QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1

Earthquake monitoring is necessary to promptly identify the affected areas, the severity of the events, and, finally, to estimate damages and plan the actions needed for the restoration process. The use of seismic stations to monitor the strength and origin of earthquakes is limited when dealing with remote areas (we cannot have global capillary coverage). Identification and analysis of all affected areas is mandatory to support areas not monitored by traditional stations. Using social media images in crisis management has proven effective in various situations. However, they are still limited by the possibility of using communication infrastructures in case of an earthquake and by the presence of people in the area. Moreover, social media images and messages cannot be used to estimate the actual severity of earthquakes and their characteristics effectively. The employment of satellites to monitor changes around the globe grants the possibility of exploiting instrumentation that is not limited by the visible spectrum, the presence of land infrastructures, and people in the affected areas. In this work, we propose a new dataset composed of images taken from Sentinel-1 and a new series of tasks to help monitor earthquakes from a new detailed view. Coupled with the data, we provide a series of traditional machine learning and deep learning models as baselines to assess the effectiveness of ML-based models in earthquake analysis

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

Forest wildfires represent one of the catastrophic events that, over the last decades, have caused huge environmental and humanitarian damage. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools

DANTE at GeoLingIt: Dialect-Aware Multi-Granularity Pre-training for Locating Tweets within Italy

This paper presents an NLP research system designed to geolocate tweets within Italy, a country renowned for its diverse linguistic landscape. Our methodology consists of a two-step process involving pre-training and fine-tuning phases. In the pre-training step, we take a semi-supervised approach and introduce two additional tasks. The primary objective of these tasks is to provide the language model with comprehensive knowledge of language varieties, focusing on both the sentence and token levels. Subsequently, during the fine-tuning phase, the model is adapted explicitly for two subtasks: coarse- and fine-grained variety geolocation. To evaluate the effectiveness of our methodology, we participate in the GeoLingIt 2023 shared task and assess our model’s performance using standard metrics. Ablation studies demonstrate the crucial role of thepre-training step in enhancing the model’s performance on both task

Designing Logic Tensor Networks for Visual Sudoku puzzle classification

Given the increasing importance of the neurosymbolic (NeSy) approach in artificial intelligence, there is a growing interest in studying benchmarks specifically designed to emphasize the ability of AI systems to combine low-level representation learning with high-level symbolic reasoning. One such recent benchmark is Visual Sudoku Puzzle Classification, that combines visual perception with relational constraints. In this work, we investigate the application of Logic Tensork Networks (LTNs) to the Visual Sudoku Classification task and discuss various alternatives in terms of logical constraint formulation, integration with the perceptual module and training procedure

A Model-based Curriculum Learning Strategy for Training SegFormer

The use of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision opened up new tracks in this area. However, a significant drawback of these models is the large amount of data required to obtain competitive results. This critical issue limits their application in domains where large labeled data collections are unavailable. Some strategies have been proposed to use relatively limited labeled data sets to train CNN-based models. Curriculum learning is one of the currently available strategies to train deep learning models faster and with less data. However, to our knowledge, curriculum learning techniques have never been used at the model level to support ViT training for semantic segmentation. We propose a new curriculum learning technique tailored to ViT models to fill this gap. The results show the effectiveness of the proposed strategy in training ViT models from scratch to solve the semantic segmentation task

Vision Transformers for Burned Area Delineation

The automatic identification of burned areas is an important task that was mainly managed manually or semi-automatically in the past. In the last years, thanks to the availability of novel deep neural network architectures, automatic segmentation solutions have been proposed also in the emergency management domain. The most recent works in burned area delineation leverage on Convolutional Neural Networks (CNNs) to automatically identify regions that were previously affected by forest wildfires. A largely adopted segmentation model, U-Net, demonstrated good performances for the task under analysis, but in some cases a high overestimation of burned areas is given, leading to low precision scores. Given the recent advances in the field of NLP and the first successes also in the vision domain, in this paper we investigate the adoption of vision transformers for semantic segmentation to address the burned area identification task. In particular, we explore the SegFormer architecture with two of its variants: the smallest MiT-B0 and the intermediate one MiT-B3. The experimental results show that SegFormer provides better predictions, with higher precision and F1 score, but also better performance in terms of the number of parameters with respect to CNNs

DQNC2S: DQN-based Cross-stream Crisis event Summarizer

Summarizing multiple disaster-relevant data streams simultaneously is particularly challenging as existing Retrieve&Re-ranking strategies suffer from the inherent redundancy of multi-stream data and limited scalability in a multi-query setting. This work proposes an online approach to crisis timeline generation based on weak annotation with Deep Q-Networks. It selects on-the-fly the relevant pieces of text without requiring neither human annotations nor content re-ranking. This makes the inference time independent of the number of input queries. The proposed approach also incorporates a redundancy filter into the reward function to effectively handle cross-stream content overlaps. The achieved ROUGE and BERTScore results are superior to those of best-performing models on the CrisisFACTS 2022 benchmark

Droplet Digital PCR for BCR–ABL1 Monitoring in Diagnostic Routine: Ready to Start?

SIMPLE SUMMARY: The introduction to clinical practice of a treatment-free remission approach in chronic myeloid leukemia patients with a stable deep molecular response highlighted how crucial it is to monitor the molecular levels of BCR–ABL1 as accurately and precisely as possible. In this context, the droplet digital PCR (ddPCR) presents an alternative methodology for such quantification. To hypothesize the introduction of this technology in routine practice, we performed a multicentric study that compares ddPCR with the standard methodology currently used. Our results demonstrate that the use of ddPCR in clinical practice is feasible and could be beneficial. ABSTRACT: BCR–ABL1 mRNA levels represent the key molecular marker for the evaluation of minimal residual disease (MRD) in chronic myeloid leukemia (CML) patients and real-time quantitative PCR (RT-qPCR) is currently the standard method to monitor it. In the era of tyrosine kinase inhibitors (TKIs) discontinuation, droplet digital PCR (ddPCR) has emerged to provide a more precise detection of MRD. To hypothesize the use of ddPCR in clinical practice, we designed a multicentric study to evaluate the potential value of ddPCR in the diagnostic routine. Thirty-seven RNA samples from CML patients and five from healthy donors were analyzed using both ddPCR QXDx(TM) BCR-ABL %IS Kit and LabNet-approved RT-qPCR methodologies in three different Italian laboratories. Our results show that ddPCR has a good agreement with RT-qPCR, but it is more precise to quantify BCR–ABL1 transcript levels. Furthermore, we did not find differences between duplicate or quadruplicate analysis in terms of BCR–ABL1% IS values. Droplet digital PCR could be confidently introduced into the diagnostic routine as a complement to the RT-qPCR