pwc-archive/papers-with-abstracts huggingface dataset:
AERIQ: SDR-Based LTE I/Q Measurement and Analysis Framework for Air-to-Ground Propagation Modeling
Authors: Sung Joon Maeng, Ozgur Ozdemir, İsmail Güvenç, Mihail Sichitiu, Rudra Dutta, Magreth Mushi
Date: 2022-10-14 00:00:00

In this paper, we introduce AERIQ: a software-defined radio (SDR) based I/Q measurement and analysis framework for wireless signals for aerial experimentation. AERIQ is integrated into controllable aerial vehicles, it is flexible, repeatable, and provides raw I/Q samples for post-processing the data to extract various key parameters of interest (KPIs) over a 3D volume. Using SDRs, we collect I/Q data with unmanned aerial vehicles (UAVs) flying at various altitudes in a radio dynamic zone (RDZ) like outdoor environment, from a 4G LTE eNB that we configure to operate at 3.51 GHz. Using the raw I/Q samples, and using Matlab's LTE Toolbox, we provide a step-by-step description for frequency offset estimation/correction, synchronization, cell search, channel estimation, and reference signal received power (RSRP). We provide various representative results for each step, such as RSRP measurements and corresponding analytical approximation at different UAV altitudes, coherence bandwidth and coherence time of the channel at different UAV altitudes and link distances, and kriging based 3D RSRP interpolation. The collected raw data as well as the software developed for obtaining and post-processing such data are provided publicly for potential use by other researchers. AERIQ is also available in emulation and testbed environments for external researchers to access and use as part of the NSF AERPAW platform at NC State University.
Effect of Standing Wave on Terahertz Channel Model
Authors: Ha Tran, Thanh Le, Suresh Singh
Date: 2022-10-13 00:00:00

There is a growing interest in exploiting the terahertz frequency band for future communication systems that demand high data rates. Given the complex propagation behavior of this frequency band, various researchers have developed channel models that can be utilized in the development of communication systems. These models however do not include a crucial aspect of terahertz propagation at short distances: the presence of standing waves. Our measurements show that at specific distances, the effect of standing waves is significant. In this paper, we extend previous terahertz channel models to include the effect of standing waves and show a good fit with our measurements. Our measurements and modeling cover the five most promising terahertz frequency bands: 140, 220, 340, 410, 460 GHz.
Reflection Channel Model for Terahertz Communications
Authors: Thanh Le, Ha Tran, Suresh Singh
Date: 2022-10-13 00:00:00
Tasks: model

Terahertz frequencies are an untapped resource for providing high-speed short-range communications. As a result, it is of interest to study the propagation characteristics of terahertz waves and to develop channel models. In previous work we used a measurement-based approach to develop an accurate channel model for line of sight (LoS) links. In this paper we extend that work by developing channel models for non-line of sight (NLoS) links where the signal suffers one reflection. We study reflections that occur off a metal plate as well as a piece of wood. Our model for received magnitude includes the effects of standing waves that develop between the transmitter and receiver. Measurements show an excellent agreement between empirical data and the model. In addition, we have analyzed the received phase of the reflected signal at frequencies in the range 320- 480 GHz. We observed a linear error between the predicted and actual phase and developed a model to accommodate that discrepancy. The final model we have developed for predicting received phase is very accurate for the entire range 320 - 480 GHz and for both materials.
The Coming of Age of Nucleic Acid Vaccines during COVID-19
Authors: Halie M. Rando, Ronan Lordan, Likhitha Kolla, Elizabeth Sell, Alexandra J. Lee, Nils Wellhausen, Amruta Naik, Jeremy P. Kamil, COVID-19 Review Consortium, Anthony Gitter, Casey S. Greene
Date: 2022-10-14 00:00:00

