pwc-archive/papers-with-abstracts huggingface dataset:
Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers (extended version)
Authors: Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad, Arian Maleki
Date: 2023-10-26 00:00:00
Tasks: Model Selection, regression

The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet computationally demanding approach to estimate OO. Recent theoretical work showed that approximate leave-one-out cross validation (ALO) is a computationally efficient and statistically reliable estimate of LO (and OO) for generalized linear models with differentiable regularizers. For problems involving non-differentiable regularizers, despite significant empirical evidence, the theoretical understanding of ALO's error remains unknown. In this paper, we present a novel theory for a wide class of problems in the generalized linear model family with non-differentiable regularizers. We bound the error |ALO - LO| in terms of intuitive metrics such as the size of leave-i-out perturbations in active sets, sample size n, number of features p and regularization parameters. As a consequence, for the $\ell_1$-regularized problems, we show that |ALO - LO| goes to zero as p goes to infinity while n/p and SNR are fixed and bounded.
Inside the black box: Neural network-based real-time prediction of US recessions
Authors: Seulki Chung
Date: 2023-10-26 00:00:00
Tasks: Feature Importance, regression

Long short-term memory (LSTM) and gated recurrent unit (GRU) are used to model US recessions from 1967 to 2021. Their predictive performances are compared to those of the traditional linear models. The out-of-sample performance suggests the application of LSTM and GRU in recession forecasting, especially for longer-term forecasts. The Shapley additive explanations (SHAP) method is applied to both groups of models. The SHAP-based different weight assignments imply the capability of these types of neural networks to capture the business cycle asymmetries and nonlinearities. The SHAP method delivers key recession indicators, such as the S&P 500 index for short-term forecasting up to 3 months and the term spread for longer-term forecasting up to 12 months. These findings are robust against other interpretation methods, such as the local interpretable model-agnostic explanations (LIME) and the marginal effects.
Coreset Markov Chain Monte Carlo
Authors: Naitong Chen, Trevor Campbell
Date: 2023-10-25 00:00:00

A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during inference in order to reduce computational cost. However, state of the art methods for tuning coreset weights are expensive, require nontrivial user input, and impose constraints on the model. In this work, we propose a new method -- Coreset MCMC -- that simulates a Markov chain targeting the coreset posterior, while simultaneously updating the coreset weights using those same draws. Coreset MCMC is simple to implement and tune, and can be used with any existing MCMC kernel. We analyze Coreset MCMC in a representative setting to obtain key insights about the convergence behaviour of the method. Empirical results demonstrate that Coreset MCMC provides higher quality posterior approximations and reduced computational cost compared with other coreset construction methods. Further, compared with other general subsampling MCMC methods, we find that Coreset MCMC has a higher sampling efficiency with competitively accurate posterior approximations.
Causality-Inspired Fair Representation Learning for Multimodal Recommendation
Authors: Weixin Chen, Li Chen, Yongxin Ni, Yuhan Zhao
Date: 2023-10-26 00:00:00
Tasks: Attribute, Causal Inference, counterfactual, Fairness, Informativeness, Multimodal Recommendation, Recommendation Systems, Representation Learning

Recently, multimodal recommendations (MMR) have gained increasing attention for alleviating the data sparsity problem of traditional recommender systems by incorporating modality-based representations. Although MMR exhibit notable improvement in recommendation accuracy, we empirically validate that an increase in the quantity or variety of modalities leads to a higher degree of users' sensitive information leakage due to entangled causal relationships, risking fair representation learning. On the other hand, existing fair representation learning approaches are mostly based on the assumption that sensitive information is solely leaked from users' interaction data and do not explicitly model the causal relationships introduced by multimodal data, which limits their applicability in multimodal scenarios. Particularly, we disentangle biased and filtered modal embeddings inspired by causal inference techniques, enabling the mining of modality-based unfair and fair user-user relations, thereby enhancing the fairness and informativeness of user representations. By addressing the causal effects of sensitive attributes on user preferences, our approach aims to achieve counterfactual fairness in multimodal recommendations. Experiments on two public datasets demonstrate the superiority of our FMMRec relative to the state-of-the-art baselines. Our source code is available at https://github.com/WeixinChen98/FMMRec.
The Word2vec Graph Model for Author Attribution and Genre Detection in Literary Analysis
Authors: Nafis Irtiza Tripto, Mohammed Eunus Ali
Date: 2023-10-25 00:00:00
Tasks: Articles, Author Attribution

