pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic Targeted Factor Analysis
Authors: Miguel C. Herculano, Santiago Montoya-Blandón
Date: 2024-12-09 00:00:00
Tasks: valid

We develop a probabilistic variant of Partial Least Squares (PLS) we call Probabilistic Targeted Factor Analysis (PTFA), which can be used to extract common factors in predictors that are useful to predict a set of predetermined target variables. Along with the technique, we provide an efficient expectation-maximization (EM) algorithm to learn the parameters and forecast the targets of interest. We develop a number of extensions to missing-at-random data, stochastic volatility, factor dynamics, and mixed-frequency data for real-time forecasting. In a simulation exercise, we show that PTFA outperforms PLS at recovering the common underlying factors affecting both features and target variables delivering better in-sample fit, and providing valid forecasts under contamination such as measurement error or outliers. Finally, we provide three applications in Economics and Finance where PTFA outperforms compared with PLS and Principal Component Analysis (PCA) at out-of-sample forecasting.
LineGS : 3D Line Segment Representation on 3D Gaussian Splatting
Authors: Chenggang Yang, Yuang Shi
Date: 2024-11-30 00:00:00
Tasks: 3D Reconstruction, Surface Reconstruction

Abstract representations of 3D scenes play a crucial role in computer vision, enabling a wide range of applications such as mapping, localization, surface reconstruction, and even advanced tasks like SLAM and rendering. Among these representations, line segments are widely used because of their ability to succinctly capture the structural features of a scene. However, existing 3D reconstruction methods often face significant challenges. Methods relying on 2D projections suffer from instability caused by errors in multi-view matching and occlusions, while direct 3D approaches are hampered by noise and sparsity in 3D point cloud data. This paper introduces LineGS, a novel method that combines geometry-guided 3D line reconstruction with a 3D Gaussian splatting model to address these challenges and improve representation ability. The method leverages the high-density Gaussian point distributions along the edge of the scene to refine and optimize initial line segments generated from traditional geometric approaches. By aligning these segments with the underlying geometric features of the scene, LineGS achieves a more precise and reliable representation of 3D structures. The results show significant improvements in both geometric accuracy and model compactness compared to baseline methods.
Discriminative features pyramid network for medical image segmentation
Authors: Xiwang Xie, Lijie Xie, Guanyu Li, Hao Guo, Weidong Zhang, Feng Shao, Wenyi Zhao, Ling Tong, Xipeng Pan, Jubai An
Date: 2024-04-25 00:00:00
Proceeding: Biocybernetics and Biomedical Engineering 2024 4
Tasks: Image Segmentation, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

The diverse shapes and scales, complicated backgrounds, blurred boundaries, and similar appearances challenge the current organ segmentation methods in medical scene images. It is difficult to acquire satisfactory performance to directly extend the object segmentation methods in the natural scene images to the medical scene images. In this paper, we propose a discriminant feature pyramid (DFPNet) network for organ segmentation in the original medical images, which consists of two sub-networks: the feature steered network and the border network. To be specific, the feature steered network takes a top-down step-wise manner to extract abundant context information, which is conducive to suppressing the cluttered background and perceiving the scale variation of objects. The border network utilizes a bottom-up step-wise manner to optimize the boundary feature map, which aims at distinguishing adjacent edge features with similar appearances but diverse labels. A series of experiments were conducted on three publicly available medical datasets ( i.e., LUNA 16, RIM-ONE-R1, and VNC datasets) to evaluate the validity and generalization of the proposed DFPNet. Experimental results indicate that our network achieves superior performance in terms of the receiver operating characteristic (ROC) curve, F-Score, Jaccard index, and Hausdorff distance. The code will be available at: https://github.com/Xie-Xiwang/DFPNet.
Improved Deformation-Driven Element Packing with RepulsionPak
Authors: Reza Adhitya Saputra, Craig S. Kaplan, Paul Asente
Date: 2019-10-30 00:00:00
Proceeding: IEEE Transactions on Visualization and Computer Graphics 2019 10

