pwc-archive/papers-with-abstracts huggingface dataset:
Molecular techniques employed in CTG(Ser1) and CTG(Ala) D-xylose metabolizing yeast clades for strain design and industrial applications
Authors: Ana Paula Wives, Isabelli Seiler de Medeiros Mendes, Sofia Turatti dos Santos, Diego Bonatto
Date: 2024-05-17 00:00:00

D-xylose is the second most abundant monosaccharide found in lignocellulose and is of biotechnological importance for producing second-generation ethanol and other high-value chemical compounds. D-xylose conversion to ethanol is promoted by microbial fermentation, mainly by bacteria, yeasts, or filamentous fungi. Considering yeasts, species belonging to the CTG(Ser1) or CTG(Ala) clade display a remarkable ability to ferment D-xylose to ethanol and other compounds; however, these yeasts are not employed on an industrial scale due to the poor fermentative performance compared to conventional yeasts, like Saccharomyces cerevisiae, and also due to the lack of a molecular toolbox for development of new strains tailored to fermentation stress tolerance and performance. Thus, the purpose of this review is to evaluate the major molecular tools (e.g., transformation markers and techniques, vectors, regulatory sequences, and gene editing techniques) available for the most studied yeasts of CTG(Ser1) clade, like Scheffersomyces, Spathaspora, Candida and Yamadazyma species, and the CTG(Ala) clade representative Pachysolen tannophilus. Furthermore, we synthesized the current state-of-the-art molecular developments and perspectives for D-xylose fermenting yeast strain design.
EPL: Empirical Prototype Learning for Deep Face Recognition
Authors: Weijia Fan, Jiajun Wen, Xi Jia, Linlin Shen, Jiancan Zhou, Qiufu Li
Date: 2024-05-21 00:00:00
Tasks: Face Recognition

Prototype learning is widely used in face recognition, which takes the row vectors of coefficient matrix in the last linear layer of the feature extraction model as the prototypes for each class. When the prototypes are updated using the facial sample feature gradients in the model training, they are prone to being pulled away from the class center by the hard samples, resulting in decreased overall model performance. In this paper, we explicitly define prototypes as the expectations of sample features in each class and design the empirical prototypes using the existing samples in the dataset. We then devise a strategy to adaptively update these empirical prototypes during the model training based on the similarity between the sample features and the empirical prototypes. Furthermore, we propose an empirical prototype learning (EPL) method, which utilizes an adaptive margin parameter with respect to sample features. EPL assigns larger margins to the normal samples and smaller margins to the hard samples, allowing the learned empirical prototypes to better reflect the class center dominated by the normal samples and finally pull the hard samples towards the empirical prototypes through the learning. The extensive experiments on MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace demonstrate the effectiveness of EPL. Our code is available at $\href{https://github.com/WakingHours-GitHub/EPL}{https://github.com/WakingHours-GitHub/EPL}$.
CoVisPose: Co-Visibility Pose Transformer for Wide-Baseline Relative Pose Estimation in 360◦ Indoor Panoramas
Authors: Will Hutchcroft, Yuguang Li, Ivaylo Boyadzhiev, Zhiqiang Wan, HaiYan Wang, Sing Bing Kang
Date: 2022-10-01 00:00:00
Proceeding: European Conference on Computer Vision 2022 10
Tasks: Camera Pose Estimation, Decoder, Pose Estimation, regression

We present CoVisPose, a new end-to-end supervised learning method for relative camera pose estimation in wide baseline 360
 indoor panoramas. To address the challenges of occlusion, perspective changes, and textureless or repetitive regions, we generate rich representations for direct pose regression by jointly learning dense bidirectional visual overlap, correspondence, and layout geometry. We estimate three image column-wise quantities: co-visibility (the probability that a given column’s image content is seen in the other panorama), angular correspondence (angular matching of columns across panoramas), and floor layout (the vertical floor-wall boundary angle). We learn these dense outputs by applying a transformer over the image-column feature sequences, which cover the full 360 field-of-view (FoV) from both panoramas. The resultant rich representation supports learning robust relative poses with an efficient 1D convolutional decoder. In addition to learned direct pose regression with scale, our network also supports pose estimation through a RANSAC-based rigid registration of the predicted corresponding layout boundary points. Our method is robust to extremely wide baselines with very low visual overlap, as well as significant occlusions. We improve upon the SOTA by a large margin, as demonstrated on a large-scale dataset of real homes, ZInD.
Neuroevolutionary representations for learning heterogeneous treatment effects
Authors: Michael C. Burkhart, Gabriel Ruiz
Date: 2023-05-25 00:00:00
Proceeding: Journal of Computational Science 2023 5
Tasks: Causal Inference

Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature representations to aid the estimation of the conditional average treatment effect or CATE. Our method focuses on an intermediate layer in a neural network trained to predict the outcome from the features. In contrast to previous approaches that encourage the distribution of representations to be treatment-invariant, we leverage a genetic algorithm that optimizes over representations useful for predicting the outcome to select those less useful for predicting the treatment. This allows us to retain information within the features useful for predicting the outcome even if that information may be related to treatment assignment. We validate our method on synthetic examples and illustrate its use on a real life dataset. This paper extends work previously presented at the International Conference on Computational Science in London (Burkhart and Ruiz, 2022).
Modelo de la inflación en Costa Rica
Authors: Aguilar Daniel, Chacon Breyner, Acuna Minor
Date: 2024-05-17 00:00:00

The stability of the inflation rate is a necessary condition for the proper functioning of any capitalist economy. In an economic environment with volatile inflation, the growth of the economy and its distribution among the agents of society is compromised. For this reason, and because in Costa Rica, as in most capitalist nations, the monetary authority is in charge of maintaining price stability, it is necessary to have models capable of predicting the behavior of inflation. on in the country. Along these lines, this work aims to compare two of the main types of basic predictive models found in the literature: univariate autoregressive models, exemplified by ARIMA models, and multivariate models formulated based on the theory. economics, like the Phillips curve.
CoCo Matrix: Taxonomy of Cognitive Contributions in Co-writing with Intelligent Agents
Authors: Ruyuan Wan, Simret Gebreegziabhe, Toby Jia-Jun Li, Karla Badillo-Urquiola
Date: 2024-05-21 00:00:00

In recent years, there has been a growing interest in employing intelligent agents in writing. Previous work emphasizes the evaluation of the quality of end product-whether it was coherent and polished, overlooking the journey that led to the product, which is an invaluable dimension of the creative process. To understand how to recognize human efforts in co-writing with intelligent writing systems, we adapt Flower and Hayes' cognitive process theory of writing and propose CoCo Matrix, a two-dimensional taxonomy of entropy and information gain, to depict the new human-agent co-writing model. We define four quadrants and situate thirty-four published systems within the taxonomy. Our research found that low entropy and high information gain systems are under-explored, yet offer promising future directions in writing tasks that benefit from the agent's divergent planning and the human's focused translation. CoCo Matrix, not only categorizes different writing systems but also deepens our understanding of the cognitive processes in human-agent co-writing. By analyzing minimal changes in the writing process, CoCo Matrix serves as a proxy for the writer's mental model, allowing writers to reflect on their contributions. This reflection is facilitated through the measured metrics of information gain and entropy, which provide insights irrespective of the writing system used.
Can We Treat Noisy Labels as Accurate?
Authors: Yuxiang Zheng, Zhongyi Han, Yilong Yin, Xin Gao, Tongliang Liu
Date: 2024-05-21 00:00:00
Tasks: Learning with noisy labels

Noisy labels significantly hinder the accuracy and generalization of machine learning models, particularly due to ambiguous instance features. Traditional techniques that attempt to correct noisy labels directly, such as those using transition matrices, often fail to address the inherent complexities of the problem sufficiently. In this paper, we introduce EchoAlign, a transformative paradigm shift in learning from noisy labels. Instead of focusing on label correction, EchoAlign treats noisy labels ($\tilde{Y}$) as accurate and modifies corresponding instance features ($X$) to achieve better alignment with $\tilde{Y}$. EchoAlign's core components are (1) EchoMod: Employing controllable generative models, EchoMod precisely modifies instances while maintaining their intrinsic characteristics and ensuring alignment with the noisy labels. (2) EchoSelect: Instance modification inevitably introduces distribution shifts between training and test sets. EchoSelect maintains a significant portion of clean original instances to mitigate these shifts. It leverages the distinct feature similarity distributions between original and modified instances as a robust tool for accurate sample selection. This integrated approach yields remarkable results. In environments with 30% instance-dependent noise, even at 99% selection accuracy, EchoSelect retains nearly twice the number of samples compared to the previous best method. Notably, on three datasets, EchoAlign surpasses previous state-of-the-art techniques with a substantial improvement.
Generalize Polyp Segmentation via Inpainting across Diverse Backgrounds and Pseudo-Mask Refinement
Authors: Jiajian Ma, Fangqi Lu, Silin Huang, Song Wu, Zhen Li
Date: 2024-05-21 00:00:00
Tasks: Data Augmentation

