pwc-archive/papers-with-abstracts huggingface dataset:
From concrete mixture to structural design -- a holistic optimization procedure in the presence of uncertainties
Authors: Atul Agrawal, Erik Tamsen, Phaedon-Stelios Koutsourelakis, Joerg F. Unger
Date: 2023-12-06 00:00:00

Designing civil structures such as bridges, dams or buildings is a complex task requiring many synergies from several experts. Each is responsible for different parts of the process. This is often done in a sequential manner, e.g. the structural engineer makes a design under the assumption of certain material properties (e.g. the strength class of the concrete), and then the material engineer optimizes the material with these restrictions. This paper proposes a holistic optimization procedure, which combines the concrete mixture design and structural simulations in a joint, forward workflow that we ultimately seek to invert. In this manner, new mixtures beyond standard ranges can be considered. Any design effort should account for the presence of uncertainties which can be aleatoric or epistemic as when data is used to calibrate physical models or identify models that fill missing links in the workflow. Inverting the causal relations established poses several challenges especially when these involve physics-based models which most often than not do not provide derivatives/sensitivities or when design constraints are present. To this end, we advocate Variational Optimization, with proposed extensions and appropriately chosen heuristics to overcome the aforementioned challenges. The proposed methodology is illustrated using the design of a precast concrete beam with the objective to minimize the global warming potential while satisfying a number of constraints associated with its load-bearing capacity after 28days according to the Eurocode, the demoulding time as computed by a complex nonlinear Finite Element model, and the maximum temperature during the hydration.
On the Estimation Performance of Generalized Power Method for Heteroscedastic Probabilistic PCA
Authors: Jinxin Wang, Chonghe Jiang, Huikang Liu, Anthony Man-Cho So
Date: 2023-12-06 00:00:00

The heteroscedastic probabilistic principal component analysis (PCA) technique, a variant of the classic PCA that considers data heterogeneity, is receiving more and more attention in the data science and signal processing communities. In this paper, to estimate the underlying low-dimensional linear subspace (simply called \emph{ground truth}) from available heterogeneous data samples, we consider the associated non-convex maximum-likelihood estimation problem, which involves maximizing a sum of heterogeneous quadratic forms over an orthogonality constraint (HQPOC). We propose a first-order method -- generalized power method (GPM) -- to tackle the problem and establish its \emph{estimation performance} guarantee. Specifically, we show that, given a suitable initialization, the distances between the iterates generated by GPM and the ground truth decrease at least geometrically to some threshold associated with the residual part of certain "population-residual decomposition". In establishing the estimation performance result, we prove a novel local error bound property of another closely related optimization problem, namely quadratic optimization with orthogonality constraint (QPOC), which is new and can be of independent interest. Numerical experiments are conducted to demonstrate the superior performance of GPM in both Gaussian noise and sub-Gaussian noise settings.
Empirical Bayes Covariance Decomposition, and a solution to the Multiple Tuning Problem in Sparse PCA
Authors: Joonsuk Kang, Matthew Stephens
Date: 2023-12-06 00:00:00

Sparse Principal Components Analysis (PCA) has been proposed as a way to improve both interpretability and reliability of PCA. However, use of sparse PCA in practice is hindered by the difficulty of tuning the multiple hyperparameters that control the sparsity of different PCs (the "multiple tuning problem", MTP). Here we present a solution to the MTP using Empirical Bayes methods. We first introduce a general formulation for penalized PCA of a data matrix $\mathbf{X}$, which includes some existing sparse PCA methods as special cases. We show that this formulation also leads to a penalized decomposition of the covariance (or Gram) matrix, $\mathbf{X}^T\mathbf{X}$. We introduce empirical Bayes versions of these penalized problems, in which the penalties are determined by prior distributions that are estimated from the data by maximum likelihood rather than cross-validation. The resulting "Empirical Bayes Covariance Decomposition" provides a principled and efficient solution to the MTP in sparse PCA, and one that can be immediately extended to incorporate other structural assumptions (e.g. non-negative PCA). We illustrate the effectiveness of this approach on both simulated and real data examples.
Algorithmic Fairness with Feedback
Authors: John W. Patty, Elizabeth Maggie Penn
Date: 2023-12-05 00:00:00
Tasks: Fairness

