pwc-archive/papers-with-abstracts huggingface dataset:
Early life exposure to measles and later-life outcomes: Evidence from the introduction of a vaccine
Authors: Gerard J. van den Berg, Stephanie von Hinke, Nicolai Vitt
Date: 2023-01-25 00:00:00

Until the mid 1960s, the UK experienced regular measles epidemics, with the vast majority of children being infected in early childhood. The introduction of a measles vaccine substantially reduced its incidence. The first part of this paper examines the long-term human capital and health effects of this change in the early childhood disease environment. The second part investigates interactions between the vaccination campaign and individuals' endowments as captured using molecular genetic data, shedding light on complementarities between public health investments and individual endowments. We use two identification approaches, based on the nationwide introduction of the vaccine in 1968 and local vaccination trials in 1966. Our results show that exposure to the vaccination in early childhood positively affects adult height, but only among those with high genetic endowments for height. We find no effects on years of education; neither a direct effect, nor evidence of complementarities.
Memory-Aware Social Learning under Partial Information Sharing
Authors: Michele Cirillo, Virginia Bordignon, Vincenzo Matta, Ali H. Sayed
Date: 2023-01-25 00:00:00
Tasks: valid

This work examines a social learning problem, where dispersed agents connected through a network topology interact locally to form their opinions (beliefs) as regards certain hypotheses of interest. These opinions evolve over time, since the agents collect observations from the environment, and update their current beliefs by accounting for: their past beliefs, the innovation contained in the new data, and the beliefs received from the neighbors. The distinguishing feature of the present work is that agents are constrained to share opinions regarding only a single hypothesis. We devise a novel learning strategy where each agent forms a valid belief by completing the partial beliefs received from its neighbors. This completion is performed by exploiting the knowledge accumulated in the past beliefs, thanks to a principled memory-aware rule inspired by a Bayesian criterion. The analysis allows us to characterize the role of memory in social learning under partial information sharing, revealing novel and nontrivial learning dynamics. Surprisingly, we establish that the standard classification rule based on selecting the maximum belief is not optimal under partial information sharing, while there exists a consistent threshold-based decision rule that allows each agent to classify correctly the hypothesis of interest. We also show that the proposed strategy outperforms previously considered schemes, highlighting that the introduction of memory in the social learning algorithm is critical to overcome the limitations arising from sharing partial information.
RIS Meets Aerodynamic HAPS: A Multi-objective Optimization Approach
Authors: Arman Azizi, Arman Farhang
Date: 2023-01-25 00:00:00

In this paper, we propose a novel network architecture for integrating terrestrial and non-terrestrial networks (NTNs) to establish connection between terrestrial ground stations which are unconnected due to blockage. We propose a new network framework where reconfigurable intelligent surface (RIS) is mounted on an aerodynamic high altitude platform station (HAPS), referred to as aerodynamic HAPS-RIS. This can be one of the promising candidates among non-terrestrial RIS (NT-RIS) platforms. We formulate a mathematical model of the cascade channel gain and time-varying effects based on the predictable mobility of the aerodynamic HAPS-RIS. We propose a multi-objective optimization problem for designing the RIS phase shifts to maximize the cascade channel gain while forcing the Doppler spread to zero, and minimizing the delay spread upper bound. Considering an RIS reference element, we find a closed-form solution to this optimization problem based on the Pareto optimality of the aforementioned objective functions. Finally, we evaluate and show the effective performance of our proposed closed-form solution through numerical simulations.
Optimal Prototype Filter Design Based on New Mathematical Model of GFDM System
Authors: Behzad Mozaffari Tazehkand, Mohammad Reza Ghavidel Aghdam, Reza Abdolee, Aysan Kamyab
Date: 2023-01-25 00:00:00

