pwc-archive/papers-with-abstracts huggingface dataset:
REGIONAL DATASETS FOR AIR QUALITY MONITORING IN EUROPEAN CITIES
Authors: G.- F. Angelis; A. Emvoliadis; T.-I. Theodorou; A. Zamichos; A. Drosou; D. Tzovaras
Date: 2024-07-12 00:00:00
Proceeding: IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium 2024 7
Tasks: Multivariate Time Series Imputation, Spatio-Temporal Forecasting, Time Series

The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005°. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.
A Dataset for Mechanical Mechanisms
Authors: Farshid Ghezelbash, Amir Hossein Eskandari, Amir J Bidhendi
Date: 2024-08-19 00:00:00

This study introduces a dataset consisting of approximately 9,000 images of mechanical mechanisms and their corresponding descriptions, aimed at supporting research in mechanism design. The dataset consists of a diverse collection of 2D and 3D sketches, meticulously curated to ensure relevance and quality. We demonstrate the application of this dataset by fine-tuning two models: 1) Stable Diffusion (for generating new mechanical designs), and 2) BLIP-2 (for captioning these designs). While the results from Stable Diffusion show promise, particularly in generating coherent 3D sketches, the model struggles with 2D sketches and occasionally produces nonsensical outputs. These limitations underscore the need for further development, particularly in expanding the dataset and refining model architectures. Nonetheless, this work serves as a step towards leveraging generative AI in mechanical design, highlighting both the potential and current limitations of these approaches.
Characterizing nonlinear systems with mixed input-output properties through dissipation inequalities
Authors: Sebastiaan van den Eijnden, Thomas Chaffey
Date: 2024-08-30 00:00:00
Tasks: LEMMA

Systems that show different characteristics, such as finite-gain and passivity, depending on the nature of the inputs, are said to possess mixed input-output properties. In this paper, we provide a constructive method for characterizing mixed input-output properties of nonlinear systems using a dissipativity framework. Our results take inspiration from the generalized Kalman-Yakubovich-Popov lemma, and show that a system is mixed if it is dissipative with respect to highly specialized supply rates. The mixed input-output characterization is used for assessing stability of feedback interconnections in which the feedback components violate conditions of classical results such as the small-gain and passivity theorem. We showcase applicability of our results through various examples.
Training-Free Sketch-Guided Diffusion with Latent Optimization
Authors: Sandra Zhang Ding, Jiafeng Mao, Kiyoharu Aizawa
Date: 2024-08-31 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Based on recent advanced diffusion models, Text-to-image (T2I) generation models have demonstrated their capabilities to generate diverse and high-quality images. However, leveraging their potential for real-world content creation, particularly in providing users with precise control over the image generation result, poses a significant challenge. In this paper, we propose an innovative training-free pipeline that extends existing text-to-image generation models to incorporate a sketch as an additional condition. To generate new images with a layout and structure closely resembling the input sketch, we find that these core features of a sketch can be tracked with the cross-attention maps of diffusion models. We introduce latent optimization, a method that refines the noisy latent at each intermediate step of the generation process using cross-attention maps to ensure that the generated images adhere closely to the desired structure outlined in the reference sketch. Through latent optimization, our method enhances the accuracy of image generation, offering users greater control and customization options in content creation.
Optimal Control applied to a SEIR model of 2019-nCoV with social distancing
Authors: Abhishek Mallela1
Date: 2020-04-19 00:00:00
Proceeding: medRxiv 2020 4

Does the implementation of social distancing measures have merit in controlling
the spread of the novel coronavirus? In this study, we develop a mathematical model
to explore the effects of social distancing on new disease infections. Mathematical
analyses of our model indicate that successful eradication of the disease is strongly
dependent on the chosen preventive measure. Numerical computations of the model
solution demonstrate that the ability to flatten the curve becomes easier as social
distancing is strictly enforced. Based on our model, we also formulate an optimal
control problem and solve it using Pontryagin’s Maximum Principle and an efficient
numerical iterative method. Our numerical results of an optimal 2019-nCoV treatment
protocol that yields a minimum disease burden from this disease indicates that social
distancing is vitally important.
NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation
Authors: Yiying Wang, Abhirup Banerjee, Vicente Grau
Date: 2024-09-06 00:00:00
Tasks: 3D geometry

Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often difficult for cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes. Moreover, due to the radiation limit, often only two angiographic projections are acquired, providing limited information of the vessel geometry and necessitating 3D coronary tree reconstruction based only on two ICA projections. In this paper, we propose a self-supervised deep learning method called NeCA, which is based on neural implicit representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer, in order to achieve 3D coronary artery tree reconstruction from two 2D projections. We validate our method using six different metrics on a dataset generated from coronary computed tomography angiography of right coronary artery and left anterior descending artery. The evaluation results demonstrate that our NeCA method, without requiring 3D ground truth for supervision or large datasets for training, achieves promising performance in both vessel topology and branch-connectivity preservation compared to the supervised deep learning model.
Training-Free Style Consistent Image Synthesis with Condition and Mask Guidance in E-Commerce
Authors: Guandong Li
Date: 2024-09-07 00:00:00
Tasks: Image Generation

