pwc-archive/papers-with-abstracts huggingface dataset:
Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods
Authors: Minsi Ren, Bowen Gao, Bo Qiang, Yanyan Lan
Date: 2023-11-01 00:00:00
Tasks: Drug Design, Drug Discovery

Structure-based drug design (SBDD) stands at the forefront of drug discovery, emphasizing the creation of molecules that target specific binding pockets. Recent advances in this area have witnessed the adoption of deep generative models and geometric deep learning techniques, modeling SBDD as a conditional generation task where the target structure serves as context. Historically, evaluation of these models centered on docking scores, which quantitatively depict the predicted binding affinity between a molecule and its target pocket. Though state-of-the-art models purport that a majority of their generated ligands exceed the docking score of ground truth ligands in test sets, it begs the question: Do these scores align with real-world biological needs? In this paper, we introduce the delta score, a novel evaluation metric grounded in tangible pharmaceutical requisites. Our experiments reveal that molecules produced by current deep generative models significantly lag behind ground truth reference ligands when assessed with the delta score. This novel metric not only complements existing benchmarks but also provides a pivotal direction for subsequent research in the domain.
Boundary-rendering network for breast lesion segmentation in ultrasound images
Authors: Ruobing Huanga, Mingrong Lina, Haoran Doua, Zehui Lina, Qilong Yinga, Xiaohong Jia b, Wenwen Xub, Zihan Mei b, Xin Yanga, Yijie Dong b, Jianqiao Zhoub, ∗, Dong Ni a
Date: 2022-12-10 00:00:00
Proceeding: Medical Image Analysis 2022 12
Tasks: Lesion Segmentation, Segmentation, Semantic Segmentation

Breast Ultrasound (BUS) has proven to be an effective tool for the early detection of cancer in the breast.

A lesion segmentation provides identification of the boundary, shape, and location of the target, and serves as a crucial step toward accurate diagnosis. Despite recent efforts in developing machine learning algorithms to automate this process, problems remain due to the blurry or occluded edges and highly irregular nodule shapes. Existing methods often produce over-smooth or inaccurate results, failing the need of identifying detailed boundary structures which are of clinical interest. To overcome these challenges, we propose a novel boundary-rendering framework that explicitly highlights the importance of boundary for automated nodule segmentation in BUS images. It utilizes a boundary selection module to automatically focuses on the ambiguous boundary region and a graph convolutional-based boundary rendering module to exploit global contour information. Furthermore, the proposed framework embeds nodule classification via semantic segmentation and encourages co-learning across tasks. Validation experiments were performed on different BUS datasets to verify the robustness of the proposed method.

Results show that the proposed method outperforms states-of-art segmentation approaches (Dice=0.854, IOU=0.919, HD=17.8) in nodule delineation, as well as obtains a higher classification accuracy than classical classification models.
Multi‑camera trajectory matching based on hierarchical clustering and constraints
Authors: Gábor Szűcs, Regő Borsodi, Dávid Papp
Date: 2023-10-19 00:00:00
Proceeding: Multimedia Tools and Applications 2023 10
Tasks: Attribute, Autonomous Driving, Constrained Clustering, image-classification, Image Classification, object-detection, Object Detection, Object Tracking, Trajectory Clustering, Unsupervised Vehicle Re-Identification, vehicle detection, Vehicle Re-Identification

The fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking. Autonomous driving and traffic monitoring systems, especially the on-premise installed fixed position multi-camera configurations, benefit greatly from recent advances. In this paper, we propose a Multi-Camera Multi-Target (MCMT) vehicle tracking system using a constrained hierarchical clustering solution, which improves trajectory matching, and thus provides a
more robust tracking of objects transitioning between cameras. YOLOv5, ByteTrack, and ResNet50-IBN ReID networks are used for vehicle detection and tracking. Static attributes such as vehicle type and vehicle color are determined from ReID features with SVM. The proposed ReID feature-based attribute categorization shows better performance, than its pure CNN counterpart. Single-camera trajectories (SCTs) are combined into multi-camera trajectories (MCTs) using hierarchical agglomerative clustering (HAC) with time and space constraints (our proposed algorithm is denoted by MCT#MAC). Similarities between SCTs are measured by comparing the mean ReID features cumulated on the trajectory. The system was evaluated on more datasets, and our experiments demonstrate that constraining HAC by manipulating the proximity matrix greatly improves the multi-camera IDF1 score.
Hierarchical Semantic Contrast for Weakly Supervised Semantic Segmentation
Authors: Yuanchen Wu, Xiaoqiang Li, Songmin Dai, Jide Li, Tong Liu, Shaorong Xie
Date: 2023-08-01 00:00:00
Proceeding: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence 2023 8
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) with image-level annotations has achieved great processes through class activation map (CAM). Since vanilla CAMs are hardly served as guidance to bridge the gap between full and weak supervision, recent studies explore semantic representations to make CAM fit for WSSS better and demonstrate encouraging results. However, they generally exploit single-level semantics, which may hamper the model to learn a comprehensive semantic structure. Motivated by the prior that each image has multiple levels of semantics, we propose hierarchical semantic contrast (HSC) to ameliorate the above problem. It conducts semantic contrast from coarse-grained to fine-grained perspective, including ROI level, class level, and pixel level, making the model learn a better object pattern understanding. To further improve CAM quality, building upon HSC, we explore consistency regularization of cross supervision and develop momentum prototype learning to utilize abundant semantics across different images. Extensive studies manifest that our plug-and-play learning paradigm, HSC, can significantly boost CAM quality on both non-saliency-guided and saliency-guided baselines, and establish new state-of-the-art WSSS performance on PASCAL VOC 2012 dataset.
Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning
Authors: Fangchen Yu, Runze Zhao, Zhan Shi, Yiwen Lu, Jicong Fan, Yicheng Zeng, Jianfeng Mao, Wenye Li
Date: 2023-11-03 00:00:00
Proceeding: 37th Conference on Neural Information Processing Systems (NeurIPS 2023) 2023 11
Tasks: Clustering, Imputation

