pwc-archive/papers-with-abstracts huggingface dataset:
Multi-hop Sub-THz-FSO: A Technique for High-rate Uninterrupted Backhauling in 6G
Authors: Praveen Kumar Singya, Behrooz Makki, Antonio D'Errico, Mohamed-Slim Alouini
Date: 2023-12-10 00:00:00

Moving towards $6^{\text{th}}$ generation (6G), backhaul networks require significant improvements to support new use-cases with restricted joint capacity and availability requirements. In this paper, we investigate the potentials and challenges of joint sub-teraHertz (sub-THz) and free space optical (FSO), in short sub-THz-FSO, multi-hop networks as a candidate technology for future backhaul communications. As we show, with a proper deployment, sub-THz-FSO networks have the potential to provide high-rate reliable backhauling, while there are multiple practical challenges to be address before they can be used in large-scale.
A Novel Discrete Phase Shift Design for RIS-Assisted Multi-User MIMO
Authors: Parisa Ramezani, Yasaman Khorsandmanesh, Emil Björnson
Date: 2023-12-09 00:00:00

Reconfigurable intelligent surface (RIS) is a newly-emerged technology that might fundamentally change how wireless networks are operated. Though extensively studied in recent years, the practical limitations of RIS are often neglected when assessing the performance of RIS-assisted communication networks. One of these limitations is that each RIS element is restricted to incur a controllable phase shift to the reflected signal from a predefined discrete set. This paper studies an RIS-assisted multi-user multiple-input multiple-output (MIMO) system, where an RIS with discrete phase shifts assists in simultaneous uplink data transmission from multiple user equipments (UEs) to a base station (BS). We aim to maximize the sum rate by optimizing the receive beamforming vectors and RIS phase shift configuration. To this end, we transform the original sum-rate maximization problem into a minimum mean square error (MMSE) minimization problem and employ the block coordinate descent (BCD) technique for iterative optimization of the variables until convergence. We formulate the discrete RIS phase shift optimization problem as a mixed-integer least squares problem and propose a novel method based on sphere decoding (SD) to solve it. Through numerical evaluation, we show that the proposed discrete phase shift design outperforms the conventional nearest point mapping method, which is prevalently used in previous works.
Sequential Processing in Cell-free Massive MIMO Uplink with Limited Memory Access Points
Authors: Vida Ranjbar, Robbert Beerten, Marc Moonen, Sofie Pollin
Date: 2023-12-09 00:00:00

Cell-free massive multiple-input multiple-output (MIMO) is an emerging technology that will reshape the architecture of next-generation networks. This paper considers the sequential fronthaul, whereby the access points (APs) are connected in a daisy chain topology with multiple sequential processing stages. With this sequential processing in the uplink, each AP refines users' signal estimates received from the previous AP based on its own local received signal vector. While this processing architecture has been shown to achieve the same performance as centralized processing, the impact of the limited memory capacity at the APs on the store and forward processing architecture is yet to be analyzed. Thus, we model the received signal vector compression using rate-distortion theory to demonstrate the effect of limited memory capacity on the optimal number of APs in the daisy chain fronthaul. Without this memory constraint, more geographically distributed antennas alleviate the adverse effect of large-scale fading on the signal-to-interference-plus-noise-ratio (SINR). However, we show that in case of limited memory capacity at each AP, the memory capacity to store the received signal vectors at the final AP of this fronthaul becomes a limiting factor. In other words, we show that when deciding on the number of APs to distribute the antennas, there is an inherent trade-off between more macro-diversity and compression noise power on the stored signal vectors at the APs. Hence, the available memory capacity at the APs significantly influences the optimal number of APs in the fronthaul.
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion
Authors: Arian Eamaz, Farhang Yeganegi, Yunqiao Hu, Shunqiao Sun, Mojtaba Soltanalian
Date: 2023-12-09 00:00:00
Tasks: Matrix Completion, Quantization

The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging. This paper investigates the impact of coarse quantization on measurements obtained from such arrays. To recover azimuth angles from quantized measurements, we leverage the low-rank properties of the constructed Hankel matrix. In particular, by addressing the one-bit Hankel matrix completion problem through a developed singular value thresholding algorithm, our proposed approach accurately estimates the azimuth angles of interest. We provide comprehensive insights into recovery performance and the required number of one-bit samples. The effectiveness of our proposed scheme is underscored by numerical results, demonstrating successful reconstruction using only one-bit data.
On Possible Indicators of Negative Selection in Germinal Centers
Authors: Bertrand Ottino-Loffler, Gabriel Victora
Date: 2023-12-10 00:00:00