The field of algorithmic fairness has rapidly emerged over the past 15 years as algorithms have become ubiquitous in everyday lives. Algorithmic fairness traditionally considers statistical notions of fairness algorithms might satisfy in decisions based on noisy data. We first show that these are theoretically disconnected from welfare-based notions of fairness. We then discuss two individual welfare-based notions of fairness, envy freeness and prejudice freeness, and establish conditions under which they are equivalent to error rate balance and predictive parity, respectively. We discuss the implications of these findings in light of the recently discovered impossibility theorem in algorithmic fairness (Kleinberg, Mullainathan, & Raghavan (2016), Chouldechova (2017)).
Maximum likelihood thresholds of Gaussian graphical models and graphical lasso
Authors: Daniel Irving Bernstein, Hayden Outlaw
Date: 2023-12-05 00:00:00

Associated to each graph G is a Gaussian graphical model. Such models are often used in high-dimensional settings, i.e. where there are relatively few data points compared to the number of variables. The maximum likelihood threshold of a graph is the minimum number of data points required to fit the corresponding graphical model using maximum likelihood estimation. Graphical lasso is a method for selecting and fitting a graphical model. In this project, we ask: when graphical lasso is used to select and fit a graphical model on n data points, how likely is it that n is greater than or equal to the maximum likelihood threshold of the corresponding graph? Our results are a series of computational experiments.
Boosting legal case retrieval by query content selection with large language models
Authors: Youchao Zhou, Heyan Huang, Zhijing Wu
Date: 2023-12-06 00:00:00
Tasks: Retrieval

Legal case retrieval, which aims to retrieve relevant cases to a given query case, benefits judgment justice and attracts increasing attention. Unlike generic retrieval queries, legal case queries are typically long and the definition of relevance is closely related to legal-specific elements. Therefore, legal case queries may suffer from noise and sparsity of salient content, which hinders retrieval models from perceiving correct information in a query. While previous studies have paid attention to improving retrieval models and understanding relevance judgments, we focus on enhancing legal case retrieval by utilizing the salient content in legal case queries. We first annotate the salient content in queries manually and investigate how sparse and dense retrieval models attend to those content. Then we experiment with various query content selection methods utilizing large language models (LLMs) to extract or summarize salient content and incorporate it into the retrieval models. Experimental results show that reformulating long queries using LLMs improves the performance of both sparse and dense models in legal case retrieval.
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
Authors: Chengcheng Wang, wei he, Ying Nie, Jianyuan Guo, Chuanjian Liu, Kai Han, Yunhe Wang
Date: 2023-09-20 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: GPU, object-detection, Object Detection, Real-Time Object Detection

In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection. Many studies pushed up the baseline to a higher level by modifying the architecture, augmenting data and designing new losses. However, we find previous models still suffer from information fusion problem, although Feature Pyramid Network (FPN) and Path Aggregation Network (PANet) have alleviated this. Therefore, this study provides an advanced Gatherand-Distribute mechanism (GD) mechanism, which is realized with convolution and self-attention operations. This new designed model named as Gold-YOLO, which boosts the multi-scale feature fusion capabilities and achieves an ideal balance between latency and accuracy across all model scales. Additionally, we implement MAE-style pretraining in the YOLO-series for the first time, allowing YOLOseries models could be to benefit from unsupervised pretraining. Gold-YOLO-N attains an outstanding 39.9% AP on the COCO val2017 datasets and 1030 FPS on a T4 GPU, which outperforms the previous SOTA model YOLOv6-3.0-N with similar FPS by +2.4%. The PyTorch code is available at https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection/Gold-YOLO, and the MindSpore code is available at https://gitee.com/mindspore/models/tree/master/research/cv/Gold_YOLO.
MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems
Authors: Zefang Zong, Meng Zheng, Yong Li, Depeng Jin
Date: 2022-10-26 00:00:00
Proceeding: AAAI -22 2022 10
Tasks: Multi-agent Reinforcement Learning