Spectral clustering has gained popularity for clustering non-convex data due to its simplicity and effectiveness. It is essential to construct a similarity graph using a high-quality affinity measure that models the local neighborhood relations among
the data samples. However, incomplete data can lead to inaccurate affinity measures, resulting in degraded clustering performance. To address these issues, we propose an imputation-free framework with two novel approaches to improve spectral clustering on incomplete data. Firstly, we introduce a new kernel correction method that enhances the quality of the kernel matrix estimated on incomplete data with a theoretical guarantee, benefiting classical spectral clustering on pre-defined kernels. Secondly, we develop a series of affinity learning methods that equip the selfexpressive framework with ℓp-norm to construct an intrinsic affinity matrix with an adaptive extension. Our methods outperform existing data imputation and distance calibration techniques on benchmark datasets, offering a promising solution to spectral clustering on incomplete data in various real-world applications.
Highly-Efficient Robinson-Foulds Distance Estimation with Matrix Correction
Authors: Fangchen Yu, Rui Bao, Jianfeng Mao, Wenye Li
Date: 2023-10-04 00:00:00
Proceeding: 26th European Conference on Artificial Intelligence 2023 10
Tasks: valid

Phylogenetic trees are essential in studying evolutionary relationships, and the Robinson-Foulds (RF) distance is a widely used metric to calculate pairwise dissimilarities between phylogenetic trees, with various applications in both the biology and computing communities. However, generating a precise RF distance matrix becomes difficult or even intractable when tree information is partially missing. To address this issue, we introduce a novel distance correction algorithm for estimating the RF distance matrix of incomplete phylogenetic trees. Our method innovatively harnesses the assumption of Euclidean embedding, correcting an approximate distance matrix into a valid distance metric, guaranteed to be closer to the unknown ground-truth. Despite its simplicity, our approach exhibits robust performance, efficiency, and scalability in empirical evaluations, outperforming classical distance correction algorithms and holding potential benefits in downstream applications. Our code is available at https://github.com/CUHKSZ-Yu/EMC.
Online Estimation of Similarity Matrices with Incomplete Data
Authors: Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li
Date: 2023-07-02 00:00:00
Proceeding: Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI) 2023 7
Tasks: Imputation

The similarity matrix measures pairwise similarities between a set of data points and is an essential concept in data processing, routinely used in practical applications. Obtaining a similarity matrix is typically straightforward when data points are completely observed. However, incomplete observations can make it challenging to obtain a high-quality similarity matrix, which becomes even more complex in online data. To address this challenge, we propose matrix correction algorithms that leverage the positive semi-definiteness (PSD) of the similarity matrix to improve similarity estimation in both offline and online scenarios. Our approaches have a solid theoretical guarantee of performance and excellent potential for parallel execution on large-scale data. Empirical evaluations demonstrate their high effectiveness and efficiency with significantly improved results over classical imputation-based methods, benefiting downstream applications with superior performance. Our code is available at https://github.com/CUHKSZ-Yu/OnMC.
Fuzzy Superpixel-based Image Segmentation
Authors: Tsz Ching Ng, Siu Kai Choy, Shu Yan Lam, Kwok Wai Yu
Date: 2023-02-01 00:00:00
Proceeding: February 2023 2023 2
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

