pwc-archive/papers-with-abstracts huggingface dataset:
High-Dimensional Canonical Correlation Analysis
Authors: Anna Bykhovskaya, Vadim Gorin
Date: 2023-06-28 00:00:00

This paper studies high-dimensional canonical correlation analysis (CCA) with an emphasis on the vectors that define canonical variables. The paper shows that when two dimensions of data grow to infinity jointly and proportionally, the classical CCA procedure for estimating those vectors fails to deliver a consistent estimate. This provides the first result on the impossibility of identification of canonical variables in the CCA procedure when all dimensions are large. As a countermeasure, the paper derives the magnitude of the estimation error, which can be used in practice to assess the precision of CCA estimates. Applications of the results to cyclical vs. non-cyclical stocks and to a limestone grassland data set are provided.
Racial Disparities in Debt Collection
Authors: Jessica LaVoice, Domonkos F. Vamossy
Date: 2019-10-07 00:00:00

This paper shows that black and Hispanic borrowers are 39% more likely to experience a debt collection judgment than white borrowers, even after controlling for credit scores and other relevant credit attributes. The racial gap in judgments is more pronounced in areas with a high density of payday lenders, a high share of income-less households, and low levels of tertiary education. State-level measures of racial discrimination cannot explain the judgment gap, nor can neighborhood-level differences in the previous share of contested judgments or cases with attorney representation. A back-of-the-envelope calculation suggests that closing the racial wealth gap could significantly reduce the racial disparity in debt collection judgments.
Scaling Laws for Discriminative Speech Recognition Rescoring Models
Authors: Yile Gu, Prashanth Gurunath Shivakumar, Jari Kolehmainen, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko
Date: 2023-06-27 00:00:00
Tasks: speech-recognition, Speech Recognition

Recent studies have found that model performance has a smooth power-law relationship, or scaling laws, with training data and model size, for a wide range of problems. These scaling laws allow one to choose nearly optimal data and model sizes. We study whether this scaling property is also applicable to second-pass rescoring, which is an important component of speech recognition systems. We focus on RescoreBERT as the rescoring model, which uses a pre-trained Transformer-based architecture fined tuned with an ASR discriminative loss. Using such a rescoring model, we show that the word error rate (WER) follows a scaling law for over two orders of magnitude as training data and model size increase. In addition, it is found that a pre-trained model would require less data than a randomly initialized model of the same size, representing effective data transferred from pre-training step. This effective data transferred is found to also follow a scaling law with the data and model size.
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning
Authors: Zhongzhi Yu, Yang Zhang, Kaizhi Qian, Yonggan Fu, Yingyan Lin
Date: 2023-06-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Transfer Learning

Despite the impressive performance recently achieved by automatic speech recognition (ASR), we observe two primary challenges that hinder its broader applications: (1) The difficulty of introducing scalability into the model to support more languages with limited training, inference, and storage overhead; (2) The low-resource adaptation ability that enables effective low-resource adaptation while avoiding over-fitting and catastrophic forgetting issues. Inspired by recent findings, we hypothesize that we can address the above challenges with modules widely shared across languages. To this end, we propose an ASR framework, dubbed \METHODNS, that, \textit{for the first time}, simultaneously achieves strong multilingual scalability and low-resource adaptation ability thanks to its modularize-then-assemble strategy. Specifically, \METHOD learns a small set of generalizable sub-modules and adaptively assembles them for different languages to reduce the multilingual overhead and enable effective knowledge transfer for low-resource adaptation. Extensive experiments and visualizations demonstrate that \METHOD can effectively discover language similarity and improve multilingual and low-resource ASR performance over state-of-the-art (SOTA) methods, e.g., under multilingual-ASR, our framework achieves a 0.13$\sim$2.41 lower character error rate (CER) with 30\% smaller inference overhead over SOTA solutions on multilingual ASR and a comparable CER, with nearly 50 times fewer trainable parameters over SOTA solutions on low-resource tuning, respectively.
Implementing contextual biasing in GPU decoder for online ASR
Authors: Iuliia Nigmatulina, Srikanth Madikeri, Esaú Villatoro-Tello, Petr Motliček, Juan Zuluaga-Gomez, Karthik Pandia, Aravind Ganapathiraju
Date: 2023-06-23 00:00:00
Tasks: CPU, Decoder, GPU, Language Modeling, Language Modelling

