pwc-archive/papers-with-abstracts huggingface dataset:
MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation
Authors: Aaron Cao, Zongyu Li, Jordan Jomsky, Andrew F. Laine, Jia Guo
Date: 2024-09-12 00:00:00
Tasks: Brain Segmentation, Mamba

Widely used traditional pipelines for subcortical brain segmentation are often inefficient and slow, particularly when processing large datasets. Furthermore, deep learning models face challenges due to the high resolution of MRI images and the large number of anatomical classes involved. To address these limitations, we developed a 3D patch-based hybrid CNN-Mamba model that leverages Mamba's selective scan algorithm, thereby enhancing segmentation accuracy and efficiency for 3D inputs. This retrospective study utilized 1784 T1-weighted MRI scans from a diverse, multi-site dataset of healthy individuals. The dataset was divided into training, validation, and testing sets with a 1076/345/363 split. The scans were obtained from 1.5T and 3T MRI machines. Our model's performance was validated against several benchmarks, including other CNN-Mamba, CNN-Transformer, and pure CNN networks, using FreeSurfer-generated ground truths. We employed the Dice Similarity Coefficient (DSC), Volume Similarity (VS), and Average Symmetric Surface Distance (ASSD) as evaluation metrics. Statistical significance was determined using the Wilcoxon signed-rank test with a threshold of P < 0.05. The proposed model achieved the highest overall performance across all metrics (DSC 0.88383; VS 0.97076; ASSD 0.33604), significantly outperforming all non-Mamba-based models (P < 0.001). While the model did not show significant improvement in DSC or VS compared to another Mamba-based model (P-values of 0.114 and 0.425), it demonstrated a significant enhancement in ASSD (P < 0.001) with approximately 20% fewer parameters. In conclusion, our proposed hybrid CNN-Mamba architecture offers an efficient and accurate approach for 3D subcortical brain segmentation, demonstrating potential advantages over existing methods. Code is available at: https://github.com/aaroncao06/MedSegMamba.
INN-PAR: Invertible Neural Network for PPG to ABP Reconstruction
Authors: Soumitra Kundu, Gargi Panda, Saumik Bhattacharya, Aurobinda Routray, Rajlakshmi Guha
Date: 2024-09-13 00:00:00
Tasks: Photoplethysmography (PPG)

Non-invasive and continuous blood pressure (BP) monitoring is essential for the early prevention of many cardiovascular diseases. Estimating arterial blood pressure (ABP) from photoplethysmography (PPG) has emerged as a promising solution. However, existing deep learning approaches for PPG-to-ABP reconstruction (PAR) encounter certain information loss, impacting the precision of the reconstructed signal. To overcome this limitation, we introduce an invertible neural network for PPG to ABP reconstruction (INN-PAR), which employs a series of invertible blocks to jointly learn the mapping between PPG and its gradient with the ABP signal and its gradient. INN-PAR efficiently captures both forward and inverse mappings simultaneously, thereby preventing information loss. By integrating signal gradients into the learning process, INN-PAR enhances the network's ability to capture essential high-frequency details, leading to more accurate signal reconstruction. Moreover, we propose a multi-scale convolution module (MSCM) within the invertible block, enabling the model to learn features across multiple scales effectively. We have experimented on two benchmark datasets, which show that INN-PAR significantly outperforms the state-of-the-art methods in both waveform reconstruction and BP measurement accuracy. Codes can be found at: https://github.com/soumitra1992/INNPAR-PPG2ABP.
Model-independent variable selection via the rule-based variable priority
Authors: Min Lu, Hemant Ishwaran
Date: 2024-09-13 00:00:00
Tasks: Prediction, Variable Selection

While achieving high prediction accuracy is a fundamental goal in machine learning, an equally important task is finding a small number of features with high explanatory power. One popular selection technique is permutation importance, which assesses a variable's impact by measuring the change in prediction error after permuting the variable. However, this can be problematic due to the need to create artificial data, a problem shared by other methods as well. Another problem is that variable selection methods can be limited by being model-specific. We introduce a new model-independent approach, Variable Priority (VarPro), which works by utilizing rules without the need to generate artificial data or evaluate prediction error. The method is relatively easy to use, requiring only the calculation of sample averages of simple statistics, and can be applied to many data settings, including regression, classification, and survival. We investigate the asymptotic properties of VarPro and show, among other things, that VarPro has a consistent filtering property for noise variables. Empirical studies using synthetic and real-world data show the method achieves a balanced performance and compares favorably to many state-of-the-art procedures currently used for variable selection.
Biomimetic Frontend for Differentiable Audio Processing
Authors: Ruolan Leslie Famularo, Dmitry N. Zotkin, Shihab A. Shamma, Ramani Duraiswami
Date: 2024-09-13 00:00:00
Tasks: Computational Efficiency

