pwc-archive/papers-with-abstracts huggingface dataset:
Characterization of differences in immune responses during bolus and continuous infusion endotoxin challenges using mathematical modeling
Authors: Kristen A. Windoloski, Susanne Janum, Ronan M. G. Berg, Mette S. Olufsen
Date: 2023-08-03 00:00:00

Endotoxin administration is commonly used to study the inflammatory response, and though traditionally given as a bolus injection, it can be administered as a continuous infusion over multiple hours. Several studies hypothesize that the latter better represents the prolonged and pronounced inflammation observed in conditions like sepsis. Yet, very few experimental studies have administered endotoxin using both strategies, leaving significant gaps in determining the underlying mechanisms responsible for their differing immune responses. We use mathematical modeling to analyze cytokine data from two studies administering a 2 ng/kg dose of endotoxin, one as a bolus and the other as a continuous infusion over four hours. Using our model, we simulate the dynamics of mean and subject-specific cytokine responses as well as the response to long-term endotoxin administration. Cytokine measurements reveal that the bolus injection leads to significantly higher peaks for IL-8, while IL-10 reaches significantly higher peaks during continuous administration. Moreover, the peak timing of all measured cytokines occurs later in the continuous infusion. We identify three model parameters that significantly differ between the two administration methods. Monocyte activation of IL-10 is greater during the continuous infusion, while recovery rates of IL-8 is faster for the bolus injection. This suggests that a continuous infusion elicits a stronger, longer-lasting systemic reaction through increased stimulation of monocyte anti-inflammatory mediator production and decreased recovery of pro-inflammatory catalysts. Furthermore, our continuous infusion model exhibits prolonged inflammation with recurrent peaks resolving within two days during long-term (20-32 hours) endotoxin administration.
Path Shadowing Monte-Carlo
Authors: Rudy Morel, Stéphane Mallat, Jean-Philippe Bouchaud
Date: 2023-08-03 00:00:00
Tasks: Diversity

We introduce a Path Shadowing Monte-Carlo method, which provides prediction of future paths, given any generative model. At any given date, it averages future quantities over generated price paths whose past history matches, or `shadows', the actual (observed) history. We test our approach using paths generated from a maximum entropy model of financial prices, based on a recently proposed multi-scale analogue of the standard skewness and kurtosis called `Scattering Spectra'. This model promotes diversity of generated paths while reproducing the main statistical properties of financial prices, including stylized facts on volatility roughness. Our method yields state-of-the-art predictions for future realized volatility and allows one to determine conditional option smiles for the S\&P500 that outperform both the current version of the Path-Dependent Volatility model and the option market itself.
Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models
Authors: Aidan O'Gara
Date: 2023-07-05 00:00:00

Are current language models capable of deception and lie detection? We study this question by introducing a text-based game called $\textit{Hoodwinked}$, inspired by Mafia and Among Us. Players are locked in a house and must find a key to escape, but one player is tasked with killing the others. Each time a murder is committed, the surviving players have a natural language discussion then vote to banish one player from the game. We conduct experiments with agents controlled by GPT-3, GPT-3.5, and GPT-4 and find evidence of deception and lie detection capabilities. The killer often denies their crime and accuses others, leading to measurable effects on voting outcomes. More advanced models are more effective killers, outperforming smaller models in 18 of 24 pairwise comparisons. Secondary metrics provide evidence that this improvement is not mediated by different actions, but rather by stronger persuasive skills during discussions. To evaluate the ability of AI agents to deceive humans, we make this game publicly available at h https://hoodwinked.ai/ .
Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
Authors: Yu-Min Chu, Chieh Liu, Ting-I Hsieh, Hwann-Tzong Chen, Tyng-Luh Liu
Date: 2023-01-27 00:00:00
Proceeding: ICML 2023 1
Tasks: 3D Anomaly Detection, 3D Anomaly Detection and Segmentation, Anomaly Detection, RGB+3D Anomaly Detection and Segmentation, Unsupervised Anomaly Detection

