pwc-archive/papers-with-abstracts huggingface dataset:
PhaseMO: A Universal Massive MIMO Architecture for Sustainable NextG
Authors: Adel Heidari, Agrim Gupta, Ish Kumar Jain, Dinesh Bharadia
Date: 2025-01-08 00:00:00

The rapid proliferation of devices and increasing data traffic in cellular networks necessitate advanced solutions to meet these escalating demands. Massive MIMO (Multiple Input Multiple Output) technology offers a promising approach, significantly enhancing throughput, coverage, and spatial multi-plexing. Despite its advantages, massive MIMO systems often lack flexible software controls over hardware, limiting their ability to optimize operational expenditure (OpEx) by reducing power consumption while maintaining performance. Current software-controlled methods, such as antenna muting combined with digital beamforming and hybrid beamforming, have notable limitations. Antenna muting struggles to maintain throughput and coverage, while hybrid beamforming faces hardware constraints that restrict scalability and future-proofing. This work presents PhaseMO, a versatile approach that adapts to varying network loads. PhaseMO effectively reduces power consumption in low-load scenarios without sacrificing coverage and overcomes the hardware limitations of hybrid beamforming, offering a scalable and future-proof solution. We will show that PhaseMO can achieve up to 30% improvement in energy efficiency while avoiding about 10% coverage reduction and 5dB increase in UE transmit power.
Constrained and Regularized Quantitative Ultrasound Parameter Estimation using ADMM
Authors: Ali K. Z. Tehrani, Hassan Rivaz, Ivan M. Rosado-Mendez
Date: 2025-01-07 00:00:00
Tasks: parameter estimation

Regularized estimation of quantitative ultrasound (QUS) parameters, such as attenuation and backscatter coefficients, has gained research interest. Recently, the alternating direction method of multipliers (ADMM) has been applied successfully to estimate these parameters, by utilizing L2 and L1 norms for attenuation and backscatter coefficient regularization, respectively. While this method improves upon previous approaches, it does not fully leverage the prior knowledge of minimum physically feasible parameter values, sometimes yielding values outside the realistic range. This work addresses this limitation by incorporating minimum QUS parameter values as constraints to enhance ADMM estimation. The proposed method is validated using experimental phantom data.
Use Cases for Terahertz Communications: An Industrial Perspective
Authors: Tommaso Zugno, Cristina Ciochina, Sharad Sambhwani, Patrick Svedman, Luis M. Pessoa, Ben Chen, Per Hjalmar Lehne, Mate Boban, Thomas Kürner
Date: 2025-01-07 00:00:00

Thanks to the vast amount of available resources and unique propagation properties, terahertz (THz) frequency bands are viewed as a key enabler for achieving ultrahigh communication performance and precise sensing capabilities in future wireless systems. Recently, the European Telecommunications Standards Institute (ETSI) initiated an Industry Specification Group (ISG) on THz which aims at establishing the technical foundation for subsequent standardization of this technology, which is pivotal for its successful integration into future networks. Starting from the work recently finalized within this group, this paper provides an industrial perspective on potential use cases and frequency bands of interest for THz communication systems. We first identify promising frequency bands in the 100 GHz - 1 THz range, offering over 500 GHz of available spectrum that can be exploited to unlock the full potential of THz communications. Then, we present key use cases and application areas for THz communications, emphasizing the role of this technology and its advantages over other frequency bands. We discuss their target requirements and show that some applications demand for multi-Tbps data rates, latency below 0.5 ms, and sensing accuracy down to 0.5 cm. Additionally, we identify the main deployment scenarios and outline other enabling technologies crucial for overcoming the challenges faced by THz system. Finally, we summarize the past and ongoing standardization efforts focusing on THz communications, while also providing an outlook towards the inclusion of this technology as an integral part of the future sixth generation (6G) and beyond communication networks.
STAR-RIS Aided Dynamic Scatterers Tracking for Integrated Sensing and Communications
Authors: Muye Li, Shun Zhang, Yao Ge, Chau Yuen
Date: 2025-01-07 00:00:00
Tasks: Beam Prediction, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has become an attractive technology for future wireless networks. In this paper, we propose a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided dynamic scatterers tracking scheme for ISAC in high mobility millimeter wave communication systems, where the STAR-RIS is employed to provide communication service for indoor user with the base station (BS) and simultaneously sense and track the interested outdoor dynamic scatterers. Specifically, we resort to an active STAR-RIS to respectively receive and further deal with the impinging signal from its double sides at the same time. Then, we develop a transmission strategy with the activation scheme of the STAR-RIS elements, and construct the signal models within the system. After acquiring the channel parameters related to the BS-RIS channel, the dynamic paths can be identified from all the scattering paths, and the dynamic targets can be classified with respect to their radar cross sections. We further track the outdoor scatterers at STAR-RIS by resorting to the Gaussian mixture-probability hypothesis density filter. With the tracked locations of the outdoor scatterers, a beam prediction strategy for both the precoder of BS and the refraction phase shift vector of STAR-RIS is developed to enhance the communication performance of the indoor user. Besides, a target mismatch detection and path collision prediction mechanism is proposed to reduce the training overhead and improve the transmission performance. Finally, the feasibility and effectiveness of our proposed STAR-RIS aided dynamic scatterers tracking scheme for ISAC are demonstrated and verified via simulation results.
The generalized phase retrieval problem over compact groups
Authors: Tamir Bendory, Dan Edidin
Date: 2025-01-07 00:00:00
Tasks: Retrieval

