pwc-archive/papers-with-abstracts huggingface dataset:
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees
Authors: Yajie Bao, Hossam S. Abbas, Javad Mohammadpour Velni
Date: 2022-06-06 00:00:00
Tasks: Bayesian Inference, Model Predictive Control, Uncertainty Quantification

This paper presents a learning- and scenario-based model predictive control (MPC) design approach for systems modeled in linear parameter-varying (LPV) framework. Using input-output data collected from the system, a state-space LPV model with uncertainty quantification is first learned through the variational Bayesian inference Neural Network (BNN) approach. The learned probabilistic model is assumed to contain the true dynamics of the system with a high probability and used to generate scenarios which ensure safety for a scenario-based MPC. Moreover, to guarantee stability and enhance performance of the closed-loop system, a parameter-dependent terminal cost and controller, as well as a terminal robust positive invariant set are designed. Numerical examples will be used to demonstrate that the proposed control design approach can ensure safety and achieve desired control performance.
Collective strategy condensation towards class-separated societies
Authors: Claudius Gros
Date: 2022-06-07 00:00:00

In physics, the wavefunctions of bosonic particles collapse when the system undergoes a Bose-Einstein condensation. In game theory, the strategy of an agent describes the probability to engage in a certain course of action. Strategies are expected to differ in competitive situations, namely when there is a penalty to do the same as somebody else. We study what happens when agents are interested how they fare not only in absolute terms, but also relative to others. This preference, denoted envy, is shown to induce the emergence of distinct social classes via a collective strategy condensation transition. Members of the lower class pursue identical strategies, in analogy to the Bose-Einstein condensation, with the upper class remaining individualistic.
Economic activity and climate change
Authors: Aránzazu de Juan, Pilar Poncela, Vladimir Rodríguez-Caballero, Esther Ruiz
Date: 2022-06-07 00:00:00
Tasks: Survey

In this paper, we survey recent econometric contributions to measure the relationship between economic activity and climate change. Due to the critical relevance of these effects for the well-being of future generations, there is an explosion of publications devoted to measuring this relationship and its main channels. The relation between economic activity and climate change is complex with the possibility of causality running in both directions. Starting from economic activity, the channels that relate economic activity and climate change are energy consumption and the consequent pollution. Hence, we first describe the main econometric contributions about the interactions between economic activity and energy consumption, moving then to describing the contributions on the interactions between economic activity and pollution. Finally, we look at the main results on the relationship between climate change and economic activity. An important consequence of climate change is the increasing occurrence of extreme weather phenomena. Therefore, we also survey contributions on the economic effects of catastrophic climate phenomena.
Cointegration and ARDL specification between the Dubai crude oil and the US natural gas market
Authors: Stavros Stavroyiannis
Date: 2022-06-03 00:00:00

This paper examines the relationship between the price of the Dubai crude oil and the price of the US natural gas using an updated monthly dataset from 1992 to 2018, incorporating the latter events in the energy markets. After employing a variety of unit root and cointegration tests, the long-run relationship is examined via the autoregressive distributed lag (ARDL) cointegration technique, along with the Toda-Yamamoto (1995) causality test. Our results indicate that there is a long-run relationship with a unidirectional causality running from the Dubai crude oil market to the US natural gas market. A variety of post specification tests indicate that the selected ARDL model is well-specified, and the results of the Toda-Yamamoto approach via impulse response functions, forecast error variance decompositions, and historical decompositions with generalized weights, show that the Dubai crude oil price retains a positive relationship and affects the US natural gas price.
Household and individual economic responses to different health shocks: The role of medical innovations
Authors: Volha Lazuka
Date: 2022-06-07 00:00:00

This study provides new evidence regarding the extent to which medical care mitigates the economic consequences of various health shocks for the individual and a wider family. To obtain causal effects, I focus on the role of medical scientific discoveries and leverage the longitudinal dimension of unique administrative data for Sweden. The results indicate that medical innovations strongly mitigate the negative economic consequences of a health shock for the individual and create spillovers to relatives. Such mitigating effects are highly heterogeneous across prognoses. These results suggest that medical innovation substantially reduces the burden of welfare costs yet produces income inequalities.
Scaling laws in global corporations as a benchmarking approach to assess environmental performance
Authors: Rossana Mastrandrea, Rob ter Burg, Yuli Shan, Klaus Hubacek, Franco Ruzzenenti
Date: 2022-06-07 00:00:00
Tasks: Benchmarking, Open-Ended Question Answering

