pwc-archive/papers-with-abstracts huggingface dataset:
First, Do No Harm: Algorithms, AI, and Digital Product Liability
Authors: Marc J. Pfeiffer
Date: 2023-11-17 00:00:00
Tasks: Ethics, Misinformation

The ethical imperative for technology should be first, do no harm. But digital innovations like AI and social media increasingly enable societal harms, from bias to misinformation. As these technologies grow ubiquitous, we need solutions to address unintended consequences. This report proposes a model to incentivize developers to prevent foreseeable algorithmic harms. It does this by expanding negligence and product liability laws. Digital product developers would be incentivized to mitigate potential algorithmic risks before deployment to protect themselves and investors. Standards and penalties would be set proportional to harm. Insurers would require harm mitigation during development in order to obtain coverage. This shifts tech ethics from move fast and break things to first, do no harm. Details would need careful refinement between stakeholders to enact reasonable guardrails without stifling innovation. Policy and harm prevention frameworks would likely evolve over time. Similar accountability schemes have helped address workplace, environmental, and product safety. Introducing algorithmic harm negligence liability would acknowledge the real societal costs of unethical tech. The timing is right for reform. This proposal provides a model to steer the digital revolution toward human rights and dignity. Harm prevention must be prioritized over reckless growth. Vigorous liability policies are essential to stop technologists from breaking things
Religious Competition, Culture and Domestic Violence: Evidence from Colombia
Authors: Hector Galindo-Silva, Guy Tchuente
Date: 2023-11-17 00:00:00

This paper studies how religious competition, as measured by the emergence of religious organizations with innovative worship styles and cultural practices, impacts domestic violence. Using data from Colombia, the study estimates a two-way fixed-effects model and reveals that the establishment of the first non-Catholic church in a predominantly Catholic municipality leads to a significant decrease in reported cases of domestic violence. This effect persists in the long run, indicating that religious competition introduces values and practices that discourage domestic violence, such as household stability and reduced male dominance. Additionally, the effect is more pronounced in municipalities with less clustered social networks, suggesting the diffusion of these values and practices through social connections. This research contributes to the understanding of how culture influences domestic violence, emphasizing the role of religious competition as a catalyst for cultural change.
Benefiting from Bias: Delegating to Encourage Information Acquisition
Authors: Ian Ball, Xin Gao
Date: 2023-11-20 00:00:00

A principal delegates decisions to a biased agent. Payoffs depend on a state that the principal cannot observe. Initially, the agent does not observe the state, but he can acquire information about it at a cost. We characterize the principal's optimal delegation set. This set features a cap on high decisions and a gap around the agent's ex ante favorite decision. It may even induce ex-post Pareto-dominated decisions. Under certain conditions on the cost of information acquisition, we show that the principal prefers delegating to an agent with a small bias than to an unbiased agent.
Ambiguity aversion as a route to randomness in a duopoly game
Authors: Davide Radi, Laura Gardini
Date: 2023-11-19 00:00:00

The global dynamics is investigated for a duopoly game where the perfect foresight hypothesis is relaxed and firms are worst-case maximizers. Overlooking the degree of product substitutability as well as the sensitivity of price to quantity, the unique and globally stable Cournot-Nash equilibrium of the complete-information duopoly game, loses stability when firms are not aware if they are playing a duopoly game, as it is, or an oligopoly game with more than two competitors. This finding resembles Theocharis' condition for the stability of the Cournot-Nash equilibrium in oligopolies without uncertainty. As opposed to complete-information oligopoly games, coexisting attractors, disconnected basins of attractions and chaotic dynamics emerge when the Cournot-Nash equilibrium loses stability. This difference in the global dynamics is due to the nonlinearities introduced by the worst-case approach to uncertainty, which mirror in bimodal best-reply functions. Conducted with techniques that require a symmetric setting of the game, the investigation of the dynamics reveals that a chaotic regime prevents firms from being ambiguity averse, that is, firms are worst-case maximizers only in the quantity-expectation space. Therefore, chaotic dynamics are the result and at the same time the source of profit uncertainty.
Modeling trading games in a stochastic non-life insurance market
Authors: Leonard Mushunje, David Edmund Allen
Date: 2023-11-18 00:00:00

