pwc-archive/papers-with-abstracts huggingface dataset:
Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning
Authors: Rohaifa Khaldi, Siham Tabik, Sergio Puertas-Ruiz, Julio Peñas de Giles, José Antonio Hódar Correa, Regino Zamora, Domingo Alcaraz Segura
Date: 2024-01-31 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

Monitoring the distribution and size of long-living large shrubs, such as junipers, is crucial for assessing the long-term impacts of global change on high-mountain ecosystems. While deep learning models have shown remarkable success in object segmentation, adapting these models to detect shrub species with polymorphic nature remains challenging. In this research, we release a large dataset of individual shrub delineations on freely available satellite imagery and use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve (Sierra Nevada, Spain). To optimize performance, we introduced a novel dual data construction approach: using photo-interpreted (PI) data for model development and fieldwork (FW) data for validation. To account for the polymorphic nature of junipers during model evaluation, we developed a soft version of the Intersection over Union metric. Finally, we assessed the uncertainty of the resulting map in terms of canopy cover and density of shrubs per size class. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data. The R2 and RMSE of the observed versus predicted relationship were 0.63 and 6.67% for canopy cover, and 0.90 and 20.62 for shrub density. The greater density of larger shrubs in lower altitudes and smaller shrubs in higher altitudes observed in the model outputs was also present in the PI and FW data, suggesting an altitudinal uplift in the optimal performance of the species. This study demonstrates that deep learning applied on freely available high-resolution satellite imagery is useful to detect medium to large shrubs of high ecological value at the regional scale, which could be expanded to other high-mountains worldwide and to historical and forthcoming imagery.
From Training-Free to Adaptive: Empirical Insights into MLLMs' Understanding of Detection Information
Authors: Qirui Jiao, Daoyuan Chen, Yilun Huang, Yaliang Li, Ying Shen
Date: 2024-01-31 00:00:00
Tasks: Hallucination, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR), Visual Question Answering

Despite the impressive capabilities of Multimodal Large Language Models (MLLMs) in integrating text and image modalities, challenges remain in accurately interpreting detailed visual elements. Vision detection models excel at recognizing fine-grained image details, prompting researchers to use them to enhance MLLMs. One effective strategy is to infuse detection information in text format, which has proven simple and effective. However, most studies utilize this method without training, leaving the potential of adaptive training largely unexplored. Adaptive training could significantly enhance MLLMs' comprehension of unique inputs while filtering out irrelevant information. This paper addresses the crucial question: How does training impact MLLMs' understanding of infused textual detection information? We systematically experiment with various representative models to evaluate the effects of training-free, retraining, and fine-tuning strategies. We also examine the influence of training on MLLMs' original abilities and the interchangeability of detection models. Our findings indicate that fine-tuning a pre-trained MLLM to incorporate textual detection information delivers superior results compared to training-free and retraining methods, improving performance by 6.71% across 10 widely recognized benchmarks. Furthermore, fine-tuning enables MLLMs to retain performance enhancements even when detection models are swapped, indicating improved understanding of formatted textual data. We release our codes to support further exploration of fusion strategies for vision detection models and the enhancement of MLLMs' fine-grained multimodal capabilities.
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning
Authors: Arnau Pastor, Pau Escofet, Sahar Ben Rached, Eduard Alarcón, Pere Barlet-Ros, Sergi Abadal
Date: 2024-01-31 00:00:00
Tasks: Deep Reinforcement Learning, graph partitioning, Quantum Circuit Mapping, reinforcement-learning, Reinforcement Learning

Quantum computing holds immense potential for solving classically intractable problems by leveraging the unique properties of quantum mechanics. The scalability of quantum architectures remains a significant challenge. Multi-core quantum architectures are proposed to solve the scalability problem, arising a new set of challenges in hardware, communications and compilation, among others. One of these challenges is to adapt a quantum algorithm to fit within the different cores of the quantum computer. This paper presents a novel approach for circuit partitioning using Deep Reinforcement Learning, contributing to the advancement of both quantum computing and graph partitioning. This work is the first step in integrating Deep Reinforcement Learning techniques into Quantum Circuit Mapping, opening the door to a new paradigm of solutions to such problems.
Understanding polysemanticity in neural networks through coding theory
Authors: Simon C. Marshall, Jan H. Kirchner
Date: 2024-01-31 00:00:00

