pwc-archive/papers-with-abstracts huggingface dataset:
Distinctive pupil and microsaccade-rate signatures in self-recognition
Authors: Lisa Schwetlick, Hendrik Graupner, Olaf Dimigen, Ralf Engbert
Date: 2023-07-28 00:00:00

Pupil dynamics and fixational eye movements are primarily involuntary processes that actively support visual perception during fixations. Both measures are known to be sensitive to ongoing cognitive and affective processing. In a visual fixation experiment (N=116) we demonstrate that self-recognition, familiar faces, and unfamiliar faces elicit specific responses in pupil dynamics and microsaccade rate. First, the pupil response comprises an immediate pupil constriction followed by a dilation in response to stimulus onsets. We observe attenuated constriction and greater dilation when faces are recognized compared to unknown faces. This effect is strongest for one's own face. Second, microsaccade rates, which generally show inhibitory responses to incoming stimuli, generate stronger inhibition for familiar faces compared to unknown faces. Again, the strongest inhibition is observed in response to one's own face. Our results imply that eye-related physiological measures expose hidden knowledge in face memory and could contribute to biometric authentication and identity validation procedures.
Quantifying interictal intracranial EEG to predict focal epilepsy
Authors: Ryan S Gallagher, Nishant Sinha, Akash R Pattnaik, William K. S. Ojemann, Alfredo Lucas, Joshua J. LaRocque, John M Bernabei, Adam S Greenblatt, Elizabeth M Sweeney, H Isaac Chen, Kathryn A Davis, Erin C Conrad, Brian Litt
Date: 2023-07-27 00:00:00
Tasks: Decision Making, EEG

Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the 5 Sense Score (5SS), a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians choice of therapeutic intervention and the patient outcome. The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts.
Integrative AI-Driven Strategies for Advancing Precision Medicine in Infectious Diseases and Beyond: A Novel Multidisciplinary Approach
Authors: Ghizal fatima, Risala H. Allami, Maitham G. Yousif
Date: 2023-07-27 00:00:00
Tasks: Management, Prognosis

Precision medicine, tailored to individual patients based on their genetics, environment, and lifestyle, shows promise in managing complex diseases like infections. Integrating artificial intelligence (AI) into precision medicine can revolutionize disease management. This paper introduces a novel approach using AI to advance precision medicine in infectious diseases and beyond. It integrates diverse fields, analyzing patients' profiles using genomics, proteomics, microbiomics, and clinical data. AI algorithms process vast data, providing insights for precise diagnosis, treatment, and prognosis. AI-driven predictive modeling empowers healthcare providers to make personalized and effective interventions. Collaboration among experts from different domains refines AI models and ensures ethical and robust applications. Beyond infections, this AI-driven approach can benefit other complex diseases. Precision medicine powered by AI has the potential to transform healthcare into a proactive, patient-centric model. Research is needed to address privacy, regulations, and AI integration into clinical workflows. Collaboration among researchers, healthcare institutions, and policymakers is crucial in harnessing AI-driven strategies for advancing precision medicine and improving patient outcomes.
Dimensions of Level-1 Group-Based Phylogenetic Networks
Authors: Elizabeth Gross, Robert Krone, Samuel Martin
Date: 2023-07-27 00:00:00

Phylogenetic networks represent evolutionary histories of sets of taxa where horizontal evolution or hybridization has occurred. Placing a Markov model of evolution on a phylogenetic network gives a model that is particularly amenable to algebraic study by representing it as an algebraic variety. In this paper, we give a formula for the dimension of the variety corresponding to a triangle-free level-1 phylogenetic network under a group-based evolutionary model. On our way to this, we give a dimension formula for codimension zero toric fiber products. We conclude by illustrating applications to identifiability.
Pairs Trading: An Optimal Selling Rule with Constraints
Authors: Ruyi Liu, Jingzhi Tie, Zhen Wu, Qing Zhang
Date: 2023-07-28 00:00:00
Tasks: Position

