pwc-archive/papers-with-abstracts huggingface dataset:
State of Health Estimation for Battery Modules with Parallel-Connected Cells Under Cell-to-Cell Variations
Authors: Qinan Zhou, Dyche Anderson, Jing Sun
Date: 2023-12-05 00:00:00
Tasks: feature selection

State of health (SOH) estimation for lithium-ion battery modules with cells connected in parallel is a challenging problem, especially with cell-to-cell variations. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are effective at the cell level, but a generalizable method to extend them to module-level SOH estimation remains missing, when only module-level measurements are available. This paper proposes a new method and demonstrates that, with multiple features systematically selected from the module-level ICA and DVA, the module-level SOH can be estimated with high accuracy and confidence in the presence of cell-to-cell variations. First, an information theory-based feature selection algorithm is proposed to find an optimal set of features for module-level SOH estimation. Second, a relevance vector regression (RVR)-based module-level SOH estimation model is proposed to provide both point estimates and three-sigma credible intervals while maintaining model sparsity. With more selected features incorporated, the proposed method achieves better estimation accuracy and higher confidence at the expense of higher model complexity. When applied to a large experimental dataset, the proposed method and the resulting sparse model lead to module-level SOH estimates with a 0.5% root-mean-square error and a 1.5% average three-sigma value. With all the training processes completed offboard, the proposed method has low computational complexity for onboard implementations.
Decomposing Thermodynamic Dissipation of Linear Langevin Systems via Oscillatory Modes and Its Application to Neural Dynamics
Authors: Daiki Sekizawa, Sosuke Ito, Masafumi Oizumi
Date: 2023-12-06 00:00:00

Recent developments in stochastic thermodynamics have elucidated various relations between the entropy production rate (thermodynamic dissipation) and the physical limits of information processing in nonequilibrium dynamical systems. These findings have opened new perspectives in analyzing real biological systems. In neuroscience, the importance of quantifying entropy production has attracted attention for understanding information processing in the brain. However, the relationship between the entropy production rate and oscillations, which are common in many biological systems, remains unclear. For instance, neural oscillations like delta, theta, and alpha waves play crucial roles in brain information processing. Here, we derive a novel decomposition of the entropy production rate of linear Langevin systems. We show that one component of the entropy production rate, called the housekeeping entropy production rate, can be decomposed into independent positive contributions from oscillatory modes. Our decomposition enables us to calculate the contribution of oscillatory modes to the housekeeping entropy production rate. In addition, when the noise matrix is diagonal, the contribution of each oscillatory mode can be further decomposed into the contribution of each system element. To demonstrate the utility of our decomposition, we applied it to an electrocorticography (ECoG) dataset recorded during awake and anesthetized conditions in monkeys, where the oscillatory properties change drastically. We showed consistent trends across different monkeys: the contribution of delta band was larger in the anesthetized condition than in the awake condition, while those from higher frequency bands, such as the theta band, were smaller. These results allow us to interpret the changes in neural oscillation in terms of stochastic thermodynamics and the physical limits of information processing.
Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers
Authors: Guy Moss, Vjeran Višnjević, Olaf Eisen, Falk M. Oraschewski, Cornelius Schröder, Jakob H. Macke, Reinhard Drews
Date: 2023-12-03 00:00:00
Tasks: Bayesian Inference

The ice shelves buttressing the Antarctic ice sheet determine the rate of ice-discharge into the surrounding oceans. The geometry of ice shelves, and hence their buttressing strength, is determined by ice flow as well as by the local surface accumulation and basal melt rates, governed by atmospheric and oceanic conditions. Contemporary methods resolve one of these rates, but typically not both. Moreover, there is little information of how they changed in time. We present a new method to simultaneously infer the surface accumulation and basal melt rates averaged over decadal and centennial timescales. We infer the spatial dependence of these rates along flow line transects using internal stratigraphy observed by radars, using a kinematic forward model of internal stratigraphy. We solve the inverse problem using simulation-based inference (SBI). SBI performs Bayesian inference by training neural networks on simulations of the forward model to approximate the posterior distribution, allowing us to also quantify uncertainties over the inferred parameters. We demonstrate the validity of our method on a synthetic example, and apply it to Ekstr\"om Ice Shelf, Antarctica, for which newly acquired radar measurements are available. We obtain posterior distributions of surface accumulation and basal melt averaging over 42, 84, 146, and 188 years before 2022. Our results suggest stable atmospheric and oceanographic conditions over this period in this catchment of Antarctica. Use of observed internal stratigraphy can separate the effects of surface accumulation and basal melt, allowing them to be interpreted in a historical context of the last centuries and beyond.
Deformable Registration of MRA and 4D Flow Images to Facilitate Accurate Estimation of Flow Properties within Blood Vessels
Authors: Dan Lior, Craig G. Rusin, Justin Weigand, Kristina V. Montez, Yimo Wang, Silvana Molossi, Daniel J. Penny, Charles Puelz
Date: 2023-12-05 00:00:00