We studied the behavior and variation of utility between the two conflicting players in a closed Nash-equilibrium loop. Our modeling approach also captured the nexus between optimal premium strategizing and firm performance using the Lotka-Volterra completion model. Our model robustly modeled the two main cases, insurer-insurer and insurer-policyholder, which we accompanied by numerical examples of premium movements and their relationship to the market equilibrium point. We found that insurers with high claim exposures tend to set high premiums. The other competitors either set a competitive premium or adopt the fixed premium charge to remain in the game; otherwise, they will operate below the optimal point. We also noted an inverse link between trading premiums and claims in general insurance games due to self-interest and utility indifferences. We concluded that while an insurer aims to charge high premiums to enjoy more, policyholders are willing to avoid these charges by paying less.
How does end-to-end speech recognition training impact speech enhancement artifacts?
Authors: Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix, Rintaro Ikeshita, Hiroshi Sato, Shoko Araki, Shigeru Katagiri
Date: 2023-11-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Jointly training a speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end has been investigated as a way to mitigate the influence of \emph{processing distortion} generated by single-channel SE on ASR. In this paper, we investigate the effect of such joint training on the signal-level characteristics of the enhanced signals from the viewpoint of the decomposed noise and artifact errors. The experimental analyses provide two novel findings: 1) ASR-level training of the SE front-end reduces the artifact errors while increasing the noise errors, and 2) simply interpolating the enhanced and observed signals, which achieves a similar effect of reducing artifacts and increasing noise, improves ASR performance without jointly modifying the SE and ASR modules, even for a strong ASR back-end using a WavLM feature extractor. Our findings provide a better understanding of the effect of joint training and a novel insight for designing an ASR agnostic SE front-end.
Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss
Authors: Hanako Segawa, Tsubasa Ochiai, Marc Delcroix, Tomohiro Nakatani, Rintaro Ikeshita, Shoko Araki, Takeshi Yamada, Shoji Makino
Date: 2023-11-20 00:00:00

Array processing performance depends on the number of microphones available. Virtual microphone estimation (VME) has been proposed to increase the number of microphone signals artificially. Neural network-based VME (NN-VME) trains an NN with a VM-level loss to predict a signal at a microphone location that is available during training but not at inference. However, this training objective may not be optimal for a specific array processing back-end, such as beamforming. An alternative approach is to use a training objective considering the array-processing back-end, such as a loss on the beamformer output. This approach may generate signals optimal for beamforming but not physically grounded. To combine the advantages of both approaches, this paper proposes a multi-task loss for NN-VME that combines both VM-level and beamformer-level losses. We evaluate the proposed multi-task NN-VME on multi-talker underdetermined conditions and show that it achieves a 33.1 % relative WER improvement compared to using only real microphones and 10.8 % compared to using a prior NN-VME approach.
APNet2: High-quality and High-efficiency Neural Vocoder with Direct Prediction of Amplitude and Phase Spectra
Authors: Hui-Peng Du, Ye-Xin Lu, Yang Ai, Zhen-Hua Ling
Date: 2023-11-20 00:00:00
Tasks: Speech Synthesis

In our previous work, we proposed a neural vocoder called APNet, which directly predicts speech amplitude and phase spectra with a 5 ms frame shift in parallel from the input acoustic features, and then reconstructs the 16 kHz speech waveform using inverse short-time Fourier transform (ISTFT). APNet demonstrates the capability to generate synthesized speech of comparable quality to the HiFi-GAN vocoder but with a considerably improved inference speed. However, the performance of the APNet vocoder is constrained by the waveform sampling rate and spectral frame shift, limiting its practicality for high-quality speech synthesis. Therefore, this paper proposes an improved iteration of APNet, named APNet2. The proposed APNet2 vocoder adopts ConvNeXt v2 as the backbone network for amplitude and phase predictions, expecting to enhance the modeling capability. Additionally, we introduce a multi-resolution discriminator (MRD) into the GAN-based losses and optimize the form of certain losses. At a common configuration with a waveform sampling rate of 22.05 kHz and spectral frame shift of 256 points (i.e., approximately 11.6ms), our proposed APNet2 vocoder outperformed the original APNet and Vocos vocoders in terms of synthesized speech quality. The synthesized speech quality of APNet2 is also comparable to that of HiFi-GAN and iSTFTNet, while offering a significantly faster inference speed.
Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition
Authors: Keqi Deng, Philip C. Woodland
Date: 2023-11-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, Language Modelling, Prediction, speech-recognition, Speech Recognition