A central feature of vertebrate immune response is affinity maturation, wherein antibody-producing B cells undergo evolutionary selection in microanatomical structures called germinal centers, which form in secondary lymphoid organs upon antigen exposure. While it has been shown that the median B cell affinity dependably increases over the course of maturation, the exact logic behind this evolution remains vague. Three potential selection methods include encouraging the reproduction of high affinity cells (``birth/positive selection''), encouraging cell death in low affinity cells (``death/negative selection''), and adjusting the mutation rate based on cell affinity (``mutational selection''). While all three forms of selection would lead to a net increase in affinity, different selection methods may lead to distinct statistical dynamics. We present a tractable model of selection, and analyze proposed signatures of negative selection. Given the simplicity of the model, such signatures should be stronger here than in real systems. However, we find a number of intuitively appealing metrics -- such as preferential ancestry ratios, terminal node counts, and mutation count skewness -- are all ill-suited for detecting selection method.
A spatial multiscale mathematical model of Plasmodium vivax transmission
Authors: Shoshana Elgart, Mark B. Flegg, Somya Mehra, Jennifer A. Flegg
Date: 2023-12-10 00:00:00

The epidemiological behavior of Plasmodium vivax malaria occurs across spatial scales including within-host, population, and metapopulation levels. On the within-host scale, P. vivax sporozoites inoculated in a host may form latent hypnozoites, the activation of which drives secondary infections and accounts for a large proportion of P. vivax illness; on the metapopulation level, the coupled human-vector dynamics characteristic of the population level are further complicated by the migration of human populations across patches with different malaria forces of (re-)infection. To explore the interplay of all three scales in a single two-patch model of Plasmodium vivax dynamics, we construct and study a system of eight integro-differential equations with periodic forcing (arising from the single-frequency sinusoidal movement of a human sub-population). Under the numerically-informed ansatz that the limiting solutions to the system are closely bounded by sinusoidal ones for certain regions of parameter space, we derive a single nonlinear equation from which all approximate limiting solutions may be drawn, and devise necessary and sufficient conditions for the equation to have only a disease-free solution. Our results illustrate the impact of movement on P. vivax transmission and suggest a need to focus vector control efforts on forest mosquito populations. The three-scale model introduced here provides a more comprehensive framework for studying the clinical, behavioral, and geographical factors underlying P. vivax malaria endemicity.
Convergence of the Chambolle-Pock Algorithm in the Absence of Monotonicity
Authors: Brecht Evens, Puya Latafat, Panagiotis Patrinos
Date: 2023-12-11 00:00:00

The Chambolle-Pock algorithm (CPA), also known as the primal-dual hybrid gradient method, has gained popularity over the last decade due to its success in solving large-scale convex structured problems. This work extends its convergence analysis for problems with varying degrees of (non)monotonicity, quantified through a so-called oblique weak Minty condition on the associated primal-dual operator. Our results reveal novel stepsize and relaxation parameter ranges which do not only depend on the norm of the linear mapping, but also on its other singular values. In particular, in nonmonotone settings, in addition to the classical stepsize conditions, extra bounds on the stepsizes and relaxation parameters are required. On the other hand, in the strongly monotone setting, the relaxation parameter is allowed to exceed the classical upper bound of two. Moreover, we build upon the recently introduced class of semimonotone operators, providing sufficient convergence conditions for CPA when the individual operators are semimonotone. Since this class of operators encompasses traditional operator classes including (hypo)- and co(hypo)-monotone operators, this analysis recovers and extends existing results for CPA. Tightness of the proposed stepsize ranges is demonstrated through several examples.
Novel Evolution of the Mineralocorticoid Receptor in Humans compared to Chimpanzees, Gorillas and Orangutans
Authors: Yoshinao Katsu, Jiawen Zhang, Michael E. Baker
Date: 2023-12-08 00:00:00

Five distinct full-length mineralocorticoid receptor (MR) genes have been identified in humans. These human MRs can be distinguished by the presence or absence of an in-frame insertion of 12 base pairs coding for Lys, Cys, Ser, Trp (KCSW) in their DNA-binding domain (DBD) and the presence of two amino acid mutations in their amino terminal domain (NTD). Two human MRs with the KCSW insertion (MR-KCSW) and three human MRs without KCSW in the DBD have been identified. The three human MRs without KCSW contain either (Ile-180, Ala-241) or (Val-180, Val-241) or (Ile-180, Val-241) in their NTD. The two human MRs with KCSW contain either (Val-180, Val-241) or (Ile-180, Val-241) in their NTD. Human MR-KCSW with (Ile-180, Ala-241) has not been cloned. In contrast, chimpanzees contain two MRs with KCSW and two MRs without KCSW in their DBD and both contain only Ile180, Val-241 in their NTDs. Each pair of chimpanzee MRs differ at another amino acid in the NTD. A chimpanzee MR with either Val-180, Val-241 or Ile-180, Ala-241 in the NTD has not been cloned. Gorillas and orangutans each contain one MR with KCSW in the DBD and one MR without KCSW. Both gorilla and orangutan MRs contain I-180, Val-241 in their NTD. Neither Val-180, Val-241 nor Ile-180, Ala-241 are found in the NTD in either a gorilla MR or an orangutan MR. These data suggest that human MRs with Val-180, Val-241 or Ile-180, Ala-241 in the NTD evolved after humans and chimpanzees diverged from their common ancestor. These unique human MRs may have had a role in the divergent evolution of humans from chimpanzees. Studies are underway to characterize transcriptional activation of the five human MRs by aldosterone, cortisol, and other corticosteroids for comparison with each other to elucidate the roles of these MRs in human physiology.
New network models facilitate analysis of biological networks
Authors: Alex Stivala
Date: 2023-12-11 00:00:00