The largest 6,529 international corporations are accountable for almost 30% of global CO2e emissions. A growing awareness of the role of the corporate world in the path toward sustainability has led many shareholders and stakeholders to pursue increasingly stringent and ambitious environmental goals. However, how to assess the corporate environmental performance objectively and efficiently remains an open question. This study reveals underlying dynamics and structures that can be used to construct a unified quantitative picture of the environmental impact of companies. This study shows that the environmental impact (metabolism) of companies CO2e energy used, water withdrawal and waste production, scales with their size according to a simple power law which is often sublinear, and can be used to derive a sector-specific, size-dependent benchmark to asses unambiguously a company's environmental performance. Enforcing such a benchmark would potentially result in a 15% emissions reduction, but a fair and effective environmental policy should consider the size of the corporation and the super or sublinear nature of the scaling relationship
Impact of micro-credit on the livelihoods of clients -- A study on Sunamganj District
Authors: Nazrul Islam
Date: 2022-06-06 00:00:00

The objective of this paper is to assess the impact of micro credit on the livelihoods of the clients in the haor area of Sunamganj district, Sylhet, Bangladesh. The major findings of the study are that 66.2 percent respondents of borrowers and 98.7 non-borrowers are head of the family and an average 76.6 percent and among the borrowers 32 percent is husband/wife while 1.3 percent of non-borrowers and on average 22.2. In terms of sex 64.7 percent of borrowers and 92.5 percent of non-borrowers are male while 35.3 percent of borrowers and 7.5 percent of non-borrowers are female. The impact of micro-credit in terms of formal and informal credit receiving households based on DID method showed that total income, total expenditure and investment have been increased 13.57 percent, 10.39 percent and 26.17 percent. All the elements of total income have been increased except debt which has been decreased by 2.39 percent. But the decrease in debt is the good sign of positive impact of debt. Consumption of food has been increased but non-food has been decreased. All the elements of investment have been increased except some factors. The savings has been decreased due excess increase in investment. The study suggested that for breaking vicious cycle of poverty by micro-credit the duration of loans should be at least five year and the volume of loans must be minimum 500,000 and repayment should at not be less than monthly. The rate of interest should not be more than 5 percent.
Pancreatic Cancer ROSE Image Classification Based on Multiple Instance Learning with Shuffle Instances
Authors: Tianyi Zhang, Youdan Feng, Yunlu Feng, Guanglei Zhang
Date: 2022-06-07 00:00:00
Tasks: Diagnostic, image-classification, Image Classification, Multiple Instance Learning

The rapid on-site evaluation (ROSE) technique can significantly ac-celerate the diagnostic workflow of pancreatic cancer by immediately analyzing the fast-stained cytopathological images with on-site pathologists. Computer-aided diagnosis (CAD) using the deep learning method has the potential to solve the problem of insufficient pathology staffing. However, the cancerous patterns of ROSE images vary greatly between different samples, making the CAD task extremely challenging. Besides, due to different staining qualities and various types of acquisition devices, the ROSE images also have compli-cated perturbations in terms of color distribution, brightness, and contrast. To address these challenges, we proposed a novel multiple instance learning (MIL) approach using shuffle patches containing the instances, which adopts the patch-based learning strategy of Vision Transformers. With the re-grouped bags of shuffle instances and their bag-level soft labels, the approach utilizes a MIL head to make the model focus on the features from the pancreatic cancer cells, rather than that from various perturbations in ROSE images. Simultaneously, combined with a classification head, the model can effectively identify the gen-eral distributive patterns across different instances. The results demonstrate the significant improvements in the classification accuracy with more accurate at-tention regions, indicating that the diverse patterns of ROSE images are effec-tively extracted, and the complicated perturbations of ROSE images are signifi-cantly eliminated. It also suggests that the MIL with shuffle instances has great potential in the analysis of cytopathological images.
Can autism be diagnosed with AI?
Authors: Ahmad Chaddad, Jiali Li, Qizong Lu, Yujie Li, Idowu Paul Okuwobi, Camel Tanougast, Christian Desrosiers, Tamim Niazi
Date: 2022-06-05 00:00:00
Tasks: Texture Classification

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks like Autism Spectrum Disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and Healthy Control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.
Introducing 4D Geometric Shell Shaping for Mitigating Nonlinear Interference Noise
Authors: Sebastiaan Goossens, Yunus Can Gültekin, Olga Vassilieva, Inwoong Kim, Paparao Palacharla, Chigo Okonkwo, Alex Alvarado
Date: 2022-06-07 00:00:00