Inpainting lesions within different normal backgrounds is a potential method of addressing the generalization problem, which is crucial for polyp segmentation models. However, seamlessly introducing polyps into complex endoscopic environments while simultaneously generating accurate pseudo-masks remains a challenge for current inpainting methods. To address these issues, we first leverage the pre-trained Stable Diffusion Inpaint and ControlNet, to introduce a robust generative model capable of inpainting polyps across different backgrounds. Secondly, we utilize the prior that synthetic polyps are confined to the inpainted region, to establish an inpainted region-guided pseudo-mask refinement network. We also propose a sample selection strategy that prioritizes well-aligned and hard synthetic cases for further model fine-tuning. Experiments demonstrate that our inpainting model outperformed baseline methods both qualitatively and quantitatively in inpainting quality. Moreover, our data augmentation strategy significantly enhances the performance of polyp segmentation models on external datasets, achieving or surpassing the level of fully supervised training benchmarks in that domain. Our code is available at https://github.com/497662892/PolypInpainter.
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
Authors: Keke Huang, Yu Guang Wang, Ming Li, and Pietro Liò
Date: 2024-05-21 00:00:00
Tasks: Graph Neural Network

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on a diverse range of real-world and synthetic datasets with varying degrees of heterophily, support the superiority of UniFilter. These results not only demonstrate the universality of UniBasis but also highlight its proficiency in graph explanation.
AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection
Authors: Zizhao Chen, Yeqiang Qian, Xiaoxiao Yang, Chunxiang Wang, Ming Yang
Date: 2024-05-21 00:00:00
Tasks: Knowledge Distillation, Pedestrian Detection

Multispectral pedestrian detection has been shown to be effective in improving performance within complex illumination scenarios. However, prevalent double-stream networks in multispectral detection employ two separate feature extraction branches for multi-modal data, leading to nearly double the inference time compared to single-stream networks utilizing only one feature extraction branch. This increased inference time has hindered the widespread employment of multispectral pedestrian detection in embedded devices for autonomous systems. To address this limitation, various knowledge distillation methods have been proposed. However, traditional distillation methods focus only on the fusion features and ignore the large amount of information in the original multi-modal features, thereby restricting the student network's performance. To tackle the challenge, we introduce the Adaptive Modal Fusion Distillation (AMFD) framework, which can fully utilize the original modal features of the teacher network. Specifically, a Modal Extraction Alignment (MEA) module is utilized to derive learning weights for student networks, integrating focal and global attention mechanisms. This methodology enables the student network to acquire optimal fusion strategies independent from that of teacher network without necessitating an additional feature fusion module. Furthermore, we present the SMOD dataset, a well-aligned challenging multispectral dataset for detection. Extensive experiments on the challenging KAIST, LLVIP and SMOD datasets are conducted to validate the effectiveness of AMFD. The results demonstrate that our method outperforms existing state-of-the-art methods in both reducing log-average Miss Rate and improving mean Average Precision. The code is available at https://github.com/bigD233/AMFD.git.
Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image
Authors: Zerui Zhang, Zhichao Sun, Zelong Liu, Bo Du, Rui Yu, Zhou Zhao, Yongchao Xu
Date: 2024-05-21 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network, Image Restoration, Semi-supervised Anomaly Detection, Supervised Anomaly Detection

Medical anomaly detection is a critical research area aimed at recognizing abnormal images to aid in diagnosis.Most existing methods adopt synthetic anomalies and image restoration on normal samples to detect anomaly. The unlabeled data consisting of both normal and abnormal data is not well explored. We introduce a novel Spatial-aware Attention Generative Adversarial Network (SAGAN) for one-class semi-supervised generation of health images.Our core insight is the utilization of position encoding and attention to accurately focus on restoring abnormal regions and preserving normal regions. To fully utilize the unlabelled data, SAGAN relaxes the cyclic consistency requirement of the existing unpaired image-to-image conversion methods, and generates high-quality health images corresponding to unlabeled data, guided by the reconstruction of normal images and restoration of pseudo-anomaly images.Subsequently, the discrepancy between the generated healthy image and the original image is utilized as an anomaly score.Extensive experiments on three medical datasets demonstrate that the proposed SAGAN outperforms the state-of-the-art methods.
Aligning Transformers with Continuous Feedback via Energy Rank Alignment
Authors: Shriram Chennakesavalu, Frank Hu, Sebastian Ibarraran, Grant M. Rotskoff
Date: 2024-05-21 00:00:00

