pwc-archive/papers-with-abstracts huggingface dataset:
Reorganization of the auditory-perceptual space across the human vocal range
Authors: Daniel Friedrichs, Volker Dellwo
Date: 2023-09-13 00:00:00

We analyzed the auditory-perceptual space across a substantial portion of the human vocal range (220-1046 Hz) using multidimensional scaling analysis of cochlea-scaled spectra from 250-ms vowel segments, initially studied in Friedrichs et al. (2017) J. Acoust. Soc. Am. 142 1025-1033. The dataset comprised the vowels /i y e {\o} {\epsilon} a o u/ (N=240) produced by three native German female speakers, encompassing a broad range of their respective voice frequency ranges. The initial study demonstrated that, during a closed-set identification task involving 21 listeners, the point vowels /i a u/ were significantly recognized at fundamental frequencies (fo) nearing 1 kHz, whereas the recognition of other vowels decreased at higher pitches. Building on these findings, our study revealed systematic spectral shifts associated with vowel height and frontness as fo increased, with a notable clustering around /i a u/ above 523 Hz. These observations underscore the pivotal role of spectral shape in vowel perception, illustrating the reliance on acoustic anchors at higher pitches. Furthermore, this study sheds light on the quantal nature of these vowels and their potential impact on language evolution, offering a plausible explanation for their widespread presence in the world's languages.
VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance
Authors: Carlos Hernandez-Olivan, Koichi Saito, Naoki Murata, Chieh-Hsin Lai, Marco A. Martínez-Ramirez, Wei-Hsiang Liao, Yuki Mitsufuji
Date: 2023-09-13 00:00:00
Tasks: Bandwidth Extension

Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation. Recent diffusion-based music restoration methods have demonstrated impressive performance, and among them, diffusion posterior sampling (DPS) stands out given its intrinsic properties, making it versatile across various restoration tasks. In this paper, we identify that there are potential issues which will degrade current DPS-based methods' performance and introduce the way to mitigate the issues inspired by diverse diffusion guidance techniques including the RePaint (RP) strategy and the Pseudoinverse-Guided Diffusion Models ($\Pi$GDM). We demonstrate our methods for the vocal declipping and bandwidth extension tasks under various levels of distortion and cutoff frequency, respectively. In both tasks, our methods outperform the current DPS-based music restoration benchmarks. We refer to \url{http://carlosholivan.github.io/demos/audio-restoration-2023.html} for examples of the restored audio samples.
ASPED: An Audio Dataset for Detecting Pedestrians
Authors: Pavan Seshadri, Chaeyeon Han, Bon-Woo Koo, Noah Posner, Subhrajit Guhathakurta, Alexander Lerch
Date: 2023-09-12 00:00:00
Tasks: Pedestrian Detection

We introduce the new audio analysis task of pedestrian detection and present a new large-scale dataset for this task. While the preliminary results prove the viability of using audio approaches for pedestrian detection, they also show that this challenging task cannot be easily solved with standard approaches.
Addressing the Blind Spots in Spoken Language Processing
Authors: Amit Moryossef
Date: 2023-09-06 00:00:00
Tasks: Spoken Language Understanding

This paper explores the critical but often overlooked role of non-verbal cues, including co-speech gestures and facial expressions, in human communication and their implications for Natural Language Processing (NLP). We argue that understanding human communication requires a more holistic approach that goes beyond textual or spoken words to include non-verbal elements. Borrowing from advances in sign language processing, we propose the development of universal automatic gesture segmentation and transcription models to transcribe these non-verbal cues into textual form. Such a methodology aims to bridge the blind spots in spoken language understanding, enhancing the scope and applicability of NLP models. Through motivating examples, we demonstrate the limitations of relying solely on text-based models. We propose a computationally efficient and flexible approach for incorporating non-verbal cues, which can seamlessly integrate with existing NLP pipelines. We conclude by calling upon the research community to contribute to the development of universal transcription methods and to validate their effectiveness in capturing the complexities of real-world, multi-modal interactions.
Media Moments and Corporate Connections: A Deep Learning Approach to Stock Movement Classification
Authors: Luke Sanborn, Matthew Sahagun
Date: 2023-09-08 00:00:00
Tasks: Graph Neural Network

