pwc-archive/papers-with-abstracts huggingface dataset:
Domain-Controlled Prompt Learning
Authors: Qinglong Cao, Zhengqin Xu, Yuntian Chen, Chao Ma, Xiaokang Yang
Date: 2023-09-30 00:00:00
Tasks: Prompt Learning

Large pre-trained vision-language models, such as CLIP, have shown remarkable generalization capabilities across various tasks when appropriate text prompts are provided. However, adapting these models to specific domains, like remote sensing images (RSIs), medical images, etc, remains unexplored and challenging. Existing prompt learning methods often lack domain-awareness or domain-transfer mechanisms, leading to suboptimal performance due to the misinterpretation of specific images in natural image patterns. To tackle this dilemma, we proposed a \textbf{Domain-Controlled Prompt Learning} for the specific domains. Specifically, the large-scale specific domain foundation model (LSDM) is first introduced to provide essential specific domain knowledge. Using lightweight neural networks, we transfer this knowledge into domain biases, which control both the visual and language branches to obtain domain-adaptive prompts in a directly incorporating manner. Simultaneously, to overcome the existing overfitting challenge, we propose a novel noisy-adding strategy, without extra trainable parameters, to help the model escape the suboptimal solution in a global domain oscillation manner. Experimental results show our method achieves state-of-the-art performance in specific domain image recognition datasets. Our code is available at https://github.com/caoql98/DCPL.
Associations between urinary phthalate metabolite concentrations and rheumatoid arthritis in the U.S. adult population
Authors: Yuting Chen a b 1, Jiran Shen c 1, Ye Wu a b, Lanlan Fang a b, Shanshan Xu a b, Yongzhen Peng a b, Faming Pan a b
Date: 2023-10-30 00:00:00
Proceeding: Chemosphere 2023 10
Tasks: Nutrition, regression

Phthalates are ubiquitous environmental contaminants. Nevertheless, limited data is available about the impacts of phthalates on rheumatoid arthritis (RA). The purpose of this study was to use National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2018 to assess the individual and combined effects of exposure to phthalate mixtures on RA in adults. A total of 8240 participants with complete data participated in the study, of whom 645 had RA. The levels of ten phthalate metabolites were detected in urine samples. In the single-pollutant models, independent associations were identified between urinary mono-(carboxyoctyl) phthalate (MCOP), mono-(3-carboxylpropyl) phthalate (MCPP), mono-isobutyl phthalate (MiBP) and mono-benzyl phthalate (MBzP) with the incidence of RA. The results of multi-pollutant models, including weighted quantile sum (WQS) regression, quantile-based g computation (qgcomp), and Bayesian kernel machine regression (BKMR) approaches consistently revealed that co-exposure to phthalates was positively associated with RA incidence. Such association was more pronounced in adults over 60 years of age, where MCOP was identified as the dominant positive driver. Overall, our findings add novel evidence that co-exposures to phthalates might be positively associated with RA incidence. Given the limitations of the NHANES study, well-designed longitudinal studies are required to verify or disprove these results.
OpenMix+: Revisiting Data Augmentation for Open Set Recognition
Authors: Guosong Jiang, Pengfei Zhu, Yu Wang, QinGhua Hu
Date: 2023-04-20 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2023 4
Tasks: Data Augmentation, Open Set Learning

Open set recognition requires models to recognize samples of known classes learned in the training set while reject unknowns not learned. Compared with the structural risk minimization theory for closed-set problems, structural risk in open set tasks remains rarely explored. In this paper, we point out that balancing between structural risk and open space risk is crucial for open set recognition, and re-formalize it as open set structural risk. This brings a new view towards the general relationship between closed set recognition and open set recognition against the common intuition, which argues that a good closed set classifier always benefits for open set recognition. Specifically, we theoretically and experimentally show that recent mix-based data augmentation methods are aggressive closed set regularization methods, which reduce structural risk at cost of sacrificing open space risk. Besides, we show that existing negative data augmentation designed for open space risk reduction also ignore the trade-off problem between structural risk and open space risk, which limits their performance. We propose an efficient negative data augmentation strategy named self-mix and a corresponding method named OpenMix. OpenMix generates high-quality negative samples by mixing samples themselves, which can take care of both risks simultaneously.  When combining OpenMix with conservative closed set regularization methods to form OpenMix+, models can achieve lower open set structural risk. Extensive experiments validate the superiority of OpenMix and OpenMix+ in terms of both effectiveness and universality.
Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo
Authors: Tharindu Cyril Weerasooriya, Alexander Ororbia, Raj Bhensadadia, Ashiqur KhudaBukhsh, Christopher Homan
Date: 2023-07-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics: ACL 2023 2023 7
Tasks: Diversity

