pwc-archive/papers-with-abstracts huggingface dataset:
Finite- and Large-Sample Inference for Ranks using Multinomial Data with an Application to Ranking Political Parties
Authors: Sergei Bazylik, Magne Mogstad, Joseph Romano, Azeem Shaikh, Daniel Wilhelm
Date: 2024-01-31 00:00:00
Tasks: Survey, valid

It is common to rank different categories by means of preferences that are revealed through data on choices. A prominent example is the ranking of political candidates or parties using the estimated share of support each one receives in surveys or polls about political attitudes. Since these rankings are computed using estimates of the share of support rather than the true share of support, there may be considerable uncertainty concerning the true ranking of the political candidates or parties. In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each category. We consider both the problem of constructing marginal confidence sets for the rank of a particular category as well as simultaneous confidence sets for the ranks of all categories. A distinguishing feature of our analysis is that we exploit the multinomial structure of the data to develop confidence sets that are valid in finite samples. We additionally develop confidence sets using the bootstrap that are valid only approximately in large samples. We use our methodology to rank political parties in Australia using data from the 2019 Australian Election Survey. We find that our finite-sample confidence sets are informative across the entire ranking of political parties, even in Australian territories with few survey respondents and/or with parties that are chosen by only a small share of the survey respondents. In contrast, the bootstrap-based confidence sets may sometimes be considerably less informative. These findings motivate us to compare these methods in an empirically-driven simulation study, in which we conclude that our finite-sample confidence sets often perform better than their large-sample, bootstrap-based counterparts, especially in settings that resemble our empirical application.
Consistent Specification Test of the Quantile Autoregression
Authors: Anthoulla Phella
Date: 2020-10-08 00:00:00

This paper proposes a test for the joint hypothesis of correct dynamic specification and no omitted latent factors for the Quantile Autoregression. If the composite null is rejected we proceed to disentangle the cause of rejection, i.e., dynamic misspecification or an omitted variable. We establish the asymptotic distribution of the test statistics under fairly weak conditions and show that factor estimation error is negligible. A Monte Carlo study shows that the suggested tests have good finite sample properties. Finally, we undertake an empirical illustration of modelling GDP growth and CPI inflation in the United Kingdom, where we find evidence that factor augmented models are correctly specified in contrast with their non-augmented counterparts when it comes to GDP growth, while also exploring the asymmetric behaviour of the growth and inflation distributions.
Deep Room Impulse Response Completion
Authors: Jackie Lin, Georg Götz, Sebastian J. Schlecht
Date: 2024-02-01 00:00:00

Rendering immersive spatial audio in virtual reality (VR) and video games demands a fast and accurate generation of room impulse responses (RIRs) to recreate auditory environments plausibly. However, the conventional methods for simulating or measuring long RIRs are either computationally intensive or challenged by low signal-to-noise ratios. This study is propelled by the insight that direct sound and early reflections encapsulate sufficient information about room geometry and absorption characteristics. Building upon this premise, we propose a novel task termed "RIR completion," aimed at synthesizing the late reverberation given only the early portion (50 ms) of the response. To this end, we introduce DECOR, Deep Exponential Completion Of Room impulse responses, a deep neural network structured as an autoencoder designed to predict multi-exponential decay envelopes of filtered noise sequences. The interpretability of DECOR's output facilitates its integration with diverse rendering techniques. The proposed method is compared against an adapted state-of-the-art network, and comparable performance shows promising results supporting the feasibility of the RIR completion task. The RIR completion can be widely adapted to enhance RIR generation tasks where fast late reverberation approximation is required.
USDnet: Unsupervised Speech Dereverberation via Neural Forward Filtering
Authors: Zhong-Qiu Wang
Date: 2024-02-01 00:00:00
Tasks: Speech Dereverberation