While models in audio and speech processing are becoming deeper and more end-to-end, they as a consequence need expensive training on large data, and are often brittle. We build on a classical model of human hearing and make it differentiable, so that we can combine traditional explainable biomimetic signal processing approaches with deep-learning frameworks. This allows us to arrive at an expressive and explainable model that is easily trained on modest amounts of data. We apply this model to audio processing tasks, including classification and enhancement. Results show that our differentiable model surpasses black-box approaches in terms of computational efficiency and robustness, even with little training data. We also discuss other potential applications.
Clean Label Attacks against SLU Systems
Authors: Henry Li Xinyuan, Sonal Joshi, Thomas Thebaud, Jesus Villalba, Najim Dehak, Sanjeev Khudanpur
Date: 2024-09-13 00:00:00
Tasks: Data Poisoning, speech-recognition, Speech Recognition, Spoken Language Understanding

Poisoning backdoor attacks involve an adversary manipulating the training data to induce certain behaviors in the victim model by inserting a trigger in the signal at inference time. We adapted clean label backdoor (CLBD)-data poisoning attacks, which do not modify the training labels, on state-of-the-art speech recognition models that support/perform a Spoken Language Understanding task, achieving 99.8% attack success rate by poisoning 10% of the training data. We analyzed how varying the signal-strength of the poison, percent of samples poisoned, and choice of trigger impact the attack. We also found that CLBD attacks are most successful when applied to training samples that are inherently hard for a proxy model. Using this strategy, we achieved an attack success rate of 99.3% by poisoning a meager 1.5% of the training data. Finally, we applied two previously developed defenses against gradient-based attacks, and found that they attain mixed success against poisoning.
A Bayesian Approach to Clustering via the Proper Bayesian Bootstrap: the Bayesian Bagged Clustering (BBC) algorithm
Authors: Federico Maria Quetti, Silvia Figini, Elena Ballante
Date: 2024-09-13 00:00:00
Tasks: Clustering

The paper presents a novel approach for unsupervised techniques in the field of clustering. A new method is proposed to enhance existing literature models using the proper Bayesian bootstrap to improve results in terms of robustness and interpretability. Our approach is organized in two steps: k-means clustering is used for prior elicitation, then proper Bayesian bootstrap is applied as resampling method in an ensemble clustering approach. Results are analyzed introducing measures of uncertainty based on Shannon entropy. The proposal provides clear indication on the optimal number of clusters, as well as a better representation of the clustered data. Empirical results are provided on simulated data showing the methodological and empirical advances obtained.
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation
Authors: Pengyun Wang, Yadi Cao, Chris Russell, Yanxin Shen, Junyu Luo, Ming Zhang, Siyu Heng, Xiao Luo
Date: 2024-09-13 00:00:00
Tasks: Domain Adaptation, GRAPH DOMAIN ADAPTATION, Transfer Learning

Graph domain adaptation has recently enabled knowledge transfer across different graphs. However, without the semantic information on target graphs, the performance on target graphs is still far from satisfactory. To address the issue, we study the problem of active graph domain adaptation, which selects a small quantitative of informative nodes on the target graph for extra annotation. This problem is highly challenging due to the complicated topological relationships and the distribution discrepancy across graphs. In this paper, we propose a novel approach named Dual Consistency Delving with Topological Uncertainty (DELTA) for active graph domain adaptation. Our DELTA consists of an edge-oriented graph subnetwork and a path-oriented graph subnetwork, which can explore topological semantics from complementary perspectives. In particular, our edge-oriented graph subnetwork utilizes the message passing mechanism to learn neighborhood information, while our path-oriented graph subnetwork explores high-order relationships from sub-structures. To jointly learn from two subnetworks, we roughly select informative candidate nodes with the consideration of consistency across two subnetworks. Then, we aggregate local semantics from its K-hop subgraph based on node degrees for topological uncertainty estimation. To overcome potential distribution shifts, we compare target nodes and their corresponding source nodes for discrepancy scores as an additional component for fine selection. Extensive experiments on benchmark datasets demonstrate that DELTA outperforms various state-of-the-art approaches. The code implementation of DELTA is available at https://github.com/goose315/DELTA.
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks
Authors: Jean Seong Bjorn Choe, Bumkyu Choi, Jong-Kook Kim
Date: 2024-09-13 00:00:00
Tasks: Acrobot, Reinforcement Learning (RL)

This report presents a solution for the swing-up and stabilisation tasks of the acrobot and the pendubot, developed for the AI Olympics competition at IROS 2024. Our approach employs the Average-Reward Entropy Advantage Policy Optimization (AR-EAPO), a model-free reinforcement learning (RL) algorithm that combines average-reward RL and maximum entropy RL. Results demonstrate that our controller achieves improved performance and robustness scores compared to established baseline methods in both the acrobot and pendubot scenarios, without the need for a heavily engineered reward function or system model. The current results are applicable exclusively to the simulation stage setup.
Multi forests: Variable importance for multi-class outcomes
Authors: Roman Hornung, Alexander Hapfelmeier
Date: 2024-09-13 00:00:00

