pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian inference for the mixed conditional heteroskedasticity model
Authors: L. BAUWENS and J.V.K. ROMBOUTS
Date: 2007-02-01 00:00:00
Proceeding: Econometrics Journal 2007 2
Tasks: Bayesian Inference, Econometrics

We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211–50). We construct a Gibbs sampler algorithm to compute posterior and predictive densities. The number of mixture components is selected by the marginal likelihood criterion.We apply the model to the SP500 daily returns.
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
Authors: Daniel Steininger, Andreas Trondl, Gerardus Croonen, Julia Simon, Verena Widhalm
Date: 2023-01-06 00:00:00
Proceeding: Winter Conference on Applications of Computer Vision (WACV) 2023 1
Tasks: Benchmarking, Crop Classification, Crop Yield Prediction, Domain Adaptation, Fine-Grained Image Classification, Instance Segmentation, Multi-Task Learning, Object Detection, Panoptic Segmentation, Plant Phenotyping, Semantic Segmentation

Precision Agriculture and especially the application of automated weed intervention represents an increasingly essential research area, as sustainability and efficiency considerations are becoming more and more relevant. While the potentials of Convolutional Neural Networks for detection, classification and segmentation tasks have successfully been demonstrated in other application areas, this relatively new field currently lacks the required quantity and quality of training data for such a highly data-driven approach. Therefore, we propose a novel large-scale image dataset specializing in the fine-grained identification of 74 relevant crop and weed species with a strong emphasis on data variability. We provide annotations of labeled bounding boxes, semantic masks and stem positions for about 112k instances in more than 8k high-resolution images of both real-world agricultural sites and specifically cultivated outdoor plots of rare weed types. Additionally, each sample is enriched with an extensive set of meta-annotations regarding environmental conditions and recording parameters. We furthermore conduct benchmark experiments for multiple learning tasks on different variants of the dataset to demonstrate its versatility and provide examples of useful mapping schemes for tailoring the annotated data to the requirements of specific applications. In the course of the evaluation, we furthermore demonstrate how incorporating multiple species of weeds into the learning process increases the accuracy of crop detection. Overall, the evaluation clearly demonstrates that our dataset represents an essential step towards overcoming the data gap and promoting further research in the area of Precision Agriculture.
Combining transformer and CNN for object detection in UAV imagery
Authors: Willy Fitra Hendria, Quang Thinh Phan, Fikriansyah Adzaka, Cheol Jeong
Date: 2021-12-28 00:00:00
Proceeding: ICT Express 2021 12
Tasks: Object, object-detection, Object Detection

Combining multiple models is a well-known technique to improve predictive performance in challenging tasks such as object detection in UAV imagery. In this paper, we propose fusion of transformer-based and convolutional neural network-based (CNN) models with two approaches. First, we ensemble Swin Transformer and DetectoRS with ResNet backbone, and conduct performance comparison on four typical methods for combining predictions of multiple object detection models. Second, we design a hybrid architecture by combining Swin Transformer backbone with a neck of DetectoRS. We show that the fusion of the transformer and the CNN-based models performs better compared to the respective baseline model.
UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices
Authors: Moataz Samir, Sanaa Sharafeddine, Chadi M. Assi, Tri Minh Nguyen, Ali Ghrayeb
Date: 2019-09-17 00:00:00
Proceeding: IEEE Transactions on Wireless Communications 2019 9
Tasks: Benchmarking, Trajectory Planning

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV's flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.
RHapTor: Rendering Haptic Torques for Virtual Reality
Authors: Kevin Roice, George Alex Koulieris
Date: 2022-07-25 00:00:00
Proceeding: ACM SIGGRAPH 2022 7

N/A
Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution
Authors: Zhongwei Qiu, Huan Yang, Jianlong Fu, Daochang Liu, Chang Xu, Dongmei Fu
Date: 2022-12-27 00:00:00
Tasks: Super-Resolution, Video Enhancement, Video Super-Resolution

Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges are remained to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. In this work, a novel Frequency-Transformer (FTVSR) is proposed for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band, so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global frequency relations and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processing in the frequency domain and discover that a ``divided attention'' which conducts a joint space-frequency attention before applying temporal-frequency attention, leads to the best video enhancement quality. Extensive experiments on three widely-used VSR datasets show that FTVSR outperforms state-of-the-art methods on different low-quality videos with clear visual margins. Code and pre-trained models are available at https://github.com/researchmm/FTVSR.
Black-box language model explanation by context length probing
Authors: Ondřej Cífka, Antoine Liutkus
Date: 2022-12-30 00:00:00
Tasks: Language Modeling, Language Modelling