Exponential-family random graph models (ERGMs) are a family of network models originating in social network analysis, which have also been applied to biological networks. Advances in estimation algorithms have increased the practical scope of these models to larger networks, however it is still not always possible to estimate a model without encountering problems of model near-degeneracy, particularly if it is desired to use only simple model parameters, rather than more complex parameters designed to overcome the problem of near-degeneracy. Two new network models related to the ERGM, the Tapered ERGM, and the latent order logistic (LOLOG) model, have recently been proposed to overcome this problem. In this work I illustrate the application of the Tapered ERGM and the LOLOG to a set of biological networks, including protein-protein interaction (PPI) networks, gene regulatory networks, and neural networks. I find that the Tapered ERGM and the LOLOG are able to estimate models for networks for which it was not possible to estimate a conventional ERGM, and are able to do so using only simple model parameters. In the case of two neural networks where data on the spatial position of neurons is available, this allows the estimation of models including terms for spatial distance and triangle structures, allowing triangle motif statistical significance to be estimated while accounting for the effect of spatial proximity on connection probability. For some larger networks, however, Tapered ERGM and LOLOG estimation was not possible in practical time, while conventional ERGM models were able to be estimated only by using the Equilibrium Expectation (EE) algorithm.
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates
Authors: Kohitij Kar, James J DiCarlo
Date: 2023-12-10 00:00:00
Tasks: Object, Object Recognition

Visual object recognition -- the behavioral ability to rapidly and accurately categorize many visually encountered objects -- is core to primate cognition. This behavioral capability is algorithmically impressive because of the myriad identity-preserving viewpoints and scenes that dramatically change the visual image produced by the same object. Until recently, the brain mechanisms that support that capability were deeply mysterious. However, over the last decade, this scientific mystery has been illuminated by the discovery and development of brain-inspired, image-computable, artificial neural network (ANN) systems that rival primates in this behavioral feat. Apart from fundamentally changing the landscape of artificial intelligence (AI), modified versions of these ANN systems are the current leading scientific hypotheses of an integrated set of mechanisms in the primate ventral visual stream that support object recognition. What separates brain-mapped versions of these systems from prior conceptual models is that they are Sensory-computable, Mechanistic, Anatomically Referenced, and Testable (SMART). Here, we review and provide perspective on the brain mechanisms that the currently leading SMART models address. We review the empirical brain and behavioral alignment successes and failures of those current models. Given ongoing advances in neurobehavioral measurements and AI, we discuss the next frontiers for even more accurate mechanistic understanding. And we outline the likely applications of that SMART-model-based understanding.
Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites
Authors: Nizar Islah, Guillaume Etter, Mashbayar Tugsbayar, Tugce Gurbuz, Blake Richards, Eilif Muller
Date: 2023-12-09 00:00:00
Tasks: Anatomy, Temporal Sequences

One of the hallmark features of neocortical anatomy is the presence of extensive top-down projections into primary sensory areas, with many impinging on the distal apical dendrites of pyramidal neurons. While it is known that they exert a modulatory effect, altering the gain of responses, their functional role remains an active area of research. It is hypothesized that these top-down projections carry contextual information that can help animals to resolve ambiguities in sensory data. One proposed mechanism of contextual integration is a non-linear integration of distinct input streams at apical and basal dendrites of pyramidal neurons. Computationally, however, it is yet to be demonstrated how such an architecture could leverage distinct compartments for flexible contextual integration and sensory processing when both sensory and context signals can be unreliable. Here, we implement an augmented deep neural network with distinct apical and basal compartments that integrates a) contextual information from top-down projections to apical compartments, and b) sensory representations driven by bottom-up projections to basal compartments, via a biophysically inspired rule. In addition, we develop a new multi-scenario contextual integration task using a generative image modeling approach. In addition to generalizing previous contextual integration tasks, it better captures the diversity of scenarios where neither contextual nor sensory information are fully reliable. To solve this task, this model successfully learns to select among integration strategies. We find that our model outperforms those without the "apical prior" when contextual information contradicts sensory input. Altogether, this suggests that the apical prior and biophysically inspired integration rule could be key components necessary for handling the ambiguities that animals encounter in the diverse contexts of the real world.
ProtoCode: Leveraging Large Language Models for Automated Generation of Machine-Readable Protocols from Scientific Publications
Authors: Shuo Jiang, Daniel Evans-Yamamoto, Dennis Bersenev, Sucheendra K. Palaniappan, Ayako Yachie-Kinoshita
Date: 2023-12-11 00:00:00

