pwc-archive/papers-with-abstracts huggingface dataset:
Implementation and Evaluation of Physical Layer Key Generation on SDR based LoRa Platform
Authors: Yingying Hu, Dongyang Xu, Tiantian Zhang
Date: 2023-08-30 00:00:00
Tasks: Quantization

Physical layer key generation technology which leverages channel randomness to generate secret keys has attracted extensive attentions in long range (LoRa)-based networks recently. We in this paper develop a software-defined radio (SDR) based LoRa communications platform using GNU Radio on universal software radio peripheral (USRP) to implement and evaluate typical physical layer key generation schemes. Thanks to the flexibility and configurability of GNU Radio to extract LoRa packets, we are able to obtain the fine-grained channel frequency response (CFR) through LoRa preamble based channel estimation for key generation. Besides, we propose a lowcomplexity preprocessing method to enhance the randomness of quantization while reducing the secret key disagreement ratio. The results indicate that we can achieve 367 key bits with a high level of randomness through just a single effective channel probing in an indoor environment at a distance of 2 meters under the circumstance of a spreading factor (SF) of 7, a preamble length of 8, a signal bandwidth of 250 kHz, and a sampling rate of 1 MHz.
Widely Separated MIMO Radar Using Matrix Completion
Authors: Shunqiao Sun, Yunqiao Hu, Kumar Vijay Mishra, Athina P. Petropulu
Date: 2023-08-29 00:00:00
Tasks: Matrix Completion

We present a low-complexity widely separated multiple-input-multiple-output (WS-MIMO) radar that samples the signals at each of its multiple receivers at reduced rates. We process the low-rate samples of all transmit-receive chains at each receiver as data matrices. We demonstrate that each of these matrices is low rank as long as the target moves slowly within a coherent processing interval. We leverage matrix completion (MC) to recover the missing samples of each receiver signal matrix at the common fusion center. Subsequently, we estimate the targets' positions and Doppler velocities via the maximum likelihood method. Our MC-WS-MIMO approach recovers missing samples and thereafter target parameters at reduced rates without discretization. Our analysis using ambiguity functions shows that antenna geometry affects the performance of MC-WS-MIMO. Numerical experiments demonstrate reasonably accurate target localization at SNR of 20 dB and sampling rate reduction to 20%.
Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model
Authors: Hongtai Jing, Zhengtao Gao, Sheng Xu, Tao Shen, Zhangzhi Peng, Shwai He, Tao You, Shuang Ye, Wei Lin, Siqi Sun
Date: 2023-08-31 00:00:00
Tasks: Language Modeling, Language Modelling

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% non-redundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold, and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials.
Imaging structurally dynamic ribosomes with cryogenic electron microscopy
Authors: Samantha M. Webster, Mira B. May, Barrett M. Powell, Joseph H. Davis
Date: 2023-08-30 00:00:00
Tasks: Cryogenic Electron Tomography, Electron Tomography, Experimental Design

Throughout the history of electron microscopy, ribosomes have served as an ideal subject for imaging and technological development, which in turn has driven our understanding of ribosomal biology. Here, we provide a historical perspective at the intersection of electron microscopy technology development and ribosome biology and reflect on how this technique has shed light on each stage of the life cycle of this dynamic macromolecular machine. With an emphasis on prokaryotic systems, we specifically describe how pairing cryo-EM with clever experimental design, time-resolved techniques, and next-generation heterogeneous structural analysis has afforded insights into the modular nature of assembly, the roles of the many transient biogenesis and translation co-factors, and the subtle variations in structure and function between strains and species. The work concludes with a prospective outlook on the field, highlighting the pivotal role cryogenic electron tomography is playing in adding cellular context to our understanding of ribosomal life cycles, and noting how this exciting technology promises to bridge the gap between cellular and structural biology.
Novel enzymes for biodegradation of polycyclic aromatic hydrocarbons: metagenomics-linked identification followed by functional analysis
Authors: Kinga K. Nagy, Kristóf Takács, Imre Németh, Bálint Varga, Vince Grolmusz, Mónika Molnár, Beáta G. Vértessy
Date: 2023-08-30 00:00:00