The focus of this paper is on identifying the most effective selling strategy for pairs trading of stocks. In pairs trading, a long position is held in one stock while a short position is held in another. The goal is to determine the optimal time to sell the long position and repurchase the short position in order to close the pairs position. The paper presents an optimal pairs-trading selling rule with trading constraints. In particular, the underlying stock prices evolve according to a two dimensional geometric Brownian motion and the trading permission process is given in terms of a two-state {trading allowed, trading not allowed} Markov chain. It is shown that the optimal policy can be determined by a threshold curve which is obtained by solving the associated HJB equations (quasi-variational inequalities). A closed form solution is obtained. A verification theorem is provided. Numerical experiments are also reported to demonstrate the optimal policies and value functions.
An exploration of the mathematical structure and behavioural biases of 21st century financial crises
Authors: Nick James, Max Menzies
Date: 2023-07-28 00:00:00

In this paper we contrast the dynamics of the 2022 Ukraine invasion financial crisis with notable financial crises of the 21st century - the dot-com bubble, global financial crisis and COVID-19. We study the similarity in market dynamics and associated implications for equity investors between various financial market crises and we introduce new mathematical techniques to do so. First, we study the strength of collective dynamics during different market crises, and compare suitable portfolio diversification strategies with respect to the unique number of sectors and stocks for optimal systematic risk reduction. Next, we introduce a new linear operator method to quantify distributional distance between equity returns during various crises. Our method allows us to fairly compare underlying stock and sector performance during different time periods, normalising for those collective dynamics driven by the overall market. Finally, we introduce a new combinatorial portfolio optimisation framework driven by random sampling to investigate whether particular equities and equity sectors are more effective in maximising investor risk-adjusted returns during market crises.
Understanding the worst-kept secret of high-frequency trading
Authors: Sergio Pulido, Mathieu Rosenbaum, Emmanouil Sfendourakis
Date: 2023-07-28 00:00:00

Volume imbalance in a limit order book is often considered as a reliable indicator for predicting future price moves. In this work, we seek to analyse the nuances of the relationship between prices and volume imbalance. To this end, we study a market-making problem which allows us to view the imbalance as an optimal response to price moves. In our model, there is an underlying efficient price driving the mid-price, which follows the model with uncertainty zones. A single market maker knows the underlying efficient price and consequently the probability of a mid-price jump in the future. She controls the volumes she quotes at the best bid and ask prices. Solving her optimization problem allows us to understand endogenously the price-imbalance connection and to confirm in particular that it is optimal to quote a predictive imbalance. Our model can also be used by a platform to select a suitable tick size, which is known to be a crucial topic in financial regulation. The value function of the market maker's control problem can be viewed as a family of functions, indexed by the level of the market maker's inventory, solving a coupled system of PDEs. We show existence and uniqueness of classical solutions to this coupled system of equations. In the case of a continuous inventory, we also prove uniqueness of the market maker's optimal control policy.
Set-Membership Inference Attacks using Data Watermarking
Authors: Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer, Asja Fischer
Date: 2023-06-22 00:00:00
Tasks: Inference Attack, Membership Inference Attack

In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques. In particular, we demonstrate how conditional sampling from a generative model can reveal the watermark that was injected into parts of the training data. Our empirical results demonstrate that the proposed watermarking technique is a principled approach for detecting the non-consensual use of image data in training generative models.
Combining transmission speckle photography and convolutional neural network for determination of fat content in cow's milk -- an exercise in classification of parameters of a complex suspension
Authors: Kwasi Nyandey, Daniel Jakubczyk
Date: 2023-07-08 00:00:00

We have combined transmission speckle photography and machine learning for direct classification and recognition of milk fat content classes. Our aim was hinged on the fact that parameters of scattering particles (and the dispersion medium) can be linked to the intensity distribution (speckle) observed when coherent light is transmitted through a scattering medium. For milk, it is primarily the size distribution and concentration of fat globules, which constitutes the total fat content. Consequently, we trained convolutional neural network to recognise and classify laser speckle from different fat content classes (0.5, 1.5, 2.0 and 3.2%). We investigated four exposure-time protocols and obtained the highest performance for shorter exposure times, in which the intensity histograms are kept similar for all images and the most probable intensity in the speckle pattern is close to zero. Our neural network was able to recognize the milk fat content classes unambiguously and we obtained the highest test and independent classification accuracies of 100 and ~99% respectively. It indicates that the parameters of other complex realistic suspensions could be classified with similar methods.
ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication
Authors: Wangjun Jiang, Dingyou Ma, Zhiqing Wei, Zhiyong Feng, Ping Zhang
Date: 2023-07-14 00:00:00
Tasks: Integrated sensing and communication, ISAC

