pwc-archive/papers-with-abstracts huggingface dataset:
FlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning
Authors: Zejun Wu, Jiechao Wang, Zunquan Chen, Qinqin Yang, Zhen Xing, Dairong Cao, Jianfeng Bao, Taishan Kang, Jianzhong Lin, Shuhui Cai, Zhong Chen, Congbo Cai
Date: 2023-08-02 00:00:00
Tasks: parameter estimation

Objective: Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme. Approach: FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared. Main results: Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error (NRMSE) by about 50% on fractional anisotropy (FA) and 15% on mean diffusivity (MD), compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme. Significance: FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network.
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
Authors: Muhammad U. Mirza, Onat Dalmaz, Hasan A. Bedel, Gokberk Elmas, Yilmaz Korkmaz, Alper Gungor, Salman UH Dar, Tolga Çukur
Date: 2023-08-02 00:00:00
Tasks: MRI Reconstruction

Deep generative models have gained recent traction in accelerated MRI reconstruction. Diffusion priors are particularly promising given their representational fidelity. Instead of the target transformation from undersampled to fully-sampled data required for MRI reconstruction, common diffusion priors are trained to learn a task-agnostic transformation from an asymptotic start-point of Gaussian noise onto the finite end-point of fully-sampled data. During inference, data-consistency projections are injected in between reverse diffusion steps to reach a compromise solution within the span of both the trained diffusion prior and the imaging operator for an accelerated MRI acquisition. Unfortunately, performance losses can occur due to the discrepancy between target and learned transformations given the asymptotic normality assumption in diffusion priors. To address this discrepancy, here we introduce a novel Fourier-constrained diffusion bridge (FDB) for MRI reconstruction that transforms between a finite start-point of moderately undersampled data and an end-point of fully-sampled data. We derive the theoretical formulation of FDB as a generalized diffusion process based on a stochastic degradation operator that performs random spatial-frequency removal. We propose an enhanced sampling algorithm with a learned correction term for soft dealiasing across reverse diffusion steps. Demonstrations on brain MRI indicate that FDB outperforms state-of-the-art methods including non-diffusion and diffusion priors.
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
Authors: Ziyi Huang, Hongshan Liu, Haofeng Zhang, Xueshen Li, Haozhe Liu, Fuyong Xing, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan
Date: 2023-08-02 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Pseudo Label, Segmentation, Semantic Segmentation, Zero-Shot Learning

Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It is particularly appealing in medical image segmentation, where the annotation process is laborious and expertise-demanding. However, the direct application of SAM often yields inferior results compared to conventional fully supervised segmentation networks. An alternative approach is to use SAM as the initial stage to generate pseudo labels for further network training. However, the performance is limited by the quality of pseudo labels. In this paper, we propose a novel label correction framework to push the boundary of SAM-based segmentation. Our model utilizes a label quality evaluation module to distinguish between noisy labels and clean labels. This enables the correction of the noisy labels using an uncertainty-based self-correction module, thereby enriching the clean training set. Finally, we retrain the segmentation network with updated labels to optimize its weights for future predictions. One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance. We demonstrate the effectiveness of our proposed model on three public datasets, indicating its ability to improve segmentation accuracy and outperform baseline methods in label correction.
Robust Spatiotemporal Fusion of Satellite Images: A Constrained Convex Optimization Approach
Authors: Ryosuke Isono, Kazuki Naganuma, Shunsuke Ono
Date: 2023-08-01 00:00:00
Tasks: Missing Values

This paper proposes a novel spatiotemporal (ST) fusion framework for satellite images, named Robust Optimization-based Spatiotemporal Fusion (ROSTF). ST fusion is a promising approach to resolve a trade-off between the temporal and spatial resolution of satellite images. Although many ST fusion methods have been proposed, most of them are not designed to explicitly account for noise in observed images, despite the inevitable influence of noise caused by the measurement equipment and environment. Our ROSTF addresses this challenge by formulating noise removal and ST fusion as a unified optimization problem. Specifically, first, we define observation models for satellite images possibly contaminated with random noise, outliers, and/or missing values, and then introduce certain assumptions that would naturally hold between the observed images and the target high-resolution image. Then, based on these models and assumptions, we formulate the fusion problem as a constrained optimization problem and develop an efficient algorithm based on a preconditioned primal-dual splitting method for solving the problem. The performance of ROSTF was verified using simulated and real data. The results show that ROSTF performs comparably to several state-of-the-art ST fusion methods in noiseless cases and outperforms them in noisy cases.
Quasisynchronous LoRa for LEO Nanosatellite Communications
Authors: Hasan Gokhan Uysal, Ferkan Yilmaz, Hakan Ali Cirpan, Oguz Kucur, Huseyin Arslan
Date: 2023-08-01 00:00:00