GPU decoding significantly accelerates the output of ASR predictions. While GPUs are already being used for online ASR decoding, post-processing and rescoring on GPUs have not been properly investigated yet. Rescoring with available contextual information can considerably improve ASR predictions. Previous studies have proven the viability of lattice rescoring in decoding and biasing language model (LM) weights in offline and online CPU scenarios. In real-time GPU decoding, partial recognition hypotheses are produced without lattice generation, which makes the implementation of biasing more complex. The paper proposes and describes an approach to integrate contextual biasing in real-time GPU decoding while exploiting the standard Kaldi GPU decoder. Besides the biasing of partial ASR predictions, our approach also permits dynamic context switching allowing a flexible rescoring per each speech segment directly on GPU. The code is publicly released and tested with open-sourced test sets.
Utilizing Segment Anything Model For Assessing Localization of GRAD-CAM in Medical Imaging
Authors: Evan Kellener, Ihina Nath, An Ngo, Thomas Nguyen, Joshua Schuman, Coen Adler, Arnav Kartikeya
Date: 2023-06-24 00:00:00

The introduction of saliency map algorithms as an approach for assessing the interoperability of images has allowed for a deeper understanding of current black-box models with Artificial Intelligence. Their rise in popularity has led to these algorithms being applied in multiple fields, including medical imaging. With a classification task as important as those in the medical domain, a need for rigorous testing of their capabilities arises. Current works examine capabilities through assessing the localization of saliency maps upon medical abnormalities within an image, through comparisons with human annotations. We propose utilizing Segment Anything Model (SAM) to both further the accuracy of such existing metrics, while also generalizing beyond the need for human annotations. Our results show both high degrees of similarity to existing metrics while also highlighting the capabilities of this methodology to beyond human-annotation. Furthermore, we explore the applications (and challenges) of SAM within the medical domain, including image pre-processing before segmenting, natural language proposals to SAM in the form of CLIP-SAM, and SAM accuracy across multiple medical imaging datasets.
Frequency Modulation Aggregation for Federated Learning
Authors: Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Date: 2023-06-28 00:00:00
Tasks: Federated Learning

Federated edge learning (FEEL) is a framework for training models in a distributed fashion using edge devices and a server that coordinates the learning process. In FEEL, edge devices periodically transmit model parameters to the server, which aggregates them to generate a global model. To reduce the burden of transmitting high-dimensional data by many edge devices, a broadband analog transmission scheme has been proposed. The devices transmit the parameters simultaneously using a linear analog modulation, which are aggregated by the superposition nature of the wireless medium. However, linear analog modulations incur in an excessive power consumption for edge devices and are not suitable for current digital wireless systems. To overcome this issue, in this paper we propose a digital frequency broadband aggregation. The scheme integrates a Multiple Frequency Shift Keying (MFSK) at the transmitters and a type-based multiple access (TBMA) at the receiver. Using concurrent transmission, the server can recover the type (i.e., a histogram) of the transmitted parameters and compute any aggregation function to generate a shared global model. We provide an extensive analysis of the communication scheme in an additive white Gaussian noise (AWGN) channel and compare it with linear analog modulations. Our experimental results show that the proposed scheme achieves no drop in performance up to $-10$ dB and outperforms the analog counterparts, while requiring 14 dB less in peak-to-average power ratio (PAPR) than linear analog modulations.
Specific Beamforming for Multi-UAV Networks: A Dual Identity-based ISAC Approach
Authors: Yanpeng Cui, Qixun Zhang, Zhiyong Feng, Fan Liu, Ce Shi, Jinpo Fan, Ping Zhang
Date: 2023-06-28 00:00:00
Tasks: Integrated sensing and communication, ISAC

Beam alignment is essential to compensate for the high path loss in the millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated sensing and communication (ISAC) technology has been envisioned as a promising solution to enable efficient beam alignment in the dynamic UAV network. However, since the digital identity (D-ID) is not contained in the reflected echoes, the conventional ISAC solution has to either periodically feed back the D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by the separation of D-ID and physical identity (P-ID). This paper presents a novel dual identity association (DIA)-based ISAC approach, the first solution that enables specific, fast, and accurate beamforming towards multiple UAVs. In particular, the P-IDs extracted from echo signals are distinguished dynamically by calculating the feature similarity according to their prevalence, and thus the DIA is accurately achieved. We also present the extended Kalman filtering scheme to track and predict P-IDs, and the specific beam is thereby effectively aligned toward the intended UAVs in dynamic networks. Numerical results show that the proposed DIA-based ISAC solution significantly outperforms the conventional methods in association accuracy and communication performance.
LoRa-based Over-the-Air Computing for Sat-IoT
Authors: Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Date: 2023-06-28 00:00:00

