pwc-archive/papers-with-abstracts huggingface dataset:
Trade, Growth, and Product Innovation
Authors: Carlos Góes
Date: 2024-06-13 00:00:00

Can trade integration induce product innovation? I document that countries that joined the European Union (EU) started producing more product varieties, investing more in R&D, and trading more compared to candidate countries that did not join at a given horizon. Additionally, I show that a plausibly exogenous increase in market access increases the probability of a given country starting production of and exporting a given product. To rationalize this reduced-form evidence, I propose a new quantitative framework that integrates the forces of specialization and market size. This is a dynamic general equilibrium model of frictional trade and endogenous growth with arbitrarily many asymmetric countries that nests the Eaton-Kortum model of trade and the Romer growth model as special cases. The key result is an analytical expression to decompose gains from trade into dynamic and static components. In this framework, the product innovation growth rate increases with higher market access. Finally, a quantitative version of the model suggests that: (a) the EU enlargement increased its long-run yearly growth rate by about 0.10pp; and (b) dynamic gains can account for between 65-90% of total welfare gains from trade.
Interconnected Markets: Exploring the Dynamic Relationship Between BRICS Stock Markets and Cryptocurrency
Authors: Wei Wang, Haibo Wang, Wendy Wang, Martin Enilov
Date: 2024-06-11 00:00:00

This study aims to examine the intricate dynamics between BRICS traditional stock assets and the evolving landscape of cryptocurrencies. Using a time-varying parameter vector autoregression model (TVP-VAR), we have analyzed data from the BRICS stock market index, cryptocurrencies, and indicators from January 6, 2015, to June 29, 2023. The results show that three out of the five BRICS stock markets serve as primary sources of shocks that subsequently affect the financial network. The transcontinental (TCI) value derived from the dynamic conditional connectedness using the TVP-VAR model demonstrates a higher explanatory power than the static connectedness observed using the standard VAR model. The discoveries from this study offer valuable insights for corporations, investors, and regulators concerning systematic risk and investment strategies.
Equality of Opportunity and Opportunity Pluralism
Authors: Giovanni Valvassori Bolgè
Date: 2024-06-13 00:00:00

This paper seeks to explore the potential trade-off arising between the theories of $\textit{Equality of Opportunity}$ and $\textit{Opportunity Pluralism}$. Whereas the first theory has received much attention in the literature on Welfare Economics, the second one has only recently been introduced with the publication of the book by Joseph Fishkin, $\textit{Bottlenecks: A New Theory of Equal Opportunity}$. After arguing extensively that any notion of human flourishing is incompatible with traditional theories of $\textit{Equality of Opportunity}$, the author proposes an alternative theory squarely based on a broad notion of human development. This paper seeks to formalize the argument made in this book through the lens of economic theory. My analysis suggests that traditional theories of $\textit{Equality of Opportunity}$ are not incompatible with $\textit{Opportunity Pluralism}$.
Mechanism Design by a Politician
Authors: Giovanni Valvassori Bolgè
Date: 2024-06-13 00:00:00

A set of agents has to make a decision about the provision of a public good and its financing. Agents have heterogeneous values for the public good and each agent's value is private information. An agenda-setter has the right to make a proposal about a public-good level and a vector of contributions. For the proposal to be approved, only the favourable votes of a subset of agents are needed. If the proposal is not approved, a type-dependent outside option is implemented. I characterize the optimal public-good provision and the coalition-formation for any outside option in dominant strategies. Optimal public-good provision might be a non-monotonic function of the outside option public-good level. Moreover, the optimal coalition might be a non-convex set of types.
Matching With Pre-Existing Binding Agreements: The Agreeable Core
Authors: Peter Doe
Date: 2024-06-12 00:00:00

Matching market models ignore prior commitments. Yet many job seekers, for example, are already employed, and the same holds for many other matching markets. I analyze two-sided matching markets with pre-existing binding agreements between market participants. In this model, a pair of participants bound to each other by a pre-existing agreement must agree to any action they take. To analyze their behavior, I propose a new solution concept, the agreeable core, consisting of the matches which cannot be renegotiated without violating the binding agreements. My main contribution is an algorithm that constructs such a match by a novel combination of the Deferred Acceptance and Top Trading Cycles algorithms. The algorithm is robust to various manipulations and has applications to numerous markets including the resident-to-hospital match, college admissions, school choice, and labor markets.
Audio-conditioned phonemic and prosodic annotation for building text-to-speech models from unlabeled speech data
Authors: Yuma Shirahata, Byeongseon Park, Ryuichi Yamamoto, Kentaro Tachibana
Date: 2024-06-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Pseudo Label, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