Despite substantial efforts, neural network interpretability remains an elusive goal, with previous research failing to provide succinct explanations of most single neurons' impact on the network output. This limitation is due to the polysemantic nature of most neurons, whereby a given neuron is involved in multiple unrelated network states, complicating the interpretation of that neuron. In this paper, we apply tools developed in neuroscience and information theory to propose both a novel practical approach to network interpretability and theoretical insights into polysemanticity and the density of codes. We infer levels of redundancy in the network's code by inspecting the eigenspectrum of the activation's covariance matrix. Furthermore, we show how random projections can reveal whether a network exhibits a smooth or non-differentiable code and hence how interpretable the code is. This same framework explains the advantages of polysemantic neurons to learning performance and explains trends found in recent results by Elhage et al.~(2022). Our approach advances the pursuit of interpretability in neural networks, providing insights into their underlying structure and suggesting new avenues for circuit-level interpretability.
MelNet: A Real-Time Deep Learning Algorithm for Object Detection
Authors: Yashar Azadvatan, Murat Kurt
Date: 2024-01-31 00:00:00
Tasks: Deep Learning, Object, object-detection, Object Detection, Transfer Learning

In this study, a novel deep learning algorithm for object detection, named MelNet, was introduced. MelNet underwent training utilizing the KITTI dataset for object detection. Following 300 training epochs, MelNet attained an mAP (mean average precision) score of 0.732. Additionally, three alternative models -YOLOv5, EfficientDet, and Faster-RCNN-MobileNetv3- were trained on the KITTI dataset and juxtaposed with MelNet for object detection. The outcomes underscore the efficacy of employing transfer learning in certain instances. Notably, preexisting models trained on prominent datasets (e.g., ImageNet, COCO, and Pascal VOC) yield superior results. Another finding underscores the viability of creating a new model tailored to a specific scenario and training it on a specific dataset. This investigation demonstrates that training MelNet exclusively on the KITTI dataset also surpasses EfficientDet after 150 epochs. Consequently, post-training, MelNet's performance closely aligns with that of other pre-trained models.
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning
Authors: Erwan Escudie, Laetitia Matignon, Jacques Saraydaryan
Date: 2024-01-31 00:00:00
Tasks: Deep Reinforcement Learning, Graph Neural Network, reinforcement-learning, Reinforcement Learning (RL), Robot Navigation, Social Navigation

Learning robot navigation strategies among pedestrian is crucial for domain based applications. Combining perception, planning and prediction allows us to model the interactions between robots and pedestrians, resulting in impressive outcomes especially with recent approaches based on deep reinforcement learning (RL). However, these works do not consider multi-robot scenarios. In this paper, we present MultiSoc, a new method for learning multi-agent socially aware navigation strategies using RL. Inspired by recent works on multi-agent deep RL, our method leverages graph-based representation of agent interactions, combining the positions and fields of view of entities (pedestrians and agents). Each agent uses a model based on two Graph Neural Network combined with attention mechanisms. First an edge-selector produces a sparse graph, then a crowd coordinator applies node attention to produce a graph representing the influence of each entity on the others. This is incorporated into a model-free RL framework to learn multi-agent policies. We evaluate our approach on simulation and provide a series of experiments in a set of various conditions (number of agents / pedestrians). Empirical results show that our method learns faster than social navigation deep RL mono-agent techniques, and enables efficient multi-agent implicit coordination in challenging crowd navigation with multiple heterogeneous humans. Furthermore, by incorporating customizable meta-parameters, we can adjust the neighborhood density to take into account in our navigation strategy.
ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields
Authors: Edward Bartrum, Thu Nguyen-Phuoc, Chris Xie, Zhengqin Li, Numair Khan, Armen Avetisyan, Douglas Lanman, Lei Xiao
Date: 2024-01-31 00:00:00
Tasks: 3D scene Editing, Object

We introduce ReplaceAnything3D model (RAM3D), a novel text-guided 3D scene editing method that enables the replacement of specific objects within a scene. Given multi-view images of a scene, a text prompt describing the object to replace, and a text prompt describing the new object, our Erase-and-Replace approach can effectively swap objects in the scene with newly generated content while maintaining 3D consistency across multiple viewpoints. We demonstrate the versatility of ReplaceAnything3D by applying it to various realistic 3D scenes, showcasing results of modified foreground objects that are well-integrated with the rest of the scene without affecting its overall integrity.
Efficient Subseasonal Weather Forecast using Teleconnection-informed Transformers
Authors: Shan Zhao, Zhitong Xiong, Xiao Xiang Zhu
Date: 2024-01-31 00:00:00
Tasks: GPU, Weather Forecasting