A method is presented for the registration of MRA and 4D Flow images, with the goal of calculating blood flow properties using both modalities simultaneously. In particular, the method produces an alignment of segmentations of vessel networks, from MRA images, with the blood velocity field within those networks, from the corresponding 4D Flow images. The alignment procedure is driven by the registration of centerlines of vessels extracted from the two modalities. Our approach is robust to noise, small deformations, and partial omissions of vessel surfaces and/or blood velocities. The alignment procedure is tested on 7 patient data sets acquired at Texas Children's Hospital. The quality of the resulting alignment is assessed by (i) an illustration of the aligned and unaligned surface segmentations for a sample patient, (ii) histograms of distances between centerline networks, and (iii) graphs of estimated blood flow. For each of the 7 analyzed data sets, medians of the distance histograms decreased an average of 83.5%, and the estimated blood flow increased significantly as a result of the alignment procedure.
A Theory Guide to Using Control Functions to Instrument Hazard Models
Authors: William Liu
Date: 2023-12-05 00:00:00

I develop the theory around using control functions to instrument hazard models, allowing the inclusion of endogenous (e.g., mismeasured) regressors. Simple discrete-data hazard models can be expressed as binary choice panel data models, and the widespread Prentice and Gloeckler (1978) discrete-data proportional hazards model can specifically be expressed as a complementary log-log model with time fixed effects. This allows me to recast it as GMM estimation and its instrumented version as sequential GMM estimation in a Z-estimation (non-classical GMM) framework; this framework can then be leveraged to establish asymptotic properties and sufficient conditions. Whilst this paper focuses on the Prentice and Gloeckler (1978) model, the methods and discussion developed here can be applied more generally to other hazard models and binary choice models. I also introduce my Stata command for estimating a complementary log-log model instrumented via control functions (available as ivcloglog on SSC), which allows practitioners to easily instrument the Prentice and Gloeckler (1978) model.
The Feasibility of Establishing A Nuclear Presence at Western Michigan University
Authors: Andrew Thomas Goheen, Zayon Deshon Mobley-Wright, Rayne Symone Strother, Ryan Thomas Guthrie
Date: 2023-12-06 00:00:00

This research report journal aims to investigate the feasibility of establishing a nuclear presence at Western Michigan University. The report will analyze the potential benefits and drawbacks of introducing nuclear technology to WMUs campus. The study will also examine the current state of nuclear technology and its applications in higher education. The report will conclude with a recommendation on whether WMU should pursue the establishment of a nuclear presence on its campus.
Revealing Sequential Rationality and Forward Induction
Authors: Pierfrancesco Guarino
Date: 2023-12-06 00:00:00

Given a dynamic ordinal game, we deem a strategy sequentially rational if there exist a Bernoulli utility function and a conditional probability system with respect to which the strategy is a maximizer. We establish a complete class theorem by characterizing sequential rationality via the new Conditional B-Dominance. Building on this notion, we introduce Iterative Conditional B-Dominance, which is an iterative elimination procedure that characterizes the implications of forward induction in the class of games under scrutiny and selects the unique backward induction outcome in dynamic ordinal games with perfect information satisfying a genericity condition. Additionally, we show that Iterative Conditional B-Dominance, as a `forward induction reasoning' solution concept, captures: $(i)$ the unique backward induction outcome obtained via sophisticated voting in binary agendas with sequential majority voting; $(ii)$ farsightedness in dynamic ordinal games derived from social environments; $(iii)$ a unique outcome in ordinal Money-Burning Games.
Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers
Authors: Umberto Cappellazzo, Daniele Falavigna, Alessio Brutti, Mirco Ravanelli
Date: 2023-12-06 00:00:00
Tasks: Audio Classification, Few-Shot Learning, Transfer Learning

Parameter-efficient transfer learning (PETL) methods have emerged as a solid alternative to the standard full fine-tuning approach. They only train a few extra parameters for each downstream task, without sacrificing performance and dispensing with the issue of storing a copy of the pre-trained model for each task. For audio classification tasks, the Audio Spectrogram Transformer (AST) model shows impressive results. However, surprisingly, how to efficiently adapt it to several downstream tasks has not been tackled before. In this paper, we bridge this gap and present a detailed investigation of common PETL methods for the adaptation of the AST model to audio/speech tasks. Furthermore, we propose a new adapter design that exploits the convolution module of the Conformer model, leading to superior performance over the standard PETL approaches and surpassing or achieving performance parity with full fine-tuning by updating only 0.29% of the parameters. Finally, we provide ablation studies revealing that our proposed adapter: 1) proves to be effective in few-shot efficient transfer learning, 2) attains optimal results regardless of the amount of the allocated parameters, and 3) can be applied to other pre-trained models.
Lightweight Speaker Verification Using Transformation Module with Feature Partition and Fusion
Authors: Yanxiong Li, Zhongjie Jiang, Qisheng Huang, Wenchang Cao, Jialong Li
Date: 2023-12-06 00:00:00
Tasks: Speaker Verification