In reverberant conditions with a single speaker, each far-field microphone records a reverberant version of the same speaker signal at a different location. In over-determined conditions, where there are multiple microphones but only one speaker, each recorded mixture signal can be leveraged as a constraint to narrow down the solutions to target anechoic speech and thereby reduce reverberation. Equipped with this insight, we propose USDnet, a novel deep neural network (DNN) approach for unsupervised speech dereverberation (USD). At each training step, we first feed an input mixture to USDnet to produce an estimate for target speech, and then linearly filter the DNN estimate to approximate the multi-microphone mixture so that the constraint can be satisfied at each microphone, thereby regularizing the DNN estimate to approximate target anechoic speech. The linear filter can be estimated based on the mixture and DNN estimate via neural forward filtering algorithms such as forward convolutive prediction. We show that this novel methodology can promote unsupervised dereverberation of single-source reverberant speech.
Real-time Stereo Speech Enhancement with Spatial-Cue Preservation based on Dual-Path Structure
Authors: Masahito Togami, Jean-Marc Valin, Karim Helwani, Ritwik Giri, Umut Isik, Michael M. Goodwin
Date: 2024-02-01 00:00:00
Tasks: Speech Enhancement

We introduce a real-time, multichannel speech enhancement algorithm which maintains the spatial cues of stereo recordings including two speech sources. Recognizing that each source has unique spatial information, our method utilizes a dual-path structure, ensuring the spatial cues remain unaffected during enhancement by applying source-specific common-band gain. This method also seamlessly integrates pretrained monaural speech enhancement, eliminating the need for retraining on stereo inputs. Source separation from stereo mixtures is achieved via spatial beamforming, with the steering vector for each source being adaptively updated using post-enhancement output signal. This ensures accurate tracking of the spatial information. The final stereo output is derived by merging the spatial images of the enhanced sources, with its efficacy not heavily reliant on the separation performance of the beamforming. The algorithm runs in real-time on 10-ms frames with a 40 ms of look-ahead. Evaluations reveal its effectiveness in enhancing speech and preserving spatial cues in both fully and sparsely overlapped mixtures.
DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography
Authors: Zhihao Xue, Fan Yang, Juan Gao, Zhuo Chen, Hao Peng, Chao Zou, Hang Jin, Chenxi Hu
Date: 2024-02-01 00:00:00
Tasks: 3D Reconstruction, compressed sensing, De-aliasing, Generative Adversarial Network, Image Reconstruction

Three-dimensional coronary magnetic resonance angiography (CMRA) demands reconstruction algorithms that can significantly suppress the artifacts from a heavily undersampled acquisition. While unrolling-based deep reconstruction methods have achieved state-of-the-art performance on 2D image reconstruction, their application to 3D reconstruction is hindered by the large amount of memory needed to train an unrolled network. In this study, we propose a memory-efficient deep compressed sensing method by employing a sparsifying transform based on a pre-trained artifact estimation network. The motivation is that the artifact image estimated by a well-trained network is sparse when the input image is artifact-free, and less sparse when the input image is artifact-affected. Thus, the artifact-estimation network can be used as an inherent sparsifying transform. The proposed method, named De-Aliasing Regularization based Compressed Sensing (DARCS), was compared with a traditional compressed sensing method, de-aliasing generative adversarial network (DAGAN), model-based deep learning (MoDL), and plug-and-play for accelerations of 3D CMRA. The results demonstrate that the proposed method improved the reconstruction quality relative to the compared methods by a large margin. Furthermore, the proposed method well generalized for different undersampling rates and noise levels. The memory usage of the proposed method was only 63% of that needed by MoDL. In conclusion, the proposed method achieves improved reconstruction quality for 3D CMRA with reduced memory burden.
A System Level Analysis for Integrated Sensing and Communication
Authors: Roberto Bomfin, Konpal Shaukat Ali, Marwa Chafii
Date: 2024-02-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this work, we provide a system level analysis of integrated sensing and communication (ISAC) systems, where a setup with a mono-static dual-functional radar communication base station is assumed. We derive the ISAC signal-to-noise ratio (SNR) equation that relates communication and radar SNR for different distances. We also derive the ISAC range equation, which can be used for sensing-assisted beamforming applications. Specifically, we show that increasing the frequency and bandwidth is more favorable to the radar application in terms of relative SNR and range while increasing the transmit power is more favorable to communications. Numerical examples reveal that if the range for communication and radar is desired to be in the same order, the ISAC system should operate in mmWave or sub-THz bands, whereas sub-6GHz allows scenarios where the communication range is of orders of magnitude higher than that of radar.
Dual-Tap Optical-Digital Feedforward Equalization Enabling High-Speed Optical Transmission in IM/DD Systems
Authors: Yu Guo, Yangbo Wu, Zhao Yang, Lei Xue, Ning Liang, Yang Ren, Zhengrui Tu, Jia Feng, Qunbi Zhuge
Date: 2024-02-01 00:00:00

