pwc-archive/papers-with-abstracts huggingface dataset:
Learning 3D Mineral Prospectivity from 3D Geological Models Using Convolutional Neural Networks: Application to a Structure-controlled Hydrothermal Gold Deposit
Authors: Hao Deng, Yang Zheng, Jin Chen, Shuyan Yu, Keyan Xiao, Xiancheng Mao
Date: 2021-09-02 00:00:00

The three-dimensional (3D) geological models are the typical and key data source in the 3D mineral prospecitivity modeling. Identifying prospectivity-informative predictor variables from the 3D geological models is a challenging and tedious task. Motivated by the ability of convolutional neural networks (CNNs) to learn the intrinsic features, in this paper, we present a novel method that leverages CNNs to learn 3D mineral prospectivity from the 3D geological models. By exploiting the learning ability of CNNs, the presented method allows for disentangling complex correlation to the mineralization and thus opens a door to circumvent the tedious work for designing the predictor variables. Specifically, to explore the unstructured 3D geological models with the CNNs whose input should be structured, we develop a 2D CNN framework in which the geometry of geological boundary is compiled and reorganized into multi-channel images and fed into the CNN. This ensures an effective and efficient training of CNNs while allowing the prospective model to approximate the ore-forming process. The presented method is applied to a typical structure-controlled hydrothermal deposit, the Dayingezhuang gold deposit, eastern China, in which the presented method was compared with the prospectivity modeling methods using hand-designed predictor variables. The results demonstrate the presented method capacitates a performance boost of the 3D prospectivity modeling and empowers us to decrease work-load and prospecting risk in prediction of deep-seated orebodies.
Non-Photorealistic Rendering of Layered Materials: A Multispectral Approach
Authors: Corey Toler-Franklin, Shashank Ranjan
Date: 2021-09-02 00:00:00

We present multispectral rendering techniques for visualizing layered materials found in biological specimens. We are the first to use acquired data from the near-infrared and ultraviolet spectra for non-photorealistic rendering (NPR). Several plant and animal species are more comprehensively understood by multispectral analysis. However, traditional NPR techniques ignore unique information outside the visible spectrum. We introduce algorithms and principles for processing wavelength dependent surface normals and reflectance. Our registration and feature detection methods are used to formulate stylization effects not considered by current NPR methods including: Spectral Band Shading which isolates and emphasizes shape features at specific wavelengths at multiple scales. Experts in our user study demonstrate the effectiveness of our system for applications in the biological sciences.
Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images
Authors: Jack Breen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar
Date: 2021-09-01 00:00:00
Tasks: Diagnostic, Domain Adaptation, Mitosis Detection, Style Transfer, Unsupervised Domain Adaptation, whole slide images

Breast cancer is the most commonly diagnosed cancer worldwide, with over two million new cases each year. During diagnostic tumour grading, pathologists manually count the number of dividing cells (mitotic figures) in biopsy or tumour resection specimens. Since the process is subjective and time-consuming, data-driven artificial intelligence (AI) methods have been developed to automatically detect mitotic figures. However, these methods often generalise poorly, with performance reduced by variations in tissue types, staining protocols, or the scanners used to digitise whole-slide images. Domain adaptation approaches have been adopted in various applications to mitigate this issue of domain shift. We evaluate two unsupervised domain adaptation methods, CycleGAN and Neural Style Transfer, using the MIDOG 2021 Challenge dataset. This challenge focuses on detecting mitotic figures in whole-slide images digitised using different scanners. Two baseline mitosis detection models based on U-Net and RetinaNet were investigated in combination with the aforementioned domain adaptation methods. Both baseline models achieved human expert level performance, but had reduced performance when evaluated on images which had been digitised using a different scanner. The domain adaptation techniques were each found to be beneficial for detection with data from some scanners but not for others, with the only average increase across all scanners being achieved by CycleGAN on the RetinaNet detector. These techniques require further refinement to ensure consistency in mitosis detection.
Real-World Application of Various Trajectory Planning Algorithms on MIT RACECAR
Authors: Oguzhan Kose
Date: 2021-08-17 00:00:00
Tasks: Trajectory Planning

