pwc-archive/papers-with-abstracts huggingface dataset:
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation
Authors: Juntong Fan, Debesh Jha, Tieyong Zeng, Dayang Wang
Date: 2023-08-15 00:00:00
Tasks: Brain Tumor Segmentation, Decoder, Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Tumor Segmentation

Polyps segmentation poses a significant challenge in medical imaging due to the flat surface of polyps and their texture similarity to surrounding tissues. This similarity gives rise to difficulties in establishing a clear boundary between polyps and the surrounding mucosa, leading to complications such as local overexposure and the presence of bright spot reflections in imaging. To counter this problem, we propose a new dual graph convolution network (Dual-GCN) and location self-attention mechanisms with weighted fast normalization fusion model, named DSFNet. First, we introduce a feature enhancement block module based on Dual-GCN module to enhance local spatial and structural information extraction with fine granularity. Second, we introduce a location fused self-attention module to enhance the model's awareness and capacity to capture global information. Finally, the weighted fast normalized fusion method with trainable weights is introduced to efficiently integrate the feature maps from encoder, bottleneck, and decoder, thus promoting information transmission and facilitating the semantic consistency. Experimental results show that the proposed model surpasses other state-of-the-art models in gold standard indicators, such as Dice, MAE, and IoU. Both quantitative and qualitative analysis indicate that the proposed model demonstrates exceptional capability in polyps segmentation and has great potential clinical significance. We have shared our code on anonymous website for evaluation.
Extended Target Parameter Estimation and Tracking with an HDA Setup for ISAC Applications
Authors: Fernando Pedraza, Saeid K. Dehkordi, Jan C. Hauffen, Shuangyang Li, Peter Jung, Giuseppe Caire
Date: 2023-08-16 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation, Position

We investigate radar parameter estimation and beam tracking with a hybrid digital-analog (HDA) architecture in a multi-block measurement framework using an extended target model. In the considered setup, the backscattered data signal is utilized to predict the user position in the next time slots. Specifically, a simplified maximum likelihood framework is adopted for parameter estimation, based on which a simple tracking scheme is also developed. Furthermore, the proposed framework supports adaptive transmitter beamwidth selection, whose effects on the communication performance are also studied. Finally, we verify the effectiveness of the proposed framework via numerical simulations over complex motion patterns that emulate a realistic integrated sensing and communication (ISAC) scenario.
Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead
Authors: Fuwang Dong, Fan Liu, Yuanhao Cui, Shihang Lu, Yunxin Li
Date: 2023-08-16 00:00:00
Tasks: Management

Sensing-as-a-service is anticipated to be the core feature of 6G perceptive mobile networks (PMN), where high-precision real-time sensing will become an inherent capability rather than being an auxiliary function as before. With the proliferation of wireless connected devices, resource allocation (RA) in terms of the users' specific quality-of-service (QoS) requirements plays a pivotal role in enhancing interference management ability and resource utilization efficiency. In this article, we comprehensively introduce the concept of sensing service in PMN, including the types of tasks, the distinctions/advantages compared to conventional networks, and the definitions of sensing QoS. Subsequently, we provide a unified RA framework in sensing-centric PMN and elaborate on the unique challenges. Furthermore, we present a typical case study named "communication-assisted sensing" and evaluate the performance trade-off between sensing and communication procedures. Finally, we shed light on several open problems and opportunities deserving further investigation in the future.
A New Heterogeneous Hybrid Massive MIMO Receiver with An Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning
Authors: Feng Shu, Baihua Shi, YiWen Chen, Jiatong Bai, YiFan Li, Tingting Liu, Zhu Han, Xiaohu You
Date: 2023-08-16 00:00:00
Tasks: Clustering

Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional Hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the remaining ones to find a true direction by received beamforming over this set. This will lead to a low time-efficiency. To address this problem, a new heterogeneous sub-connected hybrid analog and digital (HAD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity and a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. This framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). And, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB at almost all SNR regions except for extremely low SNR. Four clustering methods have an accuracy decreasing order as follows: WGMD, improved DBSCAN, ALW-K-means and WLMD.
Robust Beamforming for IRS Aided MIMO Full Duplex Systems
Authors: Chandan Kumar Sheemar, Jorge Querol, Sourabh Solanki, Sumit Kumar, Symeon Chatzinotas
Date: 2023-08-15 00:00:00
Tasks: Robust Design

In this paper, a novel robust beamforming for an intelligent reflecting surface (IRS) assisted FD system is presented. Since perfect channel state information (CSI) is often challenging to acquire in practice, we consider the case of imperfect CSI and adopt a statistically robust beamforming approach to maximize the ergodic weighted sum rate (WSR). We also analyze the achievable WSR of an IRS-assisted FD with imperfect CSI, for which the lower and the upper bounds are derived. The ergodic WSR maximization problem is tackled based on the expected Weighted Minimum Mean Squared Error (WMMSE), which is guaranteed to converge to a local optimum. The effectiveness of the proposed design is investigated with extensive simulation results. It is shown that our robust design achieves significant performance gain compared to the naive beamforming approaches and considerably outperforms the robust Half-Duplex (HD) system.
In distributive phosphorylation catalytic constants enable non-trivial dynamics
Authors: Carsten Conradi, Maya Mincheva
Date: 2023-08-16 00:00:00

Ordered distributive double phosphorylation is a recurrent motif in intracellular signaling and control. It is either sequential (where the site phosphorylated last is dephosphorylated first) or cyclic (where the site phosphorylated first is dephosphorylated first). Sequential distributive double phosphorylation has been extensively studied and an inequality involving only the catalytic constants of kinase and phosphatase is known to be sufficient for multistationarity. As multistationarity is necessary for bistability it has been argued that these constants enable bistability. Here we show for cyclic distributive double phosphorylation that if its catalytic constants satisfy the very same inequality, then Hopf bifurcations and hence sustained oscillations can occur. Hence we argue that in distributive double phosphorylation (sequential or distributive) the catalytic constants enable non-trivial dynamics. In fact, if the rate constant values in a network of cyclic distributive double phosphorylation are such that Hopf bifurcations and sustained oscillations can occur, then a network of sequential distributive double phosphorylation with the same rate constant values will show multistationarity -- albeit for different values of the total concentrations. For cyclic distributive double phosphorylation we further describe a procedure to generate rate constant values where Hopf bifurcations and hence sustained oscillations can occur. This may, for example, allow for an efficient sampling of oscillatory regions in parameter space. Our analysis is greatly simplified by the fact that it is possible to reduce the network of cyclic distributive double phosphorylation to what we call a network with a single extreme ray. We summarize key properties of these networks.
Stochastic Controlled Averaging for Federated Learning with Communication Compression
Authors: Xinmeng Huang, Ping Li, Xiaoyun Li
Date: 2023-08-16 00:00:00
Tasks: Federated Learning

Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potential of alleviating its communication overhead. However, communication compression brings forth new challenges in FL due to the interplay of compression-incurred information distortion and inherent characteristics of FL such as partial participation and data heterogeneity. Despite the recent development, the performance of compressed FL approaches has not been fully exploited. The existing approaches either cannot accommodate arbitrary data heterogeneity or partial participation, or require stringent conditions on compression. In this paper, we revisit the seminal stochastic controlled averaging method by proposing an equivalent but more efficient/simplified formulation with halved uplink communication costs. Building upon this implementation, we propose two compressed FL algorithms, SCALLION and SCAFCOM, to support unbiased and biased compression, respectively. Both the proposed methods outperform the existing compressed FL methods in terms of communication and computation complexities. Moreover, SCALLION and SCAFCOM accommodates arbitrary data heterogeneity and do not make any additional assumptions on compression errors. Experiments show that SCALLION and SCAFCOM can match the performance of corresponding full-precision FL approaches with substantially reduced uplink communication, and outperform recent compressed FL methods under the same communication budget.
Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort
Authors: Anton Orlichenko, Kuan-Jui Su, Qing Tian, Hui Shen, Hong-Wen Deng, Yu-Ping Wang
Date: 2023-08-15 00:00:00
Tasks: Functional Connectivity

Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach: We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results: We find a 6.8\% average increase in SMT-VIS connectivity from younger to older scan (from $\rho=0.39$ to $\rho=0.42$) that occurs in male, female, older subject ($>65$ years old), and younger subject ($<55$ years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57\% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5\% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.
Mathematical modeling of the treatment response of resection plus combined chemotherapy and different types of radiation therapy in a glioblastoma patient
Authors: Farshad Samadifam, Ehsan Ghafourian
Date: 2023-08-15 00:00:00
Tasks: Sensitivity

Background: Gliomas are the most common brain tumors, which vary from low to high-grade. Despite the improvements in imaging techniques in the last decade, all parts of the tumor are not visible in these images, due to having a threshold of detection often lower than most of the tumor. Method: We simulated a brain domain to model heterogeneous growth plus gross total resection following concurrent chemo- and radiation therapy and adjuvant chemotherapy using temozolomide for a patient, diagnosed with glioblastoma, for different radiobiological alpha parameter as high, medium, and low sensitivity. The extent of difference in the result of treatment, area of the tumor, and survival time relating to those sensitivity parameters were shown. Moreover, based on the threshold of detection of MRI (T1Gd), the area of the tumor visible to this imaging technique was simulated. Furthermore, the extent of difference in the result of treatment for both conventional and hyperfractionated radiotherapy was investigated. Conclusion: The survival time of patient was increased by 2 months and more than 3 months at the lowest and highest sensitivity of radiation therapy, respectively.The result showed hyperfractionated radiotherapy was more effective than conventional radiotherapy in reducing the concentration of cancerous cells,as hyperfractionated method allows administration of higher total dose.
Company Similarity using Large Language Models
Authors: Dimitrios Vamvourellis, Máté Toth, Snigdha Bhagat, Dhruv Desai, Dhagash Mehta, Stefano Pasquali
Date: 2023-08-15 00:00:00

Identifying companies with similar profiles is a core task in finance with a wide range of applications in portfolio construction, asset pricing and risk attribution. When a rigorous definition of similarity is lacking, financial analysts usually resort to 'traditional' industry classifications such as Global Industry Classification System (GICS) which assign a unique category to each company at different levels of granularity. Due to their discrete nature, though, GICS classifications do not allow for ranking companies in terms of similarity. In this paper, we explore the ability of pre-trained and finetuned large language models (LLMs) to learn company embeddings based on the business descriptions reported in SEC filings. We show that we can reproduce GICS classifications using the embeddings as features. We also benchmark these embeddings on various machine learning and financial metrics and conclude that the companies that are similar according to the embeddings are also similar in terms of financial performance metrics including return correlation.
Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach
Authors: Rosana C. B. Rego
Date: 2023-06-11 00:00:00
Tasks: regression, Time Series, Time Series Forecasting

A proper forecast of the menstrual cycle is meaningful for women's health, as it allows individuals to take preventive actions to minimize cycle-associated discomforts. In addition, precise prediction can be useful for planning important events in a woman's life, such as family planning. In this work, we explored the use of machine learning techniques to predict regular and irregular menstrual cycles. We implemented some time series forecasting algorithm approaches, such as AutoRegressive Integrated Moving Average, Huber Regression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term Memory Network. Moreover, we generated synthetic data to achieve our purposes. The results showed that it is possible to accurately predict the onset and duration of menstrual cycles using machine learning techniques.
Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
Authors: William Shen, Ge Yang, Alan Yu, Jansen Wong, Leslie Pack Kaelbling, Phillip Isola
Date: 2023-07-27 00:00:00
Tasks: 3D geometry, Few-Shot Learning, Language Modeling, Language Modelling

Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed understanding of 3D geometry, which is often lacking in 2D image features. This work bridges this 2D-to-3D gap for robotic manipulation by leveraging distilled feature fields to combine accurate 3D geometry with rich semantics from 2D foundation models. We present a few-shot learning method for 6-DOF grasping and placing that harnesses these strong spatial and semantic priors to achieve in-the-wild generalization to unseen objects. Using features distilled from a vision-language model, CLIP, we present a way to designate novel objects for manipulation via free-text natural language, and demonstrate its ability to generalize to unseen expressions and novel categories of objects.
A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction
Authors: Karan Gadgil, Sukhpal Singh Gill, Ahmed M. Abdelmoniem
Date: 2023-08-07 00:00:00
Tasks: Meta-Learning, regression, Value prediction

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue and this could be achieved only by understanding the customers more. Customer Lifetime Value (CLV) is the total monetary value of transactions/purchases made by a customer with the business over an intended period of time and is used as means to estimate future customer interactions. CLV finds application in a number of distinct business domains such as Banking, Insurance, Online-entertainment, Gaming, and E-Commerce. The existing distribution-based and basic (recency, frequency & monetary) based models face a limitation in terms of handling a wide variety of input features. Moreover, the more advanced Deep learning approaches could be superfluous and add an undesirable element of complexity in certain application areas. We, therefore, propose a system which is able to qualify both as effective, and comprehensive yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model which combines the predictions from bagging and boosting models that each is found to perform well individually. Empirical tests have been carried out on an openly available Online Retail dataset to evaluate various models and show the efficacy of the proposed approach.
Fast Adaptation with Bradley-Terry Preference Models in Text-To-Image Classification and Generation
Authors: Victor Gallego
Date: 2023-07-15 00:00:00
Tasks: image-classification, Image Classification

Recently, large multimodal models, such as CLIP and Stable Diffusion have experimented tremendous successes in both foundations and applications. However, as these models increase in parameter size and computational requirements, it becomes more challenging for users to personalize them for specific tasks or preferences. In this work, we address the problem of adapting the previous models towards sets of particular human preferences, aligning the retrieved or generated images with the preferences of the user. We leverage the Bradley-Terry preference model to develop a fast adaptation method that efficiently fine-tunes the original model, with few examples and with minimal computing resources. Extensive evidence of the capabilities of this framework is provided through experiments in different domains related to multimodal text and image understanding, including preference prediction as a reward model, and generation tasks.
Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database
Authors: Mateja Napravnik, Franko Hržić, Sebastian Tschauner, Ivan Štajduhar
Date: 2023-07-27 00:00:00
Tasks: Clustering, Medical Diagnosis, Semantic Similarity, Semantic Textual Similarity

Background and objective: The usage of machine learning in medical diagnosis and treatment has witnessed significant growth in recent years through the development of computer-aided diagnosis systems that are often relying on annotated medical radiology images. However, the availability of large annotated image datasets remains a major obstacle since the process of annotation is time-consuming and costly. This paper explores how to automatically annotate a database of medical radiology images with regard to their semantic similarity. Material and methods: An automated, unsupervised approach is used to construct a large annotated dataset of medical radiology images originating from Clinical Hospital Centre Rijeka, Croatia, utilising multimodal sources, including images, DICOM metadata, and narrative diagnoses. Several appropriate feature extractors are tested for each of the data sources, and their utility is evaluated using k-means and k-medoids clustering on a representative data subset. Results: The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation. Conclusion: The results suggest that fusing the embeddings of all three data sources together works best for the task of unsupervised clustering of large-scale medical data, resulting in the most concise clusters. Hence, this work is the first step towards building a much larger and more fine-grained annotated dataset of medical radiology images.