In prediction tasks with multi-class outcomes, identifying covariates specifically associated with one or more outcome classes can be important. Conventional variable importance measures (VIMs) from random forests (RFs), like permutation and Gini importance, focus on overall predictive performance or node purity, without differentiating between the classes. Therefore, they can be expected to fail to distinguish class-associated covariates from covariates that only distinguish between groups of classes. We introduce a VIM called multi-class VIM, tailored for identifying exclusively class-associated covariates, via a novel RF variant called multi forests (MuFs). The trees in MuFs use both multi-way and binary splitting. The multi-way splits generate child nodes for each class, using a split criterion that evaluates how well these nodes represent their respective classes. This setup forms the basis of the multi-class VIM, which measures the discriminatory ability of the splits performed in the respective covariates with regard to this split criterion. Alongside the multi-class VIM, we introduce a second VIM, the discriminatory VIM. This measure, based on the binary splits, assesses the strength of the general influence of the covariates, irrespective of their class-associatedness. Simulation studies demonstrate that the multi-class VIM specifically ranks class-associated covariates highly, unlike conventional VIMs which also rank other types of covariates highly. Analyses of 121 datasets reveal that MuFs often have slightly lower predictive performance compared to conventional RFs. This is, however, not a limiting factor given the algorithm's primary purpose of calculating the multi-class VIM.
HLTCOE JHU Submission to the Voice Privacy Challenge 2024
Authors: Henry Li Xinyuan, Zexin Cai, Ashi Garg, Kevin Duh, Leibny Paola García-Perera, Sanjeev Khudanpur, Nicholas Andrews, Matthew Wiesner
Date: 2024-09-13 00:00:00
Tasks: text-to-speech, Text to Speech, Voice Conversion

We present a number of systems for the Voice Privacy Challenge, including voice conversion based systems such as the kNN-VC method and the WavLM voice Conversion method, and text-to-speech (TTS) based systems including Whisper-VITS. We found that while voice conversion systems better preserve emotional content, they struggle to conceal speaker identity in semi-white-box attack scenarios; conversely, TTS methods perform better at anonymization and worse at emotion preservation. Finally, we propose a random admixture system which seeks to balance out the strengths and weaknesses of the two category of systems, achieving a strong EER of over 40% while maintaining UAR at a respectable 47%.
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
Authors: Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen
Date: 2024-09-13 00:00:00
Tasks: Denoising, Diversity

Dynamical generative models that produce samples through an iterative process, such as Flow Matching and denoising diffusion models, have seen widespread use, but there have not been many theoretically-sound methods for improving these models with reward fine-tuning. In this work, we cast reward fine-tuning as stochastic optimal control (SOC). Critically, we prove that a very specific memoryless noise schedule must be enforced during fine-tuning, in order to account for the dependency between the noise variable and the generated samples. We also propose a new algorithm named Adjoint Matching which outperforms existing SOC algorithms, by casting SOC problems as a regression problem. We find that our approach significantly improves over existing methods for reward fine-tuning, achieving better consistency, realism, and generalization to unseen human preference reward models, while retaining sample diversity.
FP-VEC: Fingerprinting Large Language Models via Efficient Vector Addition
Authors: Zhenhua Xu, Wenpeng Xing, Zhebo Wang, Chang Hu, Chen Jie, Meng Han
Date: 2024-09-13 00:00:00
Tasks: CPU

Training Large Language Models (LLMs) requires immense computational power and vast amounts of data. As a result, protecting the intellectual property of these models through fingerprinting is essential for ownership authentication. While adding fingerprints to LLMs through fine-tuning has been attempted, it remains costly and unscalable. In this paper, we introduce FP-VEC, a pilot study on using fingerprint vectors as an efficient fingerprinting method for LLMs. Our approach generates a fingerprint vector that represents a confidential signature embedded in the model, allowing the same fingerprint to be seamlessly incorporated into an unlimited number of LLMs via vector addition. Results on several LLMs show that FP-VEC is lightweight by running on CPU-only devices for fingerprinting, scalable with a single training and unlimited fingerprinting process, and preserves the model's normal behavior. The project page is available at https://fingerprintvector.github.io .
RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge
Authors: Alejandro Lancho, Amir Weiss, Gary C. F. Lee, Tejas Jayashankar, Binoy Kurien, Yury Polyanskiy, Gregory W. Wornell
Date: 2024-09-13 00:00:00
Tasks: Deep Learning