The increasingly widespread adoption of large language models has highlighted the need for improving their explainability. We present context length probing, a novel explanation technique for causal language models, based on tracking the predictions of a model as a function of the length of available context, and allowing to assign differential importance scores to different contexts. The technique is model-agnostic and does not rely on access to model internals beyond computing token-level probabilities. We apply context length probing to large pre-trained language models and offer some initial analyses and insights, including the potential for studying long-range dependencies. The source code and an interactive demo of the method are available.
Multimodal Explainability via Latent Shift applied to COVID-19 stratification
Authors: Valerio Guarrasi, Lorenzo Tronchin, Domenico Albano, Eliodoro Faiella, Deborah Fazzini, Domiziana Santucci, Paolo Soda
Date: 2022-12-28 00:00:00
Tasks: counterfactual, Prognosis

We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, which jointly learns modality reconstructions and sample classifications using tabular and imaging data. The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset, which contains multimodal data for the early identification of patients at risk of severe outcome. The results show that the proposed method provides meaningful explanations without degrading the classification performance.
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Authors: Vikash Gupta, Barbaros Selnur Erdal, Carolina Ramirez, Ralf Floca, Laurence Jackson, Brad Genereaux, Sidney Bryson, Christopher P Bridge, Jens Kleesiek, Felix Nensa, Rickmer Braren, Khaled Younis, Tobias Penzkofer, Andreas Michael Bucher, Ming Melvin Qin, Gigon Bae, Hyeonhoon Lee, M. Jorge Cardoso, Sebastien Ourselin, Eric Kerfoot, Rahul Choudhury, Richard D. White, Tessa Cook, David Bericat, Matthew Lungren, Risto Haukioja, Haris Shuaib
Date: 2022-12-29 00:00:00
Tasks: Image Generation, Medical Image Generation

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.
Backward Curriculum Reinforcement Learning
Authors: KyungMin Ko
Date: 2022-12-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Current reinforcement learning algorithms train an agent using forward-generated trajectories, which provide little guidance so that the agent can explore as much as possible. While realizing the value of reinforcement learning results from sufficient exploration, this approach leads to a trade-off in losing sample efficiency, an essential factor impacting algorithm performance. Previous tasks use reward-shaping techniques and network structure modification to increase sample efficiency. However, these methods require many steps to implement. In this work, we propose novel backward curriculum reinforcement learning that begins training the agent using the backward trajectory of the episode instead of the original forward trajectory. This approach provides the agent with a strong reward signal, enabling more sample-efficient learning. Moreover, our method only requires a minor change in the algorithm of reversing the order of the trajectory before agent training, allowing a straightforward application to any state-of-the-art algorithm.
A Theoretical Framework for AI Models Explainability with Application in Biomedicine
Authors: Matteo Rizzo, Alberto Veneri, Andrea Albarelli, Claudio Lucchese, Marco Nobile, Cristina Conati
Date: 2022-12-29 00:00:00
Tasks: Decision Making, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

EXplainable Artificial Intelligence (XAI) is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains. Much has been written about the subject, yet XAI still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that is a synthesis of what can be found in the literature. We recognize that explanations are not atomic but the combination of evidence stemming from the model and its input-output mapping, and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation being a true description of the model's inner workings and decision-making process) and plausibility (i.e., how much the explanation looks convincing to the user). Using our proposed theoretical framework simplifies how these properties are operationalized and it provides new insight into common explanation methods that we analyze as case studies.
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Authors: Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yichi Zhang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen
Date: 2022-12-29 00:00:00
Tasks: Entity Alignment, Knowledge Graphs, Multi-modal Entity Alignment

Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current MMEA algorithms rely on KG-level modality fusion strategies for multi-modal entity representation, which ignores the variations of modality preferences of different entities, thus compromising robustness against noise in modalities such as blurry images and relations. This paper introduces MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for more fine-grained entity-level modality fusion and alignment. Experimental results demonstrate that our model not only achieves SOTA performance in multiple training scenarios, including supervised, unsupervised, iterative, and low-resource settings, but also has a limited number of parameters, efficient runtime, and interpretability. Our code is available at https://github.com/zjukg/MEAformer.
Planning with Complex Data Types in PDDL
Authors: Mojtaba Elahi, Jussi Rintanen
Date: 2022-12-29 00:00:00