This paper proposes an audio-conditioned phonemic and prosodic annotation model for building text-to-speech (TTS) datasets from unlabeled speech samples. For creating a TTS dataset that consists of label-speech paired data, the proposed annotation model leverages an automatic speech recognition (ASR) model to obtain phonemic and prosodic labels from unlabeled speech samples. By fine-tuning a large-scale pre-trained ASR model, we can construct the annotation model using a limited amount of label-speech paired data within an existing TTS dataset. To alleviate the shortage of label-speech paired data for training the annotation model, we generate pseudo label-speech paired data using text-only corpora and an auxiliary TTS model. This TTS model is also trained with the existing TTS dataset. Experimental results show that the TTS model trained with the dataset created by the proposed annotation method can synthesize speech as naturally as the one trained with a fully-labeled dataset.
DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion
Authors: Ziqian Ning, Shuai Wang, Pengcheng Zhu, Zhichao Wang, Jixun Yao, Lei Xie, Mengxiao Bi
Date: 2024-06-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Voice Conversion

Streaming voice conversion has become increasingly popular for its potential in real-time applications. The recently proposed DualVC 2 has achieved robust and high-quality streaming voice conversion with a latency of about 180ms. Nonetheless, the recognition-synthesis framework hinders end-to-end optimization, and the instability of automatic speech recognition (ASR) model with short chunks makes it challenging to further reduce latency. To address these issues, we propose an end-to-end model, DualVC 3. With speaker-independent semantic tokens to guide the training of the content encoder, the dependency on ASR is removed and the model can operate under extremely small chunks, with cascading errors eliminated. A language model is trained on the content encoder output to produce pseudo context by iteratively predicting future frames, providing more contextual information for the decoder to improve conversion quality. Experimental results demonstrate that DualVC 3 achieves comparable performance to DualVC 2 in subjective and objective metrics, with a latency of only 50 ms.
On Annotation-free Optimization of Video Coding for Machines
Authors: Marc Windsheimer, Fabian Brand, André Kaup
Date: 2024-06-12 00:00:00

Today, image and video data is not only viewed by humans, but also automatically analyzed by computer vision algorithms. However, current coding standards are optimized for human perception. Emerging from this, research on video coding for machines tries to develop coding methods designed for machines as information sink. Since many of these algorithms are based on neural networks, most proposals for video coding for machines build upon neural compression. So far, optimizing the compression by applying the task loss of the analysis network, for which ground truth data is needed, is achieving the best coding performance. But ground truth data is difficult to obtain and thus an optimization without ground truth is preferred. In this paper, we present an annotation-free optimization strategy for video coding for machines. We measure the distortion by calculating the task loss of the analysis network. Therefore, the predictions on the compressed image are compared with the predictions on the original image, instead of the ground truth data. Our results show that this strategy can even outperform training with ground truth data with rate savings of up to 7.5 %. By using the non-annotated training data, the rate gains can be further increased up to 8.2 %.
Micro-expression recognition based on depth map to point cloud
Authors: Ren Zhang, Jianqin Yin, Chao Qi, Zehao Wang, Zhicheng Zhang, Yonghao Dang
Date: 2024-06-12 00:00:00
Tasks: Micro Expression Recognition, Micro-Expression Recognition

Micro-expressions are nonverbal facial expressions that reveal the covert emotions of individuals, making the micro-expression recognition task receive widespread attention. However, the micro-expression recognition task is challenging due to the subtle facial motion and brevity in duration. Many 2D image-based methods have been developed in recent years to recognize MEs effectively, but, these approaches are restricted by facial texture information and are susceptible to environmental factors, such as lighting. Conversely, depth information can effectively represent motion information related to facial structure changes and is not affected by lighting. Motion information derived from facial structures can describe motion features that pixel textures cannot delineate. We proposed a network for micro-expression recognition based on facial depth information, and our experiments have demonstrated the crucial role of depth maps in the micro-expression recognition task. Initially, we transform the depth map into a point cloud and obtain the motion information for each point by aligning the initiating frame with the apex frame and performing a differential operation. Subsequently, we adjusted all point cloud motion feature input dimensions and used them as inputs for multiple point cloud networks to assess the efficacy of this representation. PointNet++ was chosen as the ultimate outcome for micro-expression recognition due to its superior performance. Our experiments show that our proposed method significantly outperforms the existing deep learning methods, including the baseline, on the $CAS(ME)^3$ dataset, which includes depth information.
A Plug-and-Play Untrained Neural Network for Full Waveform Inversion in Reconstructing Sound Speed Images of Ultrasound Computed Tomography
Authors: Weicheng Yan, Qiude Zhang, Yun Wu, Zhaohui Liu, Liang Zhou, Mingyue Ding, Ming Yuchi, Wu Qiu
Date: 2024-06-12 00:00:00
Tasks: Image Reconstruction