Annotator disagreement is common whenever human judgment is needed for supervised learning. It is conventional to assume that one label per item represents ground truth. However, this obscures minority opinions, if present. We regard “ground truth” as the distribution of all labels that a population of annotators could produce, if asked (and of which we only have a small sample). We next introduce DisCo (Distribution from Context), a simple neural model that learns to predict this distribution. The model takes annotator-item pairs, rather than items alone, as input, and performs inference by aggregating over all annotators. Despite its simplicity, our experiments show that, on six benchmark datasets, our model is competitive with, and frequently outperforms, other, more complex models that either do not model specific annotators or were not designed for label distribution learning.
Optimal Data-Generation Strategy for Machine Learning Yield Functions in Anisotropic Plasticity
Authors: Ronak shoghi, Alexander Hartmaier
Date: 2022-04-27 00:00:00
Proceeding: Frontiers in Materials 2022 4

Trained machine learning (ML) algorithms can serve as numerically efficient surrogate models of sophisticated but numerically expensive constitutive models of material behavior. In the field of plasticity, ML yield functions have been proposed that serve as the basis of a constitutive model for plastic material behavior. If the training data for such ML flow rules is gained by micromechanical models, the training procedure can be considered as a homogenization method that captures essential information of microstructure-property relationships of a given material. However, generating training data with micromechanical methods, as for example, the crystal plasticity finite element method, is a numerically challenging task. Hence, in this work, it is investigated how an optimal data-generation strategy for the training of a ML model can be established that produces reliable and accurate ML yield functions with the least possible effort. It is shown that even for materials with a significant plastic anisotropy, as polycrystals with a pronounced Goss texture, 300 data points representing the yield locus of the material in stress space, are sufficient to train the ML yield function successfully. Furthermore, it is demonstrated how data-oriented flow rules can be used in standard finite element analysis.
Model Card and Evaluations for Claude Models
Authors: Anthropic
Date: 2023-07-11 00:00:00
Proceeding: Technical Report 2023 7
Tasks: Arithmetic Reasoning, Bug fixing, Code Generation, Common Sense Reasoning, Language Modeling, Language Modelling, model, Multi-task Language Understanding, Question Answering, Red Teaming, reinforcement-learning, Safety Alignment

This report includes the model card [1] for Claude models, focusing on Claude 2, along with the results of a range of safety, alignment, and capabilities evaluations. We have been iterating on the training and evaluation of Claude-type models since our first work on Reinforcement Learning from Human Feedback (RLHF) [2]; the newest Claude 2 model represents a continuous evolution from those early and less capable ‘helpful and harmless’ language assistants.
This report is not intended to be a scientific paper since most aspects of training and evaluating these models have been documented in our research papers. These include papers on preference modeling [3], reinforcement learning from human feedback for helpful and harmless models [2], red teaming language models [4], measuring representation of subjective global values in language models [5], honesty, (i.e., exploring language models’ ability to recognize what they know) [6], evaluating language models with language model-generated tests [7], moral self-correction [8], and Constitutional AI [9]. We also discussed Claude’s specific constitution in a recent blog post [10]. Our work using human evaluations to test model safety is most thoroughly documented in our paper “Red-Teaming Language Models to Reduce Harms” [4], while our recent work on automated safety evaluation is “Discovering Language Model Behaviors with Model-Written Evaluations” [7]. 
This report is also not comprehensive – we expect to release new findings as we continue our research and evaluations of frontier models. However, we hope it provides useful insight into Claude 2’s capabilities and limitations.
APE-V: Athlete Performance Evaluation using Video
Authors: Chaitanya Roygaga, Dhruva Patil, Michael Boyle, William Pickard, Raoul Reiser, Aparna Bharati, Nathaniel Blanchard
Date: 2022-02-15 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops 2022 2