The classical phase retrieval problem involves estimating a signal from its Fourier magnitudes (power spectrum) by leveraging prior information about the desired signal. This paper extends the problem to compact groups, addressing the recovery of a set of matrices from their Gram matrices. In this broader context, the missing phases in Fourier space are replaced by missing unitary or orthogonal matrices arising from the action of a compact group on a finite-dimensional vector space. This generalization is driven by applications in multi-reference alignment and single-particle cryo-electron microscopy, a pivotal technology in structural biology. We define the generalized phase retrieval problem over compact groups and explore its underlying algebraic structure. We survey recent results on the uniqueness of solutions, focusing on the significant class of semialgebraic priors. Furthermore, we present a family of algorithms inspired by classical phase retrieval techniques. Finally, we propose a conjecture on the stability of the problem based on bi-Lipschitz analysis, supported by numerical experiments.
ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks
Authors: Omar Mashaal, Elsayed Mohammed, Alec Digby, Lorne Swersky, Ashkan Eshaghbeigi, Hatem Abou-zeid
Date: 2025-01-06 00:00:00
Tasks: Beam Prediction, Domain Adaptation

Deep learning techniques have recently emerged to efficiently manage mmWave beam transmissions without requiring time consuming beam sweeping strategies. A fundamental challenge in these methods is their dependency on hardware-specific training data and their limited ability to generalize. Large drops in performance are reported in literature when DL models trained in one antenna environment are applied in another. This paper proposes the application of Prototypical Networks to address this challenge and utilizes the DeepBeam real-world dataset to validate the developed solutions. Prototypical Networks excel in extracting features to establish class-specific prototypes during the training, resulting in precise embeddings that encapsulate the defining features of the data. We demonstrate the effectiveness of PN to enable generalization of deep beam predictors across unseen antennas. Our approach, which integrates data normalization and prototype normalization with the PN, achieves an average beam classification accuracy of 74.11 percent when trained and tested on different antenna datasets. This is an improvement of 398 percent compared to baseline performances reported in literature that do not account for such domain shifts. To the best of our knowledge, this work represents the first demonstration of the value of Prototypical Networks for domain adaptation in wireless networks, providing a foundation for future research in this area.
Placement, Orientation, and Resource Allocation Optimization for Cell-Free OIRS-aided OWC Network
Authors: Jalal Jalali, Hina Tabassum, Jeroen Famaey, Walid Saad, Murat Uysal
Date: 2025-01-06 00:00:00

