pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Stochastic Block Models in the Presence of Covariates
Authors: Yuichi Kitamura, Louise Laage
Date: 2024-02-26 00:00:00
Tasks: Stochastic Block Model

In the standard stochastic block model for networks, the probability of a connection between two nodes, often referred to as the edge probability, depends on the unobserved communities each of these nodes belongs to. We consider a flexible framework in which each edge probability, together with the probability of community assignment, are also impacted by observed covariates. We propose a computationally tractable two-step procedure to estimate the conditional edge probabilities as well as the community assignment probabilities. The first step relies on a spectral clustering algorithm applied to a localized adjacency matrix of the network. In the second step, k-nearest neighbor regression estimates are computed on the extracted communities. We study the statistical properties of these estimators by providing non-asymptotic bounds.
The Unelected Hand? Bureaucratic Influence and Electoral Accountability
Authors: Simon Lodato, Christos Mavridis, Federico Vaccari
Date: 2024-02-27 00:00:00

What role do non-elected bureaucrats play when elections provide imperfect accountability and create incentives for pandering? We develop a model where politicians and bureaucrats interact to implement policy. Both can either be good, sharing the voters' preferences over policies, or bad, intent on enacting policies that favor special interests. Our analysis identifies the conditions under which good bureaucrats choose to support or oppose political pandering. When bureaucrats wield significant influence over policy decisions, good politicians lose their incentives to pander, a shift that ultimately benefits voters. An intermediate level of bureaucratic influence over policymaking can be voter-optimal: large enough to prevent pandering but small enough to avoid granting excessive influence to potentially bad bureaucrats.
Learning to Maximize Ordinal and Expected Utility, and the Indifference Hypothesis
Authors: Thomas Dohmen, Georgios Gerasimou
Date: 2024-02-26 00:00:00

We ask if participants in a choice experiment with repeated presentation of the same menus and no feedback provision: (i) learn to behave in ways that are closer to the predictions of ordinal and expected utility theory under *strict* preferences; or (ii) exhibit overall behaviour that is consistent with utility theory under *weak* preferences. To answer these questions we designed and implemented a free-choice lab experiment with 15 distinct menus. Each menu contained two, three and four lotteries with three monetary outcomes, and was shown five times. Subjects were not forced to make an active choice at any menu but could avoid/defer doing so at a positive expected cost. Among our 308 subjects from the UK and Germany, significantly more were ordinal- and expected-utility maximizers in their last 15 than in their first 15 identical decision problems. Around a quarter and a fifth of all subjects, respectively, decided in those modes *throughout* the experiment, with nearly half revealing non-trivial indifferences. A considerable overlap is found between those consistently rational individuals and the ones who satisfied core principles of*random* utility theory. Finally, choice consistency is positively correlated with cognitive ability, while subjects who learned to maximize utility were more cognitively able than those who did not. We discuss potential implications of our study's novel set of findings.
Evolving E-commerce Logistics Planning- Integrating Embedded Technology and Ant Colony Algorithm for Enhanced Efficiency
Authors: Lynn Huang
Date: 2024-02-25 00:00:00

Amidst the era of networking, the e-commerce sector has undergone notable expansion, notably with the advent of Cross-border E-commerce (CBEC) in recent times. This growth trend persists, necessitating robust logistical frameworks to sustainably support operations. However, the current e-commerce logistics paradigm faces challenges in meeting evolving user demands, prompting a quest for innovative solutions. This research endeavors to address these complexities by undertaking a comprehensive analysis of CBEC logistics models and integrating embedded technology into logistical frameworks, resulting in the development of an advanced logistics tracking system. Moreover, employing the ant colony algorithm, the study conducts experimental investigations into optimizing logistics package distribution route planning. Noteworthy enhancements are observed in key metrics such as average delivery time, signaling the efficacy of this approach. In essence, this research offers a promising pathway towards optimizing logistics package distribution routes and bolstering package transportation efficiency within the CBEC domain.
Fuzzy Classification Aggregation
Authors: Federico Fioravanti
Date: 2024-02-27 00:00:00
Tasks: Classification

We consider the problem where a set of individuals has to classify $m$ objects into $p$ categories and does so by aggregating the individual classifications. We show that if $m\geq 3$, $m\geq p\geq 2$, and classifications are fuzzy, that is, objects belong to a category to a certain degree, then an optimal and independent aggregator rule that satisfies a weak unanimity condition belongs to the family of Weighted Arithmetic Means. We also obtain characterization results for $m= p= 2$.
Distributions of Posterior Quantiles via Matching
Authors: Anton Kolotilin, Alexander Wolitzky
Date: 2024-02-27 00:00:00