We present a shape-guided expert-learning framework to tackle the problem of unsupervised 3D anomaly detection. Our method is established on the effectiveness of two specialized expert models and their synergy to localize anomalous regions from color and shape modalities. The first expert utilizes geometric information to probe 3D structural anomalies by modeling the implicit distance fields around local shapes. The second expert considers the 2D RGB features associated with the first expert to identify color appearance irregularities on the local shapes. We use the two experts to build the dual memory banks from the anomaly-free training samples and perform shape-guided inference to pinpoint the defects in the testing samples. Owing to the per-point 3D representation and the effective fusion scheme of complementary modalities, our method efficiently achieves state-of-the-art performance on the MVTec 3D-AD dataset with better recall and lower false positive rates, as preferred in real applications.
FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs
Authors: Iftekharul Abedeen, Md. Ashiqur Rahman, Fatema Zohra Prottyasha, Tasnim Ahmed, Tareque Mohmud Chowdhury, Swakkhar Shatabda
Date: 2023-08-05 00:00:00
Proceeding: Scientific Data 2023 8
Tasks: Image Segmentation, Object Detection

FractureAtlas is a musculoskeletal bone fracture dataset with annotations for deep learning tasks like classification, localization and segmentation. The dataset contains a total of 4,083 X-Ray images with annotation in COCO, VGG, YOLO and Pascal VOC format. This dataset is made freely available for any purpose. The data provided within this work are free to copy, share or redistribute in any medium or format. The data might be adapted, remixed, transformed, and built upon. The dataset is licensed under a CC-BY 4.0 license. It should be noted that to use the dataset correctly, one needs to have knowledge of medical and radiology fields to understand the results and make conclusions based on the dataset. It's also important to consider the possibility of labeling errors. 

Furthermore, any publication that utilizes this resource should acknowledge the original paper, and the authors are encouraged to share their code and models to assist the research community in replicating the experiments and promoting the field of medical imaging.
Fish Detection Using Deep Learning
Authors: Suxia Cui, Yu Zhou, Yonghui Wang, Lujun Zhai
Date: 2020-01-23 00:00:00
Proceeding: Applied Computational Intelligence and Soft Computing 2020 1
Tasks: Data Augmentation, Deep Learning, Fish Detection

Recently, human being’s curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being’s learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed. The training data set was collected from the Gulf of Mexico by a digital camera. To fit into this unique need, three optimization approaches were applied to the CNN: data augmentation, network simplification, and training process speed up. Data augmentation transformation provided more learning samples; the network was simplified to accommodate the artificial neural network; the training process speed up is introduced to make the training process more time efficient. Experimental results showed that the proposed model is promising, and has the potential to be extended to other underwear objects.
Intent-aware Radio Resource Scheduling in a RAN Slicing Scenario using Reinforcement Learning
Authors: Cleverson V. Nahum, Victor Hugo Lopes, Ryan M. Dreifuerst, Pedro Batista, Ilan Correa, Kleber V. Cardoso, Aldebaro Klautau, and Robert W. Heath Jr
Date: 2023-08-02 00:00:00
Proceeding: IEEE Transactions on Wireless Communications 2023 8
Tasks: Management, Scheduling

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization. In this scenario, radio resource scheduling (RRS) is responsible for dealing with scarce and limited frequency spectrum resources available at the RAN domain while fulfilling the slice intents. The wide variety of scenarios supported in 5G and beyond 5G networks makes the RRS problem in RAN slicing scenario a significant challenge. This paper proposes an intent-aware reinforcement learning method to perform the RRS function in a RAN slicing scenario. The slice’s quality of service intents is described in a common intent model in a service-level agreement. The proposed method tries to prevent intent faults by making the management of radio resources available among slices. This method uses slices’ and users’ equipment network metrics in the observation space. The proposed method is evaluated under different network conditions and outperforms different baselines considering the slices’ intents fulfillment.
Quantitative Metrics for Benchmarking Human-Aware Robot Navigation
Authors: Jarosław Karwowski, Wojciech Szynkiewicz
Date: 2023-07-26 00:00:00
Proceeding: IEEE Access 2023 7
Tasks: Benchmarking, Robot Navigation

