pwc-archive/papers-with-abstracts huggingface dataset:
Laser-Based Fabrication of Microstructures on Nickel Thin Films and Its Applications in On-Chip Thin Film Inductors
Authors: Srikanth Itapu, Vamsi Borra, Daniel G. Georgiev
Date: 2023-11-17 00:00:00

This work reports on the fabrication of microbump structures on Ni films by single-pulse, localized laser irradiation. Conditions for the reproducible formation of such microstructures have been identified in terms of laser-irradiation and film parameters after systematic studies involving a relevant parameter space. The cracks and voids morphology of the sputtered films was rendered undesirable and hence, smoother Ni thin film of same thickness (200nm) were deposited by vacuum evaporation. The continuous nature of the film resulted in radially symmetric thermal expansion and deformation, thus achieving a high yield of microstructures. An improvement in the inductance and the quality factor of on-chip spiral inductors incorporating such laser-microstructured ferromagnetic nickel thin films was observed, which demonstrates the potential of such a laser-based method for fabrication or fine tuning of various micro-/nanoelectric/electronic sensor and other components and systems.
Minimizing the Homogeneous $\mathcal{L}_2$-Gain of Homogeneous Differentiators
Authors: Benjamin Voß, Jaime A. Moreno, Johann Reger
Date: 2023-11-17 00:00:00

The differentiation of noisy signals using the family of homogeneous differentiators is considered. It includes the high-gain (linear) as well as robust exact (discontinuous) differentiator. To characterize the effect of noise and disturbance on the differentiation estimation error, the generalized, homogeneous $\mathcal{L}_2$-gain is utilized. Analog to the classical $\mathcal{L}_p$-gain, it is not defined for the discontinuous case w.r.t. disturbances acting on the last channel. Thus, only continuous differentiators are addressed. The gain is estimated using a differential dissipation inequality, where a scaled Lyapunov function acts as storage function for the homogeneous $\mathcal{L}_2$ supply rate. The fixed differentiator gains are scaled with a gain-scaling parameter similar to the high-gain differentiator. This paper shows the existence of an optimal scaling which (locally) minimizes the homogeneous $\mathcal{L}_2$-gain estimate and provides a procedure to obtain it. Differentiators of dimension two are considered and the results are illustrated via numerical evaluation and a simulation example.
Simultaneous Synthesis and Verification of Neural Control Barrier Functions through Branch-and-Bound Verification-in-the-loop Training
Authors: Xinyu Wang, Luzia Knoedler, Frederik Baymler Mathiesen, Javier Alonso-Mora
Date: 2023-11-17 00:00:00

Control Barrier Functions (CBFs) that provide formal safety guarantees have been widely used for safety-critical systems. However, it is non-trivial to design a CBF. Utilizing neural networks as CBFs has shown great success, but it necessitates their certification as CBFs. In this work, we leverage bound propagation techniques and the Branch-and-Bound scheme to efficiently verify that a neural network satisfies the conditions to be a CBF over the continuous state space. To accelerate training, we further present a framework that embeds the verification scheme into the training loop to synthesize and verify a neural CBF simultaneously. In particular, we employ the verification scheme to identify partitions of the state space that are not guaranteed to satisfy the CBF conditions and expand the training dataset by incorporating additional data from these partitions. The neural network is then optimized using the augmented dataset to meet the CBF conditions. We show that for a non-linear control-affine system, our framework can efficiently certify a neural network as a CBF and render a larger safe set than state-of-the-art neural CBF works. We further employ our learned neural CBF to derive a safe controller to illustrate the practical use of our framework.
Data-Driven LQR using Reinforcement Learning and Quadratic Neural Networks
Authors: Soroush Asri, Luis Rodrigues
Date: 2023-11-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a novel data-driven approach to design a linear quadratic regulator (LQR) using a reinforcement learning (RL) algorithm that does not require a system model. The key contribution is to perform policy iteration (PI) by designing the policy evaluator as a two-layer quadratic neural network (QNN). This network is trained through convex optimization. To the best of our knowledge, this is the first time that a QNN trained through convex optimization is employed as the Q-function approximator (QFA). The main advantage is that the QNN's input-output mapping has an analytical expression as a quadratic form, which can then be used to obtain an analytical expression for policy improvement. This is in stark contrast to the available techniques in the literature that must train a second neural network to obtain policy improvement. The article establishes the convergence of the learning algorithm to the optimal control, provided the system is controllable and one starts from a stabilitzing policy. A quadrotor example demonstrates the effectiveness of the proposed approach.
An Active-Sensing Approach for Bearing-based Target Localization
Authors: Beniamino Pozzan, Giulia Michieletto, Mehran Mesbahi, Angelo Cenedese
Date: 2023-11-16 00:00:00
Tasks: Position, valid