We introduce a new perspective and mathematical model for the generalized frequency division multiplexing (GFDM) to analyze the effect of self-interferences and remove their detrimental impacts. Using this model, we express the GFDM signal in a vector form where the self-interferences are represented using two independent terms. For the first time, we model these interferences in terms of prototype filter parameters in the frequency domain and derive their analytical expressions. Building on these expressions, we propose the idea of optimal filter design as one of the solutions to annihilate self-interference in GFDM systems. We introduce the relationship between the required bandwidth for each subcarrier and the number of subsymbols in the proposed optimal prototype filter and show how the prototype filter optimally exploits the total system bandwidth. We derive analytical expressions to evaluate the bit error rate (BER) of the system and show how it outperforms its recent counterparts in the literature. We also perform computer simulations to verify the analytical results and find that the analytical results match well with those of the computer simulations.
On solving decision and risk management problems subject to uncertainty
Authors: Alexander Gutfraind
Date: 2023-01-18 00:00:00
Tasks: Management

Uncertainty is a pervasive challenge in decision and risk management and it is usually studied by quantification and modeling. Interestingly, engineers and other decision makers usually manage uncertainty with strategies such as incorporating robustness, or by employing decision heuristics. The focus of this paper is then to develop a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them. Based on a review of a dataset of 100 decision problems, this paper found that many decision problems have pivotal properties, i.e. properties that enable solution strategies, and finds 14 such properties. Therefore, an analyst can first find these properties in a given problem, and then utilize the strategies they enable. Multi-objective optimization methods could be used to make investment decisions quantitatively. The analytical complexity of decision problems can also be scored by evaluating how many of the pivotal properties are available. Overall, we find that in the light of pivotal properties, complex problems under uncertainty frequently appear surprisingly tractable.
Recent Trend of Nanotechnology Applications to Improve Bio-accessibility of Lycopene by Nanocarrier: A Review
Authors: Mohammad Anwar Ul Alam, Mhamuda Khatun, Mohammad Arif Ul Alam
Date: 2023-01-25 00:00:00

Lycopene, rich in red, yellow, or orange-colored fruits and vegetables, is the most potent antioxidant among the other carotenoids available in human blood plasma. It is evident that regular lycopene intake can prevent chronic diseases like cardiovascular diseases, type-2 diabetes, hypertension, kidney diseases and cancer. However, thermal processing, light, oxygen, and enzymes in gastrointestinal tract (GIT) compromise the bioaccessibility and bioavailability of lycopene ingested through diet. Nanoencapsulation provides a potential platform to prevent lycopene from light, air oxygen, thermal processing and enzymatic activity of the human digestive system. Physicochemical properties evidenced to be the potential indicator for determining the bioaccessibility of encapsulated bioactive compounds like lycopene. By manipulating the size or hydrodynamic diameter, zeta potential value or stability, polydispersity index or homogeneity and functional activity or retention of antioxidant properties observed to be the most prominent physicochemical properties to evaluate beneficial effect of implementation of nanotechnology on bioaccessibility study. Moreover, the molecular mechanism of the bioavailability of nanoparticles is not yet to be understood due to lack of comprehensive design to identify nanoparticles' behaviors if ingested through oral route as functional food ingredients. This review paper aims to study and leverage existing techniques about how nanotechnology can be used and verified to identify the bioaccessibility of lycopene before using it as a functional food ingredient for therapeutic treatments.
Predicting attractors from spectral properties of stylized gene regulatory networks
Authors: Dzmitry Rumiantsau, Annick Lesne, Marc-Thorsten Hütt
Date: 2023-01-25 00:00:00
Tasks: Open-Ended Question Answering

How the architecture of gene regulatory networks ultimately shapes gene expression patterns is an open question, which has been approached from a multitude of angles. The dominant strategy has been to identify non-random features in these networks and then argue for the function of these features using mechanistic modelling. Here we establish the foundation of an alternative approach by studying the correlation of eigenvectors with synthetic gene expression data simulated with a basic and popular model of gene expression dynamics -- attractors of Boolean threshold dynamics in signed directed graphs. Eigenvectors of the graph Laplacian are known to explain collective dynamical states (stationary patterns) in Turing dynamics on graphs. In this study, we show that eigenvectors can also predict collective states (attractors) for a markedly different type of dynamics, Boolean threshold dynamics, and category of graphs, signed directed graphs. However, the overall predictive power depends on details of the network architecture, in a predictable fashion. Our results are a set of statistical observations, providing the first systematic step towards a further theoretical understanding of the role of eigenvectors in dynamics on graphs.
Neural Mechanisms of Temporal and Rhythmic Structure Processing in Non-Musicians
Authors: Grigoriy Radchenko, Valeriia Demareva, Kirill Gromov, Irina Zayceva, Artem Rulev, Marina Zhukova, Andrey Demarev
Date: 2023-01-25 00:00:00