Athletes typically undergo regular evaluations by trainers and coaches to assess performance and injury risk. One of the most popular movements to examine in athletes needing lower extremity strength and power is the vertical jump. Specifically, maximal effort countermovement and drop jumps performed on bilateral force plates provide a wealth of metrics. However, the expense of the equipment and expertise needed to interpret the results limits their use. Computer vision techniques applied to videos of such movements are a less expensive alternative for extracting complementary metrics. Blanchard et al. collected a dataset of 89 athletes performing these movements and showcased how OpenPose could be applied to the data. However, athlete error calls into question 46.2% of movements — in these cases, an expert assessor would have the athlete redo the movement to eliminate the error. Here, we augmented Blanchard et al. with expert labels of error and established benchmark performance on automatic error identification. In total, 14 different types of errors were identified by trained annotators. Our benchmark models identified errors with an F1 score of 0.710 and a Kappa of 0.457 (Kappa measures accuracy over chance).
A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems
Authors: Yuan-Heng Wang, Hoshin V. Gupta
Date: 2023-10-12 00:00:00

Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically-interpretable Mass Conserving Perceptron (MCP) as a way to bridge the gap between PC-based and ML-based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass-conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off-the-shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall-runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems.
Training and Predicting Visual Error for Real-Time Applications
Authors: João Libório Cardoso, Bernhard Kerbl, Lei Yang, Yury Uralsky, Michael Wimmer
Date: 2023-10-13 00:00:00

Visual error metrics play a fundamental role in the quantification of perceived image similarity. Most recently, use cases for them in real-time applications have emerged, such as content-adaptive shading and shading reuse to increase performance and improve efficiency. A wide range of different metrics has been established, with the most sophisticated being capable of capturing the perceptual characteristics of the human visual system. However, their complexity, computational expense, and reliance on reference images to compare against prevent their generalized use in real-time, restricting such applications to using only the simplest available metrics. In this work, we explore the abilities of convolutional neural networks to predict a variety of visual metrics without requiring either reference or rendered images. Specifically, we train and deploy a neural network to estimate the visual error resulting from reusing shading or using reduced shading rates. The resulting models account for 70%-90% of the variance while achieving up to an order of magnitude faster computation times. Our solution combines image-space information that is readily available in most state-of-the-art deferred shading pipelines with reprojection from previous frames to enable an adequate estimate of visual errors, even in previously unseen regions. We describe a suitable convolutional network architecture and considerations for data preparation for training. We demonstrate the capability of our network to predict complex error metrics at interactive rates in a real-time application that implements content-adaptive shading in a deferred pipeline. Depending on the portion of unseen image regions, our approach can achieve up to $2\times$ performance compared to state-of-the-art methods.
PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface
Authors: Sangwon Lim, Karim El-Basyouny, Yee Hong Yang
Date: 2023-10-12 00:00:00
Tasks: Autonomous Driving, Depth Estimation, Depth Prediction, point cloud upsampling, Self-Supervised Learning

While recent advancements in deep-learning point cloud upsampling methods have improved the input to intelligent transportation systems, they still suffer from issues of domain dependency between synthetic and real-scanned point clouds. This paper addresses the above issues by proposing a new ray-based upsampling approach with an arbitrary rate, where a depth prediction is made for each query ray and its corresponding patch. Our novel method simulates the sphere-tracing ray marching algorithm on the neural implicit surface defined with an unsigned distance function (UDF) to achieve more precise and stable ray-depth predictions by training a point-transformer-based network. The rule-based mid-point query sampling method generates more evenly distributed points without requiring an end-to-end model trained using a nearest-neighbor-based reconstruction loss function, which may be biased towards the training dataset. Self-supervised learning becomes possible with accurate ground truths within the input point cloud. The results demonstrate the method's versatility across domains and training scenarios with limited computational resources and training data. Comprehensive analyses of synthetic and real-scanned applications provide empirical evidence for the significance of the upsampling task across the computer vision and graphics domains to real-world applications of ITS.
WaveNet: Wave-Aware Image Enhancement
Authors: Jiachen Dang, Zehao Li, Yong Zhong, Lishun Wang
Date: 2023-10-10 00:00:00
Proceeding: The Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2023 10
Tasks: Image Enhancement, Image Retouching, Low-Light Image Enhancement