Perfect synchronization in LoRa communications between Low Earth Orbit (LEO) satellites and ground base stations is still challenging, despite the potential use of atomic clocks in LEO satellites, which offer high precision. Even by incorporating atomic clocks in LEO satellites, their inherent precision can be leveraged to enhance the overall synchronization process, perfect synchronization is infeasible due to a combination of factors such as signal propagation delay, Doppler effects, clock drift and atmospheric effects. These challenges require the development of advanced synchronization techniques and algorithms to mitigate their effects and ensure reliable communication from / to LEO satellites. However, maintaining acceptable levels of synchronization rather than striving for perfection, quasisynchronous (QS) communication can be adopted which maintains communication reliability, improves resource utilization, reduces power consumption, and ensures scalability as more devices join the communication. Overall, QS communication offers a practical, adaptive, and robust solution that enables LEO satellite communications to support the growing demands of IoT applications and global connectivity. In our investigation, we explore different chip waveforms such as rectangular and raised cosine. Furthermore, for the first time, we study the Symbol Error Rate (SER) performance of QS LoRa communication, for different spreading factors (SF), over Additive White Gaussian Noise (AWGN) channels.
Data Augmentation of Bridging the Delay Gap for DL-based Massive MIMO CSI Feedback
Authors: Hengyu Zhang, Zhilin Lu, Xudong Zhang, Jintao Wang
Date: 2023-08-01 00:00:00
Tasks: Data Augmentation

In massive multiple-input multiple-output (MIMO) systems under the frequency division duplexing (FDD) mode, the user equipment (UE) needs to feed channel state information (CSI) back to the base station (BS). Though deep learning approaches have made a hit in the CSI feedback problem, whether they can remain excellent in actual environments needs to be further investigated. In this letter, we point out that the real-time dataset in application often has the domain gap from the training dataset caused by the time delay. To bridge the gap, we propose bubble-shift (B-S) data augmentation, which attempts to offset performance degradation by changing the delay and remaining the channel information as much as possible. Moreover, random-generation (R-G) data augmentation is especially proposed for outdoor scenarios due to the complex distribution of its channels. It generalizes the characteristics of the channel matrix and alleviates the over-fitting problem. Simulation results show that the proposed data augmentation boosts the robustness of networks in both indoor and outdoor environments. The open source codes are available at https://github.com/zhanghy23/CRNet-Aug.
LoRa Modulation for Split Learning
Authors: Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Date: 2023-08-01 00:00:00

In this paper we introduce a task-oriented communication design for split learning (SL) over a communication channel. Our approach involves the Expressive Neural Network (ENN), a novel neural network featuring adaptive activation functions (AAF) based on the Discrete Cosine Transform (DCT). This architecture does not only provide better learning capabilities, but also facilitates data transmission using the Long Range (LoRa) modulation. The frequency nature of LoRa is adequate for the communication side of the problem, while allowing to construct the AAFs at the receiver. Additionally, we propose orthogonal chirp division multiplexing (OCDM) for multiple access and a modified modulation aimed at preserving communication bandwidth. Our experimental results demonstrate the effectiveness of this scheme, achieving high accuracy in challenging scenarios, including low signal to noise Ratio (SNR) and absence of channel state information (CSI) for both additive white Gaussian noise (AWGN) and Rayleigh fading channels.
Distributed Gaussian Mixture PHD Filtering under Communication Constraints
Authors: Shiraz Khan, Yi-Chieh Sun, Inseok Hwang
Date: 2023-08-01 00:00:00

