pwc-archive/papers-with-abstracts huggingface dataset:
Mutual gain adaptive network for segmenting brain stroke lesions
Authors: Boyu Huang, Guanru Tan, Haowen Dou, Zhihan Cui, Youyi Song, Teng Zhou
Date: 2022-08-27 00:00:00
Proceeding: journal 2022 8
Tasks: Lesion Segmentation

Brain stroke, an acute vascular disease, can lead to brain damage. As the incidence rate continues to increase, it becomes urgent and yet significantly to develop automatic tools for segmenting brain stroke lesions, for helping radiologists to detect brain stroke both effectively and accurately. Deep learning models, though have advanced this task, they are still suffering from the huge size variation and ambiguous boundaries of stroke lesions, both substantially degrading the segmentation performance. In this paper, we proposed an effective and generic learning network, called mutual gain adaptive network, to enhance the network’s ability on dealing with size variation and distinguishing ambiguous boundaries, for advancing deep neural networks in brain stroke lesion segmentation. The main contribution is that 1) a mutual gain adaptive similarity (MGAS) block and 2) a global context-awareness (GCA) block. MGAS block aims at capturing both short- and long-range spatial dependencies, for better encoding global contexts by leveraging feature similarity, to enhance feature activation for segmenting stroke lesions with different sizes. GCA block is designed to supply global context for low-semantic features, for facilitating the restoration of pixel localization to distinguish ambiguous boundaries. Our network is assessed in a publicly available dataset, e.g. Anatomical Tracings of Lesions After Stroke (ATLAS). Extensive experimental results show that it is better than state-of-the-art methods.
Risk of Text Backdoor Attacks Under Dataset Distillation
Authors: Kejun Zhang, Yutuo Song, Shaofei Xu, Pengcheng Li, Rong Qian, Pengzhi Han, Lingyun Xu
Date: 2024-10-17 00:00:00
Proceeding: Information Security Conference 2024 10
Tasks: Backdoor Attack, Dataset Distillation, text-classification, Text Classification

Dataset distillation aims to transfer knowledge from large training datasets into smaller datasets to enable rapid training of neural networks while maintaining the original dataset’s performance. However, current research on dataset distillation primarily focuses on balancing resource utilization and model capability, with limited discussion on the associated security risks, especially in the natural language processing (NLP) domain. In this paper, we focus on backdoor attacks on data distilled from text datasets. Specifically, we inject triggers into the synthetic dataset during the distillation process, rather than during the model training phase. We propose a framework for backdoor attacks in the context of text dataset distillation, termed Text Backdoor Attack under Dataset Distillation (TBADD). This framework is broadly applicable to backdoor attack methods based on dataset poisoning principles. It achieves an optimal balance between clean sample accuracy (CACC) and attack success rate (ASR) by separating clean and poisoned samples in the validation set and evaluating the distilled dataset’s performance through weighted assessment. Experimental comparisons using four popular backdoor attacks on two text classification tasks demonstrate that TBADD can achieve attack success rates comparable to those of models trained with the original dataset without significantly compromising the original task performance. Under two visible backdoor attacks, the ASR approaches 100%, while under two invisible backdoor attacks, the average ASR still achieves 83%, demonstrating effective attack outcomes. Our code is available at https://github.com/Songsci1024/TBADD.
Deep Conditional HDRI: Inverse Tone Mapping via Dual Encoder-Decoder Conditioning Method
Authors: YoonChan Nam; JoonKyu Kim; Jae-hun Shim; Suk-Ju Kang
Date: 2024-03-20 00:00:00
Proceeding: IEEE Transactions on Multimedia 2024 3
Tasks: Decoder, Image Generation, inverse tone mapping, Inverse-Tone-Mapping, Tone Mapping