Intensity-modulation and direct-detection (IM/DD) transmission is widely adopted for high-speed optical transmission scenarios due to its cost-effectiveness and simplicity. However, as the data rate increases, the fiber chromatic dispersion (CD) would induce a serious power fading effect, and direct detection could generate inter-symbol interference (ISI). Moreover, the ISI becomes more severe with the increase of fiber length, thereby highly restricting the transmission distance of IM/DD systems. This paper proposes a dual-tap optical-digital feedforward equalization (DT-ODFE) scheme, which could effectively compensate for CD-induced power fading while maintaining low cost and simplicity. A theoretical channel response is formulated for IM/DD transmission, incorporating a dual-tap optical equalizer, and the theoretical analysis reveals that for an IM/DD transmission using 1371nm over 10km standard single-mode fiber (SSMF), frequency notch is removed from 33.7GHz to 46GHz. Simulation results show that the DT- ODFE achieves an SNR gain of 2.3dB over IM/DD systems with symbol-space feedforward equalizer (FFE) alone. As the fiber length increases to 15 km, DT- ODFE performs well, while FFE, decision-feedback equalizer (DFE) and Volterra nonlinear equalizers (VNLE) all fail to compensate for the power fading and the 7% hard-decision FEC limit is not satisfied. For 200 Gb/s/$\lambda$ PAM-4 over 15km SSMF, results show that the signal-to-noise ratio (SNR) of the proposed DT- ODFE with optimal coefficients satisfies the 7% hard-decision FEC limit, which uncovers the great potential of the DT- ODFE for high-speed IM/DD systems in LR/FR scenarios.
Radio Map Assisted Approach for Interference-Aware Predictive UAV Communications
Authors: Bowen Li, Junting Chen
Date: 2024-02-01 00:00:00

Herein, an interference-aware predictive aerial-and-terrestrial communication problem is studied, where an unmanned aerial vehicle (UAV) delivers some data payload to a few nodes within a communication deadline. The first challenge is the possible interference to the ground base stations (BSs) and users possibly at unknown locations. This paper develops a radio-map-based approach to predict the channel to the receivers and the unintended nodes. Therefore, a predictive communication strategy can be optimized ahead of time to reduce the interference power and duration for the ground nodes. Such predictive optimization raises the second challenge of developing a low-complexity solution for a batch of transmission strategies over T time slots for N receivers before the flight. Mathematically, while the proposed interference-aware predictive communication problem is non-convex, it is converted into a relaxed convex problem, and solved by a novel dual-based algorithm, which is shown to achieve global optimality at asymptotically small slot duration. The proposed algorithm demonstrates orders of magnitude saving of the computational time for moderate T and N compared to several existing solvers. Simulations show that the radio-map-assisted scheme can prevent all unintended receivers with known positions from experiencing interference and significantly reduce the interference to the users at unknown locations.
A Low-Cost Multi-Band Waveform Security Framework in Resource-Constrained Communications
Authors: Tongyang Xu, Zhongxiang Wei, Tianhua Xu, Gan Zheng
Date: 2024-02-01 00:00:00