We present a method to fill a container shape with deformable instances of geometric elements selected from a library, creating a 2D artistic composition called an element packing. Each element is represented as a mass-spring system, allowing it to deform to achieve a better fit with its neighbours and the container. We start with an initial placement of small elements and gradually transform them using a physics simulation that trades off between the evenness of the packing and the deformations of the individual elements. Unlike previous work, elements can be given preferred orientations, and we can use shape matching to control the initial placement of elements in tight convex corners. We also explore the creation of tileable packings, and validate our approach using statistical measurements of the distributions of positive and negative space in packings. Our method produces compositions in which the negative space between elements is approximately uniform in width, similar to real-world examples created by artists.
Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models
Authors: Jay Shah, Yiming Che, Javad Sohankar, Ji Luo, Baoxin Li, Yi Su, Teresa  Wu
Date: 2024-12-01 00:00:00
Proceeding: Life 2024 12
Tasks: MS-SSIM, SSIM, Super-Resolution, Synthetic Data Generation

Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer’s disease (AD), allowing non-invasive detection of amyloid-β plaques in the brain. However, the low spatial resolution of PET scans limits the accurate quantification of amyloid deposition due to partial volume effects (PVE). In this study, we propose a novel approach to addressing PVE using a latent diffusion model for resolution recovery (LDM-RR) of PET imaging. We leverage a synthetic data generation pipeline to create high-resolution PET digital phantoms for model training. The proposed LDM-RR model incorporates a weighted combination of L1, L2, and MS-SSIM losses at both noise and image scales to enhance MRI-guided reconstruction. We evaluated the model’s performance in improving statistical power for detecting longitudinal changes and enhancing agreement between amyloid PET measurements from different tracers. The results demonstrate that the LDM-RR approach significantly improves PET quantification accuracy, reduces inter-tracer variability, and enhances the detection of subtle changes in amyloid deposition over time. We show that deep learning has the potential to improve PET quantification in AD, effectively contributing to the early detection and monitoring of disease progression.
Stably unactivated neurons in ReLU neural networks
Authors: Natalie Brownlowe, Christopher R. Cornwell, Ethan Montes, Gabriel Quijano, Grace Stulman, Na Zhang
Date: 2024-12-06 00:00:00

The choice of architecture of a neural network influences which functions will be realizable by that neural network and, as a result, studying the expressiveness of a chosen architecture has received much attention. In ReLU neural networks, the presence of stably unactivated neurons can reduce the network's expressiveness. In this work, we investigate the probability of a neuron in the second hidden layer of such neural networks being stably unactivated when the weights and biases are initialized from symmetric probability distributions. For networks with input dimension $n_0$, we prove that if the first hidden layer has $n_0+1$ neurons then this probability is exactly $\frac{2^{n_0}+1}{4^{n_0+1}}$, and if the first hidden layer has $n_1$ neurons, $n_1 \le n_0$, then the probability is $\frac{1}{2^{n_1+1}}$. Finally, for the case when the first hidden layer has more neurons than $n_0+1$, a conjecture is proposed along with the rationale. Computational evidence is presented to support the conjecture.
k-HyperEdge Medoids for Clustering Ensemble
Authors: Feijiang Li, Jieting Wang, Liuya zhang, Yuhua Qian, Shuai Jin, Tao Yan, Liang Du
Date: 2024-12-11 00:00:00
Tasks: Clustering, Clustering Ensemble