The financial industry poses great challenges with risk modeling and profit generation. These entities are intricately tied to the sophisticated prediction of stock movements. A stock forecaster must untangle the randomness and ever-changing behaviors of the stock market. Stock movements are influenced by a myriad of factors, including company history, performance, and economic-industry connections. However, there are other factors that aren't traditionally included, such as social media and correlations between stocks. Social platforms such as Reddit, Facebook, and X (Twitter) create opportunities for niche communities to share their sentiment on financial assets. By aggregating these opinions from social media in various mediums such as posts, interviews, and news updates, we propose a more holistic approach to include these "media moments" within stock market movement prediction. We introduce a method that combines financial data, social media, and correlated stock relationships via a graph neural network in a hierarchical temporal fashion. Through numerous trials on current S&P 500 index data, with results showing an improvement in cumulative returns by 28%, we provide empirical evidence of our tool's applicability for use in investment decisions.
Data-Driven Synthesis of Configuration-Constrained Robust Invariant Sets for Linear Parameter-Varying Systems
Authors: Manas Mejari, Sampath Kumar Mulagaleti, Alberto Bemporad
Date: 2023-09-13 00:00:00
Tasks: Scheduling

We present a data-driven method to synthesize robust control invariant (RCI) sets for linear parameter-varying (LPV) systems subject to unknown but bounded disturbances. A finite-length data set consisting of state, input, and scheduling signal measurements is used to compute an RCI set and invariance-inducing controller, without identifying an LPV model of the system. We parameterize the RCI set as a configuration-constrained polytope whose facets have a fixed orientation and variable offset. This allows us to define the vertices of the polytopic set in terms of its offset. By exploiting this property, an RCI set and associated vertex control inputs are computed by solving a single linear programming (LP) problem, formulated based on a data-based invariance condition and system constraints. We illustrate the effectiveness of our approach via two numerical examples. The proposed method can generate RCI sets that are of comparable size to those obtained by a model-based method in which exact knowledge of the system matrices is assumed. We show that RCI sets can be synthesized even with a relatively small number of data samples, if the gathered data satisfy certain excitation conditions.
A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring
Authors: Sebastian Frey, Victor Kartsch, Christoph Leitner, Andrea Cossettini, Sergei Vostrikov, Simone Benatti, Luca Benini
Date: 2023-09-13 00:00:00

Surface electromyography (sEMG) is a well-established approach to monitor muscular activity on wearable and resource-constrained devices. However, when measuring deeper muscles, its low signal-to-noise ratio (SNR), high signal attenuation, and crosstalk degrade sensing performance. Ultrasound (US) complements sEMG effectively with its higher SNR at high penetration depths. In fact, combining US and sEMG improves the accuracy of muscle dynamic assessment, compared to using only one modality. However, the power envelope of US hardware is considerably higher than that of sEMG, thus inflating energy consumption and reducing the battery life. This work proposes a wearable solution that integrates both modalities and utilizes an EMG-driven wake-up approach to achieve ultra-low power consumption as needed for wearable long-term monitoring. We integrate two wearable state-of-the-art (SoA) US and ExG biosignal acquisition devices to acquire time-synchronized measurements of the short head of the biceps. To minimize power consumption, the US probe is kept in a sleep state when there is no muscle activity. sEMG data are processed on the probe (filtering, envelope extraction and thresholding) to identify muscle activity and generate a trigger to wake-up the US counterpart. The US acquisition starts before muscle fascicles displacement thanks to a triggering time faster than the electromechanical delay (30-100 ms) between the neuromuscular junction stimulation and the muscle contraction. Assuming a muscle contraction of 200 ms at a contraction rate of 1 Hz, the proposed approach enables more than 59% energy saving (with a full-system average power consumption of 12.2 mW) as compared to operating both sEMG and US continuously.
A Critical Escape Probability Formulation for Enhancing the Transient Stability of Power Systems with System Parameter Design
Authors: Xian Wu, Kaihua Xi, Aijie Cheng, Chenghui Zhang, Hai Xiang Lin
Date: 2023-09-13 00:00:00

For the enhancement of the transient stability of power systems, the key is to define a quantitative optimization formulation with system parameters as decision variables. In this paper, we model the disturbances by Gaussian noise and define a metric named Critical Escape Probability (CREP) based on the invariant probability measure of a linearised stochastic processes. CREP characterizes the probability of the state escaping from a critical set. CREP involves all the system parameters and reflects the size of the basin of attraction of the nonlinear systems. An optimization framework that minimizes CREP with the system parameters as decision variablesis is presented. Simulations show that the mean first hitting time when the state hits the boundary of the critical set, that is often used to describe the stability of nonlinear systems, is dramatically increased by minimizing CREP. This indicates that the transient stability of the system is effectively enhanced. It also shown that suppressing the state fluctuations only is insufficient for enhancing the transient stability. In addition, the famous Braess' paradox which also exists in power systems is revisited. Surprisingly, it turned out that the paradoxes identified by the traditional metric may not exist according to CREP. This new metric opens a new avenue for the transient stability analysis of future power systems integrated with large amounts of renewable energy.
Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe
Authors: Hah Min Lew, Jae Seong Kim, Moon Hwan Lee, Jaegeun Park, Sangyeon Youn, Hee Man Kim, Jihun Kim, Jae Youn Hwang
Date: 2023-09-13 00:00:00
Tasks: Deep Learning, Image-to-Image Translation, Super-Resolution