Generating style-consistent images is a common task in the e-commerce field, and current methods are largely based on diffusion models, which have achieved excellent results. This paper introduces the concept of the QKV (query/key/value) level, referring to modifications in the attention maps (self-attention and cross-attention) when integrating UNet with image conditions. Without disrupting the product's main composition in e-commerce images, we aim to use a train-free method guided by pre-set conditions. This involves using shared KV to enhance similarity in cross-attention and generating mask guidance from the attention map to cleverly direct the generation of style-consistent images. Our method has shown promising results in practical applications.
RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation
Authors: Quoc-Bao Nguyen-Le, Tuan-Hy Le, Anh-Triet Do, Quoc-Huy Trinh
Date: 2024-09-09 00:00:00
Tasks: Cardiac Segmentation, Segmentation

Cardiovascular disease remains a predominant global health concern, responsible for a significant portion of mortality worldwide. Accurate segmentation of cardiac medical imaging data is pivotal in mitigating fatality rates associated with cardiovascular conditions. However, existing state-of-the-art (SOTA) neural networks, including both CNN-based and Transformer-based approaches, exhibit limitations in practical applicability due to their inability to effectively capture inter-slice connections alongside intra-slice information. This deficiency is particularly evident in datasets featuring intricate, long-range details along the z-axis, such as coronary arteries in axial views. Additionally, SOTA methods fail to differentiate non-cardiac components from myocardium in segmentation, leading to the "spraying" phenomenon. To address these challenges, we present RotCAtt-TransUNet++, a novel architecture tailored for robust segmentation of complex cardiac structures. Our approach emphasizes modeling global contexts by aggregating multiscale features with nested skip connections in the encoder. It integrates transformer layers to capture interactions between patches and employs a rotatory attention mechanism to capture connectivity between multiple slices (inter-slice information). Additionally, a channel-wise cross-attention gate guides the fused multi-scale channel-wise information and features from decoder stages to bridge semantic gaps. Experimental results demonstrate that our proposed model outperforms existing SOTA approaches across four cardiac datasets and one abdominal dataset. Importantly, coronary arteries and myocardium are annotated with near-perfect accuracy during inference. An ablation study shows that the rotatory attention mechanism effectively transforms embedded vectorized patches in the semantic dimensional space, enhancing segmentation accuracy.
Extracting Signal out of Chaos: Advancements on MAGI for Bayesian Analysis of Dynamical Systems
Authors: Skyler Wu
Date: 2024-08-20 00:00:00

This work builds off the manifold-constrained Gaussian process inference (MAGI) method for Bayesian parameter inference and trajectory reconstruction of ODE-based dynamical systems, focusing primarily on sparse and noisy data conditions. First, we introduce Pilot MAGI (pMAGI), a novel methodological upgrade on the base MAGI method that confers significantly-improved numerical stability, parameter inference, and trajectory reconstruction. Second, we demonstrate, for the first time to our knowledge, how one can combine MAGI-based methods with dynamical systems theory to provide probabilistic classifications of whether a system is stable or chaotic. Third, we demonstrate how pMAGI performs favorably in many settings against much more computationally-expensive and overparameterized methods. Fourth, we introduce Pilot MAGI Sequential Prediction (PMSP), a novel method building upon pMAGI that allows one to predict the trajectory of ODE-based dynamical systems multiple time steps into the future, given only sparse and noisy observations. We show that PMSP can output accurate future predictions even on chaotic dynamical systems and significantly outperform PINN-based methods. Overall, we contribute to the literature two novel methods, pMAGI and PMSP, that serve as Bayesian, uncertainty-quantified competitors to the Physics-Informed Neural Network.
The Global Minimum Tax, Investment Incentives and Asymmetric Tax Competition
Authors: Xuyang Chen
Date: 2024-09-09 00:00:00