Inverse tone mapping, a technique to restore a high dynamic range (HDR) image from a single low dynamic range (LDR) image, exhibits wide versatility since it may be easily applied to any camera device. Besides, the recent advancement in deep learning has produced great performance improvement in the field of inverse tone mapping. However, it remains a difficult task to accurately restore a wide-range HDR image from a single LDR image. A recent study attempts a spatially adaptive exposure value (EV) condition generated from luminance values to create a pseudo-multi-exposure stack. However, by adopting only luminance values as input, the conditioning method cannot precisely reflect the input image information when generating the EV condition, resulting in the loss of color expression. Moreover, there are some concerns regarding how to apply the EV condition to the image feature. Thus, the key idea of this study is to directly adopt image features in generating EV conditions that are adaptive to both color and brightness. To do this, we design a condition generation network with an encoder-decoder structure and propose a novel multi-exposure stack generation network, which bidirectionally synthesizes the image features and EV-conditioned features. Additionally, to better preserve the feature information in the synthesis of the features, we propose a spatially-adaptive feature transformation block. Our proposed method exhibits outstanding results in restoring the multi-exposure stacks for HDR image synthesis. Furthermore, our method achieves state-of-the-art performance compared to existing methods in multi-exposure stack generation and stack-based HDR restoration.
Deep reverse tone mapping
Authors: Yuki Endo, Yoshihiro Kanamori, Jun Mitani
Date: 2017-11-20 00:00:00
Proceeding: ACM Transactions on Graphics (TOG) 2017 11
Tasks: inverse tone mapping, Quantization, Tone Mapping

Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.
Design of winding pattern of filament-wound composite pressure vessel with unequal openings based on non-geodesics
Authors: Kaite Guo, Lihua Wen, Jinyou Xiao, Ming Lei, Shiyu Wang, Chengshuang Zhang, Xiao Hou
Date: 2020-06-24 00:00:00
Proceeding: Journal of Engineered Fibers and Fabrics 2020 6

In this article, we proposed a new approach to design the winding patterns of filament-wound composite pressure vessel with unequal polar openings with non-geodesics. To ensure the continuity of winding angles between trajectories along the cylinder and the dome, the non-geodesics for cylindrical part were used. The developed winding patterns of the vessels were simulated using the MATLAB software to verify the feasibility of the acquired trajectories. To demonstrate the performance in designing the winding path for big polar ratios, we analyzed vessels with polar pole ratios of 1:2–1:4, respectively. The developed winding patterns have successfully achieved uniform fiber distributions along the mandrel without severe overlap, except for the polar pole regions. To avoid the severe overlap between filament bands, we further studied the relationship among the winding pattern, bandwidth, and the number of tangent points, and derived a suitable bandwidth based on the winding pattern. These simulated results proved the effectiveness of the developed method in design of winding pattern with unequal polar openings.
The Superstatistics of confined ants: From serenity to panic
Authors: A. Reyes, M. Curbelo, F. Tejera, A. Rivera, O. Ramos, M. S. Turner, E. Altshuler
Date: 2019-04-05 00:00:00

Nonequilibrium systems with strong parameter fluctuations are challenging to describe with standard Statistical Mechanics techniques. Superstatistics -- that can be seen as statistics of an underlying family of statistical distributions -- has emerged as a tool able to describe these complex systems. This has been successfully applied, for example, to hydrodynamic turbulence, internal convection, and even DNA architecture, but not to macroscopic biological systems. Here we document the occurrence of Superstatistics in the animal world, and reveal the emergence of Log-normal Superstatistics in a living system, when ants in a confined space are exposed to a threat. We use a data-driven superstatistical model to explain both normal and "panic" dynamics, identifying non-Gaussian velocity distributions, time scale separation, and the Log-normal statistics of a stochastic diffusion coefficient. We also reveal distinct behavioral regimes in the ants' collective panic response and how it relates with the individual ant memory and cluster formation. Furthermore, our findings indicate that optical signals or simple antennation are not significant mechanisms for panic transmission. These discoveries provide a foundation for the understanding of the biological origin of Log-normal type diffusion in confined environments.
Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand
Authors: Kedi Zheng, Hanwei Xu, Zeyang Long, Yi Wang, Qixin Chen
Date: 2024-11-01 00:00:00
Tasks: quantile regression