We offer a simple analysis of the problem of choosing a statistical experiment to optimize the induced distribution of posterior medians, or more generally $q$-quantiles for any $q \in (0,1)$. We show that all implementable distributions of the posterior $q$-quantile are implemented by a single experiment, the $q$-quantile matching experiment, which pools pairs of states across the $q$-quantile of the prior in a positively assortative manner, with weight $q$ on the lower state in each pair. A dense subset of implementable distributions of posterior $q$-quantiles can be uniquely implemented by perturbing the $q$-quantile matching experiment. A linear functional is optimized over distributions of posterior $q$-quantiles by taking the optimal selection from each set of $q$-quantiles induced by the $q$-quantile matching experiment. The $q$-quantile matching experiment is the only experiment that simultaneously implements all implementable distributions of the posterior $q$-quantile.
A Stationary Equilibrium Model of Green Technology Adoption with Endogenous Carbon Price
Authors: Felix Dammann, Giorgio Ferrari
Date: 2024-02-26 00:00:00

This paper proposes and analyzes a stationary equilibrium model for a competitive industry which endogenously determines the carbon price necessary to achieve a given emission target. In the model, firms are identified by their level of technology and make production, entry, and abatement decisions. Polluting firms are subject to a carbon price and abatement is formulated as an irreversible investment, which entails a sunk cost and results in the firms switching to a carbon neutral technology. In equilibrium, we identify a carbon price and a stationary distribution of incumbent, polluting firms, that guarantee the compliance with a certain emission target. Our general theoretical framework is complemented with a case study with Brownian technology shocks, in which we discuss some implications of our model. We observe that a carbon pricing system alongside installation subsidies and tax benefits for green firms trigger earlier investment, while higher income taxes for polluting firms may be distorting. Moreover, we discuss the role of a welfare maximizing regulator, who, by optimally setting the emission target, may mitigate or revert some parameters' effects observed in the model with fixed limit.
Aggregating Incomplete Rankings
Authors: Yasunori Okumura
Date: 2024-02-26 00:00:00

This study considers the method to derive a ranking of alternatives by aggregating the rankings submitted by several individuals who may not evaluate all of them. The collection of subsets of alternatives that individuals (can) evaluate is referred to as an evaluability profile. For a given evaluability profile, we define an aggregating ranking function whose inputs are the rankings of individuals on the alternatives they evaluate. We investigate the properties of aggregating ranking functions, which are modifications of those introduced in previous studies. Whether an aggregating ranking function satisfying a combination of properties exists depends on the evaluability profile. Thus, we identify the necessary and sufficient conditions on evaluability profiles to ensure the existence of the functions satisfying four different combinations of properties. Furthermore, to examine how frequently possible or impossible evaluability profiles occur, we derive the proportion of each type in specific cases.
CLAPSep: Leveraging Contrastive Pre-trained Model for Multi-Modal Query-Conditioned Target Sound Extraction
Authors: Hao Ma, Zhiyuan Peng, Xu Li, Mingjie Shao, Xixin Wu, Ju Liu
Date: 2024-02-27 00:00:00
Tasks: Target Sound Extraction

Universal sound separation (USS) aims to extract arbitrary types of sounds from real-world recordings. This can be achieved by language-queried target sound extraction (TSE), which typically consists of two components: a query network that converts user queries into conditional embeddings, and a separation network that extracts the target sound accordingly. Existing methods commonly train models from scratch. As a consequence, substantial data and computational resources are required to make the randomly initialized model comprehend sound events and perform separation accordingly. In this paper, we propose to integrate pre-trained models into TSE models to address the above issue. To be specific, we tailor and adapt the powerful contrastive language-audio pre-trained model (CLAP) for USS, denoted as CLAPSep. CLAPSep also accepts flexible user inputs, taking both positive and negative user prompts of uni- and/or multi-modalities for target sound extraction. These key features of CLAPSep can not only enhance the extraction performance but also improve the versatility of its application. We provide extensive experiments on 5 diverse datasets to demonstrate the superior performance and zero- and few-shot generalizability of our proposed CLAPSep with fast training convergence, surpassing previous methods by a significant margin. Full codes and some audio examples are released for reproduction and evaluation.
Ambisonics Encoding For Arbitrary Microphone Arrays Incorporating Residual Channels For Binaural Reproduction
Authors: Yhonatan Gayer, Vladimir Tourbabin, Zamir Ben-Hur, Jacob Donley, Boaz Rafaely
Date: 2024-02-27 00:00:00

In the rapidly evolving fields of virtual and augmented reality, accurate spatial audio capture and reproduction are essential. For these applications, Ambisonics has emerged as a standard format. However, existing methods for encoding Ambisonics signals from arbitrary microphone arrays face challenges, such as errors due to the irregular array configurations and limited spatial resolution resulting from a typically small number of microphones. To address these limitations and challenges, a mathematical framework for studying Ambisonics encoding is presented, highlighting the importance of incorporating the full steering function, and providing a novel measure for predicting the accuracy of encoding each Ambisonics channel from the steering functions alone. Furthermore, novel residual channels are formulated supplementing the Ambisonics channels. A simulation study for several array configurations demonstrates a reduction in binaural error for this approach.
Target Speaker Extraction by Directly Exploiting Contextual Information in the Time-Frequency Domain
Authors: Xue Yang, Changchun Bao, Jing Zhou, Xianhong Chen
Date: 2024-02-27 00:00:00
Tasks: Target Speaker Extraction