Searching through chemical space is an exceptionally challenging problem because the number of possible molecules grows combinatorially with the number of atoms. Large, autoregressive models trained on databases of chemical compounds have yielded powerful generators, but we still lack robust strategies for generating molecules with desired properties. This molecular search problem closely resembles the "alignment" problem for large language models, though for many chemical tasks we have a specific and easily evaluable reward function. Here, we introduce an algorithm called energy rank alignment (ERA) that leverages an explicit reward function to produce a gradient-based objective that we use to optimize autoregressive policies. We show theoretically that this algorithm is closely related to proximal policy optimization (PPO) and direct preference optimization (DPO), but has a minimizer that converges to an ideal Gibbs-Boltzmann distribution with the reward playing the role of an energy function. Furthermore, this algorithm is highly scalable, does not require reinforcement learning, and performs well relative to DPO when the number of preference observations per pairing is small. We deploy this approach to align molecular transformers and protein language models to generate molecules and protein sequences, respectively, with externally specified properties and find that it does so robustly, searching through diverse parts of chemical space.
Strategic Deployment of Honeypots in Blockchain-based IoT Systems
Authors: Daniel Commey, Sena Hounsinou, Garth V. Crosby
Date: 2024-05-21 00:00:00
Tasks: Decision Making, Intrusion Detection

This paper addresses the challenge of enhancing cybersecurity in Blockchain-based Internet of Things (BIoTs) systems, which are increasingly vulnerable to sophisticated cyberattacks. It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on IoT nodes. This model enables the transformation of regular nodes into decoys in response to suspicious activities, thereby strengthening the security of BIoT networks. The paper analyses strategic interactions between potential attackers and the AI-enhanced IDS through a game-theoretic model, specifically Bayesian games. The model focuses on understanding and predicting sophisticated attacks that may initially appear normal, emphasizing strategic decision-making, optimized honeypot deployment, and adaptive strategies in response to evolving attack patterns.
Skin-in-the-Game: Decision Making via Multi-Stakeholder Alignment in LLMs
Authors: Bilgehan Sel, Priya Shanmugasundaram, Mohammad Kachuee, Kun Zhou, Ruoxi Jia, Ming Jin
Date: 2024-05-21 00:00:00
Tasks: Arithmetic Reasoning, Decision Making, Question Answering

Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. However, they encounter significant challenges in the domain of moral reasoning and ethical decision-making, especially in complex scenarios with multiple stakeholders. This paper introduces the Skin-in-the-Game (SKIG) framework, aimed at enhancing moral reasoning in LLMs by exploring decisions' consequences from multiple stakeholder perspectives. Central to SKIG's mechanism is simulating accountability for actions, which, alongside empathy exercises and risk assessment, is pivotal to its effectiveness. We validate SKIG's performance across various moral reasoning benchmarks with proprietary and opensource LLMs, and investigate its crucial components through extensive ablation analyses.
Panmodal Information Interaction
Authors: Chirag Shah, Ryen W. White
Date: 2024-05-21 00:00:00

The emergence of generative artificial intelligence (GenAI) is transforming information interaction. For decades, search engines such as Google and Bing have been the primary means of locating relevant information for the general population. They have provided search results in the same standard format (the so-called "10 blue links"). The recent ability to chat via natural language with AI-based agents and have GenAI automatically synthesize answers in real-time (grounded in top-ranked results) is changing how people interact with and consume information at massive scale. These two information interaction modalities (traditional search and AI-powered chat) coexist in current search engines, either loosely coupled (e.g., as separate options/tabs) or tightly coupled (e.g., integrated as a chat answer embedded directly within a traditional search result page). We believe that the existence of these two different modalities, and potentially many others, is creating an opportunity to re-imagine the search experience, capitalize on the strengths of many modalities, and develop systems and strategies to support seamless flow between them. We refer to these as panmodal experiences. Unlike monomodal experiences, where only one modality is available and/or used for the task at hand, panmodal experiences make multiple modalities available to users (multimodal), directly support transitions between modalities (crossmodal), and seamlessly combine modalities to tailor task assistance (transmodal). While our focus is search and chat, with learnings from insights from a survey of over 100 individuals who have recently performed common tasks on these two modalities, we also present a more general vision for the future of information interaction using multiple modalities and the emergent capabilities of GenAI.
Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment
Authors: Holli Sargeant, Ahmed Izzidien, Felix Steffek
Date: 2024-05-21 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Topic Classification

This paper addresses a critical gap in legal analytics by developing and applying a novel taxonomy for topic classification of summary judgment cases in the United Kingdom. Using a curated dataset of summary judgment cases, we use the Large Language Model Claude 3 Opus to explore functional topics and trends. We find that Claude 3 Opus correctly classified the topic with an accuracy of 87.13% and an F1 score of 0.87. The analysis reveals distinct patterns in the application of summary judgments across various legal domains. As case law in the United Kingdom is not originally labelled with keywords or a topic filtering option, the findings not only refine our understanding of the thematic underpinnings of summary judgments but also illustrate the potential of combining traditional and AI-driven approaches in legal classification. Therefore, this paper provides a new and general taxonomy for UK law. The implications of this work serve as a foundation for further research and policy discussions in the field of judicial administration and computational legal research methodologies.