Clustering ensemble has been a popular research topic in data science due to its ability to improve the robustness of the single clustering method. Many clustering ensemble methods have been proposed, most of which can be categorized into clustering-view and sample-view methods. The clustering-view method is generally efficient, but it could be affected by the unreliability that existed in base clustering results. The sample-view method shows good performance, while the construction of the pairwise sample relation is time-consuming. In this paper, the clustering ensemble is formulated as a k-HyperEdge Medoids discovery problem and a clustering ensemble method based on k-HyperEdge Medoids that considers the characteristics of the above two types of clustering ensemble methods is proposed. In the method, a set of hyperedges is selected from the clustering view efficiently, then the hyperedges are diffused and adjusted from the sample view guided by a hyperedge loss function to construct an effective k-HyperEdge Medoid set. The loss function is mainly reduced by assigning samples to the hyperedge with the highest degree of belonging. Theoretical analyses show that the solution can approximate the optimal, the assignment method can gradually reduce the loss function, and the estimation of the belonging degree is statistically reasonable. Experiments on artificial data show the working mechanism of the proposed method. The convergence of the method is verified by experimental analysis of twenty data sets. The effectiveness and efficiency of the proposed method are also verified on these data, with nine representative clustering ensemble algorithms as reference.
Digging into Intrinsic Contextual Information for High-fidelity 3D Point Cloud Completion
Authors: Jisheng Chu, Wenrui Li, Xingtao Wang, Kanglin Ning, Yidan Lu, Xiaopeng Fan
Date: 2024-12-11 00:00:00
Tasks: Point Cloud Completion

The common occurrence of occlusion-induced incompleteness in point clouds has made point cloud completion (PCC) a highly-concerned task in the field of geometric processing. Existing PCC methods typically produce complete point clouds from partial point clouds in a coarse-to-fine paradigm, with the coarse stage generating entire shapes and the fine stage improving texture details. Though diffusion models have demonstrated effectiveness in the coarse stage, the fine stage still faces challenges in producing high-fidelity results due to the ill-posed nature of PCC. The intrinsic contextual information for texture details in partial point clouds is the key to solving the challenge. In this paper, we propose a high-fidelity PCC method that digs into both short and long-range contextual information from the partial point cloud in the fine stage. Specifically, after generating the coarse point cloud via a diffusion-based coarse generator, a mixed sampling module introduces short-range contextual information from partial point clouds into the fine stage. A surface freezing modules safeguards points from noise-free partial point clouds against disruption. As for the long-range contextual information, we design a similarity modeling module to derive similarity with rigid transformation invariance between points, conducting effective matching of geometric manifold features globally. In this way, the high-quality components present in the partial point cloud serve as valuable references for refining the coarse point cloud with high fidelity. Extensive experiments have demonstrated the superiority of the proposed method over SOTA competitors. Our code is available at https://github.com/JS-CHU/ContextualCompletion.
Trajectory planning for autonomous intersection management of connected vehicles
Authors: Bing Liua, Qing Shia, Zhuoyue Song⁎, B, Abdelkader El Kamela
Date: 2018-10-05 00:00:00
Proceeding: Simulation Modelling Practice and Theory 90 (2019) 16–30 2018 10
Tasks: Autonomous Vehicles, Management, Scheduling, Trajectory Planning

This paper proposes a cooperative scheduling mechanism for autonomous vehicles passing
through an intersection, called TP-AIM. The main objective of this research is to ensure safe
driving while minimizing delay in an intersection without traffic lights. Firstly, an intersection
management system, used as an info-collecting-organizing center, assigns reasonable priorities
for all present vehicles and hence plans their trajectories. Secondly, a window searching algorithm is performed to find an entering window, which can produce a collision-free trajectory with
minimal delay, besides backup windows. Finally, vehicles can arrange their trajectory individually, by applying dynamic programming to compute velocity profile, in order to pass
through intersection. MATLAB/Simulink and SUMO based simulations are established among
three types of traffic mechanisms with different traffic flows. The results show that the proposed
TP-AIM mechanism significantly reduces the average evacuation time and increases throughput
by over 20%. Moreover, the paper investigates intersection delay, which can be reduced to less
than 10% compared to classical light management systems. Both safety and efficiency can be
guaranteed in our proposed mechanism.
Learning to Reason via Self-Iterative Process Feedback for Small Language Models
Authors: Kaiyuan Chen, Jin Wang, Xuejie Zhang
Date: 2024-12-11 00:00:00
Tasks: Domain Generalization, GSM8K, HumanEval, mbpp