Social robots have recently gained popularity, and many human-aware navigation approaches have emerged. This work presents a comprehensive benchmark for quantitatively assessing robot navigation methods. As an automated quantitative approach, our Social Robot Planner Benchmark (SRPB) produces invariable indicators of the algorithm’s performance that can assist the system designer in selecting the best method for the specific application. Our benchmark extends state-of-the-art task performance scores and proposes novel social metrics regarding robot motion naturalness and the perceived safety of humans surrounding the robot. Our social metrics take human tracking reliability into account. Using the SRPB integrated with the TIAGo robot, we assessed the robot’s behaviour operating with traditional and human-aware trajectory planners in simulated and real-world environments. Our experiments tested whether state-of-the-art human-aware trajectory planners significantly improve human-awareness indicators over traditional approaches yet still maintain reasonable navigation performance. An open-source implementation of our benchmark, compatible with the Robot Operating System, is provided.
Unlocking the black box of CNNs: Visualising the decision-making process with PRISM
Authors: Tomasz Szandala
Date: 2023-05-13 00:00:00
Proceeding: Information Sciences 2023 5
Tasks: Decision Making, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Technology has grown rapidly in recent years, and new solutions that rely on Machine Learning (ML) and Artificial Intelligence (AI) are introduced every day. With such fast-paced advancement, inspecting and fully comprehending how given models make decisions is becoming problematic. The complex decision-making process of these models has become a black box, making it challenging to unravel how they work; therefore, eXplainable Artificial Intelligence (XAI) methods are crucial for further development. This paper discusses how state-of-the-art techniques determine classifications and why they need to be revised to understand the prediction-generating process fully. It compares those existing solutions with the new method called Principal Image Sections Mapping - PRISM, which relies on Principal Component Analysis and allows visualising the most significant features recognised by a given Convolutional Neural Network. PRISM is implemented in a piece of software called TorchPRISM that can generate and present the clustering based on the method's output. The result can indicate ambiguous classes discrimination; thus, the possibility of automating the output analysis process is also discussed. The paper's main objective is to examine how PRISM enhances the current understanding of the decision-making process and introduce a tool that can facilitate analysing the output.
Bearing Fault Diagnosis Base on Multi-scale CNN and LSTM Model
Authors: X. Chen, B. Zhang, D. Gao
Date: 2020-06-05 00:00:00
Proceeding: journal 2020 6
Tasks: Classification, Diagnostic, Fault Detection, Fault Diagnosis, Time Series, Time Series Classification

Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis. These methods use a pre-determined transformation (such as empirical mode decomposition, fast Fourier transform, discrete wavelet transform) to convert time-series signals into frequency domain signals, the performance of diagnostic system significantly relies on the extracted features. However, extracting signal characteristics is fairly time-consuming and depends on specialized signal processing knowledge. Although some studies have developed highly accurate algorithms, the diagnostic results rely heavily on large data sets and unreliable human analysis. This study proposes an automatic feature learning neural network that utilizes raw vibration signals as inputs and uses two convolutional neural networks with different kernel sizes to automatically extract different frequency signal characteristics from raw data. Then long short-term memory was used to identify the fault type according to learned features. The data is down-sampled before inputting into the network, greatly reducing the number of parameters. The experiment shows that the proposed method can not only achieve 98.46% average accuracy, exceeding some state-of-the-art intelligent algorithms based on prior knowledge and having better performance in noisy environments.
Very Fast EM-based Mixture Model Clustering using Multiresolution kd-trees
Authors: Andrew Moore
Date: 1998-12-01 00:00:00
Proceeding: NeurIPS 1998 12
Tasks: Astronomy, Clustering

Clustering is important in many fields including manufacturing, biology, finance, and astronomy. Mixture models are a popular approach due to their statistical foundations, and EM is a very popular method for finding mixture models. EM, however, requires many accesses of the data, and thus has been dismissed as impractical (e.g. [9]) for data mining of enormous datasets. We present a new algorithm, based on the multiresolution kd-trees of [5], which dramatically reduces the cost of EM-based clustering, with savings rising linearly with the number of datapoints. Although presented here for maximum likelihood estimation of Gaussian mixture models, it is also applicable to non-Gaussian models (provided class densities are monotonic in Mahalanobis distance), mixed categorical/ numeric clusters. and Bayesian methods such as Antoclass [1].
Learning the k in k-means
Authors: Greg Hamerly, Charles Elkan
Date: 2003-12-01 00:00:00
Proceeding: NeurIPS 2003 12
Tasks: Clustering, Vector Quantization (k-means problem)