Subseasonal forecasting, which is pivotal for agriculture, water resource management, and early warning of disasters, faces challenges due to the chaotic nature of the atmosphere. Recent advances in machine learning (ML) have revolutionized weather forecasting by achieving competitive predictive skills to numerical models. However, training such foundation models requires thousands of GPU days, which causes substantial carbon emissions and limits their broader applicability. Moreover, ML models tend to fool the pixel-wise error scores by producing smoothed results which lack physical consistency and meteorological meaning. To deal with the aforementioned problems, we propose a teleconnection-informed transformer. Our architecture leverages the pretrained Pangu model to achieve good initial weights and integrates a teleconnection-informed temporal module to improve predictability in an extended temporal range. Remarkably, by adjusting 1.1% of the Pangu model's parameters, our method enhances predictability on four surface and five upper-level atmospheric variables at a two-week lead time. Furthermore, the teleconnection-filtered features improve the spatial granularity of outputs significantly, indicating their potential physical consistency. Our research underscores the importance of atmospheric and oceanic teleconnections in driving future weather conditions. Besides, it presents a resource-efficient pathway for researchers to leverage existing foundation models on versatile downstream tasks.
Making Sense of Knowledge Intensive Processes: an Oil & Gas Industry Scenario
Authors: Juliana Jansen Ferreira, Vinícius Segura, Ana Fucs, Rogério de Paula
Date: 2024-01-31 00:00:00
Tasks: Decision Making

Sensemaking is a constant and ongoing process by which people associate meaning to experiences. It can be an individual process, known as abduction, or a group process by which people give meaning to collective experiences. The sensemaking of a group is influenced by the abduction process of each person about the experience. Every collaborative process needs some level of sensemaking to show results. For a knowledge intensive process, sensemaking is central and related to most of its tasks. We present findings from a fieldwork executed in knowledge intensive process from the Oil and Gas industry. Our findings indicated that different types of knowledge can be combined to compose the result of a sensemaking process (e.g. decision, the need for more discussion, etc.). This paper presents an initial set of knowledge types that can be combined to compose the result of the sensemaking of a collaborative decision making process. We also discuss ideas for using systems powered by Artificial Intelligence to support sensemaking processes.
Manipulating Predictions over Discrete Inputs in Machine Teaching
Authors: Xiaodong Wu, Yufei Han, Hayssam Dahrouj, Jianbing Ni, Zhenwen Liang, Xiangliang Zhang
Date: 2024-01-31 00:00:00
Tasks: Combinatorial Optimization

Machine teaching often involves the creation of an optimal (typically minimal) dataset to help a model (referred to as the `student') achieve specific goals given by a teacher. While abundant in the continuous domain, the studies on the effectiveness of machine teaching in the discrete domain are relatively limited. This paper focuses on machine teaching in the discrete domain, specifically on manipulating student models' predictions based on the goals of teachers via changing the training data efficiently. We formulate this task as a combinatorial optimization problem and solve it by proposing an iterative searching algorithm. Our algorithm demonstrates significant numerical merit in the scenarios where a teacher attempts at correcting erroneous predictions to improve the student's models, or maliciously manipulating the model to misclassify some specific samples to the target class aligned with his personal profits. Experimental results show that our proposed algorithm can have superior performance in effectively and efficiently manipulating the predictions of the model, surpassing conventional baselines.
Explainable Benchmarking for Iterative Optimization Heuristics
Authors: Niki van Stein, Diederick Vermetten, Anna V. Kononova, Thomas Bäck
Date: 2024-01-31 00:00:00
Tasks: Benchmarking, Evolutionary Algorithms

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. In most current research into heuristic optimization algorithms, only a very limited number of scenarios, algorithm configurations and hyper-parameter settings are explored, leading to incomplete and often biased insights and results. This paper presents a novel approach we call explainable benchmarking. Introducing the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms and the impact of their different components and hyper-parameters. We showcase the framework in the context of two modular optimization frameworks. Through this framework, we examine the impact of different algorithmic components and configurations, offering insights into their performance across diverse scenarios. We provide a systematic method for evaluating and interpreting the behaviour and efficiency of iterative optimization heuristics in a more transparent and comprehensible manner, allowing for better benchmarking and algorithm design.
Global-Liar: Factuality of LLMs over Time and Geographic Regions
Authors: Shujaat Mirza, Bruno Coelho, Yuyuan Cui, Christina Pöpper, Damon McCoy
Date: 2024-01-31 00:00:00
Tasks: Fairness, Information Retrieval, Misinformation

The increasing reliance on AI-driven solutions, particularly Large Language Models (LLMs) like the GPT series, for information retrieval highlights the critical need for their factuality and fairness, especially amidst the rampant spread of misinformation and disinformation online. Our study evaluates the factual accuracy, stability, and biases in widely adopted GPT models, including GPT-3.5 and GPT-4, contributing to reliability and integrity of AI-mediated information dissemination. We introduce 'Global-Liar,' a dataset uniquely balanced in terms of geographic and temporal representation, facilitating a more nuanced evaluation of LLM biases. Our analysis reveals that newer iterations of GPT models do not always equate to improved performance. Notably, the GPT-4 version from March demonstrates higher factual accuracy than its subsequent June release. Furthermore, a concerning bias is observed, privileging statements from the Global North over the Global South, thus potentially exacerbating existing informational inequities. Regions such as Africa and the Middle East are at a disadvantage, with much lower factual accuracy. The performance fluctuations over time suggest that model updates may not consistently benefit all regions equally. Our study also offers insights into the impact of various LLM configuration settings, such as binary decision forcing, model re-runs and temperature, on model's factuality. Models constrained to binary (true/false) choices exhibit reduced factuality compared to those allowing an 'unclear' option. Single inference at a low temperature setting matches the reliability of majority voting across various configurations. The insights gained highlight the need for culturally diverse and geographically inclusive model training and evaluation. This approach is key to achieving global equity in technology, distributing AI benefits fairly worldwide.
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
Authors: Wenshuo Chao, Zhaopeng Qiu, Likang Wu, Zhuoning Guo, Zhi Zheng, HengShu Zhu, Hao liu
Date: 2024-01-31 00:00:00
Tasks: Decoder, Graph Learning, Time Series, Time Series Forecasting