The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is an almost exact closed-form approximation to the Bayes-optimal multi-target tracking algorithm. Due to its optimality guarantees and ease of implementation, it has been studied extensively in the literature. However, the challenges involved in implementing the GM-PHD filter efficiently in a distributed (multi-sensor) setting have received little attention. The existing solutions for distributed PHD filtering either have a high computational and communication cost, making them infeasible for resource-constrained applications, or are unable to guarantee the asymptotic convergence of the distributed PHD algorithm to an optimal solution. In this paper, we develop a distributed GM-PHD filtering recursion that uses a probabilistic communication rule to limit the communication bandwidth of the algorithm, while ensuring asymptotic optimality of the algorithm. We derive the convergence properties of this recursion, which uses weighted average consensus of Gaussian mixtures (GMs) to lower (and asymptotically minimize) the Cauchy-Schwarz divergence between the sensors' local estimates. In addition, the proposed method is able to avoid the issue of false positives, which has previously been noted to impact the filtering performance of distributed multi-target tracking. Through numerical simulations, it is demonstrated that our proposed method is an effective solution for distributed multi-target tracking in resource-constrained sensor networks.
Privacy and Security in Ubiquitous Integrated Sensing and Communication: Threats, Challenges and Future Directions
Authors: Kaiqian Qu, Jia Ye, Xuran Li, Shuaishuai Guo
Date: 2023-08-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) technology is one of the featuring technologies of the next-generation communication systems. When sensing capability becomes ubiquitous, more information can be collected, which can facilitate many applications in intelligent transportation, unmanned aerial vehicle (UAV) surveillance and healthcare. However, it also faces many information privacy leakage and security issues. This article highlights the potential threats to privacy and security and the technical challenges to realizing private and secure ISAC. Three promising combating solutions including artificial intelligence (AI)-enabled schemes, friendly jamming and reconfigurable intelligent surface (RIS)-assisted design are provided to maintain user privacy and ensure information security. Case studies demonstrate their effectiveness.
Near-Field Integrated Sensing and Communications: Unlocking Potentials and Shaping the Future
Authors: Kaiqian Qu, Shuaishuai Guo, Jia Ye, Nasir Saeed
Date: 2023-08-01 00:00:00
Tasks: ISAC

The sixth generation (6G) communication networks are featured by integrated sensing and communications (ISAC), revolutionizing base stations (BSs) and terminals. Additionally, in the unfolding 6G landscape, a pivotal physical layer technology, the Extremely Large-Scale Antenna Array (ELAA), assumes center stage. With its expansive coverage of the near-field region, ELAA's electromagnetic (EM) waves manifest captivating spherical wave properties. Embracing these distinctive features, communication and sensing capabilities scale unprecedented heights. Therefore, we systematically explore the prodigious potential of near-field ISAC technology. In particular, the fundamental principles of near-field are presented to unearth its benefits in both communication and sensing. Then, we delve into the technologies underpinning near-field communication and sensing, unraveling possibilities discussed in recent works. We then investigated the advantages of near-field ISAC through rigorous case simulations, showcasing the benefits of near-field ISAC and reinforcing its stature as a transformative paradigm. As we conclude, we confront the open frontiers and chart the future directions for near-field ISAC.
Modeling the effects of adherence to vaccination and health protocols in epidemic dynamics by means of an SIR model
Authors: Jasmin Nunuvero, Angelique Santiago, Moshe Cohen, Anca Radulescu
Date: 2023-08-02 00:00:00

Susceptible-Infected-Recovered (SIR) models have been used for decades to understand epidemic outbreak dynamics. We develop an SIR model specifically designed to study the effects of population behavior with respect to health and vaccination protocols in a generic epidemic. Through a collection of parameters, our model includes the traditional SIR components: population birth, death, infection, recovery and vaccination rates, as well as limited immunity. We first use this simple setup to compare the effects of two vaccination schemes, one in which people are vaccinated at a rate proportional with the population, and one in which vaccines are administered to a fraction of the susceptible people (both of which are know strategies in real life epidemics). We then expand on the model and the analysis by investigating how these two vaccination schemes hold under two scenarios of population behavior: one in which people abide by health protocols and work towards diminishing transmission when infection is high; one in which people relax health protocols when infection is high. We illustrate these two aspects (vaccination and adherence to health protocols) act together to control the epidemic outbreak. While it is ideal that the tow components act jointly, we also show that tight observance of health protocols may diminish the need for vaccination in the effort to clear or mitigate the outbreak. Conversely, an efficient vaccination strategy can compensate for some degree of laxity in people's behavior.
A statistical fat-tail test of predicting regulatory regions in the Drosophila genome
Authors: Jian-Jun Shu, YaJing Li
Date: 2014-02-21 00:00:00