This article presents a multi-phase image segmentation methodology based on fuzzy superpixel decomposition, aggregation and merging. First, a collection of layers of dense fuzzy superpixels is generated by the variational fuzzy decomposition algorithm. Then a layer of refined superpixels is extracted by aggregating various layers of dense fuzzy superpixels using the hierarchical normalized cuts. Finally, the refined superpixels are projected into the low dimensional feature spaces by the multidimensional scaling and the segmentation result is obtained via the mean-shift-based merging approach with the spatial bandwidth adjustment strategy. Our algorithm utilizes the superimposition of fuzzy superpixels to impose more accurate spatial constraints on the final segmentation through the fuzzy superpixel aggregation. The fuzziness of superpixels also provides spatial features to measure affinities between fuzzy superpixels and refined superpixels, and guide the merging process. Comparative experiments with the existing approaches reveal a superior performance of the proposed method.
FF-Former: Swin Fourier Transformer for Nighttime Flare Removal
Authors: Dafeng Zhang, Jia Ouyang, Guanqun Liu, Xiaobing Wang, Xiangyu Kong, Zhezhu Jin
Date: 2023-03-20 00:00:00
Proceeding: CVPR Workshop 2023 3
Tasks: Flare Removal

In the process of removing nighttime flare, it is crucial to have a large receptive field due to the fact that flare can occupy a substantial portion of an image, even potentially the entire image. However, the conventional window-based Transformer approaches restrict the receptive field within the window, limiting its ability to capture global features. And the flare can cause the dark regions to become brighter and result in a loss of contrast and alteration of the frequency characteristics of the image. To address these challenges, we introduce FF-Former, which is based on Fast Fourier Convolution (FFC) and is designed to extract global frequency features for enhancing nighttime flare removal. To achieve this, we incorporate a Spatial Frequency Block (SFB) after the Swin Transformer, which forms the Swin Fourier Transformer Block (SFTB). This configuration enables the establishment of long dependencies and the extraction of global features. Unlike the traditional Transformer, which relies on global self-attention, the SFB module only performs convolution computation, making it both effective and efficient. Additionally, during the training phase, we optimize the loss function to preserve the light source points after nighttime flare removal. Experimental results on both real-world and synthetic benchmarks demonstrate that the proposed FF-Former significantly improves the performance of nighttime flare removal.
TUR2SQL: A Cross-Domain Turkish Dataset For Text-to-SQL
Authors: Ali Buğra Kanburoğlu, F. Boray Tek
Date: 2023-10-24 00:00:00
Proceeding: 8th International Conference on Computer Science and Engineering (UBMK) 2023 10
Tasks: Text to SQL, Text-To-SQL

The field of converting natural language into corresponding SQL queries using deep learning techniques has attracted significant attention in recent years. While existing Text-to-SQL datasets primarily focus on English and other languages such as Chinese, there is a lack of resources for the Turkish language. In this study, we introduce the first publicly available cross-domain Turkish Text-to-SQL dataset, named TUR2SQL. This dataset consists of 10,809 pairs of natural language statements and their corresponding SQL queries. We conducted experiments using SQLNet and ChatGPT on the TUR2SQL dataset. The experimental results show that SQLNet has limited performance and ChatGPT has superior performance on the dataset. We believe that TUR2SQL provides a foundation for further exploration and advancements in Turkish language-based Text-to-SQL research.
Intelligent unit commitment with vehicle-to-grid —A cost-emission optimization
Authors: Ahmed Yousuf Saber ∗, Ganesh Kumar Venayagamoorthy
Date: 2010-02-01 00:00:00
Proceeding: Journal of Power Sources 2010 2
Tasks: Scheduling

A gridable vehicle (GV) can be used as a small portable power plant (S3P) to enhance the security and
reliability of utility grids. Vehicle-to-grid (V2G) technology has drawn great interest in the recent years
and its success depends on intelligent scheduling of GVs or S3Ps in constrained parking lots. V2G can
reduce dependencies on small expensive units in existing power systems, resulting in reduced operation
cost and emissions. It can also increase reserve and reliability of existing power systems. Intelligent unit
commitment (UC) with V2G for cost and emission optimization in power system is presented in this paper.
As number of gridable vehicles in V2G is much higher than small units of existing systems, UC with V2G
is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to
balance between cost and emission reductions for UC with V2G. PSO can reliably and accurately solve this
complex constrained optimization problem easily and quickly. In the proposed solution model, binary
PSO optimizes on/off states of power generating units easily. Vehicles are presented by integer numbers
instead of zeros and ones to reduce the dimension of the problem. Balanced hybrid PSO optimizes the
number of gridable vehicles of V2G in the constrained parking lots. Balanced PSO provides a balance
between local and global searching abilities, and finds a balance in reducing both operation cost and
emission. Results show a considerable amount of cost and emission reduction with intelligent UC with
V2G. Finally, the practicality of UC with V2G is discussed for real-world applications.
Privacy-Preserving Algorithmic Recourse
Authors: Sikha Pentyala, Shubham Sharma, Sanjay Kariyappa, Freddy Lecue, Daniele Magazzeni
Date: 2023-11-23 00:00:00
Tasks: counterfactual, Privacy Preserving