Ultrasound computed tomography (USCT), as an emerging technology, can provide multiple quantitative parametric images of human tissue, such as sound speed and attenuation images, distinguishing it from conventional B-mode (reflection) ultrasound imaging. Full waveform inversion (FWI) is acknowledged as a technique with the greatest potential for reconstructing high-resolution sound speed images in USCT. However, traditional FWI for sound speed image reconstruction suffers from high sensitivity to the initial model caused by its strong non-convex nonlinearity, resulting in poor performance when ultrasound signals are at high frequencies. This limitation significantly restricts the application of FWI in the USCT imaging field. In this paper, we propose an untrained neural network (UNN) that can be integrated into the traditional iteration-based FWI framework as an implicit regularization prior. This integration allows for seamless deployment as a plug-and-play module within existing FWI algorithms or their variants. Notably, the proposed UNN method can be trained in an unsupervised fashion, a vital aspect in medical imaging where ground truth data is often unavailable. Evaluations of the numerical simulation and phantom experiment of the breast demonstrate that the proposed UNN improves the robustness of image reconstruction, reduces image artifacts, and achieves great image contrast. To the best of our knowledge, this study represents the first attempt to propose an implicit UNN for FWI in reconstructing sound speed images for USCT.
Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment
Authors: Tetiana Bogodorova, Denis Osipov, Luigi Vanfretti
Date: 2024-06-13 00:00:00
Tasks: Data Augmentation

Balanced data is required for deep neural networks (DNNs) when learning to perform power system stability assessment. However, power system measurement data contains relatively few events from where power system dynamics can be learnt. To mitigate this imbalance, we propose a novel data augmentation strategy preserving the dynamic characteristics to be learnt. The augmentation is performed using Variational Mode Decomposition. The detrended and the augmented data are tested for distributions similarity using Kernel Maximum Mean Discrepancy test. In addition, the effectiveness of the augmentation methodology is validated via training an Encoder DNN utilizing original data, testing using the augmented data, and evaluating the Encoder's performance employing several metrics.
Joint Power Allocation and Beamforming Design for Active IRS-Aided Directional Modulation Secure Systems
Authors: Yifan Zhao, Xiaoyu Wang, Kaibo Zhou, Xuehui Wang, Yan Wang, Wei Gao, Ruiqi Liu, Feng Shu
Date: 2024-06-13 00:00:00

Since the secrecy rate (SR) performance improvement obtained by secure directional modulation (DM) network is limited, an active intelligent reflective surface (IRS)-assisted DM network is considered to attain a high SR. To address the SR maximization problem, a novel method based on Lagrangian dual transform and closed-form fractional programming algorithm (LDT-CFFP) is proposed, where the solutions to base station (BS) beamforming vectors and IRS reflection coefficient matrix are achieved. However, the computational complexity of LDT-CFFP method is high . To reduce its complexity, a blocked IRS-assisted DM network is designed. To meet the requirements of the network performance, a power allocation (PA) strategy is proposed and adopted in the system. Specifically, the system power between BS and IRS, as well as the transmission power for confidential messages (CM) and artificial noise (AN) from the BS, are allocated separately. Then we put forward null-space projection (NSP) method, maximum-ratio-reflecting (MRR) algorithm and PA strategy (NSP-MRR-PA) to solve the SR maximization problem. The CF solutions to BS beamforming vectors and IRS reflection coefficient matrix are respectively attained via NSP and MRR algorithms. For the PA factors, we take advantage of exhaustive search (ES) algorithm, particle swarm optimization (PSO) and simulated annealing (SA) algorithm to search for the solutions. From simulation results, it is verified that the LDT-CFFP method derives a higher SR gain over NSP-MRR-PA method. For NSP-MRR-PA method, the number of IRS units in each block possesses a significant SR performance. In addition, the application PA strategies, namely ES, PSO, SA methods outperforms the other PA strategies with fixed PA factors.
Rethinking Waveform for 6G: Harnessing Delay-Doppler Alignment Modulation
Authors: Zhiqiang Xiao, Xianda Liu, Yong Zeng, J. Andrew Zhang, Shi Jin, Rui Zhang
Date: 2024-06-13 00:00:00

Waveform design has served as a cornerstone for each generation of mobile communication systems. The future sixth-generation (6G) mobile communication networks are expected to employ larger-scale antenna arrays and exploit higher-frequency bands for further boosting data transmission rate and providing ubiquitous wireless sensing. This brings new opportunities and challenges for 6G waveform design. In this article, by leveraging the super spatial resolution of large antenna arrays and the multi-path spatial sparsity of highfrequency wireless channels, we introduce a new approach for waveform design based on the recently proposed delay-Doppler alignment modulation (DDAM). In particular, DDAM makes a paradigm shift of waveform design from the conventional manner of tolerating channel delay and Doppler spreads to actively manipulating them. First, we review the fundamental constraints and performance limitations of orthogonal frequency division multiplexing (OFDM) and introduce new opportunities for 6G waveform design. Next, the motivations and basic principles of DDAM are presented, followed by its various extensions to different wireless system setups. Finally, the main design considerations for DDAM are discussed and the new opportunities for future research are highlighted.
Joint Channel Estimation and Prediction for Massive MIMO with Frequency Hopping Sounding
Authors: Yiming Zhu, Jiawei Zhuang, Gangle Sun, Hongwei Hou, Li You, Wenjin Wang
Date: 2024-06-13 00:00:00
Tasks: parameter estimation