Recent advances in wireless communication with the enormous demands of sensing ability have given rise to the integrated sensing and communication (ISAC) technology, among which passive sensing plays an important role. The main challenge of passive sensing is how to achieve high sensing performance in the condition of communication demodulation errors. In this paper, we propose an ISAC network (ISAC-NET) that combines passive sensing with communication signal detection by using model-driven deep learning (DL). Dissimilar to existing passive sensing algorithms that first demodulate the transmitted symbols and then obtain passive sensing results from the demodulated symbols, ISAC-NET obtains passive sensing results and communication demodulated symbols simultaneously. Different from the data-driven DL method, we adopt the block-by-block signal processing method that divides the ISAC-NET into the passive sensing module, signal detection module and channel reconstruction module. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for passive sensing and communication in wireless communications.
Eco-Friendly Sensing for Human Activity Recognition
Authors: Kaede Shintani, Hamada Rizk, Hirozumi Yamaguchi
Date: 2023-07-30 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

With the increasing number of IoT devices, there is a growing demand for energy-free sensors. Human activity recognition holds immense value in numerous daily healthcare applications. However, the majority of current sensing modalities consume energy, thus limiting their sustainable adoption. In this paper, we present a novel activity recognition system that not only operates without requiring energy for sensing but also harvests energy. Our proposed system utilizes photovoltaic cells, attached to the wrist and shoes, as eco-friendly sensing devices for activity recognition. By capturing photovoltaic readings and employing a deep transformer model with powerful learning capabilities, the system effectively recognizes user activities. To ensure robust performance across various subjects, time periods, and lighting conditions, the system incorporates feature extraction and different processing modules. The evaluation of the proposed system on realistic indoor and outdoor environments demonstrated its ability to recognize activities with an accuracy of 91.7%.
Pupil Learning Mechanism
Authors: Rua-Huan Tsaih, Yu-Hang Chien, Shih-Yi Chien
Date: 2023-07-30 00:00:00

Studies on artificial neural networks rarely address both vanishing gradients and overfitting issues. In this study, we follow the pupil learning procedure, which has the features of interpreting, picking, understanding, cramming, and organizing, to derive the pupil learning mechanism (PLM) by which to modify the network structure and weights of 2-layer neural networks (2LNNs). The PLM consists of modules for sequential learning, adaptive learning, perfect learning, and less-overfitted learning. Based upon a copper price forecasting dataset, we conduct an experiment to validate the PLM module design modules, and an experiment to evaluate the performance of PLM. The empirical results indeed approve the PLM module design and show the superiority of the proposed PLM model over the linear regression model and the conventional backpropagation-based 2LNN model.
Panel Data Models with Time-Varying Latent Group Structures
Authors: Yiren Wang, Peter C B Phillips, Liangjun Su
Date: 2023-07-29 00:00:00

This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism the model may have different numbers of groups and/or different group memberships before and after the break. With the preliminary nuclear-norm-regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. It is shown that the break point, the number of groups and the group memberships can each be estimated correctly with probability approaching one. Asymptotic distributions of the estimators of the slope coefficients are established. Monte Carlo simulations demonstrate excellent finite sample performance for the proposed estimation algorithm. An empirical application to real house price data across 377 Metropolitan Statistical Areas in the US from 1975 to 2014 suggests the presence both of structural breaks and of changes in group membership.
RoseNNa: A performant, portable library for neural network inference with application to computational fluid dynamics
Authors: Ajay Bati, Spencer H. Bryngelson
Date: 2023-07-30 00:00:00

The rise of neural network-based machine learning ushered in high-level libraries, including TensorFlow and PyTorch, to support their functionality. Computational fluid dynamics (CFD) researchers have benefited from this trend and produced powerful neural networks that promise shorter simulation times. For example, multilayer perceptrons (MLPs) and Long Short Term Memory (LSTM) recurrent-based (RNN) architectures can represent sub-grid physical effects, like turbulence. Implementing neural networks in CFD solvers is challenging because the programming languages used for machine learning and CFD are mostly non-overlapping, We present the roseNNa library, which bridges the gap between neural network inference and CFD. RoseNNa is a non-invasive, lightweight (1000 lines), and performant tool for neural network inference, with focus on the smaller networks used to augment PDE solvers, like those of CFD, which are typically written in C/C++ or Fortran. RoseNNa accomplishes this by automatically converting trained models from typical neural network training packages into a high-performance Fortran library with C and Fortran APIs. This reduces the effort needed to access trained neural networks and maintains performance in the PDE solvers that CFD researchers build and rely upon. Results show that RoseNNa reliably outperforms PyTorch (Python) and libtorch (C++) on MLPs and LSTM RNNs with less than 100 hidden layers and 100 neurons per layer, even after removing the overhead cost of API calls. Speedups range from a factor of about 10 and 2 faster than these established libraries for the smaller and larger ends of the neural network size ranges tested.
Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation
Authors: Minh Hieu Phan, Zhibin Liao, Johan W. Verjans, Minh-Son To
Date: 2023-07-30 00:00:00
Tasks: Anatomy, Image Generation, Translation