Although end-to-end (E2E) automatic speech recognition (ASR) has shown state-of-the-art recognition accuracy, it tends to be implicitly biased towards the training data distribution which can degrade generalisation. This paper proposes a label-synchronous neural transducer (LS-Transducer), which provides a natural approach to domain adaptation based on text-only data. The LS-Transducer extracts a label-level encoder representation before combining it with the prediction network output. Since blank tokens are no longer needed, the prediction network performs as a standard language model, which can be easily adapted using text-only data. An Auto-regressive Integrate-and-Fire (AIF) mechanism is proposed to generate the label-level encoder representation while retaining low latency operation that can be used for streaming. In addition, a streaming joint decoding method is designed to improve ASR accuracy while retaining synchronisation with AIF. Experiments show that compared to standard neural transducers, the proposed LS-Transducer gave a 12.9% relative WER reduction (WERR) for intra-domain LibriSpeech data, as well as 21.4% and 24.6% relative WERRs on cross-domain TED-LIUM 2 and AESRC2020 data with an adapted prediction network.
Tubular Curvature Filter: Implicit Pointwise Curvature Calculation Method for Tubular Objects
Authors: Elifnur Sunger, Beyza Kalkanli, Veysi Yildiz, Tales Imbiriba, Peter Campbell, Deniz Erdogmus
Date: 2023-11-20 00:00:00

Curvature estimation methods are important as they capture salient features for various applications in image processing, especially within medical domains where tortuosity of vascular structures is of significant interest. Existing methods based on centerline or skeleton curvature fail to capture curvature gradients across a rotating tubular structure. This paper presents a Tubular Curvature Filter method that locally calculates the acceleration of bundles of curves that traverse along the tubular object parallel to the centerline. This is achieved by examining the directional rate of change in the eigenvectors of the Hessian matrix of a tubular intensity function in space. This method implicitly calculates the local tubular curvature without the need to explicitly segment the tubular object. Experimental results demonstrate that the Tubular Curvature Filter method provides accurate estimates of local curvature at any point inside tubular structures.
Wireless Regional Imaging through Reconfigurable Intelligent Surfaces: Passive Mode
Authors: Fuhai Wang, Chun Wang, Rujing Xiong, Zhengyu Wang, Tiebin Mi, Robert Caiming Qiu
Date: 2023-11-19 00:00:00

In this paper, we propose a multi-RIS-aided wireless imaging framework in 3D facing the distributed placement of multi-sensor networks. The system creates a randomized reflection pattern by adjusting the RIS phase shift, enabling the receiver to capture signals within the designated space of interest (SoI). Firstly, a multi-RIS-aided linear imaging channel modeling is proposed. We introduce a theoretical framework of computational imaging to recover the signal strength distribution of the SOI. For the RIS-aided imaging system, the impact of multiple parameters on the performance of the imaging system is analyzed. The simulation results verify the correctness of the proposal. Furthermore, we propose an amplitude-only imaging algorithm for the RIS-aided imaging system to mitigate the problem of phase unpredictability. Finally, the performance verification of the imaging algorithm is carried out by proof of concept experiments under reasonable parameter settings.
SDDPM: Speckle Denoising Diffusion Probabilistic Models
Authors: Soumee Guha, Scott T. Acton
Date: 2023-11-17 00:00:00
Tasks: Denoising, Image Denoising

Coherent imaging systems, such as medical ultrasound and synthetic aperture radar (SAR), are subject to corruption from speckle due to sub-resolution scatterers. Since speckle is multiplicative in nature, the constituent image regions become corrupted to different extents. The task of denoising such images requires algorithms specifically designed for removing signal-dependent noise. This paper proposes a novel image denoising algorithm for removing signal-dependent multiplicative noise with diffusion models, called Speckle Denoising Diffusion Probabilistic Models (SDDPM). We derive the mathematical formulations for the forward process, the reverse process, and the training objective. In the forward process, we apply multiplicative noise to a given image and prove that the forward process is Gaussian. We show that the reverse process is also Gaussian and the final training objective can be expressed as the Kullback Leibler (KL) divergence between the forward and reverse processes. As derived in the paper, the final denoising task is a single step process, thereby reducing the denoising time significantly. We have trained our model with natural land-use images and ultrasound images for different noise levels. Extensive experiments centered around two different applications show that SDDPM is robust and performs significantly better than the comparative models even when the images are severely corrupted.
Tensor-based Space Debris Detection for Satellite Mega-constellations
Authors: Olivier Daoust, Hasan Nayir, Irfan Azam, Antoine Lesage-Landry, Gunes Karabulut Kurt
Date: 2023-11-20 00:00:00
Tasks: Integrated sensing and communication, Tensor Decomposition