Analyzing the writing styles of authors and articles is a key to supporting various literary analyses such as author attribution and genre detection. Over the years, rich sets of features that include stylometry, bag-of-words, n-grams have been widely used to perform such analysis. However, the effectiveness of these features largely depends on the linguistic aspects of a particular language and datasets specific characteristics. Consequently, techniques based on these feature sets cannot give desired results across domains. In this paper, we propose a novel Word2vec graph based modeling of a document that can rightly capture both context and style of the document. By using these Word2vec graph based features, we perform classification to perform author attribution and genre detection tasks. Our detailed experimental study with a comprehensive set of literary writings shows the effectiveness of this method over traditional feature based approaches. Our code and data are publicly available at https://cutt.ly/svLjSgk
A systematic approach to deep learning-based nodule detection in chest radiographs
Authors: Finn Behrendt, Marcel Bengs, Debayan Bhattacharya, Julia Krüger, Roland Opfer, Alexander Schlaefer
Date: 2023-06-21 00:00:00
Proceeding: Nature Scientific Reports 2023 6
Tasks: Data Augmentation, Lung Nodule Detection, object-detection, Object Detection, Transfer Learning

Lung cancer is a serious disease responsible for millions of deaths every year. Early stages of lung cancer can be manifested in pulmonary lung nodules. To assist radiologists in reducing the number of overseen nodules and to increase the detection accuracy in general, automatic detection algorithms have been proposed. Particularly, deep learning methods are promising. However, obtaining clinically relevant results remains challenging. While a variety of approaches have been proposed for general purpose object detection, these are typically evaluated on benchmark data sets. Achieving competitive performance for specific real-world problems like lung nodule detection typically requires careful analysis of the problem at hand and the selection and tuning of suitable deep learning models. We present a systematic comparison of state-of-the-art object detection algorithms for the task of lung nodule detection. In this regard, we address the critical aspect of class imbalance and and demonstrate a data augmentation approach as well as transfer learning to boost performance. We illustrate how this analysis and a combination of multiple architectures results in state-of-the-art performance for lung nodule detection, which is demonstrated by the proposed model winning the detection track of the Node21 competition. The code for our approach is available at https://github.com/FinnBehrendt/node21-submit.
IRNeXt: Rethinking Convolutional Network Design for Image Restoration
Authors: Yuning Cui, Wenqi Ren, Sining Yang, Xiaochun Cao, Alois Knoll
Date: 2023-04-24 00:00:00
Proceeding: ICML 2023 4
Tasks: Deblurring, Image Deblurring, Image Dehazing, Image Restoration, Rain Removal

We present IRNeXt, a simple yet effective convolutional network architecture for image restoration. Recently, Transformer models have dominated the field of image restoration due to the powerful ability of modeling long-range pixels interactions. In this paper, we excavate the potential of the convolutional neural network (CNN) and show that our CNN-based model can receive comparable or better performance than Transformer models with low computation overhead on several image restoration tasks. By re-examining the characteristics possessed by advanced image restoration algorithms, we discover several key factors leading to the performance improvement of restoration models. This motivates us to develop a novel network for image restoration based on cheap convolution operators. Comprehensive experiments demonstrate that IRNeXt delivers state-of-the-art performance among numerous datasets on a range of image restoration tasks with low computational complexity, including image dehazing, single-image defocus/motion deblurring, image deraining, and image desnowing.
Strip Attention for Image Restoration
Authors: Yuning Cui, Yi Tao, Luoxi Jing, Alois Knoll
Date: 2023-08-01 00:00:00
Proceeding: IJCAI 2023 8
Tasks: Image Dehazing, Image Restoration, Representation Learning