Polycyclic aromatic hydrocarbons (PAHs) are highly toxic, carcinogenic substances. On soils contaminated with PAHs, crop cultivation, animal husbandry and even the survival of microflora in the soil are greatly perturbed, depending on the degree of contamination. Most microorganisms cannot tolerate PAH-contaminated soils, however, some microbial strains can adapt to these harsh conditions and survive on contaminated soils. Analysis of the metagenomes of contaminated environmental samples may lead to discovery of PAH-degrading enzymes suitable for green biotechnology methodologies ranging from biocatalysis to pollution control. In the present study, our goal was to apply a metagenomic data search to identify efficient novel enzymes in remediation of PAH-contaminated soils. The metagenomic hits were further analyzed using a set of bioinformatics tools to select protein sequences predicted to encode well-folded soluble enzymes. Three novel enzymes (two dioxygenases and one peroxidase) were cloned and used in soil remediation microcosms experiments. The novel enzymes were found to be efficient for degradation of naphthalene and phenanthrene. Adding the inorganic oxidant CaO2 further increased the degrading potential of the novel enzymes for anthracene and pyrene. We conclude that metagenome mining paired with bioinformatic predictions, structural modelling and functional assays constitutes a powerful approach towards novel enzymes for soil remediation.
Improving homology-directed repair by small molecule agents for genetic engineering in unconventional yeast? -- Learning from the engineering of mammalian systems
Authors: Min Lu, Sonja Billerbeck
Date: 2023-08-29 00:00:00

The ability to precisely edit genomes by deleting or adding genetic information enables the study of biological functions and the building of efficient cell factories. In many unconventional yeasts, such as promising new hosts for cell factory design but also human pathogenic yeasts and food spoilers, this progress has been limited by the fact that most yeasts favor non-homologous end joining (NHEJ) over homologous recombination (HR) as DNA repair mechanism, impairing genetic access to these hosts. In mammalian cells, small molecules that either inhibit proteins involved in NHEJ, enhance protein function in HR, or molecules that arrest the cell cycle in HR-dominant phases are regarded as promising agents for the simple and transient increase of HR-mediated genome editing without the need for a priori host engineering. Only a few of these chemicals have been applied to the engineering of yeast although the targeted proteins are mostly conserved; making chemical agents a yet underexplored area in enhancing yeast engineering. Here, we consolidate knowledge of available small molecules that have been used to improve HR efficiency in mammalian cells and the few ones that have been used in yeast. We include available high throughput (HTP)-compatible NHEJ/HR quantification assays that could be used to screen for and isolate yeast-specific inhibitors.
Linking discrete and continuous models of cell birth and migration
Authors: W. Duncan Martinson, Alexandria Volkening, Markus Schmidtchen, Chandrasekhar Venkataraman, José A. Carrillo
Date: 2023-08-30 00:00:00

Self-organisation of individuals within large collectives occurs throughout biology. Mathematical models can help elucidate the individual-level mechanisms behind these dynamics, but analytical tractability often comes at the cost of biological intuition. Discrete models provide straightforward interpretations by tracking each individual yet can be computationally expensive. Alternatively, continuous models supply a large-scale perspective by representing the "effective" dynamics of infinite agents, but their results are often difficult to translate into experimentally relevant insights. We address this challenge by quantitatively linking spatio-temporal dynamics of continuous models and individual-based data in settings with biologically realistic, time-varying cell numbers. Specifically, we introduce and fit scaling parameters in continuous models to account for discrepancies that can arise from low cell numbers and localised interactions. We illustrate our approach on an example motivated by zebrafish-skin pattern formation, in which we create a continuous framework describing the movement and proliferation of a single cell population by upscaling rules from a discrete model. Our resulting continuous models accurately depict ensemble average agent-based solutions when migration or proliferation act alone. Interestingly, the same parameters are not optimal when both processes act simultaneously, highlighting a rich difference in how combining migration and proliferation affects discrete and continuous dynamics.
The computational role of structure in neural activity and connectivity
Authors: Srdjan Ostojic, Stefano Fusi
Date: 2023-08-31 00:00:00

One major challenge of neuroscience is finding interesting structures in a seemingly disorganized neural activity. Often these structures have computational implications that help to understand the functional role of a particular brain area. Here we outline a unified approach to characterize these structures by inspecting the representational geometry and the modularity properties of the recorded activity, and show that this approach can also reveal structures in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent works on model networks performing three classes of computations.
The Impact of Downgrading Protected Areas (PAD) on Biodiversity
Authors: Yufei Li, Lingling Hou, PengFei Liu
Date: 2023-08-31 00:00:00