When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present an improved algorithm for learning k while clustering. The G-means algorithm is based on a statistical test for the hypothesis that a subset of data follows a Gaussian distribution. G-means runs k-means with increasing k in a hierarchical fashion until the test accepts the hypothesis that the data assigned to each k-means center are Gaussian. Two key advantages are that the hypothesis test does not limit the covariance of the data and does not compute a full covariance matrix. Additionally, G-means only requires one intuitive parameter, the standard statistical signiﬁcance level α. We present results from experiments showing that the algorithm works well, and better than a recent method based on the BIC penalty for model complexity. In these experiments, we show that the BIC is ineffective as a scoring function, since it does not penalize strongly enough the model’s complexity.
Clustering via Concave Minimization
Authors: Paul Bradley, Olvi Mangasarian, W. Street
Date: 1996-12-01 00:00:00
Proceeding: NeurIPS 1996 12
Tasks: Clustering, Diagnostic

The problem of assigning m points in the n-dimensional real space Rn to k clusters is formulated as that of determining k centers in Rn such that the sum of distances of each point to the nearest center is minimized. If a polyhedral distance is used, the problem can be formulated as that of minimizing a piecewise-linear concave function on a polyhedral set which is shown to be equivalent to a bilinear program: minimizing a bilinear function on a polyhe(cid:173) dral set. A fast finite k-Median Algorithm consisting of solving few linear programs in closed form leads to a stationary point of the bilinear program. Computational testing on a number of real(cid:173) world databases was carried out. On the Wisconsin Diagnostic Breast Cancer (WDBC) database, k-Median training set correct(cid:173) ness was comparable to that of the k-Mean Algorithm, however its testing set correctness was better. Additionally, on the Wisconsin Prognostic Breast Cancer (WPBC) database, distinct and clini(cid:173) cally important survival curves were extracted by the k-Median Algorithm, whereas the k-Mean Algorithm failed to obtain such distinct survival curves for the same database.
A Support Vector Method for Clustering
Authors: Asa Ben-Hur, David Horn, Hava Siegelmann, Vladimir Vapnik
Date: 2000-12-01 00:00:00
Proceeding: NeurIPS 2000 12
Tasks: Clustering

We present a novel method for clustering using the support vector ma(cid:173) chine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere enclosing them. The boundary of the sphere forms in data space a set of closed contours containing the data. Data points enclosed by each contour are defined as a cluster. As the width parameter of the Gaussian kernel is decreased, these contours fit the data more tightly and splitting of contours occurs. The algorithm works by separating clusters according to valleys in the un(cid:173) derlying probability distribution, and thus clusters can take on arbitrary geometrical shapes. As in other SV algorithms, outliers can be dealt with by introducing a soft margin constant leading to smoother cluster bound(cid:173) aries. The structure of the data is explored by varying the two parame(cid:173) ters. We investigate the dependence of our method on these parameters and apply it to several data sets.
Support Vector Clustering
Authors: Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
Date: 2001-12-01 00:00:00
Proceeding: JMLR 2001 12
Tasks: Clustering

We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several components, each enclosing a separate cluster of points. We present a simple algorithm for identifying these clusters. The width of the Gaussian kernel controls the scale at which the data is probed while the soft margin constant helps coping with outliers and overlapping clusters. The structure of a dataset is explored by varying the two parameters, maintaining a minimal number of support vectors to assure smooth cluster boundaries. We demonstrate the performance of our algorithm on several datasets.
Stochastic Neighbor Embedding
Authors: Geoffrey E. Hinton, Sam Roweis
Date: 2002-12-01 00:00:00
Proceeding: NeurIPS 2002 12
Tasks: Dimensionality Reduction, Object

We describe a probabilistic approach to the task of placing objects, de- scribed by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object in the high-dimensional space and the densities under this Gaussian (or the given dissimilarities) are used to deﬁne a probability distribution over all the potential neighbors of the object. The aim of the embedding is to approximate this distribu- tion as well as possible when the same operation is performed on the low-dimensional “images” of the objects. A natural cost function is a sum of Kullback-Leibler divergences, one per object, which leads to a simple gradient for adjusting the positions of the low-dimensional im- ages. Unlike other dimensionality reduction methods, this probabilistic framework makes it easy to represent each object by a mixture of widely separated low-dimensional images. This allows ambiguous objects, like the document count vector for the word “bank”, to have versions close to the images of both “river” and “ﬁnance” without forcing the images of outdoor concepts to be located close to those of corporate concepts.
Visualizing Data using t-SNE
Authors: Laurens van der Maaten, Geoffrey Hinton
Date: 2008-11-01 00:00:00
Proceeding: JMLR 2008 11
Tasks: Dimensionality Reduction

We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images ofobjects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.