As a long-standing task, image restoration aims to recover the latent sharp image from its degraded counterpart. In recent years, owing to the strong ability of self-attention in capturing longrange dependencies, Transformer based methods have achieved promising performance on multifarious image restoration tasks. However, the canonical self-attention leads to quadratic complexity with respect to input size, hindering its further applications in image restoration. In this paper, we propose a Strip Attention Network (SANet) for image restoration to integrate information in a more efficient and effective manner. Specifically, a strip attention unit is proposed to harvest the contextual information for each pixel from its adjacent pixels in the same row or column. By employing this operation in different directions, each location can perceive information from an expanded region. Furthermore, we apply various receptive fields in different feature groups to enhance representation learning. Incorporating these designs into a U-shaped backbone, our SANet performs favorably against state-of-the-art algorithms on several image restoration tasks. The code is available at https://github. com/c-yn/SANet.
Large-scale Monocular Depth Estimation in the Wild
Authors: Mohammad M. Haji-Esmaeili, Gholamali Montazer
Date: 2023-09-18 00:00:00
Proceeding: Engineering Applications of Artificial Intelligence 2023 9
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Surface Normal Estimation, Surface Normals Estimation

Estimating the relative depth of a single image (Monocular Depth Estimation) is a significant step towards understanding the general structure of the depicted scenery, the relations of entities in the scene and their interactions. When faced with the task of estimating the depth of a scene without the use of Stereo images, we are dependent on the availability of large-scale depth datasets and high-capacity models to capture the intrinsic nature of depth. Unfortunately, creating large-scale datasets of depth images is not a trivial task. To overcome this limitation, In this work, a new approach is proposed to accumulate Depth & Surface Normal datasets from the world of different Video Games in an easy and reproducible way. This work also introduces a new loss function to better incorporate the relation between the Depth and the Surface Normal of a scene which results in higher quality depth estimations that also produce more uniform surface normals. Qualitative and quantitative comparisons are provided between the proposed method and the best approaches of the last nine years and have competitive performance to the State of the Art by having 
 times less parameters for the proposed method. To further prove the effectiveness of this approach, an Ablation Study is also provided. Experiments on this dataset shows that using this new loss function alongside synthetic datasets increases the accuracy of ”Monocular Depth Estimation in the Wild” tasks where other approaches usually fail to generalize.
SequenceLab: A Comprehensive Benchmark of Computational Methods for Comparing Genomic Sequences
Authors: Maximilian-David Rumpf, Mohammed Alser, Arvid E. Gollwitzer, Joel Lindegger, Nour Almadhoun, Can Firtina, Serghei Mangul, Onur Mutlu
Date: 2023-10-25 00:00:00

Computational complexity is a key limitation of genomic analyses. Thus, over the last 30 years, researchers have proposed numerous fast heuristic methods that provide computational relief. Comparing genomic sequences is one of the most fundamental computational steps in most genomic analyses. Due to its high computational complexity, optimized exact and heuristic algorithms are still being developed. We find that these methods are highly sensitive to the underlying data, its quality, and various hyperparameters. Despite their wide use, no in-depth analysis has been performed, potentially falsely discarding genetic sequences from further analysis and unnecessarily inflating computational costs. We provide the first analysis and benchmark of this heterogeneity. We deliver an actionable overview of the 11 most widely used state-of-the-art methods for comparing genomic sequences. We also inform readers about their advantages and downsides using thorough experimental evaluation and different real datasets from all major manufacturers (i.e., Illumina, ONT, and PacBio). SequenceLab is publicly available at https://github.com/CMU-SAFARI/SequenceLab.
Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs
Authors: Chao Qian, Tianheng Ling, Gregor Schiele
Date: 2023-10-04 00:00:00

