pwc-archive/papers-with-abstracts huggingface dataset:
Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender Systems
Authors: Eoghan Cunningham, Derek Greene, Barry Smyth
Date: 2023-09-26 00:00:00
Tasks: Diversity, Document Embedding, Recommendation Systems, Transfer Learning

In the extensive recommender systems literature, novelty and diversity have been identified as key properties of useful recommendations. However, these properties have received limited attention in the specific sub-field of research paper recommender systems. In this work, we argue for the importance of offering novel and diverse research paper recommendations to scientists. This approach aims to reduce siloed reading, break down filter bubbles, and promote interdisciplinary research. We propose a novel framework for evaluating the novelty and diversity of research paper recommendations that leverages methods from network analysis and natural language processing. Using this framework, we show that the choice of representational method within a larger research paper recommendation system can have a measurable impact on the nature of downstream recommendations, specifically on their novelty and diversity. We highlight a novel paper embedding method, which we demonstrate offers more innovative and diverse recommendations without sacrificing precision, compared to other state-of-the-art baselines.
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
Authors: Haobing Liu, Jianyu Ding, Yanmin Zhu, Feilong Tang, Jiadi Yu, Ruobing Jiang, Zhongwen Guo
Date: 2023-09-26 00:00:00
Tasks: Sequential Recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model.
RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction
Authors: Song-Li Wu, Liang Du, Jia-Qi Yang, Yu-Ai Wang, De-Chuan Zhan, Shuang Zhao, Zi-Xun Sun
Date: 2023-09-26 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems, Selection bias

Click-through rate (CTR) prediction is a critical task in recommendation systems, serving as the ultimate filtering step to sort items for a user. Most recent cutting-edge methods primarily focus on investigating complex implicit and explicit feature interactions; however, these methods neglect the spurious correlation issue caused by confounding factors, thereby diminishing the model's generalization ability. We propose a CTR prediction framework that REmoves Spurious cORrelations in mulTilevel feature interactions, termed RE-SORT, which has two key components. I. A multilevel stacked recurrent (MSR) structure enables the model to efficiently capture diverse nonlinear interactions from feature spaces at different levels. II. A spurious correlation elimination (SCE) module further leverages Laplacian kernel mapping and sample reweighting methods to eliminate the spurious correlations concealed within the multilevel features, allowing the model to focus on the true causal features. Extensive experiments conducted on four challenging CTR datasets and our production dataset demonstrate that the proposed method achieves state-of-the-art performance in both accuracy and speed. The utilized codes, models and dataset will be released at https://github.com/RE-SORT.
fastMONAI: A low-code deep learning library for medical image analysis
Authors: Satheshkumar Kaliyugarasan, Alexander S. Lundervold
Date: 2023-09-26 00:00:00
Proceeding: Software Impacts 2023 9
Tasks: Medical Image Analysis, Medical Image Classification, Medical Image Segmentation, regression

We introduce fastMONAI, an open-source Python-based deep learning library for 3D medical imaging. Drawing upon the strengths of fastai, MONAI, and TorchIO, fastMONAI simplifies the use of advanced techniques for tasks like classification, regression, and segmentation. The library’s design addresses domain-specific demands while promoting best practices, facilitating efficient model development. It offers newcomers an easier entry into the field while keeping the option to make advanced, lower-level customizations if needed. This paper describes the library’s design, impact, limitations, and plans for future work.
A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?
Authors: Haoran Peng, Ching-Chieh Hsia, Zhu Han, and Li-Chun Wang
Date: 2023-09-18 00:00:00
Proceeding: IEEE Transactions on Wireless Communications 2023 9
Tasks: Scheduling

By creating multipath backscatter links and amplify signal strength, reconfigurable intelligent surfaces (RIS) and decode-and-forward (DF) relaying are shown to degrade the latency of the ultrareliable low-latency communications (URLLCs) and enhanced mobile broadband (eMBB) multiplexing system. This study investigates the delay and backlog violation behavior of URLLCs and eMBB multiplexing systems supported by different technologies, e.g. RIS and DF relay, for different scheduling policies of static priority, nonpreemption, and earliest deadline first. A tight analysis approach based on the Martingale theory was proposed to evaluate the serviceability of URLLC and eMBB multiplexing systems. On this basis, the Martingale theory analyzes the delay and backlog bounds by transforming the arrival and service processes into exponential forms of the moment generating function. Furthermore, this study derives the closed-form expression of delay and backlog bound for the URLLCs and eMBB multiplexing in two-hop heterogeneous communication networks. Numerical results demonstrate that the proposed Martingale-based tightly analytical method outperforms the state-of-the-art classic stochastic network calculus for evaluating delay and backlog violation in URLLC and eMBB multiplexing systems.
Multi-static Parameter Estimation in the Near/Far Field Beam Space for Integrated Sensing and Communication Applications
Authors: Saeid K. Dehkordi, Lorenzo Pucci, Peter Jung, Andrea Giorgetti, Enrico Paolini, Giuseppe Caire
Date: 2023-09-26 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

