pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Optimization of Sampling Densities in MRI
Authors: Alban Gossard, Frédéric de Gournay, Pierre Weiss
Date: 2022-09-15 00:00:00
Tasks: Bayesian Optimization, Dimensionality Reduction

Data-driven optimization of sampling patterns in MRI has recently received a significant attention.Following recent observations on the combinatorial number of minimizers in off-the-grid optimization, we propose a framework to globally optimize the sampling densities using Bayesian optimization. Using a dimension reduction technique, we optimize the sampling trajectories more than 20 times faster than conventional off-the-grid methods, with a restricted number of training samples. This method -- among other benefits -- discards the need of automatic differentiation.Its performance is slightly worse than state-of-the-art learned trajectories since it reduces the space of admissible trajectories, but comes with significant computational advantages.Other contributions include: i) a careful evaluation of the distance in probability space to generate trajectories ii) a specific training procedure on families of operators for unrolled reconstruction networks and iii) a gradient projection based scheme for trajectory optimization.
An Efficient 3D Indoor Positioning System Based on Visible Light Communication
Authors: Vailet Hikmat Faraj Al Khattat, Siti Barirah Ahmad Anas, Abdu Saif
Date: 2022-09-15 00:00:00

In this communication revolution era, visible light communication (VLC) is the optimum efficacious answer to the increased request for high-speed data transmission with reduced cost, besides the illumination. This technology is considered the best promising candidate for indoor positioning due to its ability to attain higher positioning accuracy than other technologies, alongside its distinct advantages. However, the existing techniques that had been applied in an indoor positioning system (IPS) based on VLC focused on the aspect of accuracy but neglected the aspects of cost and complexity. This paper proposes a new technique for positioning, namely complementary and supplementary angles based on received signal strength (CSA-RSS). The positioning error performance of the system is examined based on the single light-emitting diode (LED) and different positions of the photodiode (PD). The results demonstrate the improved accuracy and prove the effectiveness of the newly proposed technique where the average error is 3.2 cm in 80% of the examined positions and the maximum average positioning error is 4.2 cm. Moreover, the proposed approach has been proven to increase system efficiency which acts as a suitable VLC design for indoor positioning performance, avoiding weak signals
Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery?
Authors: Ziqiao Zhang, Yatao Bian, Ailin Xie, Pengju Han, Long-Kai Huang, Shuigeng Zhou
Date: 2022-08-21 00:00:00
Tasks: Drug Discovery

Self-supervised pre-training is gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pre-trained models with the promise that they can extract better feature representations for molecules. Yet, the quality of learned representations have not been fully explored. In this work, inspired by the two phenomena of Activity Cliffs (ACs) and Scaffold Hopping (SH) in traditional Quantitative Structure-Activity Relationship (QSAR) analysis, we propose a method named Representation-Property Relationship Analysis (RePRA) to evaluate the quality of the representations extracted by the pre-trained model and visualize the relationship between the representations and properties. The concepts of ACs and SH are generalized from the structure-activity context to the representation-property context, and the underlying principles of RePRA are analyzed theoretically. Two scores are designed to measure the generalized ACs and SH detected by RePRA, and therefore the quality of representations can be evaluated. In experiments, representations of molecules from 10 target tasks generated by 7 pre-trained models are analyzed. The results indicate that the state-of-the-art pre-trained models can overcome some shortcomings of canonical Extended-Connectivity FingerPrints (ECFP), while the correlation between the basis of the representation space and specific molecular substructures are not explicit. Thus, some representations could be even worse than the canonical fingerprints. Our method enables researchers to evaluate the quality of molecular representations generated by their proposed self-supervised pre-trained models. And our findings can guide the community to develop better pre-training techniques to regularize the occurrence of ACs and SH.
Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model
Authors: YaQin Li, Lingli Li, Yongjin Xu, Yi Yu
Date: 2022-08-14 00:00:00
Tasks: Drug Design, Drug Discovery, Molecular Docking, Reinforcement Learning (RL)

De novo molecular design has facilitated the exploration of large chemical space to accelerate drug discovery. Structure-based de novo method can overcome the data scarcity of active ligands by incorporating drug-target interaction into deep generative architectures. However, these strategies are bottlenecked by the small fraction of experimentally determined protein or complex structures. In addition, the cost of molecular generation is computationally expensive due to 3D representations of both molecule and protein. Here, we demonstrate a widely used and fast protein sequence-based reinforcement learning (RL) model for drug discovery. In the generative model, one of the reward components, a binding affinity predictor, is based on 1D protein sequence and molecular SMILES. As a proof of concept, the RL model was utilized to design molecules for four targets. The generated compounds showed bioactivities by the validation of both QSAR and molecular docking with experimental 3D binding pockets. We also found that the performance of generated molecules depends on the selection of data source training for the binding predictor. Furthermore, drug design for a kinase without any experimental structure, CDK20, was studied by our model. With only 1D protein sequence as input, the generated novel compounds showed favorable binding affinity based on the AlphaFold predicted structure.
Replay and compositional computation
Authors: Zeb Kurth-Nelson, Timothy Behrens, Greg Wayne, Kevin Miller, Lennart Luettgau, Ray Dolan, Yunzhe Liu, Philipp Schwartenbeck
Date: 2022-09-15 00:00:00
Tasks: Hippocampus