This paper investigates how the OECD's global minimum tax (GMT) affects multinational enterprises (MNEs) behavior and countries' corporate taxes. We consider both profit shifting and capital investment responses of the MNE in a formal model of tax competition between asymmetric countries. The GMT reduces the true tax rate differential and benefits the large country, while the revenue effect is generally ambiguous for the small country. In the short run where tax rates are fixed, due to tax deduction of the substance-based income exclusion (SBIE), a higher minimum rate exerts investment incentives but also incurs a larger revenue loss for the small country. We show that under high (low) profit shifting costs the former (latter) effect dominates so that the small country's revenue increases (decreases). In the long run where countries can adjust tax rates, the GMT reshapes the tax game and the competition pattern. In contrast to the existing literature, we reveal that the minimum rate binds the small country only if it is low. With the rise of the GMT rate, countries will undercut the minimum to boost real investments and collect top-up taxes. For small market-size asymmetry and intermediate profit shifting cost, the revenue loss from the elimination of profit shifting may dominate the revenue gain from taxing the true profits generated by substantive activities, so that even a marginal GMT reform may harm the small country. Otherwise, it can raise the small country's tax revenue.
Representational Analysis of Binding in Language Models
Authors: Qin Dai, Benjamin Heinzerling, Kentaro Inui
Date: 2024-09-09 00:00:00
Tasks: Attribute, Dimensionality Reduction

Entity tracking is essential for complex reasoning. To perform in-context entity tracking, language models (LMs) must bind an entity to its attribute (e.g., bind a container to its content) to recall attribute for a given entity. For example, given a context mentioning ``The coffee is in Box Z, the stone is in Box M, the map is in Box H'', to infer ``Box Z contains the coffee'' later, LMs must bind ``Box Z'' to ``coffee''. To explain the binding behaviour of LMs, existing research introduces a Binding ID mechanism and states that LMs use a abstract concept called Binding ID (BI) to internally mark entity-attribute pairs. However, they have not captured the Ordering ID (OI) from entity activations that directly determines the binding behaviour. In this work, we provide a novel view of the BI mechanism by localizing OI and proving the causality between OI and binding behaviour. Specifically, by leveraging dimension reduction methods (e.g., PCA), we discover that there exists a low-rank subspace in the activations of LMs, that primarily encodes the order (i.e., OI) of entity and attribute. Moreover, we also discover the causal effect of OI on binding that when editing representations along the OI encoding direction, LMs tend to bind a given entity to other attributes accordingly. For example, by patching activations along the OI encoding direction we can make the LM to infer ``Box Z contains the stone'' and ``Box Z contains the map''.
Distributed Robust Continuous-Time Optimization Algorithms for Time-Varying Constrained Cost
Authors: Zeinab Ebrahimi, Mohammad Deghat
Date: 2024-09-09 00:00:00

This paper presents a distributed continuous-time optimization framework aimed at overcoming the challenges posed by time-varying cost functions and constraints in multi-agent systems, particularly those subject to disturbances. By incorporating tools such as log-barrier penalty functions to address inequality constraints, an integral sliding mode control for disturbance mitigation is proposed. The algorithm ensures asymptotic tracking of the optimal solution, achieving a tracking error of zero. The convergence of the introduced algorithms is demonstrated through Lyapunov analysis and nonsmooth techniques. Furthermore, the framework's effectiveness is validated through numerical simulations considering two scenarios for the communication networks.
Dual-Level Cross-Modal Contrastive Clustering
Authors: Haixin Zhang, Yongjun Li, Dong Huang
Date: 2024-09-06 00:00:00
Tasks: Clustering, Contrastive Learning, Deep Clustering, Image Clustering, Multimodal Deep Learning, Representation Learning

Image clustering, which involves grouping images into different clusters without labels, is a key task in unsupervised learning. Although previous deep clustering methods have achieved remarkable results, they only explore the intrinsic information of the image itself but overlook external supervision knowledge to improve the semantic understanding of images. Recently, visual-language pre-trained model on large-scale datasets have been used in various downstream tasks and have achieved great results. However, there is a gap between visual representation learning and textual semantic learning, and how to properly utilize the representation of two different modalities for clustering is still a big challenge. To tackle the challenges, we propose a novel image clustering framwork, named Dual-level Cross-Modal Contrastive Clustering (DXMC). Firstly, external textual information is introduced for constructing a semantic space which is adopted to generate image-text pairs. Secondly, the image-text pairs are respectively sent to pre-trained image and text encoder to obtain image and text embeddings which subsquently are fed into four well-designed networks. Thirdly, dual-level cross-modal contrastive learning is conducted between discriminative representations of different modalities and distinct level. Extensive experimental results on five benchmark datasets demonstrate the superiority of our proposed method.
Evaluating Machine Learning-based Skin Cancer Diagnosis
Authors: Tanish Jain
Date: 2024-09-04 00:00:00
Tasks: Fairness