Although many efforts have been made on decreasing the model complexity for speaker verification, it is still challenging to deploy speaker verification systems with satisfactory result on low-resource terminals. We design a transformation module that performs feature partition and fusion to implement lightweight speaker verification. The transformation module consists of multiple simple but effective operations, such as convolution, pooling, mean, concatenation, normalization, and element-wise summation. It works in a plug-and-play way, and can be easily implanted into a wide variety of models to reduce the model complexity while maintaining the model error. First, the input feature is split into several low-dimensional feature subsets for decreasing the model complexity. Then, each feature subset is updated by fusing it with the inter-feature-subsets correlational information to enhance its representational capability. Finally, the updated feature subsets are independently fed into the block (one or several layers) of the model for further processing. The features that are output from current block of the model are processed according to the steps above before they are fed into the next block of the model. Experimental data are selected from two public speech corpora (namely VoxCeleb1 and VoxCeleb2). Results show that implanting the transformation module into three models (namely AMCRN, ResNet34, and ECAPA-TDNN) for speaker verification slightly increases the model error and significantly decreases the model complexity. Our proposed method outperforms baseline methods on the whole in memory requirement and computational complexity with lower equal error rate. It also generalizes well across truncated segments with various lengths.
Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages
Authors: Abdelhadi Essamlali, Vincent Millot-Maysounabe, Marion Chartier, Grégoire Salin, Aymeric Becq, Lionel Arrivé, Marine Duboc Camus, Jérôme Szewczyk, Isabelle Claude
Date: 2023-12-06 00:00:00
Tasks: 3D Reconstruction, Segmentation

This article presents an evaluation of biliary tract segmentation methods used for 3D reconstruction, which may be very usefull in various critical interventions, such as endoscopic retrograde cholangiopancreatography (ERCP), using the 3D Slicer software. This article provides an assessment of biliary tract segmentation techniques employed for 3D reconstruction, which can prove highly valuable in diverse critical procedures like endoscopic retrograde cholangiopancreatography (ERCP) through the utilization of 3D Slicer software. Three different methods, namely thresholding, flood filling, and region growing, were assessed in terms of their advantages and disadvantages. The study involved 10 patient cases and employed quantitative indices and qualitative evaluation to assess the segmentations obtained by the different segmentation methods against ground truth. The results indicate that the thresholding method is almost manual and time-consuming, while the flood filling method is semi-automatic and also time-consuming. Although both methods improve segmentation quality, they are not reproducible. Therefore, an automatic method based on region growing was developed to reduce segmentation time, albeit at the expense of quality. These findings highlight the pros and cons of different conventional segmentation methods and underscore the need to explore alternative approaches, such as deep learning, to optimize biliary tract segmentation in the context of ERCP.
Lung Cancer Detection from CT Scan Images based on Genetic-Independent Recurrent Deep Learning
Authors: Ehsan Sadeghi Pour, Mahdi Esmaeili
Date: 2023-12-05 00:00:00
Tasks: Segmentation

Lung cancer is one of the prevalence diseases in the world which cause many deaths. Detecting early stages of lung cancer is so necessary. So, modeling and simulating some intelligent medical systems is an essential which can help specialist to accurately determine and diagnose the disease. So this paper contributes a new lung cancer detection model in CT images which use machine learning methods. There are three steps in this model: noise reduction (pre-processing), segmentation (middle-processing) and optimize segmentation for detect exact are of nodules. This article use some filters for noise reduction and then use Independent Recurrent Neural Networks (IndRNN) as deep learning methods for segmentation which optimize and tune by Genetic Algorithm. The results represented that the proposed method can detect exact area of nodules in CT images.
Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study
Authors: Tanner J. Zachem, Sully F. Chen, Vishal Venkatraman, David AW Sykes, Ravi Prakash, Koumani W. Ntowe, Mikhail A. Bethell, Samantha Spellicy, Alexander D Suarez, Weston Ross, Patrick J. Codd
Date: 2023-12-03 00:00:00