Replay in the brain has been viewed as rehearsal, or, more recently, as sampling from a transition model. Here, we propose a new hypothesis: that replay is able to implement a form of compositional computation where entities are assembled into relationally-bound structures to derive qualitatively new knowledge. This idea builds on recent advances in neuroscience which indicate that the hippocampus flexibly binds objects to generalizable roles and that replay strings these role-bound objects into compound statements. We suggest experiments to test our hypothesis, and we end by noting the implications for AI systems which lack the human ability to radically generalize past experience to solve new problems.
Compositional generalization through abstract representations in human and artificial neural networks
Authors: Takuya Ito, Tim Klinger, Douglas H. Schultz, John D. Murray, Michael W. Cole, Mattia Rigotti
Date: 2022-09-15 00:00:00
Tasks: Zero-shot Generalization

Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems. Compositionality has been proposed as a key mechanism supporting generalization in humans, but evidence of its neural implementation and impact on behavior is still scarce. Here we study the computational properties associated with compositional generalization in both humans and artificial neural networks (ANNs) on a highly compositional task. First, we identified behavioral signatures of compositional generalization in humans, along with their neural correlates using whole-cortex functional magnetic resonance imaging (fMRI) data. Next, we designed pretraining paradigms aided by a procedure we term {\em primitives pretraining} to endow compositional task elements into ANNs. We found that ANNs with this prior knowledge had greater correspondence with human behavior and neural compositional signatures. Importantly, primitives pretraining induced abstract internal representations, excellent zero-shot generalization, and sample-efficient learning. Moreover, it gave rise to a hierarchy of abstract representations that matched human fMRI data, where sensory rule abstractions emerged in early sensory areas, and motor rule abstractions emerged in later motor areas. Our findings give empirical support to the role of compositional generalization in human behavior, implicate abstract representations as its neural implementation, and illustrate that these representations can be embedded into ANNs by designing simple and efficient pretraining procedures.
Bioeconomic analysis of harvesting within a predator-prey system: A case study in the Chesapeake Bay fisheries
Authors: Iordanka Panayotova, John Herrmann, Nathan Kolling
Date: 2022-09-14 00:00:00
Tasks: Time Series, Time Series Analysis

Sustainable use of biological resources is very important as over exploitation on the long run may lead to stock depletion, which in turn may threaten biodiversity. The Chesapeake Bay is an extremely complex ecosystem, and sustainable harvesting of its fisheries is essential both for the ecosystem's biodiversity and economic prosperity of the area. Here, we use ecosystem based mathematical modeling to study the population dynamics with harvesting of two key fishes in the Chesapeake Bay, the Atlantic Menhaden (Brevoortia tyrannus) as a prey and the Striped Bass (Morone saxatilis) as a predator. We start by fitting the generalized Lotka-Volterra model to actual time series abundance data of the two species obtained from fisheries in the Bay. We derive conditions for the existence of the bio-economic equilibrium and investigate the stability and the resilience of the biological system. We study the maximum sustainable yield, maximum economic yield, and resilience maximizing yield policies and their effects on the fisheries long term sustainability, particularly with respect to the menhaden-bass population dynamics. This study may be used by policy-makers to balance the economic and ecological harvesting goals while managing the populations of Atlantic menhaden and striped bass in the Chesapeake Bay fisheries.
Optimal portfolio selection of many players under relative performance criteria in the market model with random coefficients
Authors: Jeong Yin Park
Date: 2022-08-16 00:00:00

We study the optimal portfolio selection problem under relative performance criteria in the market model with random coefficients from the perspective of many players game theory. We consider five random coefficients which consist of three market parameters which are used in the risky asset price modeling and two preference parameters which are related to risk attitude and impact of relative performance. We focus on two cases; either all agents have Constant Absolute Risk Aversion (CARA) risk preferences or all agents have Constant Relative Risk Aversion (CRRA) risk preferences for their investment optimization problem. For each case, we show that the forward Nash equilibrium and the mean field equilibrium exist for the n-agent game and the corresponding mean field stochastic optimal control problem, respectively. To extend the n-agent game to the continuum of players game, we introduce a measure dependent forward relative performance process and apply an optimization over controlled dynamics of McKean-Vlasov type. We conclude that our optimal portfolio formulas extend the corresponding results of the market model with constant coefficients.
Modeling and Pricing Cyber Insurance -- Idiosyncratic, Systematic, and Systemic Risks
Authors: Kerstin Awiszus, Thomas Knispel, Irina Penner, Gregor Svindland, Alexander Voß, Stefan Weber
Date: 2022-08-18 00:00:00