The growing penetration of electric vehicles (EVs) significantly changes typical load curves in smart grids. With the development of fast charging technology, the volatility of EV charging demand is increasing, which requires additional flexibility for real-time power balance. The forecasting of EV charging demand involves probabilistic modeling of high dimensional time series dynamics across diverse electric vehicle charging stations (EVCSs). This paper studies the forecasting problem of multiple EVCS in a hierarchical probabilistic manner. For each charging station, a deep learning model based on a partial input convex neural network (PICNN) is trained to predict the day-ahead charging demand's conditional distribution, preventing the common quantile crossing problem in traditional quantile regression models. Then, differentiable convex optimization layers (DCLs) are used to reconcile the scenarios sampled from the distributions to yield coherent scenarios that satisfy the hierarchical constraint. It learns a better weight matrix for adjusting the forecasting results of different targets in a machine-learning approach compared to traditional optimization-based hierarchical reconciling methods. Numerical experiments based on real-world EV charging data are conducted to demonstrate the efficacy of the proposed method.
RSL-SQL: Robust Schema Linking in Text-to-SQL Generation
Authors: Zhenbiao Cao, Yuanlei Zheng, Zhihao Fan, Xiaojin Zhang, Wei Chen, Xiang Bai
Date: 2024-10-31 00:00:00
Tasks: Text to SQL, Text-To-SQL

Text-to-SQL generation aims to translate natural language questions into SQL statements. In Text-to-SQL based on large language models, schema linking is a widely adopted strategy to streamline the input for LLMs by selecting only relevant schema elements, therefore reducing noise and computational overhead. However, schema linking faces risks that require caution, including the potential omission of necessary elements and disruption of database structural integrity. To address these challenges, we propose a novel framework called RSL-SQL that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. We improve the recall of pattern linking using forward and backward pruning methods, achieving a strict recall of 94% while reducing the number of input columns by 83%. Furthermore, it hedges the risk by voting between a full mode and a simplified mode enhanced with contextual information. Experiments on the BIRD and Spider benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on Spider using GPT-4o. Furthermore, our approach outperforms a series of GPT-4 based Text-to-SQL systems when adopting DeepSeek (much cheaper) with same intact prompts. Extensive analysis and ablation studies confirm the effectiveness of each component in our framework. The codes are available at https://github.com/Laqcce-cao/RSL-SQL.
Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal Endoscopy
Authors: Marcel Roth, Micha V. Nowak, Adrian Krenzer, Frank Puppe
Date: 2024-10-21 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Medical Diagnosis, Medical Image Analysis, Medical Image Classification

Video capsule endoscopy has transformed gastrointestinal endoscopy (GIE) diagnostics by offering a non-invasive method for capturing detailed images of the gastrointestinal tract, enabling early disease detection. However, its potential is limited by the sheer volume of images generated during the imaging procedure, which can take anywhere from 6-8 hours and often produce up to 1 million images, necessitating automated analysis. Additionally, the variability of these images, combined with the need for expert annotations and the scarcity of large, high-quality labeled datasets, constrains the effectiveness of current medical image analysis models. To address this, we introduce a novel large GIE dataset, called EndoExtend24, created by merging ten existing public and private datasets, ensuring patient integrity across splits. EndoExtend24 includes over 226,000 labeled images, as well as dynamic class mappings, which allow unified training across datasets with differing labeling granularity, supporting up to 123 distinct pathological findings. Further, we propose to leverage domain adaptive pre-training of foundation models trained with self-supervision on generic image data, to adapt them to the task of GIE medical image diagnosis. Specifically, the EVA-02 model, which is based on the ViT architecture and trained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher), is pre-trained on the EndoExtend24 dataset to achieve domain adaptation, and finally trained on the Capsule Endoscopy 2024 Challenge dataset. Our model demonstrates robust performance, securing third place in the Capsule Endoscopy 2024 Challenge. We achieved a macro AUC of 0.762 and a balanced accuracy of 37.1% on the test set. These results emphasize the effectiveness of our domain-adaptive pre-training approach and the enriched EndoExtend24 dataset in advancing gastrointestinal endoscopy diagnostics.
Topic-Conversation Relevance (TCR) Dataset and Benchmarks
Authors: Yaran Fan, Jamie Pool, Senja Filipi, Ross Cutler
Date: 2024-10-29 00:00:00
Tasks: Diversity

