoded through a Vision Transformer (ViT). The ViT, which has demonstrated excellent performance in image classification by directly applying transformers to sequences of image patches, efficiently captures spatial features of terrain elevation. To account for both spatial and temporal features, we employ GRU blocks enhanced with graph convolution, a method widely used in the literature. We propose a hybrid graph learning structure that combines static and dynamic graph learning. A static graph, derived from transformer-encoded LiDAR data, captures terrain elevation relationships, while a dynamic graph adapts to temporal changes, improving the overall graph representation. We apply graph convolution in two layers through these static and dynamic graphs. Our method makes daily predictions up to 12 days ahead. Empirical results from multiple water stations in Quebec demonstrate that our method significantly reduces prediction error by an average of 10\% across all days, with greater improvements for longer forecasting horizons.
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized chain-of-thought technique using reinforcement learning strategies. While it has demonstrated surprisingly strong capabilities on various general language tasks, its performance in specialized fields such as medicine remains unknown. To this end, this report provides a comprehensive exploration of o1 on different medical scenarios, examining 3 key aspects: understanding, reasoning, and multilinguality. Specifically, our evaluation encompasses 6 tasks using data from 37 medical datasets, including two newly constructed and more challenging question-answering (QA) tasks based on professional medical quizzes from the New England Journal of Medicine (NEJM) and The Lancet. These datasets offer greater clinical relevance compared to standard medical QA benchmarks such as MedQA, translating more effectively into real-world clinical utility. Our analysis of o1 suggests that the enhanced reasoning ability of LLMs may (significantly) benefit their capability to understand various medical instructions and reason through complex clinical scenarios. Notably, o1 surpasses the previous GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two newly created complex QA scenarios. But meanwhile, we identify several weaknesses in both the model capability and the existing evaluation protocols, including hallucination, inconsistent multilingual ability, and discrepant metrics for evaluation. We release our raw data and model outputs at https://ucsc-vlaa.github.io/o1_medicine/ for future research.
The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Sign Language Processing (SLP) provides a foundation for a more inclusive future in language technology; however, the field faces several significant challenges that must be addressed to achieve practical, real-world applications. This work addresses multi-view isolated sign recognition (MV-ISR), and highlights the essential role of 3D awareness and geometry in SLP systems. We introduce the NGT200 dataset, a novel spatio-temporal multi-view benchmark, establishing MV-ISR as distinct from single-view ISR (SV-ISR). We demonstrate the benefits of synthetic data and propose conditioning sign representations on spatial symmetries inherent in sign language. Leveraging an SE(2) equivariant model improves MV-ISR performance by 8%-22% over the baseline.
Deciphering Cardiac Destiny: Unveiling Future Risks Through Cutting-Edge Machine Learning Approaches
Venue: arXiv cs.CY 2024
Year: 2024

Cardiac arrest remains a leading cause of death worldwide, necessitating proactive measures for early detection and intervention. This project aims to develop and assess predictive models for the timely identification of cardiac arrest incidents, utilizing a comprehensive dataset of clinical parameters and patient histories. Employing machine learning (ML) algorithms like XGBoost, Gradient Boosting, and Naive Bayes, alongside a deep learning (DL) approach with Recurrent Neural Networks (RNNs), we aim to enhance early detection capabilities. Rigorous experimentation and validation revealed the superior performance of the RNN model, which effectively captures complex temporal dependencies within the data. Our findings highlight the efficacy of these models in accurately predicting cardiac arrest likelihood, emphasizing the potential for improved patient care through early risk stratification and personalized interventions. By leveraging advanced analytics, healthcare providers can proactively mitigate cardiac arrest risk, optimize resource allocation, and improve patient outcomes. This research highlights the transformative potential of machine learning and deep learning techniques in managing cardiovascular risk and advances the field of predictive healthcare analytics.
Learning Task-Based Trainable Neuromorphic ADCs via Power-Aware Distillation
Venue: arXiv cs.NE 2024
Year: 2024