Practically all of the planning research is limited to states represented in terms of Boolean and numeric state variables. Many practical problems, for example, planning inside complex software systems, require far more complex data types, and even real-world planning in many cases requires concepts such as sets of objects, which are not convenient to express in modeling languages with scalar types only. In this work, we investigate a modeling language for complex software systems, which supports complex data types such as sets, arrays, records, and unions. We give a reduction of a broad range of complex data types and their operations to Boolean logic, and then map this representation further to PDDL to be used with domain-independent PDDL planners. We evaluate the practicality of this approach, and provide solutions to some of the issues that arise in the PDDL translation.
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
Authors: Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy
Date: 2022-12-30 00:00:00
Tasks: Meta Reinforcement Learning, Reinforcement Learning (RL)

We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with limited interactions per task. The agent can use its experience in each task and across tasks to estimate both the transition model and the distribution over tasks. We propose an algorithm to meta-learn the underlying structure across tasks, utilize it to plan in each task, and upper-bound the regret of the planning loss. Our bound suggests that the average regret over tasks decreases as the number of tasks increases and as the tasks are more similar. In the classical single-task setting, it is known that the planning horizon should depend on the estimated model's accuracy, that is, on the number of samples within task. We generalize this finding to meta-RL and study this dependence of planning horizons on the number of tasks. Based on our theoretical findings, we derive heuristics for selecting slowly increasing discount factors, and we validate its significance empirically.
UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions
Authors: Asterios Bampakis, Sofia Yfantidou, Athena Vakali
Date: 2022-12-30 00:00:00
Tasks: Activity Prediction

It is indisputable that physical activity is vital for an individual's health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to create positive health behavior change. This work is motivated by the potential of personalized and adaptive goal-setting techniques in encouraging physical activity via self-tracking. To this end, we propose UBIWEAR, an end-to-end framework for intelligent physical activity prediction, with the ultimate goal to empower data-driven goal-setting interventions. To achieve this, we experiment with numerous machine learning and deep learning paradigms as a robust benchmark for physical activity prediction tasks. To train our models, we utilize, "MyHeart Counts", an open, large-scale dataset collected in-the-wild from thousands of users. We also propose a prescriptive framework for self-tracking aggregated data preprocessing, to facilitate data wrangling of real-world, noisy data. Our best model achieves a MAE of 1087 steps, 65% lower than the state of the art in terms of absolute error, proving the feasibility of the physical activity prediction task, and paving the way for future research.
Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective
Authors: Cheng Tan, Zhangyang Gao, Hanqun Cao, Xingran Chen, Ge Wang, Lirong Wu, Jun Xia, Jiangbin Zheng, Stan Z. Li
Date: 2022-12-02 00:00:00
Tasks: Prediction

The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction. Although deep learning has shown promising results in this field, current methods suffer from poor generalization and high complexity. In this work, we reformulate the RNA secondary structure prediction as a K-Rook problem, thereby simplifying the prediction process into probabilistic matching within a finite solution space. Building on this innovative perspective, we introduce RFold, a simple yet effective method that learns to predict the most matching K-Rook solution from the given sequence. RFold employs a bi-dimensional optimization strategy that decomposes the probabilistic matching problem into row-wise and column-wise components to reduce the matching complexity, simplifying the solving process while guaranteeing the validity of the output. Extensive experiments demonstrate that RFold achieves competitive performance and about eight times faster inference efficiency than the state-of-the-art approaches. The code and Colab demo are available in (http://github.com/A4Bio/RFold).
Using Large Language Models to Generate Engaging Captions for Data Visualizations
Authors: Ashley Liew, Klaus Mueller
Date: 2022-12-27 00:00:00
Tasks: Prompt Engineering

Creating compelling captions for data visualizations has been a longstanding challenge. Visualization researchers are typically untrained in journalistic reporting and hence the captions that are placed below data visualizations tend to be not overly engaging and rather just stick to basic observations about the data. In this work we explore the opportunities offered by the newly emerging crop of large language models (LLM) which use sophisticated deep learning technology to produce human-like prose. We ask, can these powerful software devices be purposed to produce engaging captions for generic data visualizations like a scatterplot. It turns out that the key challenge lies in designing the most effective prompt for the LLM, a task called prompt engineering. We report on first experiments using the popular LLM GPT-3 and deliver some promising results.