The emergence of optical intelligent reflecting surface (OIRS) technologies marks a milestone in optical wireless communication (OWC) systems, enabling enhanced control over light propagation in indoor environments. This capability allows for the customization of channel conditions to achieve specific performance goals. This paper presents an enhancement in downlink cell-free OWC networks through the integration of OIRS. The key focus is on fine-tuning crucial parameters, including transmit power, receiver orientations, OIRS elements allocation, and strategic placement. In particular, a multi-objective optimization problem (MOOP) aimed at simultaneously improving the network's spectral efficiency (SE) and energy efficiency (EE) while adhering to the network's quality of service (QoS) constraints is formulated. The problem is solved by employing the $\epsilon$-constraint method to convert the MOOP into a single-objective optimization problem and solving it through successive convex approximation. Simulation results show the significant impact of OIRS on SE and EE, confirming its effectiveness in improving OWC network performance.
Fast Directed $q$-Analysis for Brain Graphs
Authors: Felix Windisch, Florian Unger
Date: 2025-01-08 00:00:00

Recent innovations in reconstructing large scale, full-precision, neuron-synapse-scale connectomes demand subsequent improvements to graph analysis methods to keep up with the growing complexity and size of the data. One such tool is the recently introduced directed $q$-analysis. We present numerous improvements, theoretical and applied, to this technique: on the theoretical side, we introduce modified definitions for key elements of directed $q$-analysis, which remedy a well-hidden and previously undetected bias. This also leads to new, beneficial perspectives to the associated computational challenges. Most importantly, we present a high-speed, publicly available, low-level implementation that provides speed-ups of several orders of magnitude on C. Elegans. Furthermore, the speed gains grow with the size of the considered graph. This is made possible due to the mathematical and algorithmic improvements as well as a carefully crafted implementation. These speed-ups enable, for the first time, the analysis of full-sized connectomes such as those obtained by recent reconstructive methods. Additionally, the speed-ups allow comparative analysis to corresponding null models, appropriately designed randomly structured artificial graphs that do not correspond to actual brains. This, in turn, allows for assessing the efficacy and usefulness of directed $q$-analysis for studying the brain. We report on the results in this paper.
How Large is the Universe of RNA-Like Motifs? A Clustering Analysis of RNA Graph Motifs Using Topological Descriptors
Authors: Rui Wang, Tamar Schlick
Date: 2025-01-08 00:00:00
Tasks: RAG

We introduce a computational topology-based approach with unsupervised machine-learning algorithms to estimate the database size and content of RNA-like graph topologies. Specifically, we apply graph theory enumeration to generate all 110,667 possible 2D dual graphs for vertex numbers ranging from 2 to 9. Among them, only 0.11% graphs correspond to approximately 200,000 known RNA atomic fragments (collected in 2021) using the RNA-as-Graphs (RAG) mapping method. The remaining 99.89% of the dual graphs may be RNA-like or non-RNA-like. To determine which dual graphs in the 99.89% hypothetical set are more likely to be associated with RNA structures, we apply computational topology descriptors using the Persistent Spectral Graphs (PSG) method to characterize each graph using 19 PSG-based features and use clustering algorithms that partition all possible dual graphs into two clusters, RNA-like cluster and non-RNA-like cluster. The distance of each dual graph to the center of the RNA-like cluster represents the likelihood of it belonging to RNA structures. From validation, our PSG-based RNA-like cluster includes 97.3% of the 121 known RNA dual graphs, suggesting good performance. Furthermore, 46.017% of the hypothetical RNAs are predicted to be RNA-like. Significantly, we observe that all the top 15 RNA-like dual graphs can be separated into multiple subgraphs, whereas the top 15 non-RNA-like dual graphs tend not to have any subgraphs. Moreover, a significant topological difference between top RNA-like and non-RNA-like graphs is evident when comparing their topological features. These findings provide valuable insights into the size of the RNA motif universe and RNA design strategies, offering a novel framework for predicting RNA graph topologies and guiding the discovery of novel RNA motifs, perhaps anti-viral therapeutics by subgraph assembly.
Advances in RNA secondary structure prediction and RNA modifications: Methods, data, and applications
Authors: Shu Yang, Nhat Truong Pham, Ziyang Li, Jae Young Baik, Joseph Lee, Tianhua Zhai, Weicheng Yu, BoJian Hou, Tianqi Shang, Weiqing He, Duy Duong-Tran, Mayur Naik, Li Shen
Date: 2025-01-07 00:00:00