To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique. In the past, CNNs were frequently used because they are simple to optimise for special embedded hardware such as FPGAs. This work proposes a novel LSTM cell optimisation aimed at energy-efficient inference on end devices. Using the traffic speed prediction as a case study, a vanilla LSTM model with the optimised LSTM cell achieves 17534 inferences per second while consuming only 3.8 $\mu$J per inference on the FPGA XC7S15 from Spartan-7 family. It achieves at least 5.4$\times$ faster throughput and 1.37$\times$ more energy efficient than existing approaches.
How does Module Tracking for Agrivoltaics Differ from Standard Photovoltaics? Performance & Technoeconomic Implications
Authors: Habeel Alam, Nauman Zafar Butt
Date: 2023-10-25 00:00:00

Spatial-temporal sharing of sunlight between solar modules and crops needs to be designed optimally in agrivoltaics (AV). For AV with fixed module tilts, the sunlight balance is governed through the spatial density and elevation of the modules which cannot be manipulated after the installation. For flexible food-energy balancing across various seasons and crop rotations, modules with single or dual axis mobility can be best suitable. AV tracking must be geared towards ensuring a desired sunlight balance that may depend on many factors including the crop type, module array density, socio-economic factors, and local policies. Here, we explore single axis customized tracking (CT) for the mobile AV using a techno-economic model that incorporates design parameters including crop's shade sensitivity, module to land area ratio, and module types, as well as the economic parameters including soft and hardware costs for modules, feed-in-tariff, and crop income. CT is implemented through standard tracking that tracks the sun around noon hours and its orthogonal, i.e., anti-tracking around sunrise and sunset. We evaluate the optimal CT schemes that can maximize economic performance while ensuring the desired food-energy yield thresholds. Economic feasibility for AV is evaluated in terms of the ratio (ppr) of the price for the module system customizations to the performance benefit due to the crop income. A case study for Punjab, Pakistan shows that CT schemes for moderate shade sensitive crops and typically dense AV module arrays can require 30 to 40 percent increase in the reference FIT to ensure the food-energy yield threshold of 80 percent relative to standalone food-energy farms for high and low value crops, respectively. CT schemes for a lower crop yield threshold of 70 percent require the corresponding increase in FIT to 10 to 20 percent, respectively.
Detecting Abrupt Change of Channel Covariance Matrix in IRS-Assisted Communication
Authors: Runnan Liu, Liang Liu, Yin Xu, Dazhi He, Wenjun Zhang, Chang Wen Chen
Date: 2023-10-26 00:00:00

The knowledge of channel covariance matrices is crucial to the design of intelligent reflecting surface (IRS) assisted communication. However, channel covariance matrices may change suddenly in practice. This letter focuses on the detection of the above change in IRS-assisted communication. Specifically, we consider the uplink communication system consisting of a single-antenna user (UE), an IRS, and a multi-antenna base station (BS). We first categorize two types of channel covariance matrix changes based on their impact on system design: Type I change, which denotes the change in the BS receive covariance matrix, and Type II change, which denotes the change in the IRS transmit/receive covariance matrix. Secondly, a powerful method is proposed to detect whether a Type I change occurs, a Type II change occurs, or no change occurs. The effectiveness of our proposed scheme is verified by numerical results.
Single channel speech enhancement by colored spectrograms
Authors: Sania Gul, Muhammad Salman Khan, Muhammad Fazeel
Date: 2023-10-26 00:00:00
Tasks: Denoising, Generative Adversarial Network, Speech Enhancement