This study evaluates the reliability of two deep learning models for skin cancer detection, focusing on their explainability and fairness. Using the HAM10000 dataset of dermatoscopic images, the research assesses two convolutional neural network architectures: a MobileNet-based model and a custom CNN model. Both models are evaluated for their ability to classify skin lesions into seven categories and to distinguish between dangerous and benign lesions. Explainability is assessed using Saliency Maps and Integrated Gradients, with results interpreted by a dermatologist. The study finds that both models generally highlight relevant features for most lesion types, although they struggle with certain classes like seborrheic keratoses and vascular lesions. Fairness is evaluated using the Equalized Odds metric across sex and skin tone groups. While both models demonstrate fairness across sex groups, they show significant disparities in false positive and false negative rates between light and dark skin tones. A Calibrated Equalized Odds postprocessing strategy is applied to mitigate these disparities, resulting in improved fairness, particularly in reducing false negative rate differences. The study concludes that while the models show promise in explainability, further development is needed to ensure fairness across different skin tones. These findings underscore the importance of rigorous evaluation of AI models in medical applications, particularly in diverse population groups.
HSF: Defending against Jailbreak Attacks with Hidden State Filtering
Authors: Cheng Qian, Hainan Zhang, Lei Sha, Zhiming Zheng
Date: 2024-08-31 00:00:00
Tasks: LLM Jailbreak

With the growing deployment of LLMs in daily applications like chatbots and content generation, efforts to ensure outputs align with human values and avoid harmful content have intensified. However, increasingly sophisticated jailbreak attacks threaten this alignment, aiming to induce unsafe outputs. Current defense efforts either focus on prompt rewriting or detection, which are limited in effectiveness due to the various design of jailbreak prompts, or on output control and detection, which are computationally expensive as they require LLM inference. Therefore, designing a pre-inference defense method that resists diverse jailbreak prompts is crucial for preventing LLM jailbreak attacks. We observe that jailbreak attacks, safe queries, and harmful queries exhibit different clustering patterns within the LLM's hidden state representation space. This suggests that by leveraging the LLM's hidden state representational capabilities, we can analyze the LLM's forthcoming behavior and proactively intervene for defense. In this paper, we propose a jailbreak attack defense strategy based on a Hidden State Filter (HSF), a lossless architectural defense mechanism that enables the model to preemptively identify and reject adversarial inputs before the inference process begins. We activate its defensive potential through an additional plugin module, effectively framing the defense task as a classification problem. Experimental results on two benchmark datasets, utilizing three different LLMs, show that HSF significantly enhances resilience against six cutting-edge jailbreak attacks. It significantly reduces the success rate of jailbreak attacks while minimally impacting responses to benign user queries, with negligible inference overhead, and outperforming defense baselines.Our code and data are available at https://anonymous.4open.science/r/Hidden-State-Filtering-8652/
SX-Stitch: An Efficient VMS-UNet Based Framework for Intraoperative Scoliosis X-Ray Image Stitching
Authors: Yi Li, Heting Gao, Mingde He, Jinqian Liang, Jason Gu, Wei Liu
Date: 2024-09-09 00:00:00
Tasks: Image Segmentation, Image Stitching, Mamba, Medical Image Segmentation, Segmentation, Semantic Segmentation

In scoliosis surgery, the limited field of view of the C-arm X-ray machine restricts the surgeons' holistic analysis of spinal structures .This paper presents an end-to-end efficient and robust intraoperative X-ray image stitching method for scoliosis surgery,named SX-Stitch. The method is divided into two stages:segmentation and stitching. In the segmentation stage, We propose a medical image segmentation model named Vision Mamba of Spine-UNet (VMS-UNet), which utilizes the state space Mamba to capture long-distance contextual information while maintaining linear computational complexity, and incorporates the SimAM attention mechanism, significantly improving the segmentation performance.In the stitching stage, we simplify the alignment process between images to the minimization of a registration energy function. The total energy function is then optimized to order unordered images, and a hybrid energy function is introduced to optimize the best seam, effectively eliminating parallax artifacts. On the clinical dataset, Sx-Stitch demonstrates superiority over SOTA schemes both qualitatively and quantitatively.
KAN-Based Fusion of Dual-Domain for Audio-Driven Facial Landmarks Generation
Authors: Hoang-Son Vo-Thanh, Quang-Vinh Nguyen, Soo-Hyung Kim
Date: 2024-09-09 00:00:00
Tasks: Face Generation, Speech to Facial Landmark, Talking Face Generation

Audio-driven talking face generation is a widely researched topic due to its high applicability. Reconstructing a talking face using audio significantly contributes to fields such as education, healthcare, online conversations, virtual assistants, and virtual reality. Early studies often focused solely on changing the mouth movements, which resulted in outcomes with limited practical applications. Recently, researchers have proposed a new approach of constructing the entire face, including face pose, neck, and shoulders. To achieve this, they need to generate through landmarks. However, creating stable landmarks that align well with the audio is a challenge. In this paper, we propose the KFusion of Dual-Domain model, a robust model that generates landmarks from audio. We separate the audio into two distinct domains to learn emotional information and facial context, then use a fusion mechanism based on the KAN model. Our model demonstrates high efficiency compared to recent models. This will lay the groundwork for the development of the audio-driven talking face generation problem in the future.