In the project, the vehicle was first controlled with ROS. For this purpose, the necessary nodes were prepared to be controlled with a joystick. Afterwards, DWA(Dynamic Window Approach), TEB(Timed-Elastic Band) and APF(Artificial Potential Field) path planning algorithms were applied to MIT RACECAR, respectively. These algorithms have advantages and disadvantages against each other on different issues. For this reason, a scenario was created to compare algorithms. On a curved double lane road created according to this scenario, MIT RACECAR has to follow the lanes and when it encounters an obstacle, it has to change lanes without leaving the road and pass without hitting the obstacle. In addition, an image processing algorithm was developed to obtain the position information of the lanes needed to implement this scenario. This algorithm detects the target point by processing the image taken from the ZED camera and gives the target point information to the path planning algorithm. After the necessary tools were created, the algorithms were tested against the scenario. In these tests, measurements such as how many obstacles the algorithm successfully passed, how simple routes it chose, and computational costs they have. According to these results, although it was not the algorithm that successfully passed the most obstacles, APF was chosen due to its low processing load and simple working logic. It was believed that with its uncomplicated structure, APF would also provide advantages in the future stages of the project.
Ideals and Virtual Realities
Authors: E. Canessa, L. Tenze
Date: 2021-08-29 00:00:00

A main step for world progress is to keep sharing ever-present Ideals for science and education within today Virtual Realities. On-line education is transforming human society to new levels in the way people teach and learn during the ongoing SARS-CoV-2 pandemic. There is an increasing interest in having more and more reliable, fast and simple apps to communicate and also to record, assemble and distribute videos and lectures in the fields of Physics & Maths still using traditional didactic methods. We describe here how to accurately reproduce chalkboard classes for the popular YouTube video platform using OpenEyA-YT. The audience can thus be expanded over continents to help mitigate the effects of physical isolation.
Sk-Unet Model with Fourier Domain for Mitosis Detection
Authors: Sen yang, Feng Luo, Jun Zhang, Xiyue Wang
Date: 2021-09-01 00:00:00
Tasks: Mitosis Detection, Segmentation

Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem. Swapping the low-frequency spectrum of source and target images is shown effective to alleviate the discrepancy between different scanners. Our Fourier-based segmentation method can achieve F1 with 0.7456 on the preliminary test set.
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge
Authors: Xi Long, Ying Cheng, Xiao Mu, Lian Liu, Jingxin Liu
Date: 2021-09-01 00:00:00
Tasks: Data Augmentation, Domain Generalization, Mitosis Detection

We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generalization (MIDOG) Challenge at MICCAI 2021.
Dynamic Scene Novel View Synthesis via Deferred Spatio-temporal Consistency
Authors: Beatrix-Emőke Fülöp-Balogh, Eleanor Tursman, James Tompkin, Julie Digne, Nicolas Bonneel
Date: 2021-09-02 00:00:00
Tasks: Novel View Synthesis

Structure from motion (SfM) enables us to reconstruct a scene via casual capture from cameras at different viewpoints, and novel view synthesis (NVS) allows us to render a captured scene from a new viewpoint. Both are hard with casual capture and dynamic scenes: SfM produces noisy and spatio-temporally sparse reconstructed point clouds, resulting in NVS with spatio-temporally inconsistent effects. We consider SfM and NVS parts together to ease the challenge. First, for SfM, we recover stable camera poses, then we defer the requirement for temporally-consistent points across the scene and reconstruct only a sparse point cloud per timestep that is noisy in space-time. Second, for NVS, we present a variational diffusion formulation on depths and colors that lets us robustly cope with the noise by enforcing spatio-temporal consistency via per-pixel reprojection weights derived from the input views. Together, this deferred approach generates novel views for dynamic scenes without requiring challenging spatio-temporally consistent reconstructions nor training complex models on large datasets. We demonstrate our algorithm on real-world dynamic scenes against classic and more recent learning-based baseline approaches.
Multi Expression Programming
Authors: Oltean Mihai, Dumitrescu D.
Date: 2002-12-01 00:00:00
Proceeding: Technical Report 2002 12
Tasks: regression, Symbolic Regression