This work proposes a maximum likelihood (ML)-based parameter estimation framework for a millimeter wave (mmWave) integrated sensing and communication (ISAC) system in a multi-static configuration using energy-efficient hybrid digital-analog arrays. Due to the typically large arrays deployed in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field regime. The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field assumptions, and is used to obtain a first estimate of the target parameters. In cases where the target is determined to be in the near-field of the arrays, a second estimation based on near-field assumptions is carried out to obtain more accurate estimates. In particular, we select beamfocusing array weights designed to achieve a constant gain over an extended spatial region and re-estimate the target parameters at the receivers. We evaluate the effectiveness of the proposed framework in numerous scenarios through numerical simulations and demonstrate the impact of the custom-designed flat-gain beamfocusing codewords in increasing the communication performance of the system.
Designing Effective Music Excerpts
Authors: Emaad Manzoor, Nikhil Malik
Date: 2023-09-25 00:00:00

Excerpts are widely used to preview and promote musical works. Effective excerpts induce consumption of the source musical work and thus generate revenue. Yet, what makes an excerpt effective remains unexplored. We leverage a policy change by Apple that generates quasi-exogenous variation in the excerpts of songs in the iTunes Music Store to estimate that having a 60 second longer excerpt increases songs' unique monthly listeners by 5.4% on average, by 9.7% for lesser known songs, and by 11.1% for lesser known artists. This is comparable to the impact of being featured on the Spotify Global Top 50 playlist. We develop measures of musical repetition and unpredictability to examine information provision as a mechanism, and find that the demand-enhancing effect of longer excerpts is suppressed when they are repetitive, too predictable, or too unpredictable. Our findings support platforms' adoption of longer excerpts to improve content discovery and our measures can help inform excerpt selection in practice.
Enhanced Channel Estimation in mm-Wave MIMO Systems Leveraging Integrated Communication and Sensing
Authors: Silvia Mura, Marouan Mizmizi, Umberto Spagnolini, Athina Petropulu
Date: 2023-09-26 00:00:00
Tasks: Integrated sensing and communication

This paper tackles the challenge of wideband MIMO channel estimation within indoor millimeter-wave scenarios. Our proposed approach exploits the integrated sensing and communication paradigm, where sensing information aids in channel estimation. The key innovation consists of employing both spatial and temporal sensing modes to significantly reduce the number of required training pilots. Moreover, our algorithm addresses and corrects potential mismatches between sensing and communication modes, which can arise from differing sensing and communication propagation paths. Extensive simulations demonstrate that the proposed method requires 4x less pilots compared to the current state-of-the-art, marking a substantial advancement in channel estimation efficiency.
Approximation Rates for Deep Calibration of (Rough) Stochastic Volatility Models
Authors: Francesca Biagini, Lukas Gonon, Niklas Walter
Date: 2023-09-26 00:00:00

We derive quantitative error bounds for deep neural networks (DNNs) approximating option prices on a $d$-dimensional risky asset as functions of the underlying model parameters, payoff parameters and initial conditions. We cover a general class of stochastic volatility models of Markovian nature as well as the rough Bergomi model. In particular, under suitable assumptions we show that option prices can be learned by DNNs up to an arbitrary small error $\varepsilon \in (0,1/2)$ while the network size grows only sub-polynomially in the asset vector dimension $d$ and the reciprocal $\varepsilon^{-1}$ of the accuracy. Hence, the approximation does not suffer from the curse of dimensionality. As quantitative approximation results for DNNs applicable in our setting are formulated for functions on compact domains, we first consider the case of the asset price restricted to a compact set, then we extend these results to the general case by using convergence arguments for the option prices.
Towards a Neuronally Consistent Ontology for Robotic Agents
Authors: Florian Ahrens, Mihai Pomarlan, Daniel Beßler, Thorsten Fehr, Michael Beetz, Manfred Herrmann
Date: 2023-09-26 00:00:00
Tasks: Specificity