Speech enhancement concerns the processes required to remove unwanted background sounds from the target speech to improve its quality and intelligibility. In this paper, a novel approach for single-channel speech enhancement is presented, using colored spectrograms. We propose the use of a deep neural network (DNN) architecture adapted from the pix2pix generative adversarial network (GAN) and train it over colored spectrograms of speech to denoise them. After denoising, the colors of spectrograms are translated to magnitudes of short-time Fourier transform (STFT) using a shallow regression neural network. These estimated STFT magnitudes are later combined with the noisy phases to obtain an enhanced speech. The results show an improvement of almost 0.84 points in the perceptual evaluation of speech quality (PESQ) and 1% in the short-term objective intelligibility (STOI) over the unprocessed noisy data. The gain in quality and intelligibility over the unprocessed signal is almost equal to the gain achieved by the baseline methods used for comparison with the proposed model, but at a much reduced computational cost. The proposed solution offers a comparative PESQ score at almost 10 times reduced computational cost than a similar baseline model that has generated the highest PESQ score trained on grayscaled spectrograms, while it provides only a 1% deficit in STOI at 28 times reduced computational cost when compared to another baseline system based on convolutional neural network-GAN (CNN-GAN) that produces the most intelligible speech.
Real-time Neonatal Chest Sound Separation using Deep Learning
Authors: Yang Yi Poh, Ethan Grooby, Kenneth Tan, Lindsay Zhou, Arrabella King, Ashwin Ramanathan, Atul Malhotra, Mehrtash Harandi, Faezeh Marzbanrad
Date: 2023-10-26 00:00:00
Tasks: Decoder, Deep Learning

Auscultation for neonates is a simple and non-invasive method of providing diagnosis for cardiovascular and respiratory disease. Such diagnosis often requires high-quality heart and lung sounds to be captured during auscultation. However, in most cases, obtaining such high-quality sounds is non-trivial due to the chest sounds containing a mixture of heart, lung, and noise sounds. As such, additional preprocessing is needed to separate the chest sounds into heart and lung sounds. This paper proposes a novel deep-learning approach to separate such chest sounds into heart and lung sounds. Inspired by the Conv-TasNet model, the proposed model has an encoder, decoder, and mask generator. The encoder consists of a 1D convolution model and the decoder consists of a transposed 1D convolution. The mask generator is constructed using stacked 1D convolutions and transformers. The proposed model outperforms previous methods in terms of objective distortion measures by 2.01 dB to 5.06 dB in the artificial dataset, as well as computation time, with at least a 17-time improvement. Therefore, our proposed model could be a suitable preprocessing step for any phonocardiogram-based health monitoring system.
Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning
Authors: Xinfa Zhu, Yuke Li, Yi Lei, Ning Jiang, Guoqing Zhao, Lei Xie
Date: 2023-10-26 00:00:00
Tasks: Contrastive Learning, Expressive Speech Synthesis, Speech Synthesis

This paper aims to build a multi-speaker expressive TTS system, synthesizing a target speaker's speech with multiple styles and emotions. To this end, we propose a novel contrastive learning-based TTS approach to transfer style and emotion across speakers. Specifically, contrastive learning from different levels, i.e. utterance and category level, is leveraged to extract the disentangled style, emotion, and speaker representations from speech for style and emotion transfer. Furthermore, a semi-supervised training strategy is introduced to improve the data utilization efficiency by involving multi-domain data, including style-labeled data, emotion-labeled data, and abundant unlabeled data. To achieve expressive speech with diverse styles and emotions for a target speaker, the learned disentangled representations are integrated into an improved VITS model. Experiments on multi-domain data demonstrate the effectiveness of the proposed method.
Improved Panning on Non-Equidistant Loudspeakers with Direct Sound Level Compensation
Authors: Jan-Hendrik Hanschke, Daniel Arteaga, Giulio Cengarle, Joshua Lando, Mark R. P. Thomas, Alan Seefeldt
Date: 2023-10-25 00:00:00
Tasks: Position

Loudspeaker rendering techniques that create phantom sound sources often assume an equidistant loudspeaker layout. Typical home setups might not fulfill this condition as loudspeakers deviate from canonical positions, thus requiring a corresponding calibration. The standard approach is to compensate for delays and to match the loudness of each loudspeaker at the listener's location. It was found that a shift of the phantom image occurs when this calibration procedure is applied and one of a pair of loudspeakers is significantly closer to the listener than the other. In this paper, a novel approach to panning on non-equidistant loudspeaker layouts is presented whereby the panning position is governed by the direct sound and the perceived loudness is governed by the full impulse response. Subjective listening tests are presented that validate the approach and quantify the perceived effect of the compensation. In a setup where the standard calibration leads to an average error of 10 degrees, the proposed direct sound compensation largely returns the phantom source to its intended position.