Multi Expression Programming (MEP) is a new evolutionary paradigm intended for solving computationally difficult problems. MEP individuals are linear entities that encode complex computer programs. MEP chromosomes are represented in the same way as C or Pascal compilers translate mathematical expressions into machine code. MEP is used for solving some difficult problems like symbolic regression and game strategy discovery. MEP is compared with Gene Expression Programming (GEP) and Cartesian Genetic Programming (CGP) by using several well-known test problems. For the considered problems MEP outperforms GEP and CGP. For these
examples MEP is two magnitude orders better than CGP.
Error-based or target-based? A unifying framework for learning in recurrent spiking networks
Authors: Cristiano Capone, Paolo Muratore, Pier Stanislao Paolucci
Date: 2021-09-02 00:00:00
Tasks: Imitation Learning

Learning in biological or artificial networks means changing the laws governing the network dynamics in order to better behave in a specific situation. In the field of supervised learning, two complementary approaches stand out: error-based and target-based learning. However, there exists no consensus on which is better suited for which task, and what is the most biologically plausible. Here we propose a comprehensive theoretical framework that includes these two frameworks as special cases. This novel theoretical formulation offers major insights into the differences between the two approaches. In particular, we show how target-based naturally emerges from error-based when the number of constraints on the target dynamics, and as a consequence on the internal network dynamics, is comparable to the degrees of freedom of the network. Moreover, given the experimental evidences on the relevance that spikes have in biological networks, we investigate the role of coding with specific patterns of spikes by introducing a parameter that defines the tolerance to precise spike timing during learning. Our approach naturally lends itself to Imitation Learning (and Behavioral Cloning in particular) and we apply it to solve relevant closed-loop tasks such as the button-and-food task, and the 2D Bipedal Walker. We show that a high dimensionality feedback structure is extremely important when it is necessary to solve a task that requires retaining memory for a long time (button-and-food). On the other hand, we find that coding with specific patterns of spikes enables optimal performances in a motor task (the 2D Bipedal Walker). Finally, we show that our theoretical formulation suggests protocols to deduce the structure of learning feedback in biological networks.
Noise-based cyberattacks generating fake P300 waves in brain–computer interfaces
Authors: Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Sergio López Bernal, Alberto Huertas Celdrán, Gregorio Martínez Pérez
Date: 2021-07-10 00:00:00
Proceeding: Cluster Computing 2021 7
Tasks: Adversarial Attack, Cyber Attack Investigation, EEG, Electroencephalogram (EEG), Noise Estimation

Most of the current Brain–Computer Interfaces (BCIs) application scenarios use electroencephalographic signals (EEG) containing the subject’s information. It means that if EEG were maliciously manipulated, the proper functioning of BCI frameworks could be at risk. Unfortunately, it happens in frameworks sensitive to noise-based cyberattacks, and more efforts are needed to measure the impact of these attacks. This work presents and analyzes the impact of four noise-based cyberattacks attempting to generate fake P300 waves in two different phases of a BCI framework. A set of experiments show that the greater the attacker’s knowledge regarding the P300 waves, processes, and data of the BCI framework, the higher the attack impact. In this sense, the attacker with less knowledge impacts 1% in the acquisition phase and 4% in the processing phase, while the attacker with the most knowledge impacts 22% and 74%, respectively.
Land use change in agricultural systems: an integrated ecological-social simulation model of farmer decisions and cropping system performance based on a cellular automata approach
Authors: Diego Ferraro, Daniela Blanco, Sebastián Pessah, Rodrigo Castro
Date: 2021-08-10 00:00:00