Thousands of satellites, asteroids, and rocket bodies break, collide, or degrade, resulting in large amounts of space debris in low Earth orbit. The presence of space debris poses a serious threat to satellite mega-constellations and to future space missions. Debris can be avoided if detected within the safety range of a satellite. In this paper, an integrated sensing and communication technique is proposed to detect space debris for satellite mega-constellations. The canonical polyadic (CP) tensor decomposition method is used to estimate the rank of the tensor that denotes the number of paths including line-of-sight and non-line-of-sight by exploiting the sparsity of THz channel with limited scattering. The analysis reveals that the reflected signals of the THz can be utilized for the detection of space debris. The CP decomposition is cast as an optimization problem and solved using the alternating least square (ALS) algorithm. Simulation results show that the probability of detection of the proposed tensor-based scheme is higher than the conventional energy-based detection scheme for the space debris detection.
Joint Design of ISAC Waveform under PAPR Constraints
Authors: Yating Chen, Cai Wen, Yan Huang, Le Liang, Jie Li, HUI ZHANG, Wei Hong
Date: 2023-11-20 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we formulate the precoding problem of integrated sensing and communication (ISAC) waveform as a non-convex quadratically constrainted quadratic program (QCQP), in which the weighted sum of communication multi-user interference (MUI) and the gap between dual-use waveform and ideal radar waveform is minimized with peak-to-average power ratio (PAPR) constraints. We propose an efficient algorithm based on alternating direction method of multipliers (ADMM), which is able to decouple multiple variables and provide a closed-form solution for each subproblem. In addition, to improve the sensing performance in both spatial and temporal domains, we propose a new criteria to design the ideal radar waveform, in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of interest. The limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function waveform. Numerical results indicate that the designed dual-function waveform is capable of offering good communication quality of service (QoS) and sensing performance.
Vital Signs Estimation Using a 26 GHz Multi-Beam Communication Testbed
Authors: Miquel Sellés Valls, Sofie Pollin, Ying Wang, Rizqi Hersyandika, Andre Kokkeler, Yang Miao
Date: 2023-11-19 00:00:00

This paper presents a novel pipeline for vital sign monitoring using a 26 GHz multi-beam communication testbed. In context of Joint Communication and Sensing (JCAS), the advanced communication capability at millimeter-wave bands is comparable to the radio resource of radars and is promising to sense the surrounding environment. Being able to communicate and sense the vital sign of humans present in the environment will enable new vertical services of telecommunication, i.e., remote health monitoring. The proposed processing pipeline leverages spatially orthogonal beams to estimate the vital sign - breath rate and heart rate - of single and multiple persons in static scenarios from the raw Channel State Information samples. We consider both monostatic and bistatic sensing scenarios. For monostatic scenario, we employ the phase time-frequency calibration and Discrete Wavelet Transform to improve the performance compared to the conventional Fast Fourier Transform based methods. For bistatic scenario, we use K-means clustering algorithm to extract multi-person vital signs due to the distinct frequency-domain signal feature between single and multi-person scenarios. The results show that the estimated breath rate and heart rate reach below 2 beats per minute (bpm) error compared to the reference captured by on-body sensor for the single-person monostatic sensing scenario with body-transceiver distance up to 2 m, and the two-person bistatic sensing scenario with BS-UE distance up to 4 m. The presented work does not optimize the OFDM waveform parameters for sensing; it demonstrates a promising JCAS proof-of-concept in contact-free vital sign monitoring using mmWave multi-beam communication systems.
Dynamics of a two-strain epidemic model with waning immunity -- a perturbative approach
Authors: Nir Gavish, Musa Rabiu
Date: 2023-11-19 00:00:00

Many infectious diseases are comprised of multiple strains with examples including Influenza, tuberculosis, and Dengue virus. The time evolution of such systems is linked to a complex landscape shaped by interactions between competing strains. Possible long-term dynamics include the extinction of less competitive strains, convergence to multi-strain steady-states, or self-sustained oscillations. This work considers a two-strain epidemic model in which the strains can interact indirectly via the immunity response generated following infections, and in which this immune response wanes with time. In particular, we focus on scenarios where the rate of waning immunity is significantly faster than the rate of demographic turnover. The first key result of this study is the explicit computation of the steady states of the nonlinear system of seven equations. Following this result, we take advantage of the separation of time scales in the problem and use perturbation methods to analyze the stability of the fixed points. In particular, we establish the conditions under which the system gives rise to the coexistence of the two strains and whether coexistence is attained via convergence to an endemic steady-state or via self-sustained oscillations. Our study unveils two parameter regimes of distinct qualitative behavior of the system and characterizes the separatrix between them. Within the first regime, the system gives rise to oscillatory coexistence for all feasible conditions. In the second regime, the system's behavior is governed by a solution to a quadratic equation, potentially resulting in the convergence to a multi-strain endemic equilibrium or the persistence of oscillatory coexistence.