Small language models (SLMs) are more efficient, cost-effective, and customizable than large language models (LLMs), though they often underperform in specific areas like reasoning. Past methods for enhancing SLMs' reasoning, such as supervised fine-tuning and distillation, often depend on costly external signals, resulting in SLMs being overly confident with limited supervision signals, thus limiting their abilities. Therefore, this study enables SLMs to learn to reason from self-iterative feedback. By combining odds ratio preference optimization (ORPO), we fine-tune and align SLMs using positive and negative signals generated by themselves. Additionally, we introduce process supervision for rewards in preference alignment by sampling-based inference simulation and process reward models. Compared to Supervised Fine-Tuning (SFT), our method improves the performance of Gemma-2B by 12.43 (Acc) on GSM8K and 3.95 (Pass@1) on MBPP. Furthermore, the proposed method also demonstrated superior out-of-domain generalization capabilities on MMLU_Math and HumanEval.
Utility-Scale Bifacial Solar Photovoltaic System: Optimum Sizing and Techno-Economic Evaluation
Authors: Sharaf K. Magableh, Caisheng Wang, Feng Lin
Date: 2024-12-10 00:00:00
Tasks: energy management

Classical monofacial solar photovoltaic systems have gained prevalence and are widely reported in the literature because they have a lower initial cost compared with bifacial systems. However, limited investigation of both systems has been done on a utility scale with different performance indicators. This paper introduces a multifaceted comparative analysis including various aspects like energy generation, reliability, environmental effect, economic viability, and footprint area. Real measured data, including ambient temperature, solar irradiance, and a utility-scale load, were used for studying both systems in the City of Detroit. The optimal system sizing and energy management strategy are attained using the Whale optimization algorithm. Minimizing the loss of power supply probability and sizing the number of photovoltaic panels (NPV) are carried out for both cases. Results revealed that the bifacial solar system generates more power with a lower NPV, a smaller installation area, and hence a lower levelized cost of energy for the entire project lifetime compared to the monofacial system. Accordingly, the bifacial system outlined in this paper is recommended and can be implemented in various locations to establish a sustainable solar energy system that is economically feasible with clean energy production for the entire project's lifespan.
FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning
Authors: Saket Upadhyay
Date: 2024-12-11 00:00:00

Fuzz testing is a fundamental technique employed to identify vulnerabilities within software systems. However, the process can be protracted and resource-intensive, especially when confronted with extensive codebases. In this work, I present FuzzDistill, an approach that harnesses compile-time data and machine learning to refine fuzzing targets. By analyzing compile-time information, such as function call graphs' features, loop information, and memory operations, FuzzDistill identifies high-priority areas of the codebase that are more probable to contain vulnerabilities. I demonstrate the efficacy of my approach through experiments conducted on real-world software, demonstrating substantial reductions in testing time.
SRFS: Parallel Processing Fault-tolerant ROS2-based Flight Software for the Space Ranger Cubesat
Authors: Zebei Zhao, Yinghao Xiang, Ziyu Zhou, Kehan Chong, Haoran Ma, Pei Chen
Date: 2024-12-11 00:00:00
Tasks: Management, Scheduling

Traditional real-time operating systems (RTOS) often exhibit poor parallel performance, while thread monitoring in Linux-based systems presents significant challenges. To address these issues, this paper proposes a satellite flight software system design based on the Robot Operating System (ROS), leveraging ROS's built-in reliable publish-subscribe messaging mechanism for inter-application communication. Considering the complex functional requirements of modern small satellites, the design incorporates both hardware and software architecture, alongside system scheduling and error-correction mechanisms. This approach ensures efficient parallel data processing and system reliability, while also reducing the development cycle through code reuse. Comprehensive testing, including system time delay, system management, fault tolerance, and system maintenance, was conducted to validate the system's capabilities in telemetry, remote control, new feature integration, and autonomous error correction. The results demonstrate the high reliability and ease of maintenance of the satellite flight software offering a reference framework for the rapid development of high-performance small satellite operations systems.
NLPineers@ NLU of Devanagari Script Languages 2025: Hate Speech Detection using Ensembling of BERT-based models
Authors: Anmol Guragain, Nadika Poudel, Rajesh Piryani, Bishesh Khanal
Date: 2024-12-11 00:00:00
Tasks: Data Augmentation, Hate Speech Detection