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.
A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction
Authors: Antreas Palamas, Nicolas Souli, Tania Panayiotou, Panayiotis Kolios, Georgios Ellinas
Date: 2023-09-13 00:00:00
Tasks: Multi-Task Learning, Trajectory Prediction

The rise of unmanned aerial vehicle (UAV) operations, as well as the vulnerability of the UAVs' sensors, has led to the need for proper monitoring systems for detecting any abnormal behavior of the UAV. This work addresses this problem by proposing an innovative multi-task learning framework (MLF-ST) for UAV state identification and trajectory prediction, that aims to optimize the performance of both tasks simultaneously. A deep neural network with shared layers to extract features from the input data is employed, utilizing drone sensor measurements and historical trajectory information. Moreover, a novel loss function is proposed that combines the two objectives, encouraging the network to jointly learn the features that are most useful for both tasks. The proposed MLF-ST framework is evaluated on a large dataset of UAV flights, illustrating that it is able to outperform various state-of-the-art baseline techniques in terms of both state identification and trajectory prediction. The evaluation of the proposed framework, using real-world data, demonstrates that it can enable applications such as UAV-based surveillance and monitoring, while also improving the safety and efficiency of UAV operations.
Immersion and Invariance-based Disturbance Observer and Its Application to Safe Control
Authors: Yujie Wang, Xiangru Xu
Date: 2023-09-13 00:00:00

When the disturbance input matrix is nonlinear, existing disturbance observer design methods rely on the solvability of a partial differential equation or the existence of an output function with a uniformly well-defined disturbance relative degree, which can pose significant limitations. This note introduces a systematic approach for designing an Immersion and Invariance-based Disturbance Observer (IIDOB) that circumvents these strong assumptions. The proposed IIDOB ensures the disturbance estimation error is globally uniformly ultimately bounded by approximately solving a partial differential equation while compensating for the approximation error. Furthermore, by integrating IIDOB into the framework of control barrier functions, a filter-based safe control design method for control-affine systems with disturbances is established where the filter is used to generate an alternative disturbance estimation signal with a known derivative. Sufficient conditions are established to guarantee the safety of the disturbed systems. Simulation results demonstrate the effectiveness of the proposed method.
21cmEMU: an emulator of 21cmFAST summary observables
Authors: Daniela Breitman, Andrei Mesinger, Steven Murray, David Prelogovic, Yuxiang Qin, Roberto Trotta
Date: 2023-09-11 00:00:00

Recent years have witnessed rapid progress in observations of the Epoch of Reionization (EoR). These have enabled high-dimensional inference of galaxy and intergalactic medium (IGM) properties during the first billion years of our Universe. However, even using efficient, semi-numerical simulations, traditional inference approaches that compute 3D lightcones on-the-fly can take $10^5$ core hours. Here we present 21cmEMU: an emulator of several summary observables from the popular 21cmFAST simulation code. 21cmEMU takes as input nine parameters characterizing EoR galaxies, and outputs the following summary statistics: (i) the IGM mean neutral fraction; (ii) the 21-cm power spectrum; (iii) the mean 21-cm spin temperature; (iv) the sky-averaged (global) 21-cm signal; (v) the ultraviolet (UV) luminosity functions (LFs); and (vi) the Thomson scattering optical depth to the cosmic microwave background (CMB). All observables are predicted with sub-percent median accuracy, with a reduction of the computational cost by a factor of over 10$^4$. After validating inference results, we showcase a few applications, including: (i) quantifying the relative constraining power of different observational datasets; (ii) seeing how recent claims of a late EoR impact previous inferences; and (iii) forecasting upcoming constraints from the sixth observing season of the Hydrogen Epoch of Reionization Array (HERA) telescope. 21cmEMU is publicly-available, and is included as an alternative simulator in the public 21CMMC sampler.
Evolution of trust in structured populations
Authors: Chaoqian Wang
Date: 2023-09-12 00:00:00