Agricultural systems experience land-use changes that are driven by population growth and intensification of technological inputs. This results in land-use and cover change (LUCC) dynamics representing a complex landscape transformation process. In order to study the LUCC process we developed a spatially explicit agent-based model in the form of a Cellular Automata implemented with the Cell-DEVS formalism. The resulting model called AgroDEVS is used for predicting LUCC dynamics along with their associated economic and environmental changes. AgroDEVS is structured using behavioral rules and functions representing a) crop yields, b) weather conditions, c) economic profit, d) farmer preferences, e) technology level adoption and f) natural resources consumption based on embodied energy accounting. Using data from a typical location of the Pampa region (Argentina) for the 1988-2015 period, simulation exercises showed that the economic goals were achieved, on average, each 6 out of 10 years, but the environmental thresholds were only achieved in 1.9 out of 10 years. In a set of 50-years simulations, LUCC patterns quickly converge towards the most profitable crop sequences, with no noticeable tradeoff between the economic and environmental conditions.
COVID-19 epidemic control using short-term lockdowns for collective gain
Authors: Mauro Bisiacco, Gianluigi Pillonetto
Date: 2021-09-02 00:00:00

While many efforts are currently devoted to vaccines development and administration, social distancing measures, including severe restrictions such as lockdowns, remain fundamental tools to contain the spread of COVID-19. A crucial point for any government is to understand, on the basis of the epidemic curve, the right temporal instant to set up a lockdown and then to remove it. Different strategies are being adopted with distinct shades of intensity. USA and Europe tend to introduce restrictions of considerable temporal length. They vary in time: a severe lockdown may be reached and then gradually relaxed. An interesting alternative is the Australian model where short and sharp responses have repeatedly tackled the virus and allowed people a return to near normalcy. After a few positive cases are detected, a lockdown is immediately set. In this paper we show that the Australian model can be generalized and given a rigorous mathematical analysis, casting strategies of the type short-term pain for collective gain in the context of sliding-mode control, an important branch of nonlinear control theory. This allows us to gain important insights regarding how to implement short-term lockdowns, obtaining a better understanding of their merits and possible limitations. Our model predicts the duration of the severe lockdown to be set to maintain e.g. the number of people in intensive care under a certain threshold. After tuning our strategy exploiting data collected in Italy, it turns out that COVID-19 epidemic could be e.g. controlled by alternating one or two weeks of complete lockdown with one or two months of freedom, respectively. Control strategies of this kind, where the lockdown's duration is well circumscribed, could be important also to alleviate coronavirus impact on economy.
Steady-state Real-time Optimization Using Transient Measurements on an Experimental Rig
Authors: J. Matias, J. P. C. Oliveira, G. A. C. Le Roux, J. Jaschke
Date: 2021-09-02 00:00:00

Real-time optimization with persistent parameter adaptation (ROPA) is an RTO approach, where the steady-state model parameters are updated dynamically using transient measurements. Consequently, we avoid waiting for a steady-state before triggering the optimization cycle, and the steady-state economic optimization can be scheduled at any desired rate. The steady-state wait has been recognized as a fundamental limitation of the traditional RTO approach. In this paper, we implement ROPA on an experimental rig that emulates a subsea oil well network. For comparison, we also implement traditional and dynamic RTO. The experimental results confirm the in-silico findings that ROPA's performance is similar to dynamic RTO's performance with a much lower computational cost. Finally, we present some guidelines for ROPA's practical implementation.
Using Temperature Sensitivity to Estimate Shiftable Electricity Demand: Implications for power system investments and climate change
Authors: Michael J. Roberts, Sisi Zhang, Eleanor Yuan, James Jones, Matthias Fripp
Date: 2021-09-01 00:00:00
Tasks: Sensitivity