Music is increasingly being used as a therapeutic tool in the field of rehabilitation medicine and psychophysiology. One of the main key components of music is its temporal organization. The characteristics of neurocognitive processes during music perception of meter in different tempo variations technique have been studied by using the event-related potentials technique. The study involved 20 volunteers (6 men, the median age of the participants was 23 years). The participants were asked to listen to 4 experimental series that differed in tempo (fast vs. slow) and meter (duple vs. triple). Each series consisted of 625 audio stimuli, 85% of which were organized with a standard metric structure (standard stimulus) while 15% included unexpected accents (deviant stimulus). The results revealed that the type of metric structure influences the detection of the change in stimuli. The analysis showed that the N200 wave occurred significantly faster for stimuli with duple meter and fast tempo and was the slowest for stimuli with triple meter and fast pace.
Fundamental Lack of Information in Observed Disease and Hospitalization Data
Authors: Adam Mielke, Lasse Engbo Christiansen
Date: 2023-01-25 00:00:00

We present a proof based on SEIR-models that shows it is impossible to identify the level of under reporting based on traditional observables of the disease dynamics alone. This means that the true attack rate must be determined through other means.
Individualized prescriptive inference in ischaemic stroke
Authors: Dominic Giles, Robert Gray, Chris Foulon, Guilherme Pombo, James K. Ruffle, Tianbo Xu, H. Rolf Jäger, Jorge Cardoso, Sebastien Ourselin, Geraint Rees, Ashwani Jha, Parashkev Nachev
Date: 2023-01-25 00:00:00
Tasks: 4k, counterfactual

The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, based on simple descriptions of presumptively homogeneous populations. Yet the manifest complexity of the brain's functional, connective, and vascular architectures introduces heterogeneities that violate the underlying statistical premisses, potentially leading to substantial errors at both individual and population levels. The counterfactual nature of interventional inference renders quantifying the impact of this defect difficult. Here we conduct a comprehensive series of semi-synthetic, biologically plausible, virtual interventional trials across 100M+ distinct simulations. We generate empirically grounded virtual trial data from large-scale meta-analytic connective, functional, genetic expression, and receptor distribution data, with high-resolution maps of 4K+ acute ischaemic lesions. Within each trial, we estimate treatment effects using models varying in complexity, in the presence of increasingly confounded outcomes and noisy treatment responses. Individualized prescriptions inferred from simple models, fitted to unconfounded data, were less accurate than those from complex models, fitted to confounded data. Our results indicate that complex modelling with richly represented lesion data is critical to individualized prescriptive inference in ischaemic stroke.
Valuation of the Convertible Bonds under Penalty TF model using Finite Element Method
Authors: Rakhymzhan Kazbek, Yogi Erlangga, Yerlan Amanbek, Dongming Wei
Date: 2023-01-25 00:00:00

In this paper, the TF system of two-coupled Black-Scholes equations for pricing the convertible bonds is solved numerically by using the P1 and P2 finite elements with the inequality constraints approximated by the penalty method. The corresponding finite element ODE system is numerically solved by using a modified Crank-Nicolson scheme, in which the non-linear system is solved at each time step by the Newton-Raphson method for non-smooth functions. Moreover, the corresponding Greeks are also calculated by taking advantage of the P1-P2 finite element approximation functions. Numerical solutions by the finite element method compare favorably with the solutions by the finite difference method in literature.
A Fast Feature Point Matching Algorithm Based on IMU Sensor
Authors: Lu Cao
Date: 2023-01-03 00:00:00
Tasks: Simultaneous Localization and Mapping