The paper provides a comprehensive overview of modeling and pricing cyber insurance and includes clear and easily understandable explanations of the underlying mathematical concepts. We distinguish three main types of cyber risks: idiosyncratic, systematic, and systemic cyber risks. While for idiosyncratic and systematic cyber risks, classical actuarial and financial mathematics appear to be well-suited, systemic cyber risks require more sophisticated approaches that capture both network and strategic interactions. In the context of pricing cyber insurance policies, issues of interdependence arise for both systematic and systemic cyber risks; classical actuarial valuation needs to be extended to include more complex methods, such as concepts of risk-neutral valuation and (set-valued) monetary risk measures.
Transaction time models in multi-state life insurance
Authors: Kristian Buchardt, Christian Furrer, Oliver Lunding Sandqvist
Date: 2022-08-11 00:00:00

In life insurance contracts, benefits and premiums are typically paid contingent on the biometric state of the insured. Due to delays between the occurrence, reporting, and settlement of changes to the biometric state, the state process is not fully observable in real-time. This fact implies that the classic multi-state models for the biometric state of the insured are not able to describe the development of the policy in real-time, which encompasses handling of incurred-but-not-reported and reported-but-not-settled claims. We give a fundamental treatment of the problem in the setting of continuous-time multi-state life insurance by introducing a new class of models: transaction time models. The relation between the transaction time model and the classic model is studied and a result linking the present values in the two models is derived. The results and their practical implications are illustrated for disability coverages, where we obtain explicit expressions for the transaction time reserve in specific models.
Hole Filling Method for Depth Image Based Rendering Based on Boundary Decision
Authors: Jea-Hyung Cho, Wonseok Song, Hyuk Choi, Taejeong Kim
Date: 2017-03-01 00:00:00
Proceeding: Signal Processing Letters 2017 3
Tasks: Image Inpainting

In three-dimensional display systems, depth image based rendering is the most commonly used technique for generating images captured from a virtual viewpoint through reference views and depth maps. However, disoccluded hole filling remain a challenging issue as newly exposed area appears in the virtual view. Image inpainting based hole filling is a popular approach for filling in the hole, but the conventional methods generate annoying artifacts along the boundaries of the foreground objects. This letter presents a robust hole filling method that generates a virtual view with high quality. First, by using the local and neighboring property of the depth map, the background region is filled prior to the foreground region. Also, in the matching step to find the data to fill in, depth homogeneity is considered. Finally, based on the boundary decision using the depth property, the boundary regions are filled with background-relevant data to reduce boundary artifacts. Experimental results show that the views synthesized by the proposed method achieve higher visual comfort than the view synthesized by the existing methods.
MDOE: A Spatiotemporal Event Representation Considering the Magnitude and Density of Events
Authors: Fuqiang Gu, Yong Lee, Yuan Zhuang, You Li, Jingbin Liu, Fangwen Yu, Ruiyuan Li, Chao Chen
Date: 2022-06-27 00:00:00
Proceeding: RA-L 2022 6

Event-based sensors (e.g., DVS cameras) are capable of higher dynamic range, higher temporal resolution, lower time latency, and better power efficiency compared to conventional devices (e.g., RGB cameras). However, learning from these sensors remains challenging; event-based sensors output a stream of asynchronous events, which cannot be directly used by state-of-the-art convolutional neural networks (CNNs). In this paper, we present a novel event-based representation called MDOE that considers both the magnitude and density of events. Compared to existing representations, which discard one or more types of information about event polarity, temporal information, and/or density, MDOE contains richer information about events. It has two benefits: (i) it is a conceptually-simple generic representation that is task-independent; (ii) it achieves superior performance relative to existing representations on a variety of event-based datasets.
A data imputation method for multivariate time series based on generative adversarial network
Authors: Zijian Guo，Yiming Wan，Hao Ye
Date: 2019-09-30 00:00:00
Proceeding: Neurocomputing 2019 9
Tasks: Generative Adversarial Network, Imputation, Time Series, Time Series Analysis