In massive multiple-input multiple-output (MIMO) systems, the downlink transmission performance heavily relies on accurate channel state information (CSI). Constrained by the transmitted power, user equipment always transmits sounding reference signals (SRSs) to the base station through frequency hopping, which will be leveraged to estimate uplink CSI and subsequently predict downlink CSI. This paper aims to investigate joint channel estimation and prediction (JCEP) for massive MIMO with frequency hopping sounding (FHS). Specifically, we present a multiple-subband (MS) delay-angle-Doppler (DAD) domain channel model with off-grid basis to tackle the energy leakage problem. Furthermore, we formulate the JCEP problem with FHS as a multiple measurement vector (MMV) problem, facilitating the sharing of common CSI across different subbands. To solve this problem, we propose an efficient Off-Grid-MS hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) framework. Aiming to address the lack of prior CSI in practical scenarios, the proposed algorithm can adaptively learn the hyper-parameters of the channel by minimizing the corresponding terms in the BFE expression. To alleviate the complexity of channel hyper-parameter learning, we leverage the approximations of the off-grid matrices to simplify the off-grid hyper-parameter estimation. Numerical results illustrate that the proposed algorithm can effectively mitigate the energy leakage issue and exploit the common CSI across different subbands, acquiring more accurate CSI compared to state-of-the-art counterparts.
Site-Specific Radio Channel Representation for 5G and 6G
Authors: Thomas Zemen, Jorge Gomez-Ponce, Aniruddha Chandra, Michael Walter, Enes Aksoy, Ruisi He, David Matolak, Minseok Kim, Jun-ichi Takada, Sana Salous, Reinaldo Valenzuela, Andreas F. Molisch
Date: 2024-06-13 00:00:00

A site-specific radio channel representation (SSCR) takes the surroundings of the communication system into account by considering the environment geometry, including buildings, vegetation, and mobile objects with their material and surface properties. We present methods for an SSCR that is spatially consistent, such that mobile transmitter and receiver cause a correlated time-varying channel impulse response and closely spaced antennas are correctly correlated. An SSCR is composed of a dynamically varying number of multipath components solely defined by the environment geometry and the material of the environmental objects. Hence, the environment geometry is the only natural scenario parameterization and specific calibration procedures shall be avoided. 5G and 6G physical layer technologies are increasingly able to exploit the properties of a wide range of environments from dense urban areas to railways, road transportation, industrial automation, and unmanned aerial vehicles. The channel impulse response in this wide range of scenarios has generally non-stationary statistical properties, i.e., the Doppler spectrum, power delay profile, K-factor and spatial correlation are all spatially variant (or time-variant for mobile receivers). SSCRs will enable research and development of emerging 5G and 6G technologies such as distributed multiple-input multiple-output systems, reconfigurable intelligent surfaces, multi-band communication, and joint communication and sensing. We highlight the state of the art and summarize research directions for future work towards an SSCR.
Towards Unified AI Models for MU-MIMO Communications: A Tensor Equivariance Framework
Authors: Yafei Wang, Hongwei Hou, Xinping Yi, Wenjin Wang, Shi Jin
Date: 2024-06-13 00:00:00
Tasks: Scheduling

In this paper, we propose a unified framework based on equivariance for the design of artificial intelligence (AI)-assisted technologies in multi-user multiple-input-multiple-output (MU-MIMO) systems. We first provide definitions of multidimensional equivariance, high-order equivariance, and multidimensional invariance (referred to collectively as tensor equivariance). On this basis, by investigating the design of precoding and user scheduling, which are key techniques in MU-MIMO systems, we delve deeper into revealing tensor equivariance of the mappings from channel information to optimal precoding tensors, precoding auxiliary tensors, and scheduling indicators, respectively. To model mappings with tensor equivariance, we propose a series of plug-and-play tensor equivariant neural network (TENN) modules, where the computation involving intricate parameter sharing patterns is transformed into concise tensor operations. Building upon TENN modules, we propose the unified tensor equivariance framework that can be applicable to various communication tasks, based on which we easily accomplish the design of corresponding AI-assisted precoding and user scheduling schemes. Simulation results demonstrate that the constructed precoding and user scheduling methods achieve near-optimal performance while exhibiting significantly lower computational complexity and generalization to inputs with varying sizes across multiple dimensions. This validates the superiority of TENN modules and the unified framework.