This paper explores hate speech detection in Devanagari-scripted languages, focusing on Hindi and Nepali, for Subtask B of the CHIPSAL@COLING 2025 Shared Task. Using a range of transformer-based models such as XLM-RoBERTa, MURIL, and IndicBERT, we examine their effectiveness in navigating the nuanced boundary between hate speech and free expression. Our best performing model, implemented as ensemble of multilingual BERT models achieve Recall of 0.7762 (Rank 3/31 in terms of recall) and F1 score of 0.6914 (Rank 17/31). To address class imbalance, we used backtranslation for data augmentation, and cosine similarity to preserve label consistency after augmentation. This work emphasizes the need for hate speech detection in Devanagari-scripted languages and presents a foundation for further research.
Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains
Authors: Arion Das, Asutosh Mishra, Amitesh Patel, Soumilya De, V. Gurucharan, Kripabandhu Ghosh
Date: 2024-11-25 00:00:00

Large Language Models (LLMs) have significantly impacted nearly every domain of human knowledge. However, the explainability of these models esp. to laypersons, which are crucial for instilling trust, have been examined through various skeptical lenses. In this paper, we introduce a novel notion of LLM explainability to laypersons, termed $\textit{ReQuesting}$, across three high-priority application domains -- law, health and finance, using multiple state-of-the-art LLMs. The proposed notion exhibits faithful generation of explainable layman-understandable algorithms on multiple tasks through high degree of reproducibility. Furthermore, we observe a notable alignment of the explainable algorithms with intrinsic reasoning of the LLMs.
GPD-1: Generative Pre-training for Driving
Authors: Zixun Xie, Sicheng Zuo, Wenzhao Zheng, Yunpeng Zhang, Dalong Du, Jie zhou, Jiwen Lu, Shanghang Zhang
Date: 2024-12-11 00:00:00
Tasks: Autonomous Driving, Decision Making, Motion Planning, motion prediction, Scene Generation, Trajectory Planning

Modeling the evolutions of driving scenarios is important for the evaluation and decision-making of autonomous driving systems. Most existing methods focus on one aspect of scene evolution such as map generation, motion prediction, and trajectory planning. In this paper, we propose a unified Generative Pre-training for Driving (GPD-1) model to accomplish all these tasks altogether without additional fine-tuning. We represent each scene with ego, agent, and map tokens and formulate autonomous driving as a unified token generation problem. We adopt the autoregressive transformer architecture and use a scene-level attention mask to enable intra-scene bi-directional interactions. For the ego and agent tokens, we propose a hierarchical positional tokenizer to effectively encode both 2D positions and headings. For the map tokens, we train a map vector-quantized autoencoder to efficiently compress ego-centric semantic maps into discrete tokens. We pre-train our GPD-1 on the large-scale nuPlan dataset and conduct extensive experiments to evaluate its effectiveness. With different prompts, our GPD-1 successfully generalizes to various tasks without finetuning, including scene generation, traffic simulation, closed-loop simulation, map prediction, and motion planning. Code: https://github.com/wzzheng/GPD.
DMin: Scalable Training Data Influence Estimation for Diffusion Models
Authors: Huawei Lin, Yingjie Lao, Weijie Zhao
Date: 2024-12-11 00:00:00
Tasks: Retrieval

Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models, yet existing influence estimation methods are constrained to small-scale or LoRA-tuned models due to computational limitations. As diffusion models scale up, these methods become impractical. To address this challenge, we propose DMin (Diffusion Model influence), a scalable framework for estimating the influence of each training data sample on a given generated image. By leveraging efficient gradient compression and retrieval techniques, DMin reduces storage requirements from 339.39 TB to only 726 MB and retrieves the top-k most influential training samples in under 1 second, all while maintaining performance. Our empirical results demonstrate DMin is both effective in identifying influential training samples and efficient in terms of computational and storage requirements.