In simultaneous localization and mapping (SLAM), image feature point matching process consume a lot of time. The capacity of low-power systems such as embedded systems is almost limited. It is difficult to ensure the timely processing of each image information. To reduce time consuming when matching feature points in SLAM, an algorithm of using inertial measurement unit (IMU) to optimize the efficiency of image feature point matching is proposed. When matching two image feature points, the presented algorithm does not need to traverse the whole image for matching feature points, just around the predicted point within a small range traversal search to find matching feature points. After compared with the traditional algorithm, the experimental results show that this method has greatly reduced the consumption of image feature points matching time. All the conclusions will help research how to use the IMU optimize the efficiency of image feature point matching and improve the real-time performance in SLAM.
SPINA Carb: a simple mathematical model supporting fast in-vivo estimation of insulin sensitivity and beta cell function
Authors: Dietrich JW, Dasgupta R, Anoop S, Jebasingh F, Kurian ME, Inbakumari M, Boehm BO, Thomas N
Date: 2022-10-21 00:00:00
Proceeding: Scientific Reports 2022 10
Tasks: Diagnostic, Sensitivity

Modelling insulin-glucose homeostasis may provide novel functional insights. In particular, simple models are clinically useful if they yield diagnostic methods. Examples include the homeostasis model assessment (HOMA) and the quantitative insulin sensitivity check index (QUICKI). However, limitations of these approaches have been criticised. Moreover, recent advances in physiological and biochemical research prompt further refinement in this area. We have developed a nonlinear model based on fundamental physiological motifs, including saturation kinetics, non-competitive inhibition, and pharmacokinetics. This model explains the evolution of insulin and glucose concentrations from perturbation to steady-state. Additionally, it lays the foundation of a structure parameter inference approach (SPINA), providing novel biomarkers of carbohydrate homeostasis, namely the secretory capacity of beta-cells (SPINA-GBeta) and insulin receptor gain (SPINA-GR). These markers correlate with central parameters of glucose metabolism, including average glucose infusion rate in hyperinsulinemic glucose clamp studies, response to oral glucose tolerance testing and HbA1c. Moreover, they mirror multiple measures of body composition. Compared to normal controls, SPINA-GR is significantly reduced in subjects with diabetes and prediabetes. The new model explains important physiological phenomena of insulin-glucose homeostasis. Clinical validation suggests that it may provide an efficient biomarker panel for screening purposes and clinical research.
Distilling Cognitive Backdoor Patterns within an Image
Authors: Hanxun Huang, Xingjun Ma, Sarah Erfani, James Bailey
Date: 2023-01-26 00:00:00

This paper proposes a simple method to distill and detect backdoor patterns within an image: \emph{Cognitive Distillation} (CD). The idea is to extract the "minimal essence" from an input image responsible for the model's prediction. CD optimizes an input mask to extract a small pattern from the input image that can lead to the same model output (i.e., logits or deep features). The extracted pattern can help understand the cognitive mechanism of a model on clean vs. backdoor images and is thus called a \emph{Cognitive Pattern} (CP). Using CD and the distilled CPs, we uncover an interesting phenomenon of backdoor attacks: despite the various forms and sizes of trigger patterns used by different attacks, the CPs of backdoor samples are all surprisingly and suspiciously small. One thus can leverage the learned mask to detect and remove backdoor examples from poisoned training datasets. We conduct extensive experiments to show that CD can robustly detect a wide range of advanced backdoor attacks. We also show that CD can potentially be applied to help detect potential biases from face datasets. Code is available at \url{https://github.com/HanxunH/CognitiveDistillation}.
Domain-Agnostic Molecular Generation with Chemical Feedback
Authors: Yin Fang, Ningyu Zhang, Zhuo Chen, Lingbing Guo, Xiaohui Fan, Huajun Chen
Date: 2023-01-26 00:00:00
Tasks: Drug Design, Language Modeling, Language Modelling, Molecular Docking, Transfer Learning

