pwc-archive/papers-with-abstracts huggingface dataset:
On the Learning of Digital Self-Interference Cancellation in Full-Duplex Radios
Authors: Jungyeon Kim, Hyowon Lee, Heedong Do, Jinseok Choi, Jeonghun Park, Wonjae Shin, Yonina C. Eldar, Namyoon Lee
Date: 2023-08-11 00:00:00

Full-duplex communication systems have the potential to achieve significantly higher data rates and lower latency compared to their half-duplex counterparts. This advantage stems from their ability to transmit and receive data simultaneously. However, to enable successful full-duplex operation, the primary challenge lies in accurately eliminating strong self-interference (SI). Overcoming this challenge involves addressing various issues, including the nonlinearity of power amplifiers, the time-varying nature of the SI channel, and the non-stationary transmit data distribution. In this article, we present a review of recent advancements in digital self-interference cancellation (SIC) algorithms. Our focus is on comparing the effectiveness of adaptable model-based SIC methods with their model-free counterparts that leverage data-driven machine learning techniques. Through our comparison study under practical scenarios, we demonstrate that the model-based SIC approach offers a more robust solution to the time-varying SI channel and the non-stationary transmission, achieving optimal SIC performance in terms of the convergence rate while maintaining low computational complexity. To validate our findings, we conduct experiments using a software-defined radio testbed that conforms to the IEEE 802.11a standards. The experimental results demonstrate the robustness of the model-based SIC methods, providing practical evidence of their effectiveness.
Development of a One Dollar Blood Pressure Monitor
Authors: Yinan Xuan, Ava J. Fascetti, Colin Barry, Edward J. Wang
Date: 2023-08-11 00:00:00

BPClip is an ultra-low-cost cuffless blood pressure monitor. As a universal smartphone attachment, BPClip leverages the computational imaging power of smartphones to perform oscillometry based blood pressure measurements. This paper examines different design considerations in BPClip's development. The cost and accuracy of blood pressure measurements are the central design goals. Both of these requirements are achieved with the initial prototype that achieves a $0.80 USD material cost and a mean absolute error of 8.72 and 5.49 mmHg for systolic and diastolic blood pressure, respectively. Since a main motivator to develop BPClip is making blood pressure monitoring more accessible, usability is also central to the design. User studies were conducted throughout the design process to inform the most intuitive and accessible design features. In this paper, we demystify the design process to share effective design practices with future developers working towards expanding health monitoring access beyond traditional clinical settings.
Physical Layer Security for NOMA Systems: Requirements, Issues, and Recommendations
Authors: Saeid Pakravan, Jean-Yves Chouinard, Xingwang Li, Ming Zeng, Wanming Hao, Quoc-Viet Pham, Octavia A. Dobre
Date: 2023-08-10 00:00:00

Non-orthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security view-point, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems are combined with relay and reconfigurable intelligent surfaces (RIS). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this paper, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given.
WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System
Authors: Beitong Tian, Kuan-Chieh Lu, Ahmadreza Eslaminia, Yaohui Wang, Chenhui Shao, Klara Nahrstedt
Date: 2023-08-05 00:00:00
Tasks: Classification, Data Augmentation

Ultrasonic welding machines play a critical role in the lithium battery industry, facilitating the bonding of batteries with conductors. Ensuring high-quality welding is vital, making tool condition monitoring systems essential for early-stage quality control. However, existing monitoring methods face challenges in cost, downtime, and adaptability. In this paper, we present WeldMon, an affordable ultrasonic welding machine condition monitoring system that utilizes a custom data acquisition system and a data analysis pipeline designed for real-time analysis. Our classification algorithm combines auto-generated features and hand-crafted features, achieving superior cross-validation accuracy (95.8% on average over all testing tasks) compared to the state-of-the-art method (92.5%) in condition classification tasks. Our data augmentation approach alleviates the concept drift problem, enhancing tool condition classification accuracy by 8.3%. All algorithms run locally, requiring only 385 milliseconds to process data for each welding cycle. We deploy WeldMon and a commercial system on an actual ultrasonic welding machine, performing a comprehensive comparison. Our findings highlight the potential for developing cost-effective, high-performance, and reliable tool condition monitoring systems.
Probabilistic Neural Transfer Function Estimation with Bayesian System Identification
Authors: Nan Wu, Isabel Valera, Fabian Sinz, Alexander Ecker, Thomas Euler, Yongrong Qiu
Date: 2023-08-11 00:00:00
Tasks: Variational Inference