Cooperative Pickup and Delivery Problem (PDP), as a variant of the typical Vehicle Routing Problems (VRP), is an important formulation in many real-world applications, such as
on-demand delivery, industrial warehousing, etc. It is of great
importance to efficiently provide high-quality solutions of cooperative PDP. However, it is not trivial to provide effective
solutions directly due to two major challenges: 1) the structural dependency between pickup and delivery pairs require
explicit modeling and representation. 2) the cooperation between different vehicles is highly related to solution exploration and is difficult to model. In this paper, we propose a
novel multi-agent reinforcement learning-based framework
to solve the cooperative PDP (MAPDP). First, we design a
paired context embedding to well measure the dependency of
different nodes considering their structural limits. Second, we
utilize cooperative multi-agent decoders to leverage the decision dependence among different vehicle agents based on a
special communication embedding. Third, we design a novel
cooperative A2C algorithm to train the integrated model.
We conduct extensive experiments on a randomly generated
dataset and a real-world dataset. Experiments result shown
that the proposed MAPDP outperforms all other baselines by
at least 1.64% in all settings, and shows significant computation speed during solution inference.
A Prior Instruction Representation Framework for Remote Sensing Image-text Retrieval
Authors: Jiancheng Pan, Qing Ma, Cong Bai
Date: 2023-10-27 00:00:00
Proceeding: ACMMM 2023 10
Tasks: Cross-Modal Retrieval, Image-text Retrieval, Retrieval, Scene Recognition, Text Retrieval

This paper presents a prior instruction representation framework (PIR) for remote sensing image-text retrieval, aimed at remote sensing vision-language understanding tasks to solve the semantic noise problem. Our highlight is the proposal of a paradigm that draws on prior knowledge to instruct adaptive learning of vision and text representations. Concretely, two progressive attention encoder (PAE) structures, Spatial-PAE and Temporal-PAE, are proposed to perform long-range dependency modeling to enhance key feature representation. In vision representation, Vision Instruction Representation (VIR) based on Spatial-PAE exploits the prior-guided knowledge of the remote sensing scene recognition by building a belief matrix to select key features for reducing the impact of semantic noise. In text representation, Language Cycle Attention (LCA) based on Temporal-PAE uses the previous time step to cyclically activate the current time step to enhance text representation capability. A cluster-wise affiliation loss is proposed to constrain the inter-classes and to reduce the semantic confusion zones in the common subspace. Comprehensive experiments demonstrate that using prior knowledge instruction could enhance vision and text representations and could outperform the state-of-the-art methods on two benchmark datasets, RSICD and RSITMD.
Reducing Semantic Confusion: Scene-aware Aggregation Network for Remote Sensing Cross-modal Retrieval
Authors: Jiancheng Pan, Qing Ma, Cong Bai
Date: 2023-06-12 00:00:00
Proceeding: ICMR 2023 6
Tasks: Cross-Modal Retrieval, Retrieval

Recently, remote sensing cross-modal retrieval has received incredible attention from researchers. However, the unique nature of remote-sensing images leads to many semantic confusion zones in the semantic space, which greatly affects retrieval performance. We propose a novel scene-aware aggregation network (SWAN) to reduce semantic confusion by improving scene perception capability. In visual representation, a visual multiscale fusion module (VMSF) is presented to fuse visual features with different scales as a visual representation backbone. Meanwhile, a scene fine-grained sensing module (SFGS) is proposed to establish the associations of salient features at different granularity. A scene-aware visual aggregation representation is formed by the visual information generated by these two modules. In textual representation, a textual coarse-grained enhancement module (TCGE) is designed to enhance the semantics of text and to align visual information. Furthermore, as the diversity and differentiation of remote sensing scenes weaken the understanding of scenes, a new metric, namely, scene recall is proposed to measure the perception of scenes by evaluating scene-level retrieval performance, which can also verify the effectiveness of our approach in reducing semantic confusion. By performance comparisons, ablation studies and visualization analysis, we validated the effectiveness and superiority of our approach on two datasets, RSICD and RSITMD. The source code is available at https://github.com/kinshingpoon/SWAN-pytorch.
CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence
Authors: Jiaojiao Zhao, Haoqiang Jiang, Guoyang Zou, Qian Lin, Qiang Wang, Jia Liu, Leina Ma
Date: 2022-10-17 00:00:00
Proceeding: Molecular Omics 2022 10
Tasks: Deep Learning