Growth of intermittent renewable energy and climate change make it increasingly difficult to manage electricity demand variability. Centralized storage can help but is costly. An alternative is to shift demand. Cooling and heating demands are substantial and can be economically shifted using thermal storage. To estimate what thermal storage, employed at scale, might do to reshape electricity loads, we pair fine-scale weather data with hourly electricity use to estimate the share of temperature-sensitive demand across 31 regions that span the continental United States. We then show how much variability can be reduced by shifting temperature-sensitive loads, with and without improved transmission between regions. We find that approximately three quarters of within-day, within-region demand variability can be eliminated by shifting just half of temperature-sensitive demand. The variability-reducing benefits of shifting temperature-sensitive demand complement those gained from improved interregional transmission, and greatly mitigate the challenge of serving higher peaks under climate change.
Backstepping Mean-Field Density Control for Large-Scale Heterogeneous Nonlinear Stochastic Systems
Authors: Tongjia Zheng, Qing Han, Hai Lin
Date: 2021-09-01 00:00:00
Tasks: Computational Efficiency

This work studies the problem of controlling the mean-field density of large-scale stochastic systems, which has applications in various fields such as swarm robotics. Recently, there is a growing amount of literature that employs mean-field partial differential equations (PDEs) to model the density evolution and uses density feedback to design control laws which, by acting on individual systems, stabilize their density towards a target profile. In spite of its stability property and computational efficiency, the success of density feedback relies on assuming the systems to be homogeneous first-order integrators (plus white noise) and ignores higher-order dynamics, making it less applicable in practice. In this work, we present a backstepping design algorithm that extends density control to heterogeneous and higher-order stochastic systems in strict-feedback forms. We show that the strict-feedback form in the individual level corresponds to, in the collective level, a PDE (of densities) distributedly driven by a collection of heterogeneous stochastic systems. The presented backstepping design then starts with a density feedback design for the PDE, followed by a sequence of stabilizing design for the remaining stochastic systems. We present a candidate control law with stability proof and apply it to nonholonomic mobile robots. A simulation is included to verify the effectiveness of the algorithm.
Towards a taxonomy of learning dynamics in 2 x 2 games
Authors: Marco Pangallo, James Sanders, Tobias Galla, Doyne Farmer
Date: 2017-01-31 00:00:00

Do boundedly rational players learn to choose equilibrium strategies as they play a game repeatedly? A large literature in behavioral game theory has proposed and experimentally tested various learning algorithms, but a comparative analysis of their equilibrium convergence properties is lacking. In this paper we analyze Experience-Weighted Attraction (EWA), which generalizes fictitious play, best-response dynamics, reinforcement learning and also replicator dynamics. Studying $2\times 2$ games for tractability, we recover some well-known results in the limiting cases in which EWA reduces to the learning rules that it generalizes, but also obtain new results for other parameterizations. For example, we show that in coordination games EWA may only converge to the Pareto-efficient equilibrium, never reaching the Pareto-inefficient one; that in Prisoner Dilemma games it may converge to fixed points of mutual cooperation; and that limit cycles or chaotic dynamics may be more likely with longer or shorter memory of previous play.
de novo gene birth as an inevitable consequence of adaptive evolution
Authors: Somya Mani, Tsvi Tlusty
Date: 2021-09-02 00:00:00

Phylostratigraphy suggests that new genes are continually born de novo from non-genic sequences, and the genes that persist found new lineages, contributing to the adaptive evolution of organisms. While recent evidence supports the view that de novo gene birth is frequent and widespread, the mechanisms underlying this process are yet to be discovered. Here we hypothesize and examine a potential general mechanism of gene birth driven by the accumulation of beneficial mutations at non-genic loci. To demonstrate this possibility, we model this mechanism within the boundaries set by current knowledge on mutation effects. Estimates from this analysis are in line with observations of recurrent and extensive gene birth in genomics studies. Thus, we propose that, rather than being inactive and silent, non-genic regions are likely to be dynamic storehouses of potential genes.