Neural population responses in sensory systems are driven by external physical stimuli. This stimulus-response relationship is typically characterized by receptive fields, which have been estimated by neural system identification approaches. Such models usually requires a large amount of training data, yet, the recording time for animal experiments is limited, giving rise to epistemic uncertainty for the learned neural transfer functions. While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as the stimuli driving neurons optimally, from in silico experiments. Here, we present a Bayesian system identification approach to predict neural responses to visual stimuli, and explore whether explicitly modeling network weight variability can be beneficial for identifying neural response properties. To this end, we use variational inference to estimate the posterior distribution of each model weight given the training data. Tests with different neural datasets demonstrate that this method can achieve higher or comparable performance on neural prediction, with a much higher data efficiency compared to Monte Carlo dropout methods and traditional models using point estimates of the model parameters. Furthermore, our approach enables to identify response properties with credible intervals and perform statistical test for the learned neural features, which avoid the idiosyncrasy of a single model. Finally, in silico experiments show that our model generates stimuli driving neuronal activity significantly better than traditional models, particularly in the limited-data regime.
Turning hazardous volatile matter compounds into fuel by catalytic steam reforming: An evolutionary machine learning approach
Authors: Alireza Shafizadeh, Hossein Shahbeik, Mohammad Hossein Nadian, Vijai Kumar Gupta, Abdul-Sattar Nizami, Su Shiung Lam, WanXi Peng, Junting Pan, Meisam Tabatabaei, Mortaza Aghbashlo
Date: 2023-07-25 00:00:00
Tasks: Feature Importance, TAR

Chemical and biomass processing systems release volatile matter compounds into the environment daily. Catalytic reforming can convert these compounds into valuable fuels, but developing stable and efficient catalysts is challenging. Machine learning can handle complex relationships in big data and optimize reaction conditions, making it an effective solution for addressing the mentioned issues. This study is the first to develop a machine-learning-based research framework for modeling, understanding, and optimizing the catalytic steam reforming of volatile matter compounds. Toluene catalytic steam reforming is used as a case study to show how chemical/textural analyses (e.g., X-ray diffraction analysis) can be used to obtain input features for machine learning models. Literature is used to compile a database covering a variety of catalyst characteristics and reaction conditions. The process is thoroughly analyzed, mechanistically discussed, modeled by six machine learning models, and optimized using the particle swarm optimization algorithm. Ensemble machine learning provides the best prediction performance (R2 > 0.976) for toluene conversion and product distribution. The optimal tar conversion (higher than 77.2%) is obtained at temperatures between 637.44 and 725.62 {\deg}C, with a steam-to-carbon molar ratio of 5.81-7.15 and a catalyst BET surface area 476.03-638.55 m2/g. The feature importance analysis satisfactorily reveals the effects of input descriptors on model prediction. Operating conditions (50.9%) and catalyst properties (49.1%) are equally important in modeling. The developed framework can expedite the search for optimal catalyst characteristics and reaction conditions, not only for catalytic chemical processing but also for related research areas.
Glow in the Dark: Low-Light Image Enhancement with External Memory
Authors: Dongjie Ye, Zhangkai Ni, Wenhan Yang, Hanli Wang, Shiqi Wang, Sam Kwong
Date: 2023-07-10 00:00:00
Proceeding: IEEE Transactions on Multimedia 2023 7
Tasks: Image Enhancement, Low-Light Image Enhancement