We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset for data-driven analyses of RF signal problems. Specifically, we adopt a simplified signal model for developing and analyzing interference rejection algorithms. For this signal model, we introduce a set of carefully chosen deep learning architectures, incorporating key domain-informed modifications alongside traditional benchmark solutions to establish baseline performance metrics for this intricate, ubiquitous problem. Through extensive simulations involving eight different signal mixture types, we demonstrate the superior performance (in some cases, by two orders of magnitude) of architectures such as UNet and WaveNet over traditional methods like matched filtering and linear minimum mean square error estimation. Our findings suggest that the data-driven approach can yield scalable solutions, in the sense that the same architectures may be similarly trained and deployed for different types of signals. Moreover, these findings further corroborate the promising potential of deep learning algorithms for enhancing communication systems, particularly via interference mitigation. This work also includes results from an open competition based on the RF Challenge, hosted at the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'24).
Can Kans (re)discover predictive models for Direct-Drive Laser Fusion?
Authors: Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Aarne Lees, Christopher Kanan
Date: 2024-09-13 00:00:00
Tasks: Kolmogorov-Arnold Networks, Symbolic Regression

The domain of laser fusion presents a unique and challenging predictive modeling application landscape for machine learning methods due to high problem complexity and limited training data. Data-driven approaches utilizing prescribed functional forms, inductive biases and physics-informed learning (PIL) schemes have been successful in the past for achieving desired generalization ability and model interpretation that aligns with physics expectations. In complex multi-physics application domains, however, it is not always obvious how architectural biases or discriminative penalties can be formulated. In this work, focusing on nuclear fusion energy using high powered lasers, we present the use of Kolmogorov-Arnold Networks (KANs) as an alternative to PIL for developing a new type of data-driven predictive model which is able to achieve high prediction accuracy and physics interpretability. A KAN based model, a MLP with PIL, and a baseline MLP model are compared in generalization ability and interpretation with a domain expert-derived symbolic regression model. Through empirical studies in this high physics complexity domain, we show that KANs can potentially provide benefits when developing predictive models for data-starved physics applications.
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
Authors: James Sharpnack, Phoebe Mulcaire, Klinton Bicknell, Geoff LaFlair, Kevin Yancey
Date: 2024-09-13 00:00:00
Tasks: AutoML

Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally, these are fit using parametric mixed effects models on the probability of a test taker getting the correct answer to a test item (i.e., question). Neural net extensions of these models, such as BertIRT, require specialized architectures and parameter tuning. We propose a multistage fitting procedure that is compatible with out-of-the-box Automated Machine Learning (AutoML) tools. It is based on a Monte Carlo EM (MCEM) outer loop with a two stage inner loop, which trains a non-parametric AutoML grade model using item features followed by an item specific parametric model. This greatly accelerates the modeling workflow for scoring tests. We demonstrate its effectiveness by applying it to the Duolingo English Test, a high stakes, online English proficiency test. We show that the resulting model is typically more well calibrated, gets better predictive performance, and more accurate scores than existing methods (non-explanatory IRT models and explanatory IRT models like BERT-IRT). Along the way, we provide a brief survey of machine learning methods for calibration of item parameters for CATs.
Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
Authors: Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo, Aaqib Saeed
Date: 2024-09-13 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model, Question Answering, Retrieval, Self-Supervised Learning

Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retrieval-based approach for ECG-to-text report generation and question answering. Our method leverages a self-supervised learning for the ECG encoder, enabling efficient similarity searches and report retrieval. By combining pre-training with dynamic retrieval and Large Language Model (LLM)-based refinement, ECG-ReGen effectively analyzes ECG data and answers related queries, with the potential of improving patient care. Experiments conducted on the PTB-XL and MIMIC-IV-ECG datasets demonstrate superior performance in both in-domain and cross-domain scenarios for report generation. Furthermore, our approach exhibits competitive performance on ECG-QA dataset compared to fully supervised methods when utilizing off-the-shelf LLMs for zero-shot question answering. This approach, effectively combining self-supervised encoder and LLMs, offers a scalable and efficient solution for accurate ECG interpretation, holding significant potential to enhance clinical decision-making.
Modular Neural Wiretap Codes for Fading Channels
Authors: Daniel Seifert, Onur Günlü, Rafael F. Schaefer
Date: 2024-09-13 00:00:00
Tasks: Deep Learning

The wiretap channel is a well-studied problem in the physical layer security literature. Although it is proven that the decoding error probability and information leakage can be made arbitrarily small in the asymptotic regime, further research on finite-blocklength codes is required on the path towards practical, secure communication systems. This work provides the first experimental characterization of a deep learning-based, finite-blocklength code construction for multi-tap fading wiretap channels without channel state information. In addition to the evaluation of the average probability of error and information leakage, we examine the designed codes in the presence of fading in terms of the equivocation rate and illustrate the influence of (i) the number of fading taps, (ii) differing variances of the fading coefficients, and (iii) the seed selection for the hash function-based security layer.