Protocol standardization and sharing are crucial for reproducibility in life sciences. In spite of numerous efforts for standardized protocol description, adherence to these standards in literature remains largely inconsistent. Curation of protocols are especially challenging due to the labor intensive process, requiring expert domain knowledge of each experimental procedure. Recent advancements in Large Language Models (LLMs) offer a promising solution to interpret and curate knowledge from complex scientific literature. In this work, we develop ProtoCode, a tool leveraging fine-tune LLMs to curate protocols which can be interpretable by both human and machine interfaces. Our proof-of-concept, focused on polymerase chain reaction (PCR) protocols, retrieves information from PCR protocols at an accuracy ranging 69-100% depending on the information content. In all the tested protocols, we demonstrate that ProtoCode successfully converts literature-based protocols into correct operational files for multiple thermal cycler systems. In conclusion, ProtoCode can alleviate labor intensive curation and standardization of life science protocols to enhance research reproducibility by providing a reliable, automated means to process and standardize protocols. ProtoCode is freely available as a web server at https://curation.taxila.io/ProtoCode/.
Could dropping a few cells change the takeaways from differential expression?
Authors: Miriam Shiffman, Ryan Giordano, Tamara Broderick
Date: 2023-12-11 00:00:00

Differential expression (DE) plays a fundamental role toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells/samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, this step is justified by assuming that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we hope that any deviation from the assumption is small enough that A) conclusions of the analysis still hold and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard tools, if conclusions could be substantively changed by dropping a very small fraction of data. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of DE; namely, we cast the analysis in a form suitable to the approximation, extend the approximation to models with data-dependent hyperparameters, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We assess robustness of DE for published single-cell RNA-seq data and discover that 1000s of genes can have their results flipped by dropping <1% of the data, including 100s that are sensitive to dropping a single cell (0.07%). Surprisingly, this non-robustness extends to high-level takeaways; half of the top 10 gene sets can be changed by dropping 1-2% of cells, and 2/10 can be changed by dropping a single cell.
Grid-free stochastic simulations of reaction-diffusion processes at cell-cell contacts
Authors: Thorsten Prüstel, Martin Meier-Schellersheim
Date: 2023-12-09 00:00:00

Biological cells can exchange messages through soluble molecules or membrane-bound receptors. In particular in the latter case, the interaction is usually located in specific regions of the interacting cells and may depend on or induce local morphological features or reorganizations of the membrane-associated or membrane-proximal biochemistry. Examples are interactions among cells that probe their environment and the surfaces of neighboring cells with dendrite-like protrusions. Previously, we introduced an algorithm capable of creating spatially-resolved, stochastic particle-based computational models of cellular biochemistry. However, its applicability was limited to the case of soluble ligands. Here, building on this earlier approach, we introduce a simulation method that accounts for the unique features that distinguish the sites of cell-cell contact from other regions of the cell membrane. Employing a smooth, grid-free computational representation of the contact sites, we extend the applicability of Green's function-based stochastic simulation algorithms to study intermembrane bimolecular interactions between particles on adjacent curved surfaces. We demonstrate the utility of this computational approach by exploring the effects of contact geometry and local biochemistry for the early phase of T-cell receptor activation.
Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography
Authors: William McGough, Thomas Buddenkotte, Stephan Ursprung, Zeyu Gao, Grant Stewart, Mireia Crispin-Ortuzar
Date: 2023-11-24 00:00:00
Tasks: Specificity

This study introduces an automated pipeline for renal cancer (RC) detection in non-contrast computed tomography (NCCT). In the development of our pipeline, we test three detections models: a shape model, a 2D-, and a 3D axial-sample model. Training (n=1348) and testing (n=64) data were gathered from open sources (KiTS23, Abdomen1k, CT-ORG) and Cambridge University Hospital (CUH). Results from cross-validation and testing revealed that the 2D axial sample model had the highest small ($\leq$40mm diameter) RC detection area under the curve (AUC) of 0.804. Our pipeline achieves 61.9\% sensitivity and 92.7\% specificity for small kidney cancers on unseen test data. Our results are much more accurate than previous attempts to automatically detect small renal cancers in NCCT, the most likely imaging modality for RC screening. This pipeline offers a promising advance that may enable screening for kidney cancers.