Objectives Computer vision (CV) is a field of artificial intelligence that enables machines to interpret and understand images and videos. CV has the potential to be of assistance in the operating room (OR) to track surgical instruments. We built a CV algorithm for identifying surgical instruments in the neurosurgical operating room as a potential solution for surgical instrument tracking and management to decrease surgical waste and opening of unnecessary tools. Methods We collected 1660 images of 27 commonly used neurosurgical instruments. Images were labeled using the VGG Image Annotator and split into 80% training and 20% testing sets in order to train a U-Net Convolutional Neural Network using 5-fold cross validation. Results Our U-Net achieved a tool identification accuracy of 80-100% when distinguishing 25 classes of instruments, with 19/25 classes having accuracy over 90%. The model performance was not adequate for sub classifying Adson, Gerald, and Debakey forceps, which had accuracies of 60-80%. Conclusions We demonstrated the viability of using machine learning to accurately identify surgical instruments. Instrument identification could help optimize surgical tray packing, decrease tool usage and waste, decrease incidence of instrument misplacement events, and assist in timing of routine instrument maintenance. More training data will be needed to increase accuracy across all surgical instruments that would appear in a neurosurgical operating room. Such technology has the potential to be used as a method to be used for proving what tools are truly needed in each type of operation allowing surgeons across the world to do more with less.
Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting
Authors: Francesco Binucci, Mattia Merluzzi, Paolo Banelli, Emilio Calvanese Strinati, Paolo Di Lorenzo
Date: 2023-12-06 00:00:00

Deep Neural Network (DNN) splitting is one of the key enablers of edge Artificial Intelligence (AI), as it allows end users to pre-process data and offload part of the computational burden to nearby Edge Cloud Servers (ECSs). This opens new opportunities and degrees of freedom in balancing energy consumption, delay, accuracy, privacy, and other trustworthiness metrics. In this work, we explore the opportunity of DNN splitting at the edge of 6G wireless networks to enable low energy cooperative inference with target delay and accuracy with a goal-oriented perspective. Going beyond the current literature, we explore new trade-offs that take into account the accuracy degradation as a function of the Splitting Point (SP) selection and wireless channel conditions. Then, we propose an algorithm that dynamically controls SP selection, local computing resources, uplink transmit power and bandwidth allocation, in a goal-oriented fashion, to meet a target goal-effectiveness. To the best of our knowledge, this is the first work proposing adaptive SP selection on the basis of all learning performance (i.e., energy, delay, accuracy), with the aim of guaranteeing the accomplishment of a goal (e.g., minimize the energy consumption under latency and accuracy constraints). Numerical results show the advantages of the proposed SP selection and resource allocation, to enable energy frugal and effective edge AI.
Beyond Low Rank: A Graph-Based Propagation Approach to Tensor Completion for Multi-Acquisition Scenarios
Authors: Iain Rolland, Sivasakthy Selvakumaran, Andrea Marinoni
Date: 2023-12-06 00:00:00

Tensor completion refers to the problem of recovering the missing, corrupted or unobserved entries in data represented by tensors. In this paper, we tackle the tensor completion problem in the scenario in which multiple tensor acquisitions are available and do so without placing constraints on the underlying tensor's rank. Whereas previous tensor completion work primarily focuses on low-rank completion methods, we propose a novel graph-based diffusion approach to the problem. Referred to as GraphProp, the method propagates observed entries around a graph-based representation of the tensor in order to recover the missing entries. A series of experiments have been performed to validate the presented approach, including a synthetically-generated tensor recovery experiment which shows that the method can be used to recover both low and high rank tensor entries. The successful tensor completion capabilities of the approach are also demonstrated on a real-world completion problem from the field of multispectral remote sensing completion. Using data acquired from the Landsat 7 platform, we synthetically obscure image sections in order to simulate the scenario in which image acquisitions overlap only partially. In these tests, we benchmark against alternative tensor completion approaches as well as existing graph signal recovery methods, demonstrating the superior reconstruction performance of our method versus the state of the art.
Markov Chain Monte Carlo Multi-Scan Data Association for Sets of Trajectories
Authors: Yuxuan Xia, Ángel F. García-Fernández, Lennart Svensson
Date: 2023-12-06 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

This paper considers a batch solution to the multi-object tracking problem based on sets of trajectories. Specifically, we present two offline implementations of the trajectory Poisson multi-Bernoulli mixture (TPMBM) filter for batch data based on Markov chain Monte Carlo (MCMC) sampling of the data association hypotheses. In contrast to online TPMBM implementations, the proposed offline implementations solve a large-scale, multi-scan data association problem across the entire time interval of interest, and therefore they can fully exploit all the measurement information available. Furthermore, by leveraging the efficient hypothesis structure of TPMBM filters, the proposed implementations compare favorably with other MCMC-based multi-object tracking algorithms. Simulation results show that the TPMBM implementation using the Metropolis-Hastings algorithm presents state-of-the-art multiple trajectory estimation performance.