In the 21st century, several emergent viruses have posed a global threat. Each pathogen has emphasized the value of rapid and scalable vaccine development programs. The ongoing SARS-CoV-2 pandemic has made the importance of such efforts especially clear. New biotechnological advances in vaccinology allow for recent advances that provide only the nucleic acid building blocks of an antigen, eliminating many safety concerns. During the COVID-19 pandemic, these DNA and RNA vaccines have facilitated the development and deployment of vaccines at an unprecedented pace. This success was attributable at least in part to broader shifts in scientific research relative to prior epidemics; the genome of SARS-CoV-2 was available as early as January 2020, facilitating global efforts in the development of DNA and RNA vaccines within two weeks of the international community becoming aware of the new viral threat. Additionally, these technologies that were previously only theoretical are not only safe but also highly efficacious. Although historically a slow process, the rapid development of vaccines during the COVID-19 crisis reveals a major shift in vaccine technologies. Here, we provide historical context for the emergence of these paradigm-shifting vaccines. We describe several DNA and RNA vaccines and in terms of their efficacy, safety, and approval status. We also discuss patterns in worldwide distribution. The advances made since early 2020 provide an exceptional illustration of how rapidly vaccine development technology has advanced in the last two decades in particular and suggest a new era in vaccines against emerging pathogens.
Scalable lipid droplet microarray fabrication, validation, and screening
Authors: Tracey N. Bell, Aubrey E. Kusi-Appiaha, Pengfei Lyu, L. Zhu, F. Zhu, David Van Winkle, Hongyuan Cao, M. Singh, Steven Lenhert
Date: 2022-10-13 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Drug Discovery

High throughput screening of small molecules and natural products is costly, requiring significant amounts of time, reagents, and operating space. Although microarrays have proven effective in the miniaturization of screening for certain biochemical assays, such as nucleic acid hybridization or antibody binding, they are not widely used for drug discovery in cell culture due to the need for cells to internalize lipophilic drug candidates. Lipid droplet microarrays are a promising solution to this problem as they are capable of delivering lipophilic drugs to cells at dosages comparable to solution delivery. However, the scalablility of the array fabrication, assay validation, and screening steps has limited the utility of this approach. Here we demonstrate a scalable process for lipid droplet array fabrication, assay validation in cell culture, and drug screening. A nanointaglio printing process has been adapted for use with a printing press. The arrays are stabilized for immersion into aqueous solution using a vapor coating process. In addition to delivery of lipophilic compounds, we found that we are also able to encapsulate and deliver a water-soluble compound in this way. The arrays can be functionalized by extracellular matrix proteins such as collagen prior to cell culture as the mechanism for uptake is based on direct contact with the lipid delivery vehicles rather than diffusion of the drug out of the microarray spots. We demonstrate this method for delivery to 3 different cell types and the screening of 90 natural product extracts on a microarray covering an area of less than 0.1 cm2. The arrays are suitable for miniaturized screening, for instance in BSL-3 conditions where space is limited and for applications where cell numbers are limited, such as in functional precision medicine.
Neurobiology and Changing Ecosystems: toward understanding the impact of anthropogenic influences on neurons and circuits
Authors: Angie Michaiel, Amy Bernard
Date: 2022-10-14 00:00:00

Rapid anthropogenic environmental changes, including those due to habitat contamination, degradation, and climate change, have far-reaching effects on biological systems that may outpace animals' adaptive responses (Radchuk et al., 2019). Neurobiological systems mediate interactions between animals and their environments and evolved over millions of years to detect and respond to change. To gain an understanding of the adaptive capacity of nervous systems given and unprecedented pace of environmental change, mechanisms of physiology and behavior at the cellular and biophysical level must be examined. While behavioral changes resulting from anthropogenic activity are becoming increasingly described, identification and examination of the cellular, molecular, and circuit-level processes underlying those changes are profoundly under-explored. Hence, the field of neuroscience lacks predictive frameworks to describe which neurobiology systems may be resilient or vulnerable to rapidly changing ecosystems, or what modes of adaptation are represented in our natural world. In this review, we highlight examples of animal behavior modification and corresponding nervous system adaptation in response to rapid environmental change. The underlying cellular, molecular, and circuit-level component processes underlying these behaviors are not known and emphasize the unmet need for rigorous scientific enquiry into the neurobiology of changing ecosystems.
A Neural Model of Number Comparison with Surprisingly Robust Generalization
Authors: Thomas R. Shultz, Ardavan S. Nobandegani, Zilong Wang
Date: 2022-10-13 00:00:00