In target speaker extraction, many studies rely on the speaker embedding which is obtained from an enrollment of the target speaker and employed as the guidance. However, solely using speaker embedding may not fully utilize the contextual information contained in the enrollment. In this paper, we directly exploit this contextual information in the time-frequency (T-F) domain. Specifically, the T-F representations of the enrollment and the mixed signal are interacted to compute the weighting matrices through an attention mechanism. These weighting matrices reflect the similarity among different frames of the T-F representations and are further employed to obtain the consistent T-F representations of the enrollment. These consistent representations are served as the guidance, allowing for better exploitation of the contextual information. Furthermore, the proposed method achieves the state-of-the-art performance on the benchmark dataset and shows its effectiveness in the complex scenarios.
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation
Authors: Chunxi Wang, Maoshen Jia, Meiran Li, Changchun Bao, Wenyu Jin
Date: 2024-02-25 00:00:00
Tasks: Data Augmentation, parameter estimation, Transfer Learning

Dynamic parameterization of acoustic environments has drawn widespread attention in the field of audio processing. Precise representation of local room acoustic characteristics is crucial when designing audio filters for various audio rendering applications. Key parameters in this context include reverberation time (RT60) and geometric room volume. In recent years, neural networks have been extensively applied in the task of blind room parameter estimation. However, there remains a question of whether pure attention mechanisms can achieve superior performance in this task. To address this issue, this study employs blind room parameter estimation based on monaural noisy speech signals. Various model architectures are investigated, including a proposed attention-based model. This model is a convolution-free Audio Spectrogram Transformer, utilizing patch splitting, attention mechanisms, and cross-modality transfer learning from a pretrained Vision Transformer. Experimental results suggest that the proposed attention mechanism-based model, relying purely on attention mechanisms without using convolution, exhibits significantly improved performance across various room parameter estimation tasks, especially with the help of dedicated pretraining and data augmentation schemes. Additionally, the model demonstrates more advantageous adaptability and robustness when handling variable-length audio inputs compared to existing methods.
Text-guided HuBERT: Self-Supervised Speech Pre-training via Generative Adversarial Networks
Authors: Duo Ma, Xianghu Yue, Junyi Ao, Xiaoxue Gao, Haizhou Li
Date: 2024-02-24 00:00:00
Tasks: Pseudo Label, Self-Supervised Learning

Human language can be expressed in either written or spoken form, i.e. text or speech. Humans can acquire knowledge from text to improve speaking and listening. However, the quest for speech pre-trained models to leverage unpaired text has just started. In this paper, we investigate a new way to pre-train such a joint speech-text model to learn enhanced speech representations and benefit various speech-related downstream tasks. Specifically, we propose a novel pre-training method, text-guided HuBERT, or T-HuBERT, which performs self-supervised learning over speech to derive phoneme-like discrete representations. And these phoneme-like pseudo-label sequences are firstly derived from speech via the generative adversarial networks (GAN) to be statistically similar to those from additional unpaired textual data. In this way, we build a bridge between unpaired speech and text in an unsupervised manner. Extensive experiments demonstrate the significant superiority of our proposed method over various strong baselines, which achieves up to 15.3% relative Word Error Rate (WER) reduction on the LibriSpeech dataset.
A Performance Evaluation of Filtered Delay Multiply and Sum Beamforming for Ultrasound Localization Microscopy: Preliminary Results
Authors: A. N. Madhavanunni, Niya Mariam Benoy, Mahesh Raveendranatha Panicker, Himanshu Shekhar
Date: 2024-02-27 00:00:00
Tasks: Image Reconstruction

Ultrafast ultrasound localization microscopy (ULM), which has shown promising results in microvascular imaging, overcomes the typical trade-off between resolution and penetration depth. Combining ultrasound contrast agents and high frame rate imaging enables ULM to visualize microvasculature and quantify flow. However, the quality of the microvascular maps obtained depends on the signal-to-noise ratio of the received signals, image reconstruction techniques, and the microbubble (MB) localization and tracking algorithms used. Most reported research in ULM employs the conventional delay and sum (DAS) beamforming technique for image reconstruction despite its limited contrast and resolution. In this work, a filtered delay multiply and sum (F-DMAS) beamforming approach with non-steered plane wave transmit was employed for ULM, and its performance was compared with the conventional DAS-based approach for the different localization algorithms available in the Localization and Tracking Toolbox for Ultrasound Localization Microscopy. We also introduce two novel image quality measures that can overcome the limitations of conventional quality metrics that require suitable targets for evaluation. We also report the preliminary in-vitro investigation of F-DMAS with B-mode and power Doppler maps for microvascular imaging. The results are promising with enhanced contrast and lateral resolution, and suggest that further experimental studies are warranted.
VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis
Authors: Linshan Wu, Jiaxin Zhuang, Hao Chen
Date: 2024-02-27 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Medical Image Analysis, Position, Self-Supervised Learning

Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical image analysis. However, the lack of high-level semantics in pre-training still heavily hinders the performance of downstream tasks. We observe that 3D medical images contain relatively consistent contextual position information, i.e., consistent geometric relations between different organs, which leads to a potential way for us to learn consistent semantic representations in pre-training. In this paper, we propose a simple-yet-effective Volume Contrast (VoCo) framework to leverage the contextual position priors for pre-training. Specifically, we first generate a group of base crops from different regions while enforcing feature discrepancy among them, where we employ them as class assignments of different regions. Then, we randomly crop sub-volumes and predict them belonging to which class (located at which region) by contrasting their similarity to different base crops, which can be seen as predicting contextual positions of different sub-volumes. Through this pretext task, VoCo implicitly encodes the contextual position priors into model representations without the guidance of annotations, enabling us to effectively improve the performance of downstream tasks that require high-level semantics. Extensive experimental results on six downstream tasks demonstrate the superior effectiveness of VoCo. Code will be available at https://github.com/Luffy03/VoCo.
Rate Splitting Multiple Access-Enabled Adaptive Panoramic Video Semantic Transmission
Authors: Haixiao Gao, Mengying Sun, Xiaodong Xu, Shujun Han, Bizhu Wang, JingXuan Zhang, Ping Zhang
Date: 2024-02-26 00:00:00
Tasks: Deep Reinforcement Learning, Semantic Communication, SSIM

In this paper, we propose an adaptive panoramic video semantic transmission (APVST) framework enabled by rate splitting multiple access (RSMA). The APVST framework consists of a semantic transmitter and receiver, utilizing a deep joint source-channel coding structure to adaptively extract and encode semantic features from panoramic frames. To achieve higher spectral efficiency and conserve bandwidth, APVST employs an entropy model and a dimension-adaptive module to control the transmission rate. Additionally, we take weighted-to-spherically-uniform peak signal-to-noise ratio (WS-PSNR) and weighted-to-spherically-uniform structural similarity (WS-SSIM) as distortion evaluation metrics for panoramic videos and design a weighted self-attention module for APVST. This module integrates weights and feature maps to enhance the quality of the immersive experience. Considering the overlap in the field of view when users watch panoramic videos, we further utilize RSMA to split the required panoramic video semantic streams into common and private messages for transmission. We propose an RSMA-enabled semantic stream transmission scheme and formulate a joint problem of latency and immersive experience quality by optimizing the allocation ratios of power, common rate, and channel bandwidth, aiming to maximize the quality of service (QoS) scores for users. To address the above problem, we propose a deep reinforcement learning algorithm based on proximal policy optimization (PPO) with high efficiency to handle dynamically changing environments. Simulation results demonstrate that our proposed APVST framework saves up to 20% and 50% of channel bandwidth compared to other semantic and traditional video transmission schemes, respectively. Moreover, our study confirms the efficiency of RSMA in panoramic video transmission, achieving performance gains of 13% and 20% compared to NOMA and OFDMA.
Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison
Authors: Daniela Lupu, Joseph L. Garrett, Tor Arne Johansen, Milica Orlandic, Ion Necoara
Date: 2024-02-26 00:00:00
Tasks: Dimensionality Reduction, Earth Observation

Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, particularly in the case of remote sensing platforms (e.g., drones, satellites), but also in the case of multi-year datasets. Moreover, the computational strategies of unsupervised dimensionality reduction often provide the basis for more complicated supervised techniques. Seven unsupervised dimensionality reduction algorithms are tested on hyperspectral data from the HYPSO-1 earth observation satellite. Each particular algorithm is chosen to be representative of a broader collection. The experiments probe the computational complexity, reconstruction accuracy, signal clarity, sensitivity to artifacts, and effects on target detection and classification of the different algorithms. No algorithm consistently outperformed the others across all tests, but some general trends regarding the characteristics of the algorithms did emerge. With half a million pixels, computational time requirements of the methods varied by 5 orders of magnitude, and the reconstruction error varied by about 3 orders of magnitude. A relationship between mutual information and artifact susceptibility was suggested by the tests. The relative performance of the algorithms differed significantly between the target detection and classification tests. Overall, these experiments both show the power of dimensionality reduction and give guidance regarding how to evaluate a technique prior to incorporating it into a processing pipeline.