Characterized by a cross-disciplinary nature, the bearing-based target localization task involves estimating the position of an entity of interest by a group of agents capable of collecting noisy bearing measurements. In this work, this problem is tackled by resting both on the weighted least square estimation approach and on the active-sensing control paradigm. Indeed, we propose an iterative algorithm that provides an estimate of the target position under the assumption of Gaussian noise distribution, which can be considered valid when more specific information is missing. Then, we present a seeker agents control law that aims at minimizing the localization uncertainty by optimizing the covariance matrix associated with the estimated target position. The validity of the designed bearing-based target localization solution is confirmed by the results of an extensive Monte Carlo simulation campaign.
Layer-to-Layer Melt Pool Control in Laser Powder Bed Fusion
Authors: Dominic Liao-McPherson, Efe C. Balta, Mohamadreza Afrasiabi, Alisa Rupenyan, Markus Bambach, John Lygeros
Date: 2023-11-16 00:00:00

Additive manufacturing processes are flexible and efficient technologies for producing complex geometries. However, ensuring reliability and repeatability is challenging due to the complex physics and various sources of uncertainty in the process. In this work, we investigate closed-loop control of the melt pool dimensions in a laser powder bed fusion (LPBF) process. We propose a trajectory optimization-based layer-to-layer controller that adjusts the laser power input to the next layer to track a desired melt pool depth and validate our controller by placing it in closed-loop high-fidelity multi-layer smoothed particle hydrodynamics simulator of a 2D LPBF process. Detailed numerical case studies demonstrate successful regulation of the melt pool depth on brick and overhang geometries and provide first of its kind results on the effectiveness of layer-to-layer input optimization for the LPBF process as well as detailed insight into the physics of the controlled process. Computational complexity and process performance results illustrate the method's effectiveness and provide an outlook for its implementation onto real systems.
Optimal navigability of weighted human brain connectomes in physical space
Authors: Laia Barjuan, Jordi Soriano, M. Ángeles Serrano
Date: 2023-11-17 00:00:00
Tasks: Diversity

The architecture of the human connectome supports efficient communication protocols relying either on distances between brain regions or on the intensities of connections. However, none of these protocols combines information about the two or reaches full efficiency. Here, we introduce a continuous spectrum of decentralized routing strategies that combine link weights and the spatial embedding of connectomes to transmit signals. We applied the protocols to individual connectomes in two cohorts, and to cohort archetypes designed to capture weighted connectivity properties. We found that there is an intermediate region, a sweet spot, in which navigation achieves maximum communication efficiency at low transmission cost. Interestingly, this phenomenon is robust and independent of the particular configuration of weights.Our results indicate that the intensity and topology of neural connections and brain geometry interplay to boost communicability, fundamental to support effective responses to external and internal stimuli and the diversity of brain functions.
ToSkA: Topological Skeleton Analysis for Network-Based Shape Representation and Evaluation of Objects from Cells to Death Stars
Authors: Allyson Quinn Ryan, Johannes Soltwedel, Carl D. Modes
Date: 2023-11-17 00:00:00
Tasks: Instance Segmentation, Object, Semantic Segmentation

Shape analysis and classification are popular methods for biologists, biophysicists and mathematicians investigating relationships between object function and form. Classic shape descriptors, such as sphericity, can be powerful but may be insufficient for more complex shapes. Here, we present 'napari-toska' a topological skeleton based method to analyze complex objects by representing their shape asymmetries as networks. Using global neighborhood principles, classic network science metrics and spatial feature embedding we create instance segmentation object profiles to be used for immediate or downstream classification. napari-toska can also follow temporal dynamics and identify network features capable of differentiating between experimental phenotypes. We incorporated the capacity to measure absolute spatial features of objects to bring in aspects of scale. Furthermore, napari-toska identifies certain segmentation errors through the emergence or loss of network cycles. Combined, napari-toska functions allow for flexible and in-depth shape profiling of intricate shapes often observed in biological and physical settings where robust, yet precise, system configuration is essential to downstream processes.
High-Throughput Asset Pricing
Authors: Andrew Y. Chen, Chukwuma Dim
Date: 2023-11-17 00:00:00

We apply empirical Bayes (EB) to mine data on 136,000 long-short strategies constructed from accounting ratios, past returns, and ticker symbols. This ``high-throughput asset pricing'' matches the out-of-sample performance of top journals while eliminating look-ahead bias. Naively mining for the largest Sharpe ratios leads to similar performance, consistent with our theoretical results, though EB uniquely provides unbiased predictions with transparent intuition. Predictability is concentrated in accounting strategies, small stocks, and pre-2004 periods, consistent with limited attention theories. Multiple testing methods popular in finance fail to identify most out-of-sample performers. High-throughput methods provide a rigorous, unbiased framework for understanding asset prices.
Unveiling spatial patterns of population in Italian municipalities
Authors: Davide Fiaschi, Angela Parenti, Cristiano Ricci
Date: 2023-11-17 00:00:00