Four dimensional geometric shell shaping (4D-GSS) is introduced as an approach for closing the nonlinearity-caused shaping gap. This format is designed at the spectral efficiency of 8 b/4D-sym and is compared against polarization-multiplexed 16QAM (PM-16QAM) and probabilistically shaped PM-16QAM (PS-PM-16QAM) in a 400ZR-compatible transmission setup with high amount of nonlinearities. Reach increase and nonlinearity tolerance are evaluated in terms of achievable information rates and post-FEC bit-error rate. Numerical simulations for a single-span, single-channel show that 4D-GSS achieves increased nonlinear tolerance and reach increase against PM-16QAM and PS-PM-16QAM when optimized for bit-metric decoding (RBMD). In terms of RBMD, gains are small with a reach increase of 1.7% compared to PM-16QAM. When optimizing for mutual information, a larger reach increase of 3% is achieved compared to PM-16QAM. Moreover, the introduced GSS scheme provides a scalable framework for designing well-structured 4D modulation formats with low complexity.
4D Geometric Shell Shaping with Applications to 400ZR
Authors: Sebastiaan Goossens, Yunus Can Gültekin, Olga Vassilieva, Inwoong Kim, Paparao Palacharla, Chigo Okonkwo, Alex Alvarado
Date: 2022-06-07 00:00:00

Geometric shell shaping is introduced and evaluated for reach increase and nonlinearity tolerance in terms of MI against PM-16QAM and PS-PM-16QAM in a 400ZR compatible transmission setup.
Quickest Change Detection in the Presence of Transient Adversarial Attacks
Authors: Thirupathaiah Vasantam, Don Towsley, Venugopal V. Veeravalli
Date: 2022-06-07 00:00:00
Tasks: Change Detection

We study a monitoring system in which the distributions of sensors' observations change from a nominal distribution to an abnormal distribution in response to an adversary's presence. The system uses the quickest change detection procedure, the Shewhart rule, to detect the adversary that uses its resources to affect the abnormal distribution, so as to hide its presence. The metric of interest is the probability of missed detection within a predefined number of time-slots after the changepoint. Assuming that the adversary's resource constraints are known to the detector, we find the number of required sensors to make the worst-case probability of missed detection less than an acceptable level. The distributions of observations are assumed to be Gaussian, and the presence of the adversary affects their mean. We also provide simulation results to support our analysis.
UAVs-Enabled Maritime Communications: Opportunities and Challenges
Authors: Muhammad Waseem Akhtar, Nasir Saeed
Date: 2022-06-07 00:00:00
Tasks: Management

The next generation of wireless communication systems will integrate terrestrial and non-terrestrial networks targeting to cover the undercovered regions, especially connecting the marine activities. Unmanned aerial vehicles (UAVs) based connectivity solutions offer significant advances to support the conventional terrestrial networks. However, the use of UAVs for maritime communication is still an unexplored area of research. Therefore, this paper highlights different aspects of UAV-based maritime communication, including the basic architecture, various channel characteristics, and use cases. The article, afterward, discusses several open research problems such as mobility management, trajectory optimization, interference management, and beamforming.
An Indoor Environment Sensing and Localization System via mmWave Phased Array
Authors: Yifei Sun, Jie Li, Tong Zhang, Rui Wang, Xiaohui Peng, Tony Xiao Han, Haisheng Tan
Date: 2022-06-07 00:00:00

An indoor layout sensing and localization system in 60GHz millimeter wave (mmWave) band, named mmReality, is elaborated in this paper. The mmReality system consists of one transmitter and one mobile receiver, each with a phased array and a single radio frequency (RF) chain. To reconstruct the room layout, the pilot signal is delivered from the transmitter to the receiver via different pairs of transmission and receiving beams, so that the signals at all antenna elements can be resolved. Then, the spatial smoothing and two-dimensional multiple signal classification (MUSIC) algorithm is applied to detect the angle-of-arrival (AoAs) and angle-of-departure (AoDs) of the rays from the transmitter to the receiver. Moreover, the technique of multi-carrier ranging is adopted to measure the distance of each propagation path. Synthesizing the above geometrical parameters, the location of receiver relative to the transmitter can be pinpointed, both line-of-sight (LoS) and non-line-of-sight (NLoS) paths can also be determined. Therefore, the room layout can be reconstructed by moving the receiver and repeating the above measurement in different locations of the room. At the end, we show that the reconstructed room layout can be utilized to locate a mobile device according to its AoA spectrum, even with single access point.
Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking
Authors: Benjamin W. Domae, Veljko Boljanovic, Ruifu Li, Danijela Cabric
Date: 2022-06-06 00:00:00
Tasks: BIG-bench Machine Learning, Compressive Sensing