Due to the hierarchical organization of RNA structures and their pivotal roles in fulfilling RNA functions, the formation of RNA secondary structure critically influences many biological processes and has thus been a crucial research topic. This review sets out to explore the computational prediction of RNA secondary structure and its connections to RNA modifications, which have emerged as an active domain in recent years. We first examine the progression of RNA secondary structure prediction methodology, focusing on a set of representative works categorized into thermodynamic, comparative, machine learning, and hybrid approaches. Next, we survey the advances in RNA modifications and computational methods for identifying RNA modifications, focusing on the prominent modification types. Subsequently, we highlight the interplay between RNA modifications and secondary structures, emphasizing how modifications such as m6A dynamically affect RNA folding and vice versa. In addition, we also review relevant data sources and provide a discussion of current challenges and opportunities in the field. Ultimately, we hope our review will be able to serve as a cornerstone to aid in the development of innovative methods for this emerging topic and foster therapeutic applications in the future.
Transcriptome signature for the identification of bevacizumab responders in ovarian cancer
Authors: Olga Zolotareva, Karen Legler, Olga Tsoy, Anna Esteve, Alexey Sergushichev, Vladimir Sukhov, Jan Baumbach, Kathrin Eylmann, Minyue Qi, Malik Alawi, Stefan Kommoss, Barbara Schmalfeldt, Leticia Oliveira-Ferrer
Date: 2025-01-08 00:00:00

The standard of care for ovarian cancer comprises cytoreductive surgery, followed by adjuvant platinum-based chemotherapy plus taxane therapy and maintenance therapy with the antiangiogenic compound bevacizumab and/or a PARP inhibitor. Nevertheless, there is currently no clear clinical indication for the use of bevacizumab, highlighting the urgent need for biomarkers to assess the response to bevacizumab. In the present study, based on a novel RNA-seq dataset (n=181) and a previously published microarray-based dataset (n=377), we have identified an expression signature potentially associated with benefit from bevacizumab addition and assumed to reflect cancer stemness acquisition driven by activation of CTCFL. Patients with this signature demonstrated improved overall survival when bevacizumab was added to standard chemotherapy in both novel (HR=0.41(0.23-0.74), adj.p-value=7.70e-03) and previously published cohorts (HR=0.51(0.34-0.75), adj.p-value=3.25e-03), while no significant differences in survival explained by treatment were observed in patients negative for this signature. In addition to the CTCFL signature, we found several other reproducible expression signatures which may also represent biomarker candidates not related to established molecular subtypes of ovarian cancer and require further validation studies based on additional RNA-seq data.
Curated loci prime editing (cliPE) for accessible multiplexed assays of variant effect (MAVEs)
Authors: Carina G Biar, Nicholas Bodkin, Gemma L Carvill, Jeffrey D Calhoun
Date: 2025-01-08 00:00:00

Multiplexed assays of variant effect (MAVEs) perform simultaneous characterization of many variants. Prime editing has been recently adopted for introducing many variants in their native genomic contexts. However, robust protocols and standards are limited, preventing widespread uptake. Herein, we describe curated loci prime editing (cliPE) which is an accessible, low-cost experimental pipeline to perform MAVEs using prime editing of a target gene, as well as a companion Shiny app (pegRNA Designer) to rapidly and easily design user-specific MAVE libraries.
Deep Learning-based Feature Discovery for Decoding Phenotypic Plasticity in Pediatric High-Grade Gliomas Single-Cell Transcriptomics
Authors: Abicumaran Uthamacumaran
Date: 2025-01-07 00:00:00