The Collaborative Research Center for Everyday Activity Science & Engineering (CRC EASE) aims to enable robots to perform environmental interaction tasks with close to human capacity. It therefore employs a shared ontology to model the activity of both kinds of agents, empowering robots to learn from human experiences. To properly describe these human experiences, the ontology will strongly benefit from incorporating characteristics of neuronal information processing which are not accessible from a behavioral perspective alone. We, therefore, propose the analysis of human neuroimaging data for evaluation and validation of concepts and events defined in the ontology model underlying most of the CRC projects. In an exploratory analysis, we employed an Independent Component Analysis (ICA) on functional Magnetic Resonance Imaging (fMRI) data from participants who were presented with the same complex video stimuli of activities as robotic and human agents in different environments and contexts. We then correlated the activity patterns of brain networks represented by derived components with timings of annotated event categories as defined by the ontology model. The present results demonstrate a subset of common networks with stable correlations and specificity towards particular event classes and groups, associated with environmental and contextual factors. These neuronal characteristics will open up avenues for adapting the ontology model to be more consistent with human information processing.
Simultaneously Learning Speaker's Direction and Head Orientation from Binaural Recordings
Authors: Harshvardhan Takawale, Nirupam Roy
Date: 2023-09-26 00:00:00

Estimation of a speaker's direction and head orientation with binaural recordings can be a critical piece of information in many real-world applications with emerging `earable' devices, including smart headphones and AR/VR headsets. However, it requires predicting the mutual head orientations of both the speaker and the listener, which is challenging in practice. This paper presents a system for jointly predicting speaker-listener head orientations by leveraging inherent human voice directivity and listener's head-related transfer function (HRTF) as perceived by the ear-mounted microphones on the listener. We propose a convolution neural network model that, given binaural speech recording, can predict the orientation of both speaker and listener with respect to the line joining the two. The system builds on the core observation that the recordings from the left and right ears are differentially affected by the voice directivity as well as the HRTF. We also incorporate the fact that voice is more directional at higher frequencies compared to lower frequencies.
Segment-Level Vectorized Beam Search Based on Partially Autoregressive Inference
Authors: Masao Someki, Nicholas Eng, Yosuke Higuchi, Shinji Watanabe
Date: 2023-09-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

Attention-based encoder-decoder models with autoregressive (AR) decoding have proven to be the dominant approach for automatic speech recognition (ASR) due to their superior accuracy. However, they often suffer from slow inference. This is primarily attributed to the incremental calculation of the decoder. This work proposes a partially AR framework, which employs segment-level vectorized beam search for improving the inference speed of an ASR model based on the hybrid connectionist temporal classification (CTC) attention-based architecture. It first generates an initial hypothesis using greedy CTC decoding, identifying low-confidence tokens based on their output probabilities. We then utilize the decoder to perform segment-level vectorized beam search on these tokens, re-predicting in parallel with minimal decoder calculations. Experimental results show that our method is 12 to 13 times faster in inference on the LibriSpeech corpus over AR decoding whilst preserving high accuracy.
Optimization Techniques for a Physical Model of Human Vocalisation
Authors: Mateo Cámara, Zhiyuan Xu, Yisu Zong, José Luis Blanco, Joshua D. Reiss
Date: 2023-09-26 00:00:00
Tasks: Benchmarking

We present a non-supervised approach to optimize and evaluate the synthesis of non-speech audio effects from a speech production model. We use the Pink Trombone synthesizer as a case study of a simplified production model of the vocal tract to target non-speech human audio signals --yawnings. We selected and optimized the control parameters of the synthesizer to minimize the difference between real and generated audio. We validated the most common optimization techniques reported in the literature and a specifically designed neural network. We evaluated several popular quality metrics as error functions. These include both objective quality metrics and subjective-equivalent metrics. We compared the results in terms of total error and computational demand. Results show that genetic and swarm optimizers outperform least squares algorithms at the cost of executing slower and that specific combinations of optimizers and audio representations offer significantly different results. The proposed methodology could be used in benchmarking other physical models and audio types.
Exploring RWKV for Memory Efficient and Low Latency Streaming ASR
Authors: Keyu An, Shiliang Zhang
Date: 2023-09-26 00:00:00
Tasks: Chunking

Recently, self-attention-based transformers and conformers have been introduced as alternatives to RNNs for ASR acoustic modeling. Nevertheless, the full-sequence attention mechanism is non-streamable and computationally expensive, thus requiring modifications, such as chunking and caching, for efficient streaming ASR. In this paper, we propose to apply RWKV, a variant of linear attention transformer, to streaming ASR. RWKV combines the superior performance of transformers and the inference efficiency of RNNs, which is well-suited for streaming ASR scenarios where the budget for latency and memory is restricted. Experiments on varying scales (100h - 10000h) demonstrate that RWKV-Transducer and RWKV-Boundary-Aware-Transducer achieve comparable to or even better accuracy compared with chunk conformer transducer, with minimal latency and inference memory cost.
Rethinking Session Variability: Leveraging Session Embeddings for Session Robustness in Speaker Verification
Authors: Hee-Soo Heo, Kihyun Nam, Bong-Jin Lee, Youngki Kwon, Minjae Lee, You Jin Kim, Joon Son Chung
Date: 2023-09-26 00:00:00
Tasks: Speaker Verification