The ability to process signals in digital form depends on analog-to-digital converters (ADCs). Traditionally, ADCs are designed to ensure that the digital representation closely matches the analog signal. However, recent studies have shown that significant power and memory savings can be achieved through task-based acquisition, where the acquisition process is tailored to the downstream processing task. An emerging technology for task-based acquisition involves the use of memristors, which are considered key enablers for neuromorphic computing. Memristors can implement ADCs with tunable mappings, allowing adaptation to specific system tasks or power constraints. In this work, we study task-based acquisition for a generic classification task using memristive ADCs. We consider the unique characteristics of this such neuromorphic ADCs, including their power consumption and noisy read-write behavior, and propose a physically compliant model based on resistive successive approximation register ADCs integrated with memristor components, enabling the adjustment of quantization regions. To optimize performance, we introduce a data-driven algorithm that jointly tunes task-based memristive ADCs alongside both digital and analog processing. Our design addresses the inherent stochasticity of memristors through power-aware distillation, complemented by a specialized learning algorithm that adapts to their unique analog-to-digital mapping. The proposed approach is shown to enhance accuracy by up to 27% and reduce power consumption by up to 66% compared to uniform ADCs. Even under noisy conditions, our method achieves substantial gains, with accuracy improvements of up to 19% and power reductions of up to 57%. These results highlight the effectiveness of our power-aware neuromorphic ADCs in improving system performance across diverse tasks.
Transforming Redaction: How AI is Revolutionizing Data Protection
Venue: arXiv cs.CY 2024
Year: 2024

Document redaction is a crucial process in various sectors to safeguard sensitive information from unauthorized access and disclosure. Traditional manual redaction methods, such as those performed using Adobe Acrobat, are labor-intensive, error-prone, and time-consuming. With the burgeoning volume of digital documents, the demand for more efficient and accurate redaction techniques is intensifying. This study presents the findings from a controlled experiment that compares traditional manual redaction, a redaction tool powered by classical machine learning algorithm, and AI-assisted redaction tools (iDox.ai Redact). The results indicate that iDox.ai Redact significantly outperforms manual methods, achieving higher accuracy and faster completion times. Conversely, the competitor product, classical machine learning algorithm and with necessitates manual intervention for certain sensitive data types, did not exhibit a statistically significant improvement over manual redaction. These findings suggest that while advanced AI technologies like iDox.ai Redact can substantially enhance data protection practices by reducing human error and improving compliance with data protection regulations, there remains room for improvement in AI tools that do not fully automate the redaction process. Future research should aim to enhance AI capabilities and explore their applicability across various document types and professional settings.
Texture Discrimination via Hilbert Curve Path Based Information Quantifiers
Venue: arXiv cs.CV 2024
Year: 2024

The analysis of the spatial arrangement of colors and roughness/smoothness of figures is relevant due to its wide range of applications. This paper proposes a texture classification method that extracts data from images using the Hilbert curve. Three information theory quantifiers are then computed: permutation entropy, permutation complexity, and Fisher information measure. The proposal exhibits some important properties: (i) it allows to discriminate figures according to varying degrees of correlations (as measured by the Hurst exponent), (ii) it is invariant to rotation and symmetry transformations, (iii) it can be used either in black and white or color images. Validations have been made not only using synthetic images but also using the well-known Brodatz image database.
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
Venue: arXiv cs.IR 2024
Year: 2024

The Brazilian Constitution, known as the Citizen's Charter, provides mechanisms for citizens to petition the Judiciary, including the so-called special appeal. This specific type of appeal aims to standardize the legal interpretation of Brazilian legislation in cases where the decision contradicts federal laws. The handling of special appeals is a daily task in the Judiciary, regularly presenting significant demands in its courts. We propose a new method called GLARE, based on unsupervised machine learning, to help the legal analyst classify a special appeal on a topic from a list made available by the National Court of Brazil (STJ). As part of this method, we propose a modification of the graph-based LexRank algorithm, which we call Guided LexRank. This algorithm generates the summary of a special appeal. The degree of similarity between the generated summary and different topics is evaluated using the BM25 algorithm. As a result, the method presents a ranking of themes most appropriate to the analyzed special appeal. The proposed method does not require prior labeling of the text to be evaluated and eliminates the need for large volumes of data to train a model. We evaluate the effectiveness of the method by applying it to a special appeal corpus previously classified by human experts.
Fine-Tuning a Time Series Foundation Model with Wasserstein Loss
Venue: arXiv cs.LG 2024
Year: 2024

Inspired by recent advancements in large language models (LLMs) for Natural Language Processing (NLP), there has been a surge in research focused on developing foundational models for time series forecasting. One approach involves training LLM architectures on tokenized time series data using cross-entropy loss. Although this method has demonstrated promising results, cross-entropy loss is primarily designed for classification tasks and does not account for the distance between classes. To address this limitation, we propose using the Wasserstein loss for such architectures. To validate our approach, we fine-tuned a foundational time series model on $22$ zero-shot datasets, comparing the performance of cross-entropy loss with that of Wasserstein loss. Our results demonstrate that replacing cross-entropy loss with Wasserstein loss significantly improves point estimation.
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
Venue: arXiv cs.CR 2024
Year: 2024