Satellite Internet of Things (Sat-IoT) is a novel framework in which satellites integrate sensing, communication and computing capabilities to carry out task-oriented communications. In this paper we propose to use the Long Range (LoRa) modulation for the purpose of estimation in a Sat-IoT scenario. Then we realize that the collisions generated by LoRa can be harnessed in an Over-the-Air Computing (AirComp) framework. Specifically, we propose to use LoRa for Type-based Multiple Access (TBMA), a semantic-aware scheme in which communication resources are assigned to different parameters, not users. Our experimental results show that LoRa-TBMA is suitable as a massive access scheme, provides large gains in terms of mean squared error (MSE) and saves scarce satellite communication resources (i.e., power, latency and bandwidth) with respect to orthogonal multiple access schemes. We also analyze the satellite scenarios that could take advantage of the LoRa-TBMA scheme. In summary, that angular modulations, which are very useful in satellite communications, can also benefit from AirComp.
DCT-based Air Interface Design for Function Computation
Authors: Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Date: 2023-06-28 00:00:00

With the integration of communication and computing, it is expected that part of the computing is transferred to the transmitter side. In this paper we address the general problem of Frequency Modulation (FM) for function approximation through a communication channel. We exploit the benefits of the Discrete Cosine Transform (DCT) to approximate the function and design the waveform. In front of other approximation schemes, the DCT uses basis of controlled dynamic, which is a desirable property for a practical implementation. Furthermore, the proposed modulation allows to recover both the measurement and the function in a single transmission. Our experiments show that this scheme outperforms the double side-band (DSB) modulation in terms of mean squared error (MSE). This can also be implemented with an agnostic receiver, in which the function is unknown to the receiver. Finally, the proposed modulation is compatible with some of the existing transmission technologies for sensor networks.
Over the Air Computing for Satellite Networks in 6G
Authors: Marc Martinez-Gost, Ana Pérez-Neira
Date: 2023-06-28 00:00:00

6G and beyond networks will merge communication and computation capabilities in order to adapt to changes. As they will consist of many sensors gathering information from its environment, new schemes for managing these large amounts of data are needed. For this purpose, we review Over the Air (OTA) computing in the context of estimation and detection. For distributed scenarios, such as a Wireless Sensor Network, it has been proven that a separation theorem does not necessarily hold, whereas analog schemes may outperform digital designs. We outline existing gaps in the literature, evincing that current state of the art requires a theoretical framework based on analog and hybrid digital-analog schemes that will boost the evolution of OTA computing. Furthermore, we motivate the development of 3D networks based on OTA schemes, where satellites function as sensors. We discuss its integration within the satellite segment, delineate current challenges and present a variety of use cases that benefit from OTA computing in 3D networks.
GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces
Authors: Sun Yuan, Salami Pargoo Navid, Ortiz Jorge
Date: 2023-06-28 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

We introduce GeXSe (Generative Explanatory Sensor System), a novel framework designed to extract interpretable sensor-based and vision domain features from non-invasive smart space sensors. We combine these to provide a comprehensive explanation of sensor-activation patterns in activity recognition tasks. This system leverages advanced machine learning architectures, including transformer blocks, Fast Fourier Convolution (FFC), and diffusion models, to provide a more detailed understanding of sensor-based human activity data. A standout feature of GeXSe is our unique Multi-Layer Perceptron (MLP) with linear, ReLU, and normalization layers, specially devised for optimal performance on small datasets. It also yields meaningful activation maps to explain sensor-based activation patterns. The standard approach is based on a CNN model, which our MLP model outperforms.GeXSe offers two types of explanations: sensor-based activation maps and visual domain explanations using short videos. These methods offer a comprehensive interpretation of the output from non-interpretable sensor data, thereby augmenting the interpretability of our model. Utilizing the Frechet Inception Distance (FID) for evaluation, it outperforms established methods, improving baseline performance by about 6\%. GeXSe also achieves a high F1 score of up to 0.85, demonstrating precision, recall, and noise resistance, marking significant progress in reliable and explainable smart space sensing systems.
IRS-assisted UAV Communications: A Comprehensive Review
Authors: Syed Agha Hassnain Mohsan, Yanlong Li
Date: 2023-06-28 00:00:00