Future wireless networks may operate at millimeter-wave (mmW) and sub-terahertz (sub-THz) frequencies to enable high data rate requirements. While large antenna arrays are critical for reliable communications at mmW and sub-THz bands, these antenna arrays would also mandate efficient and scalable initial beam alignment and link maintenance algorithms for mobile devices. Low-power phased-array architectures and phase-less power measurements due to high frequency oscillator phase noise pose additional challenges for practical beam tracking algorithms. Traditional beam tracking protocols require exhaustive sweeps of all possible beam directions and scale poorly with high mobility and large arrays. Compressive sensing and machine learning designs have been proposed to improve measurement scaling with array size but commonly degrade under hardware impairments or require raw samples respectively. In this work, we introduce a novel long short-term memory (LSTM) network assisted beam tracking and prediction algorithm utilizing only phase-less measurements from fixed compressive codebooks. We demonstrate comparable beam alignment accuracy to state-of-the-art phase-less beam alignment algorithms, while reducing the average number of required measurements over time.
Epidemics: towards understanding undulation and decay
Authors: Niko Sauer
Date: 2022-06-07 00:00:00

Undulation of infection levels, usually called waves, are not well understood. In this paper we propose a mathematical model that exhibits undulation and decay towards a stable state. The model is a re-interpretation of the original SIR-model obtained by postulating different constitutive relations whereby classical logistic growth with recovery is obtained. The recovery relation is based on the premise that infectiousness only lasts for some time. This leads to a differential-difference (delay) equation which intrinsically exhibits periodicity in its solutions but not necessarily decay to asymptotically stable equilibrium. Limit cycles can indeed occur. An appropriate linearization of the governing equation provides a firm basis for heuristic reasoning as well as confidence in numerical calculations.
A framework for model-assisted T x E x M exploration in maize
Authors: Jennifer Hsiao, Soo-Hyung Kim, Dennis J. Timlin, Nathaniel D. Mueller, Abigail L. S. Swann
Date: 2022-06-06 00:00:00
Tasks: Management

Breeding for new crop characteristics and adjusting management practices are critical avenues to mitigate yield loss and maintain yield stability under a changing climate. However, identifying high-performing plant traits and management options for different growing regions through traditional breeding practices and agronomic field trials is often time and resource-intensive. Mechanistic crop simulation models can serve as powerful tools to help synthesize cropping information, set breeding targets, and develop adaptation strategies to sustain food production. In this study, we develop a modeling framework for a mechanistic crop model (MAIZSIM) to run many simulations within a trait x environment x management landscape and demonstrate how such a modeling framework could be used to identify ideal trait-management combinations that maximize yield and yield stability for different agro-climate regions in the US.
Petri nets in epidemiology
Authors: Carlos Segovia
Date: 2022-05-30 00:00:00
Tasks: Epidemiology

This work provides a geometric version of the next-generation matrix method for obtaining the basic reproduction number of an epidemiological model. We exhibit a certain correspondence between any system of ODEs and Petri nets. We observe that any epidemiological model has the basic structures found in the SIR model of Kermack-McKendrick. This means that the basic reproduction number depends only on three substructures inside the Petri net, which are also given by three Petri nets inside, representing the susceptible population, the infection process, and the infected population. The five assumptions of the next-generation matrix method given by van den Driessche-Watmough can be described geometrically using Petri nets. Thus, the next-generation matrix results in a matrix of flows between the infection compartments with a dominant eigenvalue given by the basic reproduction number.
Current status of antihistamines repurposing for infectious diseases
Authors: Bruno L. Travi
Date: 2022-06-07 00:00:00

Objectives. This review gathers information on the potential role of antihistamines as anti-infective agents and identifies gaps in research that have impaired its applicability in human health. Methods. The literature search encompassed MEDLINE, PubMed and Google Scholar from 1990 to 2022. Results. The literature search identified 12 antihistamines with activity against different pathogens. Eight molecules were second-generation antihistamines with intrinsically lower tendency to cross the blood brain barrier thereby with reduced side effects. Only five antihistamines had in vivo evaluations in rodents while one study utilized a wax moth model to determine astemizole anti-Cryptococcus sp. activity combined with fluconazole. In vitro studies showed that clemastine was active against Plasmodium, Leishmania, and Trypanosoma, while terfenadine suppressed Candida spp. and Staphylococcus aureus growth. In vitro assays found that SARS-coV-2 was inhibited by doxepin, azelastine, desloratadine, and clemastine. Different antihistamines inhibited Ebola virus (diphenhydramine, chlorcyclizine), Hepatitis C virus (chlorcyclizine), and Influenza virus (carbinoxamine, chlorpheniramine). Generally, in vitro activity (IC50) of antihistamines was in the low to sub-microM range, except for Staphylococcus epidermidis (loratadine MIC=50 microM) and SARS-coV-2 (desloratadine 70% inhibition at 20 microM). Conclusion. Many antihistamine drugs showed potential to progress to clinical trials based on in vitro data and availability of toxicological and pharmacological data. However, the overall lack of systematic preclinical trials has hampered the advance of repurposed antihistamines for off label evaluation. The low interest of pharmaceutical companies has to be counterbalanced through collaborations between research groups, granting agencies and government to support the needed clinical trials.