By use of complex network dynamics and graph-based machine learning, we identified critical determinants of lineage-specific plasticity across the single-cell transcriptomics of pediatric high-grade glioma (pHGGs) subtypes: IDHWT glioblastoma and K27M-mutant glioma. Our study identified network interactions regulating glioma morphogenesis via the tumor-immune microenvironment, including neurodevelopmental programs, calcium dynamics, iron metabolism, metabolic reprogramming, and feedback loops between MAPK/ERK and WNT signaling. These relationships highlight the emergence of a hybrid spectrum of cellular states navigating a disrupted neuro-differentiation hierarchy. We identified transition genes such as DKK3, NOTCH2, GATAD1, GFAP, and SEZ6L in IDHWT glioblastoma, and H3F3A, ANXA6, HES6/7, SIRT2, FXYD6, PTPRZ1, MEIS1, CXXC5, and NDUFAB1 in K27M subtypes. We also identified MTRNR2L1, GAPDH, IGF2, FKBP variants, and FXYD7 as transition genes that influence cell fate decision-making across both subsystems. Our findings suggest pHGGs are developmentally trapped in states exhibiting maladaptive behaviors, and hybrid cellular identities. In effect, tumor heterogeneity (metastability) and plasticity emerge as stress-response patterns to immune-inflammatory microenvironments and oxidative stress. Furthermore, we show that pHGGs are steered by developmental trajectories from radial glia predominantly favoring neocortical cell fates, in telencephalon and prefrontal cortex (PFC) differentiation. By addressing underlying patterning processes and plasticity networks as therapeutic vulnerabilities, our findings provide precision medicine strategies aimed at modulating glioma cell fates and overcoming therapeutic resistance. We suggest transition therapy toward neuronal-like lineage differentiation as a potential therapy to help stabilize pHGG plasticity and aggressivity.
Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective
Authors: Meng Xiao, Weiliang Zhang, Xiaohan Huang, HengShu Zhu, Min Wu, XiaoLi Li, Yuanchun Zhou
Date: 2025-01-02 00:00:00

Gene panel selection aims to identify the most informative genomic biomarkers in label-free genomic datasets. Traditional approaches, which rely on domain expertise, embedded machine learning models, or heuristic-based iterative optimization, often introduce biases and inefficiencies, potentially obscuring critical biological signals. To address these challenges, we present an iterative gene panel selection strategy that harnesses ensemble knowledge from existing gene selection algorithms to establish preliminary boundaries or prior knowledge, which guide the initial search space. Subsequently, we incorporate reinforcement learning through a reward function shaped by expert behavior, enabling dynamic refinement and targeted selection of gene panels. This integration mitigates biases stemming from initial boundaries while capitalizing on RL's stochastic adaptability. Comprehensive comparative experiments, case studies, and downstream analyses demonstrate the effectiveness of our method, highlighting its improved precision and efficiency for label-free biomarker discovery. Our results underscore the potential of this approach to advance single-cell genomics data analysis.
Role of connectivity anisotropies in the dynamics of cultured neuronal networks
Authors: Akke Mats Houben, Jordi Garcia-Ojalvo, Jordi Soriano
Date: 2025-01-08 00:00:00

Laboratory-grown, engineered living neuronal networks in vitro have emerged in the last years as an experimental technique to understand the collective behavior of neuronal assemblies in relation to their underlying connectivity. An inherent obstacle in the design of such engineered systems is the difficulty to predict the dynamic repertoire of the emerging network and its dependence on experimental variables. To fill this gap, and inspired on recent experimental studies, here we present a numerical model that aims at, first, replicating the anisotropies in connectivity imprinted through engineering, to next realize the collective behavior of the neuronal network and make predictions. We use experimentally measured, biologically-realistic data combined with the Izhikevich model to quantify the dynamics of the neuronal network in relation to tunable structural and dynamical parameters. These parameters include the synaptic noise, strength of the imprinted anisotropies, and average axon lengths. The latter are involved in the simulation of the development of neurons in vitro. We show that the model captures the behavior of engineered neuronal cultures, in which a rich repertoire of activity patterns emerge but whose details are strongly dependent on connectivity details and noise. Results also show that the presence of connectivity anisotropies substantially improves the capacity of reconstructing structural connectivity from activity data, an aspect that is important in the quest for understanding the structure-to-function relationship in neuronal networks. Our work provides the in silico basis to assist experimentalists in the design of laboratory in vitro networks and anticipate their outcome, an aspect that is particularly important in the effort to conceive reliable brain-on-a-chip circuits and explore key aspects such as input-output relationships or information coding.