The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market. Existing efforts in this area either rely on domain-expert knowledge or regarding skill evolution as a simplified time series forecasting problem. However, both approaches overlook the sophisticated relationships among different skills and the inner-connection between skill demand and supply variations. In this paper, we propose a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework for joint skill demand-supply prediction. Specifically, CHGH is an encoder-decoder network consisting of i) a cross-view graph encoder to capture the interconnection between skill demand and supply, ii) a hierarchical graph encoder to model the co-evolution of skills from a cluster-wise perspective, and iii) a conditional hyper-decoder to jointly predict demand and supply variations by incorporating historical demand-supply gaps. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed framework compared to seven baselines and the effectiveness of the three modules.
Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation
Authors: Rozhan Ahmadi, Shohreh Kasaei
Date: 2024-01-31 00:00:00
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision. Most existing methods have addressed the challenges arising from the lack of spatial information in these labels by focusing on facilitating supervised learning through the generation of pseudo-labels from class activation maps (CAMs). Due to the localized pattern detection of CNNs, CAMs often emphasize only the most discriminative parts of an object, making it challenging to accurately distinguish foreground objects from each other and the background. Recent studies have shown that Vision Transformer (ViT) features, due to their global view, are more effective in capturing the scene layout than CNNs. However, the use of hierarchical ViTs has not been extensively explored in this field. This work explores the use of Swin Transformer by proposing "SWTformer" to enhance the accuracy of the initial seed CAMs by bringing local and global views together. SWTformer-V1 generates class probabilities and CAMs using only the patch tokens as features. SWTformer-V2 incorporates a multi-scale feature fusion mechanism to extract additional information and utilizes a background-aware mechanism to generate more accurate localization maps with improved cross-object discrimination. Based on experiments on the PascalVOC 2012 dataset, SWTformer-V1 achieves a 0.98% mAP higher localization accuracy, outperforming state-of-the-art models. It also yields comparable performance by 0.82% mIoU on average higher than other methods in generating initial localization maps, depending only on the classification network. SWTformer-V2 further improves the accuracy of the generated seed CAMs by 5.32% mIoU, further proving the effectiveness of the local-to-global view provided by the Swin transformer. Code available at: https://github.com/RozhanAhmadi/SWTformer
Neural Machine Translation for Malayalam Paraphrase Generation
Authors: Christeena Varghese, Sergey Koshelev, Ivan P. Yamshchikov
Date: 2024-01-31 00:00:00
Tasks: Machine Translation, NMT, Paraphrase Generation, Translation

This study explores four methods of generating paraphrases in Malayalam, utilizing resources available for English paraphrasing and pre-trained Neural Machine Translation (NMT) models. We evaluate the resulting paraphrases using both automated metrics, such as BLEU, METEOR, and cosine similarity, as well as human annotation. Our findings suggest that automated evaluation measures may not be fully appropriate for Malayalam, as they do not consistently align with human judgment. This discrepancy underscores the need for more nuanced paraphrase evaluation approaches especially for highly agglutinative languages.
Deterministic Computing Power Networking: Architecture, Technologies and Prospects
Authors: Qingmin Jia, Yujiao Hu, Xiaomao Zhou, Qianpiao Ma, Kai Guo, Huayu Zhang, Renchao Xie, Tao Huang, Yunjie Liu
Date: 2024-01-31 00:00:00

With the development of new Internet services such as computation-intensive and delay-sensitive tasks, the traditional "Best Effort" network transmission mode has been greatly challenged. The network system is urgently required to provide end-to-end transmission determinacy and computing determinacy for new applications to ensure the safe and efficient operation of services. Based on the research of the convergence of computing and networking, a new network paradigm named deterministic computing power networking (Det-CPN) is proposed. In this article, we firstly introduce the research advance of computing power networking. And then the motivations and scenarios of Det-CPN are analyzed. Following that, we present the system architecture, technological capabilities, workflow as well as key technologies for Det-CPN. Finally, the challenges and future trends of Det-CPN are analyzed and discussed.