Traditional physical layer secure beamforming is achieved via precoding before signal transmission using channel state information (CSI). However, imperfect CSI will compromise the performance with imperfect beamforming and potential information leakage. In addition, multiple RF chains and antennas are needed to support the narrow beam generation, which complicates hardware implementation and is not suitable for resource-constrained Internet-of-Things (IoT) devices. Moreover, with the advancement of hardware and artificial intelligence (AI), low-cost and intelligent eavesdropping to wireless communications is becoming increasingly detrimental. In this paper, we propose a multi-carrier based multi-band waveform-defined security (WDS) framework, independent from CSI and RF chains, to defend against AI eavesdropping. Ideally, the continuous variations of sub-band structures lead to an infinite number of spectral features, which can potentially prevent brute-force eavesdropping. Sub-band spectral pattern information is efficiently constructed at legitimate users via a proposed chaotic sequence generator. A novel security metric, termed signal classification accuracy (SCA), is used to evaluate the security robustness under AI eavesdropping. Communication error probability and complexity are also investigated to show the reliability and practical capability of the proposed framework. Finally, compared to traditional secure beamforming techniques, the proposed multi-band WDS framework reduces power consumption by up to six times.
Optimized Parameter Design for Channel State Information-Free Location Spoofing
Authors: Jianxiu Li, Urbashi Mitra
Date: 2024-02-01 00:00:00

In this paper, an augmented analysis of a delay-angle information spoofing (DAIS) is provided for location-privacy preservation, where the location-relevant delays and angles are artificially shifted to obfuscate the eavesdropper with an incorrect physical location. A simplified misspecified Cramer-Rao bound (MCRB) is derived, which clearly manifests that not only estimation error, but also the geometric mismatch introduced by DAIS can lead to a significant increase in localization error for an eavesdropper. Given an assumption of the orthogonality among wireless paths, the simplified MCRB can be further expressed as a function of delay-angle shifts in a closed-form, which enables the more straightforward optimization of these design parameters for location-privacy enhancement. Numerical results are provided, validating the theoretical analysis and showing that the root-mean-square error for eavesdropper's localization can be more than 150 m with the optimized delay-angle shifts for DAIS.
On the Performance Analysis of Zero-Padding OFDM for Monostatic ISAC Systems
Authors: Roberto Bomfin, Marwa Chafii
Date: 2024-01-31 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper considers an integrated sensing and communication (ISAC) system with monostatic radar functionality using a zero-padding orthogonal frequency division multiplexing (ZP-OFDM) downlink transmission. We focus on ISAC's sensing aspect, employing an energy-detection (ED) method. The ZP-OFDM transmission is motivated by the fact that sensing can be performed during the silent periods of the transmitter, thereby avoiding self-interference (SI) cancellation processing of the in-band full duplex operation, which is needed for the cyclic prefix (CP)-OFDM. Additionally, we also show that ZP-OFDM can reject nearby clutter interference. We derive the probability of detection (PD) for the ZP and CP-OFDM systems, allowing useful performance analyses. In particular, we show that the PD expressions lead to an upper bound for the ZP-OFDM transmission, which is useful for selecting the best ZP size for a given system configuration. We also provide an expression that allows range comparison between ZP and CP-OFDM, where we consider a general case of imperfect SI cancellation for the CP-OFDM system. The results show that when the ZP size is 25% of the fast Fourier transform size, the range loss of the ZP system range is only 17% larger than the CP transmission.
Optimal vaccination strategies on networks and in metropolitan areas
Authors: Lucas Machado Moschen, María Soledad Aronna
Date: 2024-01-31 00:00:00

This study presents a mathematical model for optimal vaccination strategies in interconnected metropolitan areas, considering commuting patterns. It is a compartmental model with a vaccination rate for each city, acting as a control function. The commuting patterns are incorporated through a weighted adjacency matrix and a parameter that selects day and night periods. The optimal control problem is formulated to minimize a functional cost that balances the number of hospitalizations and vaccines, including restrictions of a weekly availability cap and an application capacity of vaccines per unit of time. The key findings of this work are bounds for the basic reproduction number, particularly in the case of a metropolitan area, and the study of the optimal control problem. Theoretical analysis and numerical simulations provide insights into disease dynamics and the effectiveness of control measures. The research highlights the importance of prioritizing vaccination in the capital to better control the disease spread, as we depicted in our numerical simulations. This model serves as a tool to improve resource allocation in epidemic control across metropolitan regions.
A Diffusion-Based Approach for Simulating Forward-in-Time State-Dependent Speciation and Extinction Dynamics
Authors: Albert C. Soewongsono, Michael J. Landis
Date: 2024-02-01 00:00:00