Intelligent reflecting surface (IRS) can smartly adjust the wavefronts in terms of phase, frequency, amplitude and polarization via passive reflections and without any need of radio frequency (RF) chains. It is envisaged as an emerging technology which can change wireless communication to improve both energy and spectrum efficiencies with low energy consumption and low cost. It can intelligently configure the wireless channels through a massive number of cost effective passive reflecting elements to improve the system performance. Similarly, unmanned aerial vehicle (UAV) communication has gained a viable attention due to flexible deployment, high mobility and ease of integration with several technologies. However, UAV communication is prone to security issues and obstructions in real-time applications. Recently, it is foreseen that UAV and IRS both can integrate together to attain unparalleled capabilities in difficult scenarios. Both technologies can ensure improved performance through proactively altering the wireless propagation using smart signal reflections and maneuver control in three dimensional (3D) space. IRS can be integrated in both aerial and terrene environments to reap the benefits of smart reflections. This study briefly discusses UAV communication, IRS and focuses on IRS-assisted UAC communications. It surveys the existing literature on this emerging research topic and highlights several promising technologies which can be implemented in IRS-assisted UAV communication. This study also presents several application scenarios and open research challenges. This study goes one step further to elaborate research opportunities to design and optimize wireless systems with low energy footprint and at low cost. Finally, we shed some light on future research aspects for IRS-assisted UAV communication.
Blind Graph Matching Using Graph Signals
Authors: Hang Liu, Anna Scaglione, Hoi-To Wai
Date: 2023-06-27 00:00:00
Tasks: Graph Matching

Classical graph matching aims to find a node correspondence between two unlabeled graphs of known topologies. This problem has a wide range of applications, from matching identities in social networks to identifying similar biological network functions across species. However, when the underlying graphs are unknown, the use of conventional graph matching methods requires inferring the graph topologies first, a process that is highly sensitive to observation errors. In this paper, we tackle the blind graph matching problem with unknown underlying graphs directly using observations of graph signals, which are generated from graph filters applied to graph signal excitations. We propose to construct sample covariance matrices from the observed signals and match the nodes based on the selected sample eigenvectors. Our analysis shows that the blind matching outcome converges to the result obtained with known graph topologies when the signal sampling size is large and the signal noise is small. Numerical results showcase the performance improvement of the proposed algorithm compared to matching two estimated underlying graphs learned from the graph signals.
Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning
Authors: Yufan Liu, Boxue Tian
Date: 2023-06-28 00:00:00
Tasks: Contrastive Learning, Drug Design, Language Modeling, Language Modelling, Protein Language Model

Protein-DNA interaction is critical for life activities such as replication, transcription, and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called CLAPE, which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein-DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the AUC values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein-ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.
Quantum-Enhanced Diamond Molecular Tension Microscopy for Quantifying Cellular Forces
Authors: Feng Xu, Shuxiang Zhang, Linjie Ma, Yong Hou, Jie Li, Andrej Denisenko, Zifu Li, Joachim Spatz, Jörg Wrachtrup, Qiang Wei, Zhiqin Chu
Date: 2023-06-28 00:00:00

The constant interplay and information exchange between cells and their micro-environment are essential to their survival and ability to execute biological functions. To date, a few leading technologies such as traction force microscopy, have been broadly used in measuring cellular forces. However, the considerable limitations, regarding the sensitivity and ambiguities in data interpretation, are hindering our thorough understanding of mechanobiology. Herein, we propose an innovative approach, namely quantum-enhanced diamond molecular tension microscopy (QDMTM), to precisely quantify the integrin-based cell adhesive forces. Specifically, we construct a force sensing platform by conjugating the magnetic nanotags labeled, force-responsive polymer to the surface of diamond membrane containing nitrogen vacancy (NV) centers. Thus, the coupled mechanical information can be quantified through optical readout of spin relaxation of NV centers modulated by those magnetic nanotags. To validate QDMTM, we have carefully performed corresponding measurements both in control and real cell samples. Particularly, we have obtained the quantitative cellular adhesion force mapping by correlating the measurement with established theoretical model. We anticipate that our method can be routinely used in studying important issues like cell-cell or cell-material interactions and mechanotransduction.