We propose a relatively simple computational neural-network model of number comparison. Training on comparisons of the integers 1-9 enable the model to efficiently and accurately simulate a wide range of phenomena, including distance and ratio effects and robust generalization to multidigit integers, negative numbers, and decimal numbers. An accompanying logical model of number comparison provides further insights into the workings of number comparison and its relation to the Arabic number system. These models provide a rational basis for the psychology of number comparison and the ability of neural networks to efficiently learn a powerful system with robust generalization.
A Topological Data Analysis Study on Murine Pulmonary Arterial Trees with Pulmonary Hypertension
Authors: Megan Chambers, Natalie Johnston, Ian Livengood, Miya Spinelli, Radmila Sazdanovic, Mette S Olufsen
Date: 2022-10-13 00:00:00
Tasks: Topological Data Analysis

Pulmonary hypertension (PH), defined by a mean pulmonary arterial blood pressure above 20 mmHg, is a cardiovascular disease impacting the pulmonary vasculature. PH is accompanied by vascular remodeling, wherein vessels become stiffer, large vessels dilate, and smaller vessels constrict. Some types of PH, including hypoxia-induced PH (HPH), lead to microvascular rarefaction. The goal of this study is to analyze the change in pulmonary arterial network morphometry in the presence of HPH. To do so, we use novel methods from topological data analysis (TDA), employing persistent homology to quantify arterial network morphometry for control and hypertensive mice. These methods are used to characterize arterial trees extracted from micro-computed tomography (micro-CT) images. To compare results between control and hypertensive animals, we normalize generated networks using three pruning algorithms. This proof-of-concept study shows that the pruning methods effects the spatial tree statistics and complexities of the trees. Results show that HPH trees have higher depth and that the directional complexities correlate with branch number, except for trees pruned by vessel radius, where the left and anterior complexity are lower compared to control trees. While more data is required to make a conclusion about the overall effect of HPH on network topology, this study provides a framework for analyzing the topology of biological networks and is a step towards the extraction of relevant information for diagnosing and detecting HPH.
TechRank
Authors: Anita Mezzetti, Loïc Maréchal, Dimitri Percia David, William Lacube, Sébastien Gillard, Michael Tsesmelis, Thomas Maillart, Alain Mermoud
Date: 2022-10-14 00:00:00
Tasks: Management

We introduce TechRank, a recursive algorithm based on a bi-partite graph with weighted nodes. We develop TechRank to link companies and technologies based on the method of reflection. We allow the algorithm to incorporate exogenous variables that reflect an investor's preferences. We calibrate the algorithm in the cybersecurity sector. First, our results help estimate each entity's influence and explain companies' and technologies' ranking. Second, they provide investors with a quantitative optimal ranking of technologies and thus, help them design their optimal portfolio. We propose this method as an alternative to traditional portfolio management and, in the case of private equity investments, as a new way to price assets for which cash flows are not observable.
Impact of WACC on Firm Profitability: Evidence from Food and Allied Industry of Bangladesh
Authors: Farhana Rahman
Date: 2022-10-14 00:00:00

The research paper aims to analyze the underlying relationship in between the profitability and cost of funds of a firm. A total of twelve companies were selected as a sample for this study which are listed in Dhaka Stock Exchange under Food and Allied Industry. A panel data set of 15 years from 2005 to 2019 was used to conduct the necessary analysis. In this paper, Return on Asset (ROA) is used as the accounting criteria of profitability. WACC is the independent variable while Firm Size, Firm Age and Firm Leverage are used as control variable for the study. Fixed Effects Panel Regression Model is used to analyze the dataset. The result of the analysis shows that WACC is negatively related with the profitability measure and this relationship is significant. The study has potential to be replicated by other industries like textile, cement, pharmaceutical & chemical, fuel & power, tannery etc.
Non-fungible token transactions: data and challenges
Authors: Jason B. Cho, Sven Serneels, David S. Matteson
Date: 2022-10-13 00:00:00

Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both owing to the non-fungible nature of the tokens and to a blockchain being the primary data source, NFT transaction data pose several challenges not commonly encountered in traditional financial data. Using data that consist of the transaction history of eight highly valued NFT collections, a selection of such challenges is illustrated. These are: price differentiation by token traits, the possible existence of lateral swaps and wash trades in the transaction history and finally, severe volatility. While this paper merely scratches the surface of how data analytics can be applied in this context, the data and challenges laid out here may present opportunities for future research on the topic.
Wild Animal Classifier Using CNN
Authors: Sahil Faizal, Sanjay Sundaresan
Date: 2022-10-03 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Classification and identification of wild animals for tracking and protection purposes has become increasingly important with the deterioration of the environment, and technology is the agent of change which augments this process with novel solutions. Computer vision is one such technology which uses the abilities of artificial intelligence and machine learning models on visual inputs. Convolution neural networks (CNNs) have multiple layers which have different weights for the purpose of prediction of a particular input. The precedent for classification, however, is set by the image processing techniques which provide nearly ideal input images that produce optimal results. Image segmentation is one such widely used image processing method which provides a clear demarcation of the areas of interest in the image, be it regions or objects. The Efficiency of CNN can be related to the preprocessing done before training. Further, it is a well-established fact that heterogeneity in image sources is detrimental to the performance of CNNs. Thus, the added functionality of heterogeneity elimination is performed by the image processing techniques, introducing a level of consistency that sets the tone for the excellent feature extraction and eventually in classification.
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion
Authors: Lev Grossman, Brian Plancher
Date: 2022-10-14 00:00:00
Tasks: Deep Reinforcement Learning, Quantization, reinforcement-learning, Reinforcement Learning (RL)

Deep reinforcement learning (DRL) is one of the most powerful tools for synthesizing complex robotic behaviors. But training DRL models is incredibly compute and memory intensive, requiring large training datasets and replay buffers to achieve performant results. This poses a challenge for the next generation of field robots that will need to learn on the edge to adapt to their environment. In this paper, we begin to address this issue through observation space quantization. We evaluate our approach using four simulated robot locomotion tasks and two state-of-the-art DRL algorithms, the on-policy Proximal Policy Optimization (PPO) and off-policy Soft Actor-Critic (SAC) and find that observation space quantization reduces overall memory costs by as much as 4.2x without impacting learning performance.
Instance Segmentation with Cross-Modal Consistency
Authors: Alex Zihao Zhu, Vincent Casser, Reza Mahjourian, Henrik Kretzschmar, Sören Pirk
Date: 2022-10-14 00:00:00
Tasks: Autonomous Driving, Contrastive Learning, Instance Segmentation, Object Tracking, Segmentation, Semantic Segmentation

Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance segmentation that jointly leverages measurements from multiple sensor modalities, such as cameras and LiDAR. Our method learns to predict embeddings for each pixel or point that give rise to a dense segmentation of the scene. Specifically, our technique applies contrastive learning to points in the scene both across sensor modalities and the temporal domain. We demonstrate that this formulation encourages the models to learn embeddings that are invariant to viewpoint variations and consistent across sensor modalities. We further demonstrate that the embeddings are stable over time as objects move around the scene. This not only provides stable instance masks, but can also provide valuable signals to downstream tasks, such as object tracking. We evaluate our method on the Cityscapes and KITTI-360 datasets. We further conduct a number of ablation studies, demonstrating benefits when applying additional inputs for the contrastive loss.
Cross-domain Variational Capsules for Information Extraction
Authors: Akash Nagaraj, Akhil K, Akshay Venkatesh, Srikanth HR
Date: 2022-10-13 00:00:00