In the field of speaker verification, session or channel variability poses a significant challenge. While many contemporary methods aim to disentangle session information from speaker embeddings, we introduce a novel approach using an additional embedding to represent the session information. This is achieved by training an auxiliary network appended to the speaker embedding extractor which remains fixed in this training process. This results in two similarity scores: one for the speakers information and one for the session information. The latter score acts as a compensator for the former that might be skewed due to session variations. Our extensive experiments demonstrate that session information can be effectively compensated without retraining of the embedding extractor.
NoLACE: Improving Low-Complexity Speech Codec Enhancement Through Adaptive Temporal Shaping
Authors: Jan Büthe, Ahmed Mustafa, Jean-Marc Valin, Karim Helwani, Michael M. Goodwin
Date: 2023-09-25 00:00:00

Speech codec enhancement methods are designed to remove distortions added by speech codecs. While classical methods are very low in complexity and add zero delay, their effectiveness is rather limited. Compared to that, DNN-based methods deliver higher quality but they are typically high in complexity and/or require delay. The recently proposed Linear Adaptive Coding Enhancer (LACE) addresses this problem by combining DNNs with classical long-term/short-term postfiltering resulting in a causal low-complexity model. A short-coming of the LACE model is, however, that quality quickly saturates when the model size is scaled up. To mitigate this problem, we propose a novel adatpive temporal shaping module that adds high temporal resolution to the LACE model resulting in the Non-Linear Adaptive Coding Enhancer (NoLACE). We adapt NoLACE to enhance the Opus codec and show that NoLACE significantly outperforms both the Opus baseline and an enlarged LACE model at 6, 9 and 12 kb/s. We also show that LACE and NoLACE are well-behaved when used with an ASR system.
Noise-Robust DSP-Assisted Neural Pitch Estimation with Very Low Complexity
Authors: Krishna Subramani, Jean-Marc Valin, Jan Buethe, Paris Smaragdis, Mike Goodwin
Date: 2023-09-25 00:00:00

Pitch estimation is an essential step of many speech processing algorithms, including speech coding, synthesis, and enhancement. Recently, pitch estimators based on deep neural networks (DNNs) have have been outperforming well-established DSP-based techniques. Unfortunately, these new estimators can be impractical to deploy in real-time systems, both because of their relatively high complexity, and the fact that some require significant lookahead. We show that a hybrid estimator using a small deep neural network (DNN) with traditional DSP-based features can match or exceed the performance of pure DNN-based models, with a complexity and algorithmic delay comparable to traditional DSP-based algorithms. We further demonstrate that this hybrid approach can provide benefits for a neural vocoding task.
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''
Authors: Arthur Pimentel, Heitor Guimarães, Anderson R. Avila, Mehdi Rezagholizadeh, Tiago H. Falk
Date: 2023-09-25 00:00:00
Tasks: Data Augmentation, Model Compression, Quantization, Self-Supervised Learning, speech-recognition, Speech Recognition

Recent advances with self-supervised learning have allowed speech recognition systems to achieve state-of-the-art (SOTA) word error rates (WER) while requiring only a fraction of the labeled training data needed by its predecessors. Notwithstanding, while such models achieve SOTA performance in matched train/test conditions, their performance degrades substantially when tested in unseen conditions. To overcome this problem, strategies such as data augmentation and/or domain shift training have been explored. Available models, however, are still too large to be considered for edge speech applications on resource-constrained devices, thus model compression tools are needed. In this paper, we explore the effects that train/test mismatch conditions have on speech recognition accuracy based on compressed self-supervised speech models. In particular, we report on the effects that parameter quantization and model pruning have on speech recognition accuracy based on the so-called robust wav2vec 2.0 model under noisy, reverberant, and noise-plus-reverberation conditions.
Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization
Authors: Axel Grönland, Alessio Russo, Yassir Jedra, Bleron Klaiqi, Xavier Gelabert
Date: 2023-09-26 00:00:00
Tasks: Deep Reinforcement Learning, Quantization, reinforcement-learning, Reinforcement Learning

In the Centralized-Radio Access Network (C-RAN) architecture, functions can be placed in the central or distributed locations. This architecture can offer higher capacity and cost savings but also puts strict requirements on the fronthaul (FH). Adaptive FH compression schemes that adapt the compression amount to varying FH traffic are promising approaches to deal with stringent FH requirements. In this work, we design such a compression scheme using a model-free off policy deep reinforcement learning algorithm which accounts for FH latency and packet loss constraints. Furthermore, this algorithm is designed for model transparency and interpretability which is crucial for AI trustworthiness in performance critical domains. We show that our algorithm can successfully choose an appropriate compression scheme while satisfying the constraints and exhibits a roughly 70\% increase in FH utilization compared to a reference scheme.