Workplace meetings are vital to organizational collaboration, yet a large percentage of meetings are rated as ineffective. To help improve meeting effectiveness by understanding if the conversation is on topic, we create a comprehensive Topic-Conversation Relevance (TCR) dataset that covers a variety of domains and meeting styles. The TCR dataset includes 1,500 unique meetings, 22 million words in transcripts, and over 15,000 meeting topics, sourced from both newly collected Speech Interruption Meeting (SIM) data and existing public datasets. Along with the text data, we also open source scripts to generate synthetic meetings or create augmented meetings from the TCR dataset to enhance data diversity. For each data source, benchmarks are created using GPT-4 to evaluate the model accuracy in understanding transcription-topic relevance.
Randomized Autoregressive Visual Generation
Authors: Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, Liang-Chieh Chen
Date: 2024-11-01 00:00:00
Tasks: Image Generation, Language Modeling, Language Modelling

This paper presents Randomized AutoRegressive modeling (RAR) for visual generation, which sets a new state-of-the-art performance on the image generation task while maintaining full compatibility with language modeling frameworks. The proposed RAR is simple: during a standard autoregressive training process with a next-token prediction objective, the input sequence-typically ordered in raster form-is randomly permuted into different factorization orders with a probability r, where r starts at 1 and linearly decays to 0 over the course of training. This annealing training strategy enables the model to learn to maximize the expected likelihood over all factorization orders and thus effectively improve the model's capability of modeling bidirectional contexts. Importantly, RAR preserves the integrity of the autoregressive modeling framework, ensuring full compatibility with language modeling while significantly improving performance in image generation. On the ImageNet-256 benchmark, RAR achieves an FID score of 1.48, not only surpassing prior state-of-the-art autoregressive image generators but also outperforming leading diffusion-based and masked transformer-based methods. Code and models will be made available at https://github.com/bytedance/1d-tokenizer
A New Benchmark In Vivo Paired Dataset for Laparoscopic Image De-smoking
Authors: Wenyao Xia, Victoria Fan, Terry Peters, Elvis C. S. Chen
Date: 2024-10-03 00:00:00
Proceeding: International Conference on Medical Image Computing and Computer-Assisted Intervention 2024 10
Tasks: Image Enhancement

The single greatest obstacle in developing effective algorithms for removing surgical smoke in laparoscopic surgery is the lack of a paired dataset featuring real smoky and smoke-free surgical scenes. Consequently, existing de-smoking algorithms are developed and evaluated based on atmospheric scattering models, synthetic data, and non-reference image enhancement metrics, which do not adequately capture the complexity and essence of in vivo surgical scenes with smoke. To bridge this gap, we propose creating a paired dataset by identifying video sequences with relatively stationary scenes from existing laparoscopic surgical recordings where smoke emerges. In addition, we developed an approach to facilitate robust motion tracking through smoke to compensate for patients’ involuntary movements. As a result, we obtained 21 video sequences from 63 laparoscopic prostatectomy procedure recordings, comprising 961 pairs of smoky images and their corresponding smoke-free ground truth. Using this unique dataset, we compared a representative set of current de-smoking methods, confirming their efficacy and revealing their limitations, thereby offering insights for future directions.
SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models
Authors: José Ignacio Olalde-Verano, Sascha Kirch, Clara Pérez-Molina, Sergio Martin
Date: 2024-10-31 00:00:00
Tasks: Li-ion State of Health Estimation, Mamba, State Space Models