Protein arginine methylation (PRme), as one post-translational modification, plays a critical role in numerous cellular processes and regulates critical cellular functions. Though several in silico models for predicting PRme sites have been reported, new models may be required to develop due to the significant increase of identified PRme sites. In this study, we constructed multiple machine-learning and deep-learning models. The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe. CNNArginineMe performed best in AUC scoring metrics in comparisons with several reported predictors. Additionally, we employed CNNArginineMe to predict arginine methylation proteome and performed functional analysis. The arginine methylated proteome is significantly enriched in the amyotrophic lateral sclerosis (ALS) pathway. CNNArginineMe is freely available at https://github.com/guoyangzou/CNNArginineMe.
SUT: a new multi-purpose synthetic dataset for Farsi document image analysis
Authors: Elham Shabaninia, Fatemeh sadat Eslami, Ali Afkari Fahandari, Hossein Nezamabadi-pour
Date: 2023-11-27 00:00:00
Proceeding: 13th International Conference on Computer and Knowledge Engineering (ICCKE) 2023 11
Tasks: Document Classification, document-image-classification, Document Image Classification, image-classification, Image Classification, Information Retrieval, Optical Character Recognition (OCR), Retrieval, Text Detection

This paper introduces a new large-scale dataset for Farsi document images, named SUT, which aims to tackle the challenges associated with obtaining diverse and substantial ground-truth data for supervised models in document image analysis (DIA) tasks, such as document image classification, text detection and recognition, and information retrieval. The dataset comprises 62,453 images that have been categorized into 21 distinct classes, including identity documents featuring synthetically generated personal information superimposed on various backgrounds. The dataset also includes corresponding files with labeling information for the images. The ground-truth data is organized in CSV files containing compiled image file paths and associated information about the embedded data. To demonstrate the efficacy of the SUT dataset in DIA tasks, it was utilized for document classification (achieving an accuracy of 86% using a convolutional neural network) and OCR (achieving a CER of 0.083 and 0.072 using Tesseract and EasyOCR engines, respectively). The SUT dataset represents a valuable resource for researchers who are interested in developing and evaluating supervised models in Farsi document image analysis.
Real-World Image Variation by Aligning Diffusion Inversion Chain
Authors: Yuechen Zhang, Jinbo Xing, Eric Lo, Jiaya Jia
Date: 2023-05-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Image Generation, Image-Variation, Semantic Similarity, Semantic Textual Similarity, Text to Image Generation, Text-to-Image Generation

Recent diffusion model advancements have enabled high-fidelity images to be generated using text prompts. However, a domain gap exists between generated images and real-world images, which poses a challenge in generating high-quality variations of real-world images. Our investigation uncovers that this domain gap originates from a latents' distribution gap in different diffusion processes. To address this issue, we propose a novel inference pipeline called Real-world Image Variation by ALignment (RIVAL) that utilizes diffusion models to generate image variations from a single image exemplar. Our pipeline enhances the generation quality of image variations by aligning the image generation process to the source image's inversion chain. Specifically, we demonstrate that step-wise latent distribution alignment is essential for generating high-quality variations. To attain this, we design a cross-image self-attention injection for feature interaction and a step-wise distribution normalization to align the latent features. Incorporating these alignment processes into a diffusion model allows RIVAL to generate high-quality image variations without further parameter optimization. Our experimental results demonstrate that our proposed approach outperforms existing methods concerning semantic similarity and perceptual quality. This generalized inference pipeline can be easily applied to other diffusion-based generation tasks, such as image-conditioned text-to-image generation and stylization.
Free3D: Consistent Novel View Synthesis without 3D Representation
Authors: Chuanxia Zheng, Andrea Vedaldi
Date: 2023-12-07 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Reconstruction, Novel View Synthesis