Medical image synthesis is a challenging task due to the scarcity of paired data. Several methods have applied CycleGAN to leverage unpaired data, but they often generate inaccurate mappings that shift the anatomy. This problem is further exacerbated when the images from the source and target modalities are heavily misaligned. Recently, current methods have aimed to address this issue by incorporating a supplementary segmentation network. Unfortunately, this strategy requires costly and time-consuming pixel-level annotations. To overcome this problem, this paper proposes MaskGAN, a novel and cost-effective framework that enforces structural consistency by utilizing automatically extracted coarse masks. Our approach employs a mask generator to outline anatomical structures and a content generator to synthesize CT contents that align with these structures. Extensive experiments demonstrate that MaskGAN outperforms state-of-the-art synthesis methods on a challenging pediatric dataset, where MR and CT scans are heavily misaligned due to rapid growth in children. Specifically, MaskGAN excels in preserving anatomical structures without the need for expert annotations. The code for this paper can be found at https://github.com/HieuPhan33/MaskGAN.
Uneven Bi-Classifier Learning for Domain Adaptation
Authors: Zhiqi Yu
Date: 2023-06-01 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2023 6
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

The bi-classifier paradigm is widely adopted as an adversarial method to address domain shift challenge in unsupervised domain adaptation (UDA) by evenly training two classifiers. In this paper, we report that although the generalization ability of the feature extractor can be strengthened by the two even classifiers, the decision boundaries of the two classifiers would be shrank to the source domain in the adversarial process, which weakens the discriminative ability of the learned model. To tame this dilemma, we disentangle the function of the two classifiers and introduce uneven bi-classifier learning for domain adaptation. Specifically, we leverage the F-norm (Frobenius Norm) of classifier predictions instead of the classifier disagreement to achieve adversarial learning. By this way, our feature extractor can be adversarially trained with a single classifier and the other classifier is used for preserving the target-specific decision boundaries. The proposed uneven bi-classifier learning protocol can simultaneously enhance the generalization ability of the feature extractor and expand the decision boundary of the target classifier. Extensive experiments on large-scale datasets prove that our method can significantly surpass previous domain adaptation methods, even with only a single classifier being involved.
Towards General Low-Light Raw Noise Synthesis and Modeling
Authors: Feng Zhang, Bin Xu, Zhiqiang Li, Xinran Liu, Qingbo Lu, Changxin Gao, Nong Sang
Date: 2023-07-31 00:00:00
Proceeding: ICCV 2023 1
Tasks: Denoising, Image Denoising, Low-Light Image Enhancement, Noise Estimation

Modeling and synthesizing low-light raw noise is a fundamental problem for computational photography and image processing applications. Although most recent works have adopted physics-based models to synthesize noise, the signal-independent noise in low-light conditions is far more complicated and varies dramatically across camera sensors, which is beyond the description of these models. To address this issue, we introduce a new perspective to synthesize the signal-independent noise by a generative model. Specifically, we synthesize the signal-dependent and signal-independent noise in a physics- and learning-based manner, respectively. In this way, our method can be considered as a general model, that is, it can simultaneously learn different noise characteristics for different ISO levels and generalize to various sensors. Subsequently, we present an effective multi-scale discriminator termed Fourier transformer discriminator (FTD) to distinguish the noise distribution accurately. Additionally, we collect a new low-light raw denoising (LRD) dataset for training and benchmarking. Qualitative validation shows that the noise generated by our proposed noise model can be highly similar to the real noise in terms of distribution. Furthermore, extensive denoising experiments demonstrate that our method performs favorably against state-of-the-art methods on different sensors.