The state of health (SOH) of a Li-ion battery is a critical parameter that determines the remaining capacity and the remaining lifetime of the battery. In this paper, we propose SambaMixer a novel structured state space model (SSM) for predicting the state of health of Li-ion batteries. The proposed SSM is based on the MambaMixer architecture, which is designed to handle multi-variate time signals. We evaluate our model on the NASA battery discharge dataset and show that our model outperforms the state-of-the-art on this dataset. We further introduce a novel anchor-based resampling method which ensures time signals are of the expected length while also serving as augmentation technique. Finally, we condition prediction on the sample time and the cycle time difference using positional encodings to improve the performance of our model and to learn recuperation effects. Our results proof that our model is able to predict the SOH of Li-ion batteries with high accuracy and robustness.
Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy
Authors: Mianyong Ding, Matteo Maspero, Annemieke S Littooij, Martine van Grotel, Raquel Davila Fajardo, Max M van Noesel, Marry M van den Heuvel-Eibrink, Geert O Janssens
Date: 2024-11-01 00:00:00
Tasks: Computed Tomography (CT), Organ Segmentation

Purposes: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets. Materials and methods: In-house postoperative CTs from pediatric patients with renal tumors and neuroblastoma (n=189) and a public dataset (n=189) with CTs covering thoracoabdominal regions were used. Seventeen OARs were delineated: nine by clinicians (Type 1) and eight using TotalSegmentator (Type 2). Auto-segmentation models were trained using in-house (ModelPMC-UMCU) and a combined dataset of public data (Model-Combined). Performance was assessed with Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and mean surface distance (MSD). Two clinicians rated clinical acceptability on a 5-point Likert scale across 15 patient contours. Model robustness was evaluated against sex, age, intravenous contrast, and tumor type. Results: Model-PMC-UMCU achieved mean DSC values above 0.95 for five of nine OARs, while spleen and heart ranged between 0.90 and 0.95. The stomach-bowel and pancreas exhibited DSC values below 0.90. Model-Combined demonstrated improved robustness across both datasets. Clinical evaluation revealed good usability, with both clinicians rating six of nine Type 1 OARs above four and six of eight Type 2 OARs above three. Significant performance 2 differences were only found across age groups in both datasets, specifically in the left lung and pancreas. The 0-2 age group showed the lowest performance. Conclusion: A multi-organ segmentation model was developed, showcasing enhanced robustness when trained on combined datasets. This model is suitable for various OARs and can be applied to multiple datasets in clinical settings.
Evolutionary accumulation modelling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance
Authors: Jessica Renz, Kazeem A. Dauda, Olav N. L. Aga, Ramon Diaz-Uriarte, Iren H. Löhr, Bjørn Blomberg, Iain G. Johnston
Date: 2024-10-31 00:00:00

Can we understand and predict the evolutionary pathways by which bacteria acquire multi-drug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of antimicrobial resistance (AMR). Here, we review how a class of machine learning approaches called evolutionary accumulation modelling (EvAM) may help reveal these dynamics using genetic and/or phenotypic AMR datasets, without requiring longitudinal sampling. These approaches are well-established in cancer progression and evolutionary biology, but currently less used in AMR research. We discuss how EvAM can learn the evolutionary pathways by which drug resistances and AMR features are acquired as pathogens evolve, predict next evolutionary steps, identify influences between AMR features, and explore differences in MDR evolution between regions, demographics, and more. We demonstrate a case study on MDR evolution in Mycobacterium tuberculosis and discuss the strengths and weaknesses of these approaches, providing links to some approaches for implementation.