We study the evolution of population density across Italian municipalities on the based of their trajectories in the Moran space. We find evidence of spatial dynamical patterns of concentrated urban growth, urban sprawl, agglomeration, and depopulation. Over the long run, three distinct settlement systems emerge: urban, suburban, and rural. We discuss how estimating these demographic trends at the municipal level can help the design and validation of policies contrasting the socio-economic decline in specific Italian areas, as in the case of the Italian National Strategy for Inner Areas (Strategia Nazionale per le Aree Interne, SNAI).
Deriving Weeklong Activity-Travel Dairy from Google Location History: Survey Tool Development and A Field Test in Toronto
Authors: Melvyn Li, Kaili Wang, Yicong Liu, Khandker Nurul Habib
Date: 2023-11-16 00:00:00
Tasks: Survey

This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis. By leveraging the accuracy and omnipresence among smartphone users of GLH, the proposed methodology avoids the need for proprietary GPS tracking applications to collect smartphone-based GPS data. This research enhanced an existing travel survey designer, Travel Activity Internet Survey Interface (TRAISI), to make it capable of deriving travel diaries from the respondents' GLH. The feasibility of this data collection approach is showcased through the Google Timeline Travel Survey (GTTS) conducted in the Greater Toronto Area, Canada. The resultant dataset from the GTTS is demographically representative and offers detailed and accurate travel behavioural insights.
GhostVec: A New Threat to Speaker Privacy of End-to-End Speech Recognition System
Authors: Xiaojiao Chen, Sheng Li, Jiyi Li, Hao Huang, Yang Cao, Liang He
Date: 2023-11-17 00:00:00
Tasks: Decoder, Privacy Preserving, Speaker Verification, speech-recognition, Speech Recognition

Speaker adaptation systems face privacy concerns, for such systems are trained on private datasets and often overfitting. This paper demonstrates that an attacker can extract speaker information by querying speaker-adapted speech recognition (ASR) systems. We focus on the speaker information of a transformer-based ASR and propose GhostVec, a simple and efficient attack method to extract the speaker information from an encoder-decoder-based ASR system without any external speaker verification system or natural human voice as a reference. To make our results quantitative, we pre-process GhostVec using singular value decomposition (SVD) and synthesize it into waveform. Experiment results show that the synthesized audio of GhostVec reaches 10.83\% EER and 0.47 minDCF with target speakers, which suggests the effectiveness of the proposed method. We hope the preliminary discovery in this study to catalyze future speech recognition research on privacy-preserving topics.
Reprogramming Self-supervised Learning-based Speech Representations for Speaker Anonymization
Authors: Xiaojiao Chen, Sheng Li, Jiyi Li, Hao Huang, Yang Cao, Liang He
Date: 2023-11-17 00:00:00
Tasks: Self-Supervised Learning, Speaker anonymization

Current speaker anonymization methods, especially with self-supervised learning (SSL) models, require massive computational resources when hiding speaker identity. This paper proposes an effective and parameter-efficient speaker anonymization method based on recent End-to-End model reprogramming technology. To improve the anonymization performance, we first extract speaker representation from large SSL models as the speaker identifies. To hide the speaker's identity, we reprogram the speaker representation by adapting the speaker to a pseudo domain. Extensive experiments are carried out on the VoicePrivacy Challenge (VPC) 2022 datasets to demonstrate the effectiveness of our proposed parameter-efficient learning anonymization methods. Additionally, while achieving comparable performance with the VPC 2022 strong baseline 1.b, our approach consumes less computational resources during anonymization.
LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement
Authors: Zili Qi, Xinhui Hu, Wangjin Zhou, Sheng Li, Hao Wu, Jian Lu, Xinkang Xu
Date: 2023-11-17 00:00:00
Tasks: Ensemble Learning, Language Modelling, Prediction, Self-Supervised Learning, Speech Synthesis