As a low-level vision task, image enhancement is widely used in various computer vision applications. Recently, multiple methods combined with CNNs, MLP, Transformer, and the Fourier transform have achieved promising results on image enhancement tasks. However, these methods cannot achieve a balance between accuracy and computational cost. In this paper, we formulate the enhancement into a signal modulation problem and propose the WaveNet architecture, which performs well in various parameters and improves the feature expression using wave-like feature representation. Specifically, to better capture wave-like feature representations, we propose to represent a pixel as a sampled value of a signal function with three wave functions (Cosine Wave (CW), Sine Wave (SW), and Gating Wave (GW)) inspired by the Fourier transform. The amplitude and phase are required to generate the wave-like features. The amplitude term includes the original contents of features, and the phase term modulates the relationship between various inputs and fixed weights. To dynamically obtain the phase and the amplitude, we build the Wave Transform Block (WTB) that adaptively generates the waves and modulates the wave superposition mode. Based on the WTB, we establish an effective architecture WaveNet for image enhancement. Extensive experiments on six real-world datasets show that our model achieves better quantitative and qualitative results than state-of-the-art methods. The source code and pretrained model are available at https://github.com/DeniJsonC/WaveNet.
Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models
Authors: Jeya Balaji Balasubramanian, Parichoy Pal Choudhury, Srijon Mukhopadhyay, Thomas Ahearn, Nilanjan Chatterjee, Montserrat García-Closas, Jonas S. Almeida
Date: 2023-10-13 00:00:00
Tasks: Privacy Preserving

Objective: Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, such as the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face serious limitations in portability and privacy due to their need for circulating user data in remote servers for operation. Our objective was to overcome these limitations. Materials and Methods: We refactored R-iCARE into a Python package (Py-iCARE) then compiled it to WebAssembly (Wasm-iCARE): a portable web module, which operates entirely within the privacy of the user's device. Results: We showcase the portability and privacy of Wasm-iCARE through two applications: for researchers to statistically validate risk models, and to deliver them to end-users. Both applications run entirely on the client-side, requiring no downloads or installations, and keeps user data on-device during risk calculation. Conclusions: Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.
Retro-fallback: retrosynthetic planning in an uncertain world
Authors: Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin Segler, José Miguel Hernández-Lobato
Date: 2023-10-13 00:00:00
Tasks: Retrosynthesis

Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-cost), these works generally overlook the fact that we have imperfect knowledge of the space of possible reactions, meaning plans created by algorithms may not work in a laboratory. In this paper we propose a novel formulation of retrosynthesis in terms of stochastic processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one synthesis plan can be executed in the lab. Using in-silico benchmarks we demonstrate that retro-fallback generally produces better sets of synthesis plans than the popular MCTS and retro* algorithms.
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Authors: Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, Surajit Chaudhuri
Date: 2023-10-13 00:00:00
Tasks: Probing Language Models

Language models, such as GPT-3.5 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks. However, when probing language models using a range of basic table-understanding tasks, we observe that today's language models are still sub-optimal in many table-related tasks, likely because they are pre-trained predominantly on \emph{one-dimensional} natural-language texts, whereas relational tables are \emph{two-dimensional} objects. In this work, we propose a new "\emph{table-tuning}" paradigm, where we continue to train/fine-tune language models like GPT-3.5 and ChatGPT, using diverse table-tasks synthesized from real tables as training data, with the goal of enhancing language models' ability to understand tables and perform table tasks. We show that our resulting Table-GPT models demonstrate (1) better \emph{table-understanding} capabilities, by consistently outperforming the vanilla GPT-3.5 and ChatGPT, on a wide-range of table tasks, including holdout unseen tasks, and (2) strong \emph{generalizability}, in its ability to respond to diverse human instructions to perform new table-tasks, in a manner similar to GPT-3.5 and ChatGPT.
It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models
Authors: Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar
Date: 2023-10-13 00:00:00
Tasks: Deep Learning

Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a \emph{trade-off}. However, in this paper, we show that for an ensemble of deep learning based classification models, bias and variance are \emph{aligned} at a sample level, where squared bias is approximately \emph{equal} to variance for correctly classified sample points. We present empirical evidence confirming this phenomenon in a variety of deep learning models and datasets. Moreover, we study this phenomenon from two theoretical perspectives: calibration and neural collapse. We first show theoretically that under the assumption that the models are well calibrated, we can observe the bias-variance alignment. Second, starting from the picture provided by the neural collapse theory, we show an approximate correlation between bias and variance.