A statistical study of cis-regulatory modules (CRMs) is presented based on the estimation of similar-word set distribution. It is observed that CRMs tend to have a fat-tail distribution. A new statistical fat-tail test with two kurtosis-based fatness coefficients is proposed to distinguish CRMs from non-CRMs. As compared with the existing fluffy-tail test, the first fatness coefficient is designed to reduce computational time, making the novel fat-tail test very suitable for long sequences and large database analysis in the post-genome time and the second one to improve separation accuracy between CRMs and non-CRMs. These two fatness coefficients may be served as valuable filtering indexes to predict CRMs experimentally.
The Study and Optimization Of Production/Fermentation Processes In Biofuel Production
Authors: Amardeep Singh
Date: 2023-08-01 00:00:00

The production process involved in the creation of biofuels consists of a number of operations and steps that require a meticulous understanding of the parameters and metrics. The production techniques again differ depending on the pre-treatment systems, source material, the methods used for extraction, types of nutrients used, cell cultures employed, time undertaken and temperature. Due to the strategic and crucial role that bioethanol holds in supporting the energy demands of the future, it becomes important to run such processes to a highly optimized extent. One of the frontiers of leading such optimized designs is by studying the data from the production processes, formulating design experiments from said data and correlating the results with the parameters using analytical tools. While the case examples analyzed relate to bioethanol mostly, an additional analysis has been performed for data on biodiesel. Coupled with confirmatory methods such as Principal Component Analysis, researchers can help narrow down the extent or degree to which the parameters affect the final outcome and even configure inputs that may not play a definitive role in greater outputs. The project first tackles through some conventional case studies involving biofuel production using an FIS(Fuzzy Interface System) and provides certain insights into the ways in which fuel yields can be enhanced depending on the particular cases. For the purpose of analysis, tools such as MATLAB, Python and WEKA have been employed. Python and WEKA have been used extensively in building principal component analysis reviews for the purpose of this project while MATLAB has been used for building the FIS models.
Hypercomplex cross-correlation of DNA sequences
Authors: Jian-Jun Shu, YaJing Li
Date: 2014-02-21 00:00:00

A hypercomplex representation of DNA is proposed to facilitate comparison of DNA sequences with fuzzy composition. Using hypercomplex number representation, conventional sequence analysis method, such as, dot matrix analysis, dynamic programming, and cross-correlation method are extended and improved to align the DNA sequences with fuzzy composition. The hypercomplex dot matrix analysis can provide more control over the degree of alignment desired. A new scoring system has been proposed to accommodate the hypercomplex number representation of DNA and integrated with dynamic programming alignment method. By using hypercomplex cross-correlation, matching and mismatching alignment information between two aligned DNA sequences are stored in the resultant real part and imaginary parts, respectively. Mismatching alignment information is particularly useful for refining consensus sequence-based motif scanning.
Bayesian framework for characterizing cryptocurrency market dynamics, structural dependency, and volatility using potential field
Authors: Anoop C V, Neeraj Negi, Anup Aprem
Date: 2023-08-02 00:00:00

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Validated the existence of the non-linear potential function in cryptocurrency market through Lyapunov stability analysis. (iii) Developed a Bayesian framework for inferring the non-linear potential function from observed cryptocurrency prices. (iv) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes, such as, mean, open price or close price of an observation window, in the literature. (v) Analysis of cryptocurrency market during various Bitcoin crash durations from April 2017 to November 2021, shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (vi) The structural dependence inferred by the proposed approach was found to be consistent with results obtained using the popular wavelet coherence approach. (vii) The proposed market indicators (attractors and repellers) can be used to improve the prediction performance of state-of-art deep learning price prediction models. As, an example, we show improvement in Litecoin price prediction up to a horizon of 12 days.