As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. Despite growing academic interest in adversarial risks for generative AI, there is limited guidance tailored for practitioners to assess and mitigate these challenges in real-world environments. To address this, our contributions include: (1) a practical examination of red- and blue-teaming strategies for securing generative AI, (2) identification of key challenges and open questions in defense development and evaluation, and (3) the Attack Atlas, an intuitive framework that brings a practical approach to analyzing single-turn input attacks, placing it at the forefront for practitioners. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
Exploring Requirements Elicitation from App Store User Reviews Using Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Mobile applications have become indispensable companions in our daily lives. Spanning over the categories from communication and entertainment to healthcare and finance, these applications have been influential in every aspect. Despite their omnipresence, developing apps that meet user needs and expectations still remains a challenge. Traditional requirements elicitation methods like user interviews can be time-consuming and suffer from limited scope and subjectivity. This research introduces an approach leveraging the power of Large Language Models (LLMs) to analyze user reviews for automated requirements elicitation. We fine-tuned three well-established LLMs BERT, DistilBERT, and GEMMA, on a dataset of app reviews labeled for usefulness. Our evaluation revealed BERT's superior performance, achieving an accuracy of 92.40% and an F1-score of 92.39%, demonstrating its effectiveness in accurately classifying useful reviews. While GEMMA displayed a lower overall performance, it excelled in recall (93.39%), indicating its potential for capturing a comprehensive set of valuable user insights. These findings suggest that LLMs offer a promising avenue for streamlining requirements elicitation in mobile app development, leading to the creation of more user-centric and successful applications.
Framework for Robust Localization of UUVs and Mapping of Net Pens
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a general framework integrating vision and acoustic sensor data to enhance localization and mapping in highly dynamic and complex underwater environments, with a particular focus on fish farming. The proposed pipeline is suited to obtain both the net-relative pose estimates of an Unmanned Underwater Vehicle (UUV) and the depth map of the net pen purely based on vision data. Furthermore, this paper presents a method to estimate the global pose of an UUV fusing the net-relative pose estimates with acoustic data. The pipeline proposed in this paper showcases results on datasets obtained from industrial-scale fish farms and successfully demonstrates that the vision-based TRU-Depth model, when provided with sparse depth priors from the FFT method and combined with the Wavemap method, can estimate both net-relative and global position of the UUV in real time and generate detailed 3D maps suitable for autonomous navigation and inspection purposes.
Adapting Segment Anything Model for Unseen Object Instance Segmentation
Venue: arXiv cs.RO 2024
Year: 2024

Unseen Object Instance Segmentation (UOIS) is crucial for autonomous robots operating in unstructured environments. Previous approaches require full supervision on large-scale tabletop datasets for effective pretraining. In this paper, we propose UOIS-SAM, a data-efficient solution for the UOIS task that leverages SAM's high accuracy and strong generalization capabilities. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder, mitigating issues introduced by the SAM baseline, such as background confusion and over-segmentation, especially in scenarios involving occlusion and texture-rich objects. Extensive experimental results on OCID, OSD, and additional photometrically challenging datasets including PhoCAL and HouseCat6D, demonstrate that, even using only 10% of the training samples compared to previous methods, UOIS-SAM achieves state-of-the-art performance in unseen object segmentation, highlighting its effectiveness and robustness in various tabletop scenes.
AgriNeRF: Neural Radiance Fields for Agriculture in Challenging Lighting Conditions
Venue: arXiv cs.RO 2024
Year: 2024

Neural Radiance Fields (NeRFs) have shown significant promise in 3D scene reconstruction and novel view synthesis. In agricultural settings, NeRFs can serve as digital twins, providing critical information about fruit detection for yield estimation and other important metrics for farmers. However, traditional NeRFs are not robust to challenging lighting conditions, such as low-light, extreme bright light and varying lighting. To address these issues, this work leverages three different sensors: an RGB camera, an event camera and a thermal camera. Our RGB scene reconstruction shows an improvement in PSNR and SSIM by +2.06 dB and +8.3% respectively. Our cross-spectral scene reconstruction enhances downstream fruit detection by +43.0% in mAP50 and +61.1% increase in mAP50-95. The integration of additional sensors leads to a more robust and informative NeRF. We demonstrate that our multi-modal system yields high quality photo-realistic reconstructions under various tree canopy covers and at different times of the day. This work results in the development of a resilient NeRF, capable of performing well in visibly degraded scenarios, as well as a learnt cross-spectral representation, that is used for automated fruit detection.