Recently, researchers have shown an increasing interest in automatically predicting the subjective evaluation for speech synthesis systems. This prediction is a challenging task, especially on the out-of-domain test set. In this paper, we proposed a novel fusion model for MOS prediction that combines supervised and unsupervised approaches. In the supervised aspect, we developed an SSL-based predictor called LE-SSL-MOS. The LE-SSL-MOS utilizes pre-trained self-supervised learning models and further improves prediction accuracy by utilizing the opinion scores of each utterance in the listener enhancement branch. In the unsupervised aspect, two steps are contained: we fine-tuned the unit language model (ULM) using highly intelligible domain data to improve the correlation of an unsupervised metric - SpeechLMScore. Another is that we utilized ASR confidence as a new metric with the help of ensemble learning. To our knowledge, this is the first architecture that fuses supervised and unsupervised methods for MOS prediction. With these approaches, our experimental results on the VoiceMOS Challenge 2023 show that LE-SSL-MOS performs better than the baseline. Our fusion system achieved an absolute improvement of 13% over LE-SSL-MOS on the noisy and enhanced speech track. Our system ranked 1st and 2nd, respectively, in the French speech synthesis track and the challenge's noisy and enhanced speech track.
Improving fairness for spoken language understanding in atypical speech with Text-to-Speech
Authors: Helin Wang, Venkatesh Ravichandran, Milind Rao, Becky Lammers, Myra Sydnor, Nicholas Maragakis, Ankur A. Butala, Jayne Zhang, Lora Clawson, Victoria Chovaz, Laureano Moro-Velazquez
Date: 2023-11-16 00:00:00
Tasks: Data Augmentation, Fairness, Spoken Language Understanding, text-to-speech, Text to Speech, Voice Conversion

Spoken language understanding (SLU) systems often exhibit suboptimal performance in processing atypical speech, typically caused by neurological conditions and motor impairments. Recent advancements in Text-to-Speech (TTS) synthesis-based augmentation for more fair SLU have struggled to accurately capture the unique vocal characteristics of atypical speakers, largely due to insufficient data. To address this issue, we present a novel data augmentation method for atypical speakers by finetuning a TTS model, called Aty-TTS. Aty-TTS models speaker and atypical characteristics via knowledge transferring from a voice conversion model. Then, we use the augmented data to train SLU models adapted to atypical speech. To train these data augmentation models and evaluate the resulting SLU systems, we have collected a new atypical speech dataset containing intent annotation. Both objective and subjective assessments validate that Aty-TTS is capable of generating high-quality atypical speech. Furthermore, it serves as an effective data augmentation strategy, contributing to more fair SLU systems that can better accommodate individuals with atypical speech patterns.
MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
Authors: Teresa Zulueta-Coarasa, Florian Jug, Aastha Mathur, Josh Moore, Arrate Muñoz-Barrutia, Liviu Anita, Kola Babalola, Pete Bankhead, Perrine Gilloteaux, Nodar Gogoberidze, Martin Jones, Gerard J. Kleywegt, Paul Korir, Anna Kreshuk, Aybüke Küpcü Yoldaş, Luca Marconato, Kedar Narayan, Nils Norlin, Bugra Oezdemir, Jessica Riesterer, Norman Rzepka, Ugis Sarkans, Beatriz Serrano, Christian Tischer, Virginie Uhlmann, Vladimír Ulman, Matthew Hartley
Date: 2023-11-17 00:00:00

Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but access to such data is often hindered by the lack of standards for sharing datasets. We brought together community experts in a workshop to develop guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are positive that the MIFA (Metadata, Incentives, Formats, and Accessibility) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high quality training data.
Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography
Authors: Gangwon Jeong, Fu Li, Trevor M. Mitcham, Umberto Villa, Nebojsa Duric, Mark A. Anastasio
Date: 2023-11-16 00:00:00
Tasks: SSIM

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-input CNNs were trained separately using inputs from each modality alone (TT or RT) for comparison. The accuracy of the methods was systematically assessed using normalized root mean squared error (NRMSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). For tumor detection performance, receiver operating characteristic analysis was employed. The dual-channel IILR method was also tested on clinical human breast data. Ensemble average of the NRMSE, SSIM, and PSNR evaluated on this clinical dataset were 0.2355, 0.8845, and 28.33 dB, respectively.
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution
Authors: Mansoor Hayat, Supavadee Armvith, Dr. Titipat Achakulvisut
Date: 2023-11-16 00:00:00
Tasks: Anatomy, Image Super-Resolution, SSIM, Super-Resolution

Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients' organs. Like other technologies, stereo cameras have limitations, e.g., low resolution (LR) and blurry output images. Currently, most of the proposed techniques for super-resolution focus on developing complex blocks and complicated loss functions, which cause high system complexity. We proposed a combined channel and spatial attention block to extract features incorporated with a specific but very strong parallax attention module (PAM) for endoscopic image super-resolution. The proposed model is trained using the da Vinci dataset on scales 2 and 4. Our proposed model has improved PSNR up to 2.12 dB for scale 2 and 1.29 dB for scale 4, while SSIM is improved by 0.03 for scale 2 and 0.0008 for scale 4. By incorporating this method, diagnosis and treatment for endoscopic images can be more accurate and effective.