In this paper, we present a characteristic extraction algorithm and the Multi-domain Image Characteristics Dataset of characteristic-tagged images to simulate the way a human brain classifies cross-domain information and generates insight. The intent was to identify prominent characteristics in data and use this identification mechanism to auto-generate insight from data in other unseen domains. An information extraction algorithm is proposed which is a combination of Variational Autoencoders (VAEs) and Capsule Networks. Capsule Networks are used to decompose images into their individual features and VAEs are used to explore variations on these decomposed features. Thus, making the model robust in recognizing characteristics from variations of the data. A noteworthy point is that the algorithm uses efficient hierarchical decoding of data which helps in richer output interpretation. Noticing a dearth in the number of datasets that contain visible characteristics in images belonging to various domains, the Multi-domain Image Characteristics Dataset was created and made publicly available. It consists of thousands of images across three domains. This dataset was created with the intent of introducing a new benchmark for fine-grained characteristic recognition tasks in the future.
Break The Spell Of Total Correlation In betaTCVAE
Authors: Zihao Chen, Wenyong Wang, Sai Zou
Date: 2022-10-17 00:00:00
Tasks: Disentanglement, Representation Learning

In the absence of artificial labels, the independent and dependent features in the data are cluttered. How to construct the inductive biases of the model to flexibly divide and effectively contain features with different complexity is the main focal point of unsupervised disentangled representation learning. This paper proposes a new iterative decomposition path of total correlation and explains the disentangled representation ability of VAE from the perspective of model capacity allocation. The newly developed objective function combines latent variable dimensions into joint distribution while relieving the independence constraints of marginal distributions in combination, leading to latent variables with a more manipulable prior distribution. The novel model enables VAE to adjust the parameter capacity to divide dependent and independent data features flexibly. Experimental results on various datasets show an interesting relevance between model capacity and the latent variable grouping size, called the "V"-shaped best ELBO trajectory. Additionally, we empirically demonstrate that the proposed method obtains better disentangling performance with reasonable parameter capacity allocation.
Digital Image Forensics using Deep Learning
Authors: Akash Nagaraj, Mukund Sood, Vivek Kapoor, Yash Mathur, Bishesh Sinha
Date: 2022-10-14 00:00:00
Tasks: Deep Learning, Image Forensics

During the investigation of criminal activity when evidence is available, the issue at hand is determining the credibility of the video and ascertaining that the video is real. Today, one way to authenticate the footage is to identify the camera that was used to capture the image or video in question. While a very common way to do this is by using image meta-data, this data can easily be falsified by changing the video content or even splicing together content from two different cameras. Given the multitude of solutions proposed to this problem, it is yet to be sufficiently solved. The aim of our project is to build an algorithm that identifies which camera was used to capture an image using traces of information left intrinsically in the image, using filters, followed by a deep neural network on these filters. Solving this problem would have a big impact on the verification of evidence used in criminal and civil trials and even news reporting.
Real-Time Automated Answer Scoring
Authors: Akash Nagaraj, Mukund Sood, Gowri Srinivasa
Date: 2022-10-13 00:00:00
Tasks: Multiple-choice

In recent years, the role of big data analytics has exponentially grown and is now slowly making its way into the education industry. Several attempts are being made in this sphere in order to improve the quality of education being provided to students and while many collaborations have been carried out before, automated scoring of answers has been explored to a rather limited extent. One of the biggest hurdles to choosing constructed-response assessments over multiple-choice assessments is the effort and large cost that comes with their evaluation and this is precisely the issue that this project aims to solve. The aim is to accept raw-input from the student in the form of their answer, preprocess the answer, and automatically score the answer. In addition, we have made this a real-time system that captures "snapshots" of the writer's progress with respect to the answer, allowing us to unearth trends with respect to the way a student thinks, and how the student has arrived at their final answer.