We introduce Free3D, a simple accurate method for monocular open-set novel view synthesis (NVS). Similar to Zero-1-to-3, we start from a pre-trained 2D image generator for generalization, and fine-tune it for NVS. Compared to other works that took a similar approach, we obtain significant improvements without resorting to an explicit 3D representation, which is slow and memory-consuming, and without training an additional network for 3D reconstruction. Our key contribution is to improve the way the target camera pose is encoded in the network, which we do by introducing a new ray conditioning normalization (RCN) layer. The latter injects pose information in the underlying 2D image generator by telling each pixel its viewing direction. We further improve multi-view consistency by using light-weight multi-view attention layers and by sharing generation noise between the different views. We train Free3D on the Objaverse dataset and demonstrate excellent generalization to new categories in new datasets, including OmniObject3D and GSO. The project page is available at https://chuanxiaz.com/free3d/.
Continuous Authentication Using Mouse Clickstream Data Analysis
Authors: Sultan Almalki, Prosenjit Chatterjee, Kaushik Roy
Date: 2023-11-23 00:00:00
Tasks: User Identification

Biometrics is used to authenticate an individual based on physiological or behavioral traits. Mouse dynamics is an example of a behavioral biometric that can be used to perform continuous authentication as protection against security breaches. Recent research on mouse dynamics has shown promising results in identifying users; however, it has not yet reached an acceptable level of accuracy. In this paper, an empirical evaluation of different classification techniques is conducted on a mouse dynamics dataset, the Balabit Mouse Challenge dataset. User identification is carried out using three mouse actions: mouse move, point and click, and drag and drop. Verification and authentication methods are conducted using three machine-learning classifiers: the Decision Tree classifier, the K-Nearest Neighbors classifier, and the Random Forest classifier. The results show that the three classifiers can distinguish between a genuine user and an impostor with a relatively high degree of accuracy. In the verification mode, all the classifiers achieve a perfect accuracy of 100%. In authentication mode, all three classifiers achieved the highest accuracy (ACC) and Area Under Curve (AUC) from scenario B using the point and click action data: (Decision Tree ACC:87.6%, AUC:90.3%), (K-Nearest Neighbors ACC:99.3%, AUC:99.9%), and (Random Forest ACC:89.9%, AUC:92.5%).
High Performance Multiple Sequence Alignment Algorithms for Comparison of Microbial Genomes
Authors: Manal Helal, Hossam El-Gindy, Bruno Gaeta, Vitali Sinchenko
Date: 2023-11-29 00:00:00
Tasks: Multiple Sequence Alignment

Advances in gene sequencing have enabled in silico analyses of microbial genomes and have led to the revision of concepts of microbial taxonomy and evolution. We explore deficiencies in existing multiple sequence global alignment algorithms and introduce a new indexing scheme to partition the dynamic programming algorithm hypercube scoring tensor over processors based on the dependency between partitions to be scored in parallel. The performance of algorithms is compared in the study of rpoB gene sequences of Mycoplasma species.
Efficient Incremental Potential Contact for Actuated Face Simulation
Authors: Bo Li, Lingchen Yang, Barbara Solenthaler
Date: 2023-12-03 00:00:00

We present a quasi-static finite element simulator for human face animation. We model the face as an actuated soft body, which can be efficiently simulated using Projective Dynamics (PD). We adopt Incremental Potential Contact (IPC) to handle self-intersection. However, directly integrating IPC into the simulation would impede the high efficiency of the PD solver, since the stiffness matrix in the global step is no longer constant and cannot be pre-factorized. We notice that the actual number of vertices affected by the collision is only a small fraction of the whole model, and by utilizing this fact we effectively decrease the scale of the linear system to be solved. With the proposed optimization method for collision, we achieve high visual fidelity at a relatively low performance overhead.