We establish a general framework using a diffusion approximation to simulate forward-in-time state counts or frequencies for cladogenetic state-dependent speciation-extinction (ClaSSE) models. We apply the framework to various two- and three-region geographic-state speciation-extinction (GeoSSE) models. We show that the species range state dynamics simulated under tree-based and diffusion-based processes are comparable. We derive a method to infer rate parameters that are compatible with given observed stationary state frequencies and obtain an analytical result to compute stationary state frequencies for a given set of rate parameters. We also describe a procedure to find the time to reach the stationary frequencies of a ClaSSE model using our diffusion-based approach, which we demonstrate using a worked example for a two-region GeoSSE model. Finally, we discuss how the diffusion framework can be applied to formalize relationships between evolutionary patterns and processes under state-dependent diversification scenarios.
Can Large Language Models Understand Molecules?
Authors: Shaghayegh Sadeghi, Alan Bui, Ali Forooghi, Jianguo Lu, Alioune Ngom
Date: 2024-01-05 00:00:00
Tasks: Drug Discovery, Language Modelling, Large Language Model, Molecular Property Prediction, molecular representation, Prediction, Property Prediction

Purpose: Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) from OpenAI and LLaMA (Large Language Model Meta AI) from Meta AI are increasingly recognized for their potential in the field of cheminformatics, particularly in understanding Simplified Molecular Input Line Entry System (SMILES), a standard method for representing chemical structures. These LLMs also have the ability to decode SMILES strings into vector representations. Method: We investigate the performance of GPT and LLaMA compared to pre-trained models on SMILES in embedding SMILES strings on downstream tasks, focusing on two key applications: molecular property prediction and drug-drug interaction prediction. Results: We find that SMILES embeddings generated using LLaMA outperform those from GPT in both molecular property and DDI prediction tasks. Notably, LLaMA-based SMILES embeddings show results comparable to pre-trained models on SMILES in molecular prediction tasks and outperform the pre-trained models for the DDI prediction tasks. Conclusion: The performance of LLMs in generating SMILES embeddings shows great potential for further investigation of these models for molecular embedding. We hope our study bridges the gap between LLMs and molecular embedding, motivating additional research into the potential of LLMs in the molecular representation field. GitHub: https://github.com/sshaghayeghs/LLaMA-VS-GPT
Predicting loss-of-function impact of genetic mutations: a machine learning approach
Authors: Arshmeet Kaur, Morteza Sarmadi
Date: 2024-01-26 00:00:00
Tasks: feature selection

The innovation of next-generation sequencing (NGS) techniques has significantly reduced the price of genome sequencing, lowering barriers to future medical research; it is now feasible to apply genome sequencing to studies where it would have previously been cost-inefficient. Identifying damaging or pathogenic mutations in vast amounts of complex, high-dimensional genome sequencing data may be of particular interest to researchers. Thus, this paper's aims were to train machine learning models on the attributes of a genetic mutation to predict LoFtool scores (which measure a gene's intolerance to loss-of-function mutations). These attributes included, but were not limited to, the position of a mutation on a chromosome, changes in amino acids, and changes in codons caused by the mutation. Models were built using the univariate feature selection technique f-regression combined with K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Sample Consensus (RANSAC), Decision Trees, Random Forest, and Extreme Gradient Boosting (XGBoost). These models were evaluated using five-fold cross-validated averages of r-squared, mean squared error, root mean squared error, mean absolute error, and explained variance. The findings of this study include the training of multiple models with testing set r-squared values of 0.97.