Multivariate time series (MTS) processing plays an important role in many fields such as industry, finance, medical, etc. However, the presence of missing data in MTS makes data analysis process more complicated. To address this issue, MTS imputation reconstructs missing data with a high accuracy by exploiting a precise model of MTS distribution. Despite being an effective tool for modeling distribution on images, generative adversarial networks (GANs) have limitations in modeling MTS distribution. In this paper, to improve MTS imputation performance, a multivariate time series generative adversarial network (MTS-GAN) is proposed for MTS distribution modeling by introducing the multi-channel convolution into GANs. It is then applied to MTS imputation by formulating a constrained MTS generation task. Experimental results show that MTS-GAN performs well in modeling MTS distribution. Compared with several approaches, the proposed MTS-GAN based imputation method not only achieves a higher imputation accuracy under different missing rates, but also performs more robustly as the missing rate increases.
GRACE-FO accelerometers and data processing
Authors: Phuong-Anh Huynh, Françoise Liorzou, Damien Boulanger, Bruno Christophe, Marine Dalin, and Vincent Lebat ONERA, Châtillon, France (phuong-anh.huynh@onera.fr)
Date: 2018-03-20 00:00:00
Proceeding: 2018 2018 3

The GRACE FO mission, led by the JPL (Jet Propulsion Laboratory), is an Earth-orbiting gravity mission,
continuation of the GRACE mission, which will produce an accurate model of the Earth’s gravity field variation
providing global climatic data during five years at least. The mission involves two satellites in a loosely controlled
tandem formation, with a micro-wave link measuring the inter-satellites distance variation. Earth’s mass distribution non-uniformities cause variations of the inter-satellite distance. This variation is measured to recover gravity,
after subtracting the non-gravitational contributors, as the residual drag. ONERA (the French Aerospace Lab) is
developing, manufacturing and testing electrostatic accelerometers measuring this residual drag applied on the
satellites.
The launch of GRACE-FO twin satellites is scheduled on April 14th from Vanderbergh US Air force Base,
California with a SpaceX launcher. The last tests of the accelerometers have been performed in January on the
launch site. In parallel, ONERA team prepares the post-processing of the data, based on the experience acquired
through previous geodesic missions but also through the fundamental physics MICROSCOPE mission. Thanks to
a physical model of the accelerometer and on-ground or flight calibration of the thermal sensitivity, it would be
possible to improve the science return. Methods of detection and suppression of twangs have been developed for
MICROSCOPE and can be applied also for GRACE-FO
Longitudinal data analysis using generalized linear models
Authors: KUNG-YEE LIANG, Scott L. Zeger
Date: 1986-04-01 00:00:00
Proceeding: Biometrika 1986 4
Tasks: Causal Inference, Parameter Prediction, regression

This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for niultivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the pioposecl estimators in two simple situations is considered. The approach is closely related to quasi-likelihood.
Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs
Authors: Bharath Keshavamurthy, Nicolo Michelusi
Date: 2022-09-16 00:00:00
Tasks: Scheduling

We describe the orchestration of a decentralized swarm of rotary-wing UAV-relays, augmenting the coverage and service capabilities of a terrestrial base station. Our goal is to minimize the time-average service latencies involved in handling transmission requests from ground users under Poisson arrivals, subject to an average UAV power constraint. Equipped with rate adaptation to efficiently leverage air-to-ground channel stochastics, we first derive the optimal control policy for a single relay via a semi-Markov decision process formulation, with competitive swarm optimization for UAV trajectory design. Accordingly, we detail a multiscale decomposition of this construction: outer decisions on radial wait velocities and end positions optimize the expected long-term delay-power trade-off; consequently, inner decisions on angular wait velocities, service schedules, and UAV trajectories greedily minimize the instantaneous delay-power costs. Next, generalizing to UAV swarms via replication and consensus-driven command-and-control, this policy is embedded with spread maximization and conflict resolution heuristics. We demonstrate that our framework offers superior performance with respect to average service latencies and average per-UAV power consumption: 11x faster data payload delivery relative to static UAV-relay deployments and 2x faster than a deep-Q network solution; remarkably, one relay with our scheme outclasses three relays under a joint successive convex approximation policy by 62%.
SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data
Authors: Junyi Ma, Xieyuanli Chen, Jingyi Xu, Guangming Xiong
Date: 2022-09-16 00:00:00
Tasks: Autonomous Vehicles

Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this paper, we tackle the problem of place recognition based on sequential 3D LiDAR scans obtained by an onboard LiDAR sensor. We propose a transformer-based network named SeqOT to exploit the temporal and spatial information provided by sequential range images generated from the LiDAR data. It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion. During online operation, our SeqOT finds similar places by matching such descriptors between the current query sequence and those stored in the map. We evaluate our approach on four datasets collected with different types of LiDAR sensors in different environments. The experimental results show that our method outperforms the state-of-the-art LiDAR-based place recognition methods and generalizes well across different environments. Furthermore, our method operates online faster than the frame rate of the sensor. The implementation of our method is released as open source at: https://github.com/BIT-MJY/SeqOT.