We quantitatively assess the impacts of Downgrading Protected Areas (PAD) on biodiversity in the U.S.. Results show that PAD events significantly reduce biodiversity. The proximity to PAD events decreases the biodiversity by 26.0% within 50 km compared with records of species further away from the PAD events. We observe an overall 32.3% decrease in abundance after those nearest PAD events are enacted. Abundance declines more in organisms living in contact with water and non-mammals. Species abundance is more sensitive to the negative impacts in areas where PAD events were later reversed, as well as in areas close to protected areas belonging to the International Union for Conservation of Nature (IUCN) category. The enacted PAD events between the period 1903 to 2018 in the U.S. lead to economic losses of approximately $689.95 million due to decrease in abundance. Our results contribute to the understanding on the impact of environmental interventions such as PAD events on biodiversity change and provide important implications on biodiversity conservation policies.
Understanding step selection analysis through numerical integration
Authors: Théo Michelot, Natasha J. Klappstein, Jonathan R. Potts, John Fieberg
Date: 2023-08-30 00:00:00
Tasks: Numerical Integration, parameter estimation

Step selection functions (SSFs) are flexible models to jointly describe animals' movement and habitat preferences. Their popularity has grown rapidly and extensions have been developed to increase their utility, including various distributions to describe movement constraints, interactions to allow movements to depend on local environmental features, and random effects and latent states to account for within- and among-individual variability. Although the SSF is a relatively simple statistical model, its presentation has not been consistent in the literature, leading to confusion about model flexibility and interpretation. We believe that part of the confusion has arisen from the conflation of the SSF model with the methods used for parameter estimation. Notably, conditional logistic regression can be used to fit SSFs in exponential form, and this approach is often presented interchangeably with the actual model (the SSF itself). However, reliance on conditional logistic regression reduces model flexibility, and suggests a misleading interpretation of step selection analysis as being equivalent to a case-control study. In this review, we explicitly distinguish between model formulation and inference technique, presenting a coherent framework to fit SSFs based on numerical integration and maximum likelihood estimation. We provide an overview of common numerical integration techniques, and explain how they relate to step selection analyses. This framework unifies different model fitting techniques for SSFs, and opens the way for improved inference. In particular, it makes it straightforward to model movement with distributions outside the exponential family, and to apply different SSF formulations to a data set and compare them with AIC. By separating the model formulation from the inference technique, we hope to clarify many important concepts in step selection analysis.
The Quantitative Genetics of Human Disease: 1 Foundations
Authors: David J. Cutler, Kiana Jodeiry, Andrew J. Bass, Michael P. Epstein
Date: 2023-08-29 00:00:00

In this the first of an anticipated four paper series, fundamental results of quantitative genetics are presented from a first principles approach. While none of these results are in any sense new, they are presented in extended detail to precisely distinguish between definition and assumption, with a further emphasis on distinguishing quantities from their usual approximations. Terminology frequently encountered in the field of human genetic disease studies will be defined in terms of their quantitive genetics form. Methods for estimation of both quantitative genetics and the related human genetics quantities will be demonstrated. While practitioners in the field of human quantitative disease studies may find this work pedantic in detail, the principle target audience for this work is trainees reasonably familiar with population genetics theory, but with less experience in its application to human disease studies. We introduce much of this formalism because in later papers in this series, we demonstrate that common areas of confusion in human disease studies can be resolved be appealing directly to these formal definitions. The second paper in this series will discuss polygenic risk scores. The third paper will concern the question of "missing" heritability and the role interactions may play. The fourth paper will discuss sexually dimorphic disease and the potential role of the X chromosome.
Research directions for kidney stone disease
Authors: Vincent Blay, Felix Grases
Date: 2023-08-31 00:00:00

Kidney stone disease poses a major burden to patients and healthcare systems around the world. The formation of kidney stones may occur over months or years, but many patients are diagnosed at a late stage, suffer excruciating pain, and require surgical intervention to physically remove the stones. The prevalence of kidney stones has increased during recent decades to over 10% in many developed countries, suggesting a link with environmental and behavioral factors. Recurrence rates are also high. In terms of their impact and scale, kidney stones are an ongoing pandemic. The causes and mechanisms of kidney stone formation are diverse and often unknown, resulting in varied compositions and different anatomical locations being affected. A better understanding of these processes could enable earlier diagnoses through more sensitive and scalable biomarkers, as well as more effective preventives and therapeutics.
Linking microblogging sentiments to stock price movement: An application of GPT-4
Authors: Rick Steinert, Saskia Altmann
Date: 2023-08-31 00:00:00
Tasks: Prompt Engineering, Sentiment Analysis