When individuals are subject to adverse outcomes from machine learning models, providing a recourse path to help achieve a positive outcome is desirable. Recent work has shown that counterfactual explanations - which can be used as a means of single-step recourse - are vulnerable to privacy issues, putting an individuals' privacy at risk. Providing a sequential multi-step path for recourse can amplify this risk. Furthermore, simply adding noise to recourse paths found from existing methods can impact the realism and actionability of the path for an end-user. In this work, we address privacy issues when generating realistic recourse paths based on instance-based counterfactual explanations, and provide PrivRecourse: an end-to-end privacy preserving pipeline that can provide realistic recourse paths. PrivRecourse uses differentially private (DP) clustering to represent non-overlapping subsets of the private dataset. These DP cluster centers are then used to generate recourse paths by forming a graph with cluster centers as the nodes, so that we can generate realistic - feasible and actionable - recourse paths. We empirically evaluate our approach on finance datasets and compare it to simply adding noise to data instances, and to using DP synthetic data, to generate the graph. We observe that PrivRecourse can provide paths that are private and realistic.
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding
Authors: Peng Xia, Xingtong Yu, Ming Hu, Lie Ju, Zhiyong Wang, Peibo Duan, ZongYuan Ge
Date: 2023-11-23 00:00:00
Tasks: Fine-Grained Visual Recognition, Graph Representation Learning, Representation Learning

Object categories are typically organized into a multi-granularity taxonomic hierarchy. When classifying categories at different hierarchy levels, traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios. Recent studies integrating Vision-Language Models (VLMs) with class hierarchies have shown promise, yet they fall short of fully exploiting the hierarchical relationships. These efforts are constrained by their inability to perform effectively across varied granularity of categories. To tackle this issue, we propose a novel framework (HGCLIP) that effectively combines CLIP with a deeper exploitation of the Hierarchical class structure via Graph representation learning. We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category. After passing through a graph encoder, the textual features incorporate hierarchical structure information, while the image features emphasize class-aware features derived from prototypes through the attention mechanism. Our approach demonstrates significant improvements on 11 diverse visual recognition benchmarks. Our codes are fully available at https://github.com/richard-peng-xia/HGCLIP.
Minimizing Factual Inconsistency and Hallucination in Large Language Models
Authors: Muneeswaran I, Shreya Saxena, Siva Prasad, M V Sai Prakash, Advaith Shankar, Varun V, Vishal Vaddina, Saisubramaniam Gopalakrishnan
Date: 2023-11-23 00:00:00
Tasks: Hallucination, RAG, Retrieval, Retrieval-augmented Generation

Large Language Models (LLMs) are widely used in critical fields such as healthcare, education, and finance due to their remarkable proficiency in various language-related tasks. However, LLMs are prone to generating factually incorrect responses or "hallucinations," which can lead to a loss of credibility and trust among users. To address this issue, we propose a multi-stage framework that generates the rationale first, verifies and refines incorrect ones, and uses them as supporting references to generate the answer. The generated rationale enhances the transparency of the answer and our framework provides insights into how the model arrived at this answer, by using this rationale and the references to the context. In this paper, we demonstrate its effectiveness in improving the quality of responses to drug-related inquiries in the life sciences industry. Our framework improves traditional Retrieval Augmented Generation (RAG) by enabling OpenAI GPT-3.5-turbo to be 14-25% more faithful and 16-22% more accurate on two datasets. Furthermore, fine-tuning samples based on our framework improves the accuracy of smaller open-access LLMs by 33-42% and competes with RAG on commercial models.
Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs
Authors: Jonathan Roberts, Timo Lüddecke, Rehan Sheikh, Kai Han, Samuel Albanie
Date: 2023-11-24 00:00:00
Tasks: Disaster Response

Multimodal large language models (MLLMs) have shown remarkable capabilities across a broad range of tasks but their knowledge and abilities in the geographic and geospatial domains are yet to be explored, despite potential wide-ranging benefits to navigation, environmental research, urban development, and disaster response. We conduct a series of experiments exploring various vision capabilities of MLLMs within these domains, particularly focusing on the frontier model GPT-4V, and benchmark its performance against open-source counterparts. Our methodology involves challenging these models with a small-scale geographic benchmark consisting of a suite of visual tasks, testing their abilities across a spectrum of complexity. The analysis uncovers not only where such models excel, including instances where they outperform humans, but also where they falter, providing a balanced view of their capabilities in the geographic domain. To enable the comparison and evaluation of future models, our benchmark will be publicly released.