Deep learning-based methods have achieved remarkable success with powerful modeling capabilities. However, the weights of these models are learned over the entire training dataset, which inevitably leads to the ignorance of sample specific properties in the learned enhancement mapping. This situation causes ineffective enhancement in the testing phase for the samples that differ significantly from the training distribution. In this paper, we introduce external memory to form an external memory-augmented network (EMNet) for low-light image enhancement. The external memory aims to capture the sample specific properties of the training dataset to guide the enhancement in the testing phase. Benefiting from the learned memory, more complex distributions of reference images in the entire dataset can be “remembered” to facilitate the adjustment of the testing samples more adaptively. To further augment the capacity of the model, we take the transformer as our baseline network, which specializes in capturing long-range spatial redundancy. Experimental results demonstrate that our proposed method has a promising performance and outperforms state-of-the-art methods. It is noted that, the proposed external memory is a plug-and-play mechanism that can be integrated with any existing method to further improve the enhancement quality. More practices of integrating external memory with other image enhancement methods are qualitatively and quantitatively analyzed. The results further confirm that the effectiveness of our proposed memory mechanism when combing with existing enhancement methods.
FastAiAlloc: A real-time multi-resources allocation framework proposal based on predictive model and multiple optimization strategies
Authors: Marcos de Souza Oliveira, Francisco Erivaldo Fernandes, Lukas Cerveny, Flávia Akemi, Leonardo Neri, Beatriz Bonafini, Victor Outtes, Alan da Silva, Orion Darshan
Date: 2023-08-09 00:00:00
Proceeding: Future Generation Computer Systems 2023 8
Tasks: Cloud Computing, CPU, Evolutionary Algorithms, Self-Organized Clustering

In cloud platforms, a common task is to allocate computational resources, e.g., memory and CPU, requested by applications and users. The allocation of these resources, which is an optimal load-balancing task, is considered an NP-Hard problem, being a challenging research area. There are many works proposed in the literature to address this problem. They use several strategies to deal with this optimization problem such as evolutionary algorithms, exact programming and also heuristics. However, some steps of the allocation process are not considered by these works, which are sometimes treated separately and not in an integrated manner. These steps include the applications and users resource consumption request profile as part of the optimization process and defining adequate metrics to check the optimized allocation. To integrate these steps, this work proposes a framework based on the following strategies, widely used in the literature: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Linear Programming, besides our Heuristic approach. Furthermore, we restricted the resource allocation optimization to a real-time scenario, facilitating its use in an industrial process. In addition, Key Performance Indicators (KPIs) are proposed to carry out a comparative study in a public dataset. Our experiments showed that our proposed framework achieved better results than the baseline one, e.g., using a random allocation. In some cases, we observed an increase of almost 60% in performance compared with the baseline. In addition, when trying to balance memory and CPU consumption in the cluster, the linear approach performed the best, while the GA achieved the best result in allocating different user profiles across the cluster.
Assessing Neural Network Representations During Training Using Data Diffusion Spectra
Authors: Danqi Liao*, Chen Liu*, Alexander Tong, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy
Date: 2023-05-31 00:00:00
Proceeding: International Conference on Machine Learning Workshop on TAGML 2023 5
Tasks: Mutual Information Estimation, Representation Learning

Here we present information theoretic measures based on the data diffusion operator as characterisations of the representations learned by neural networks. Specifically, we define diffusion spectral entropy (DSE), i.e., entropy of the diffusion operator computed on the neural representation of a dataset as well as diffusion spectral mutual information (DSMI), which assesses the relationship between different sets of variables representing data. First, we show that these definitions form robust measures of intrinsic dimensionality and relationship strength respectively on toy data, outperforming binned Shannon entropy in terms of accuracy.
Then we study the evolution of representations within classification networks and networks with self-supervised losses. In both cases, we see that generalizable training results in decrease in DSE over epochs — starting from a random initialization. We also see that there is an increase in DSMI with the class label over time. On the other hand, training with corrupt labels results in a maintenance or increase in entropy and near-zero DSMI with labels. We also assess DSMI with the input and observe differing trends. On MNIST it grows until plateaus, whereas on CIFAR it increases and then decreases. Overall results show that these measures can elucidate characteristics of network performance as well as data complexity. Code is available at https://github.com/ChenLiu-1996/DiffusionSpectralEntropy.
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
Authors: Lihe Yang, Zhen Zhao, Lei Qi, Yu Qiao, Yinghuan Shi, Hengshuang Zhao
Date: 2023-08-13 00:00:00
Proceeding: ICCV 2023 1
Tasks: Semi-Supervised Image Classification

Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples. This practice ensures high-quality pseudo labels, but incurs a relatively low utilization of the whole unlabeled set. In this work, our key insight is that these uncertain samples can be turned into certain ones, as long as the confusion classes for the top-1 class are detected and removed. Invoked by this, we propose a novel method dubbed ShrinkMatch to learn uncertain samples. For each uncertain sample, it adaptively seeks a shrunk class space, which merely contains the original top-1 class, as well as remaining less likely classes. Since the confusion ones are removed in this space, the re-calculated top-1 confidence can satisfy the pre-defined threshold. We then impose a consistency regularization between a pair of strongly and weakly augmented samples in the shrunk space to strive for discriminative representations. Furthermore, considering the varied reliability among uncertain samples and the gradually improved model during training, we correspondingly design two reweighting principles for our uncertain loss. Our method exhibits impressive performance on widely adopted benchmarks. Code is available at https://github.com/LiheYoung/ShrinkMatch.
Modeling the Dashboard Provenance
Authors: Johne Jarske, Jorge Rady, Lucia V. L. Filgueiras, Leandro M. Velloso, Tania L. Santos
Date: 2023-08-13 00:00:00
Tasks: Data Visualization, Decision Making

Organizations of all kinds, whether public or private, profit-driven or non-profit, and across various industries and sectors, rely on dashboards for effective data visualization. However, the reliability and efficacy of these dashboards rely on the quality of the visual and data they present. Studies show that less than a quarter of dashboards provide information about their sources, which is just one of the expected metadata when provenance is seriously considered. Provenance is a record that describes people, organizations, entities, and activities that had a role in the production, influence, or delivery of a piece of data or an object. This paper aims to provide a provenance representation model, that entitles standardization, modeling, generation, capture, and visualization, specifically designed for dashboards and its visual and data components. The proposed model will offer a comprehensive set of essential provenance metadata that enables users to evaluate the quality, consistency, and reliability of the information presented on dashboards. This will allow a clear and precise understanding of the context in which a specific dashboard was developed, ultimately leading to better decision-making.
UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment
Authors: Xinyi Wang, Angeliki Katsenou, David Bull
Date: 2023-08-13 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

The volume of User Generated Content (UGC) has increased in recent years. The challenge with this type of content is assessing its quality. So far, the state-of-the-art metrics are not exhibiting a very high correlation with perceptual quality. In this paper, we explore state-of-the-art metrics that extract/combine natural scene statistics and deep neural network features. We experiment with these by introducing saliency maps to improve perceptibility. We train and test our models using public datasets, namely, YouTube-UGC and KoNViD-1k. Preliminary results indicate that high correlations are achieved by using only deep features while adding saliency is not always boosting the performance. Our results and code will be made publicly available to serve as a benchmark for the research community and can be found on our project page: https://github.com/xinyiW915/SPIE-2023-Supplementary.
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Authors: Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
Date: 2023-08-14 00:00:00
Tasks: knowledge editing

Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, there is no standard implementation framework available for the community, which hinders practitioners from applying knowledge editing to applications. To address these issues, we propose EasyEdit, an easy-to-use knowledge editing framework for LLMs. It supports various cutting-edge knowledge editing approaches and can be readily applied to many well-known LLMs such as T5, GPT-J, LlaMA, etc. Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization. We have released the source code on GitHub, along with Google Colab tutorials and comprehensive documentation for beginners to get started. Besides, we present an online system for real-time knowledge editing, and a demo video.
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Authors: Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, Wei Yang
Date: 2023-08-13 00:00:00
Tasks: Diffusion Personalization Tuning Free, Image Generation, Personalized Image Generation, Prompt Engineering

Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tricky to generate desired images using only text prompt as it often involves complex prompt engineering. An alternative to text prompt is image prompt, as the saying goes: "an image is worth a thousand words". Although existing methods of direct fine-tuning from pretrained models are effective, they require large computing resources and are not compatible with other base models, text prompt, and structural controls. In this paper, we present IP-Adapter, an effective and lightweight adapter to achieve image prompt capability for the pretrained text-to-image diffusion models. The key design of our IP-Adapter is decoupled cross-attention mechanism that separates cross-attention layers for text features and image features. Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model. As we freeze the pretrained diffusion model, the proposed IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools. With the benefit of the decoupled cross-attention strategy, the image prompt can also work well with the text prompt to achieve multimodal image generation. The project page is available at \url{https://ip-adapter.github.io}.