The trust game, derived from an economics experiment, has recently attracted interest in the field of evolutionary dynamics. In a recent version of the evolutionary trust game, players adopt one of three strategies: investor, trustworthy trustee, or untrustworthy trustee. Trustworthy trustees enhance and share the investment with the investor, whereas untrustworthy trustees retain the full amount, betraying the investor. Following this setup, we investigate a two-player trust game, which is analytically feasible under weak selection. We explore the evolution of trust in structured populations, factoring in four strategy updating rules: pairwise comparison (PC), birth-death (BD), imitation (IM), and death-birth (DB). Comparing structured populations with well-mixed populations, we arrive at two main conclusions. First, in the absence of untrustworthy trustees, there is a saddle point between investors and trustworthy trustees, with collaboration thriving best in well-mixed populations. The collaboration diminishes sequentially from DB to IM to PC/BD updating rules in structured populations. Second, an invasion of untrustworthy trustees makes this saddle point unstable and leads to the extinction of investors. The 3-strategy system stabilizes at an equilibrium line where the trustworthy and untrustworthy trustees coexist. The stability span of trustworthy trustees is maximally extended under the PC and BD updating rules in structured populations, while it decreases in a sequence from IM to DB updating rules, with the well-mixed population being the least favorable. This research thus adds an analytical lens to the evolution of trust in structured populations.
Landau model to illustrate the process of learning and unlearning of nociplastic pain
Authors: Belén Valenzuela
Date: 2023-09-11 00:00:00

Recent advances in the comprehension of the consolidation of nociplastic pain points to a complex nonconscious learnt process of threat perception. Neurobiological education is emerging as a promising approach to unlearn nociplastic pain supported by biopsychosocial tools (exposition to movement, mindfulness, sharing group format...). However this approach is still poorly known by clinisians and society in general forming a communication problem that, unfortunately, perpetuate the suffering of the patients. We propose a Landau model to describe the process of learning and unlearning nociplastic pain to help to clarify this complex situation and facilitate communication between different sectors of society. Nociplastic pain corresponds to a first order transition with attention more likely in the alert-protection state than in the trust-explore state. Two appealing results of the model are that the perception of the critical context depends on the personal history about the symptom and that biopsychosocial loops are formed when there are alarming learnt historic information about the symptom together with confused and contradictory expert information as in nocebo messages. Learning and unlearning in the model correspond to a change in control parameters able to weight more alert-protected state, trust-explore state or neutral state. This description makes clear why neurobiological education is the ground therapy from which others must be built to embody the pertinent, clear and trustful information.
Stochastic Learning of Semiparametric Monotone Index Models with Large Sample Size
Authors: Qingsong Yao
Date: 2023-09-13 00:00:00
Tasks: Stochastic Optimization

I study the estimation of semiparametric monotone index models in the scenario where the number of observation points $n$ is extremely large and conventional approaches fail to work due to heavy computational burdens. Motivated by the mini-batch gradient descent algorithm (MBGD) that is widely used as a stochastic optimization tool in the machine learning field, I proposes a novel subsample- and iteration-based estimation procedure. In particular, starting from any initial guess of the true parameter, I progressively update the parameter using a sequence of subsamples randomly drawn from the data set whose sample size is much smaller than $n$. The update is based on the gradient of some well-chosen loss function, where the nonparametric component is replaced with its Nadaraya-Watson kernel estimator based on subsamples. My proposed algorithm essentially generalizes MBGD algorithm to the semiparametric setup. Compared with full-sample-based method, the new method reduces the computational time by roughly $n$ times if the subsample size and the kernel function are chosen properly, so can be easily applied when the sample size $n$ is large. Moreover, I show that if I further conduct averages across the estimators produced during iterations, the difference between the average estimator and full-sample-based estimator will be $1/\sqrt{n}$-trivial. Consequently, the average estimator is $1/\sqrt{n}$-consistent and asymptotically normally distributed. In other words, the new estimator substantially improves the computational speed, while at the same time maintains the estimation accuracy.
Government Investments and Entrepreneurship
Authors: Joao Ricardo Faria, Laudo Ogura, Mauricio Prado, Christopher J. Boudreaux
Date: 2023-09-13 00:00:00

How can governments attract entrepreneurs and their businesses? The view that new business creation grows with the optimal level of government investments remains appealing to policymakers. In contrast with this active approach, we build a model where governments may adopt a passive approach to stimulating business creation. The insights from this model suggest new business creation depends positively on factors beyond government investments--attracting high-skilled migrants to the region and lower property prices, taxes, and fines on firms in the informal sector. These findings suggest whether entrepreneurs generate business creation in the region does not only depend on government investments. It also depends on location and skilled migration. Our model also provides methodological implications--the relationship between government investments and new business creation is endogenously determined, so unless adjustments are made, econometric estimates will be biased and inconsistent. We conclude with policy and managerial implications.