The generation of molecules with desired properties has become increasingly popular, revolutionizing the way scientists design molecular structures and providing valuable support for chemical and drug design. However, despite the potential of language models in molecule generation, they face challenges such as generating syntactically or chemically flawed molecules, having narrow domain focus, and struggling to create diverse and feasible molecules due to limited annotated data or external molecular databases. To tackle these challenges, we introduce MolGen, a pre-trained molecular language model tailored specifically for molecule generation. Through the reconstruction of over 100 million molecular SELFIES, MolGen internalizes structural and grammatical insights. This is further enhanced by domain-agnostic molecular prefix tuning, fostering robust knowledge transfer across diverse domains. Importantly, our chemical feedback paradigm steers the model away from molecular hallucinations, ensuring alignment between the model's estimated probabilities and real-world chemical preferences. Extensive experiments on well-known benchmarks underscore MolGen's optimization capabilities in properties such as penalized logP, QED, and molecular docking. Additional analyses confirm its proficiency in accurately capturing molecule distributions, discerning intricate structural patterns, and efficiently exploring the chemical space. Code is available at https://github.com/zjunlp/MolGen.
Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing
Authors: Shaobo Qiu, Ji Li, Guoxi Chen, Hong Wang, Boqi Li
Date: 2023-01-26 00:00:00
Tasks: Common Sense Reasoning, Decision Making

Although a typical autopilot system far surpasses humans in term of sensing accuracy, performance stability and response agility, such a system is still far behind humans in the wisdom of understanding an unfamiliar environment with creativity, adaptivity and resiliency. Current AD brains are basically expert systems featuring logical computations, which resemble the thinking flow of a left brain working at tactical level. A right brain is needed to upgrade the safety of automated driving vehicle onto next generation by making intuitive strategical judgements that can supervise the tactical action planning. In this work, we present the concept of an Automated Driving Strategical Brain (ADSB): a framework of a scene perception and scene safety evaluation system that works at a higher abstraction level, incorporating experience referencing, common-sense inferring and goal-and-value judging capabilities, to provide a contextual perspective for decision making within automated driving planning. The ADSB brain architecture is made up of the Experience Referencing Engine (ERE), the Common-sense Referencing Engine (CIE) and the Goal and Value Keeper (GVK). 1,614,748 cases from FARS/CRSS database of NHTSA in the period 1975 to 2018 are used for the training of ERE model. The kernel of CIE is a trained model, COMET-BART by ATOMIC, which can be used to provide directional advice when tactical-level environmental perception conclusions are ambiguous; it can also use future scenario models to remind tactical-level decision systems to plan ahead of a perceived hazard scene. GVK can take in any additional expert-hand-written rules that are of qualitative nature. Moreover, we believe that with good scalability, the ADSB approach provides a potential solution to the problem of long-tail corner cases encountered in the validation of a rule-based planning algorithm.
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Authors: Brett Daley, Martha White, Christopher Amato, Marlos C. Machado
Date: 2023-01-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Off-policy learning from multistep returns is crucial for sample-efficient reinforcement learning, but counteracting off-policy bias without exacerbating variance is challenging. Classically, off-policy bias is corrected in a per-decision manner: past temporal-difference errors are re-weighted by the instantaneous Importance Sampling (IS) ratio after each action via eligibility traces. Many off-policy algorithms rely on this mechanism, along with differing protocols for cutting the IS ratios to combat the variance of the IS estimator. Unfortunately, once a trace has been fully cut, the effect cannot be reversed. This has led to the development of credit-assignment strategies that account for multiple past experiences at a time. These trajectory-aware methods have not been extensively analyzed, and their theoretical justification remains uncertain. In this paper, we propose a multistep operator that can express both per-decision and trajectory-aware methods. We prove convergence conditions for our operator in the tabular setting, establishing the first guarantees for several existing methods as well as many new ones. Finally, we introduce Recency-Bounded Importance Sampling (RBIS), which leverages trajectory awareness to perform robustly across $\lambda$-values in an off-policy control task.