This paper investigates the potential improvement of the GPT-4 Language Learning Model (LLM) in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. We recorded daily adjusted closing prices and translated them into up-down movements. Sentiment for each day was extracted from messages on the Stocktwits platform using both LLMs. We develop a novel method to engineer a comprehensive prompt for contextual sentiment analysis which unlocks the true capabilities of modern LLM. This enables us to carefully retrieve sentiments, perceived advantages or disadvantages, and the relevance towards the analyzed company. Logistic regression is used to evaluate whether the extracted message contents reflect stock price movements. As a result, GPT-4 exhibited substantial accuracy, outperforming BERT in five out of six months and substantially exceeding a naive buy-and-hold strategy, reaching a peak accuracy of 71.47 % in May. The study also highlights the importance of prompt engineering in obtaining desired outputs from GPT-4's contextual abilities. However, the costs of deploying GPT-4 and the need for fine-tuning prompts highlight some practical considerations for its use.
The Financial Market of Environmental Indices
Authors: Thisari K. Mahanama, Abootaleb Shirvani, Svetlozar Rachev, Frank J. Fabozzi
Date: 2023-08-29 00:00:00

This paper introduces the concept of a global financial market for environmental indices, addressing sustainability concerns and aiming to attract institutional investors. Risk mitigation measures are implemented to manage inherent risks associated with investments in this new financial market. We monetize the environmental indices using quantitative measures and construct country-specific environmental indices, enabling them to be viewed as dollar-denominated assets. Our primary goal is to encourage the active engagement of institutional investors in portfolio analysis and trading within this emerging financial market. To evaluate and manage investment risks, our approach incorporates financial econometric theory and dynamic asset pricing tools. We provide an econometric analysis that reveals the relationships between environmental and economic indicators in this market. Additionally, we derive financial put options as insurance instruments that can be employed to manage investment risks. Our factor analysis identifies key drivers in the global financial market for environmental indices. To further evaluate the market's performance, we employ pricing options, efficient frontier analysis, and regression analysis. These tools help us assess the efficiency and effectiveness of the market. Overall, our research contributes to the understanding and development of the global financial market for environmental indices.
Capital Structure Dynamics and Financial Performance in Indian Banks (An Analysis of Mergers and Acquisitions)
Authors: Kurada T S S Satyanarayana, Addada Narasimha Rao, Kumpatla jaya surya
Date: 2023-08-30 00:00:00
Tasks: Decision Making

This research investigates the multifaceted relationship underlying capital structure dynamics along with financial performance as a result of mergers and acquisitions, or M&As, in Indian banks. In the face of increasing competition, banks have deliberately embraced M&A as a strategy of improving commercial prospects and maintaining financial stability. The primary goal of this study is to examine the changes in the capital framework and financial results of banks before and after M&A transactions. The investigation, which employs a paired t-test as a method of statistical analysis, is based on a review of annual reports from selected banks over a two-year period before and after M&A transactions. The paired t-test approach allows for a thorough statistical analysis of interconnected datasets, revealing the subtle influence of M&A attempts on both bank financial performance as well as capital structure dynamics. The study's findings have the potential to add to the current body of knowledge on organisational planning, managing finances, and capital structure optimisation. The research has practical significance for financial companies, legislators, and scholars interested in understanding the profound effects of M&A inside the arena of financial institutions that operate within fiercely competitive landscapes because it provides comprehensive insights regarding the complex consequences of banking merger and acquisition (M&A) deals on capital structure as well as financial performance. Finally, the goal of this research is to provide the banking sector with educated decision-making capabilities and strategic guidance to businesses facing heightened competition while coping with the complexities of capital structure.
Asymptotics for Short Maturity Asian Options in Jump-Diffusion models with Local Volatility
Authors: Dan Pirjol, Lingjiong Zhu
Date: 2023-08-30 00:00:00

We present a study of the short maturity asymptotics for Asian options in a jump-diffusion model with a local volatility component, where the jumps are modeled as a compound Poisson process. The analysis for out-of-the-money Asian options is extended to models with L\'evy jumps, including the exponential L\'{e}vy model as a special case. Both fixed and floating strike Asian options are considered. Explicit results are obtained for the first-order asymptotics of the Asian options prices for a few popular models in the literature: the Merton jump-diffusion model, the double-exponential jump model, and the Variance Gamma model. We propose an analytical approximation for Asian option prices which satisfies the constraints from the short-maturity asymptotics, and test it against Monte Carlo simulations. The asymptotic results are in good agreement with numerical simulations for sufficiently small maturity.