pwc-archive/papers-with-abstracts huggingface dataset:
Sequential attractors in combinatorial threshold-linear networks
Authors: Caitlyn Parmelee, Juliana Londono Alvarez, Carina Curto, Katherine Morrison
Date: 2021-07-21 00:00:00
Tasks: Hippocampus

Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and central pattern generator circuits that underlie rhythmic behaviors like locomotion. While network architectures supporting sequence generation vary considerably, a common feature is an abundance of inhibition. In this work, we focus on architectures that support sequential activity in recurrently connected networks with inhibition-dominated dynamics. Specifically, we study emergent sequences in a special family of threshold-linear networks, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrices are defined from directed graphs. Such networks naturally give rise to an abundance of sequences whose dynamics are tightly connected to the underlying graph. We find that architectures based on generalizations of cycle graphs produce limit cycle attractors that can be activated to generate transient or persistent (repeating) sequences. Each architecture type gives rise to an infinite family of graphs that can be built from arbitrary component subgraphs. Moreover, we prove a number of graph rules for the corresponding CTLNs in each family. The graph rules allow us to strongly constrain, and in some cases fully determine, the fixed points of the network in terms of the fixed points of the component subnetworks. Finally, we also show how the structure of certain architectures gives insight into the sequential dynamics of the corresponding attractor.
The Evolution of Imitation Without Cultural Transmission
Authors: Lee Altenberg, Susanne Still, Christopher J. Watkins
Date: 2021-07-20 00:00:00

The evolution and function of imitation have always been placed within the confines of animal learning and associated with its crucial role in cultural transmission and cultural evolution. Can imitation evolve as a form of phenotypic plasticity in the absence of cultural transmission, in phenotypes beyond behavior? We investigate a model in which imitation is unbundled from cultural transmission: an organism's adult phenotype is plastically altered by its experiences as a juvenile of other juveniles' genetically determined traits. The only information transmitted between generations is genetic. We find that during a period of directional selection towards a phenotypic optimum, natural selection favors modifiers which cause an organism to bias its plastic phenotype in the direction opposite to the mean phenotype of the population -- anti-imitation. As the population approaches the phenotypic optimum and shifts into stabilizing selection, selection on the modifier reverses and favors strong imitation of the population mean. Imitation can evolve to overshoot the target and produce an evolutionary pathology where mean fitness decreases. When purifying selection for an extreme phenotype is modeled, only selection for anti-imitation occurs, even at a mutation-selection balance. Imitation and anti-imitation emerge from these models in the absence of any goal representation, cognitive understanding of its purpose, or discernment of any kind. These theoretical outcomes are all novel evolutionary and biological phenomena, and we discuss their implications.
Phylogenetic consensus networks: Computing a consensus of 1-nested phylogenetic networks
Authors: Katharina T. Huber, Vincent Moulton, Andreas Spillner
Date: 2021-07-20 00:00:00

An important and well-studied problem in phylogenetics is to compute a \emph{consensus tree} so as to summarize the common features within a collection of rooted phylogenetic trees, all whose leaf-sets are bijectively labeled by the same set~(X) of species. More recently, however, it has become of interest to find a consensus for a collection of more general, rooted directed acyclic graphs all of whose sink-sets are bijectively labeled by~(X), so called rooted \emph{phylogenetic networks}. These networks are used to analyse the evolution of species that cross with one another, such as plants and viruses. In this paper, we introduce an algorithm for computing a consensus for a collection of so-called 1-\emph{nested} phylogenetic networks. Our approach builds on a previous result by Rosell\'o et al. that describes an encoding for any 1-nested phylogenetic network in terms of a collection of ordered pairs of subsets of (X).More specifically, we characterize those collections of ordered pairs that arise as the encoding of some 1-nested phylogenetic network, and then use this characterization to compute a \emph{consensus network} for a collection of~$t$ 1-nested networks in $O(t|X|^2+|X|^3)$ time. Applying our algorithm to a collection of phylogenetic trees yields the well-known majority rule consensus tree. Our approach leads to several new directions for futurework, and we expect that it should provide a useful new tool to help understand complex evolutionary scenarios.
Multiple species animal movements: network properties, disease dynamic and the impact of targeted control actions
Authors: Nicolas C. Cardenas, Abagael L. Sykes, Francisco P. N. Lopes, Gustavo Machado
Date: 2021-07-21 00:00:00
Tasks: Time Series Analysis

Infectious diseases in livestock are well-known to infect multiple hosts and persist through the combination of within- and between-host transmission pathways. Uncertainty remains about the epidemic consequences of the disease being introduced on farms with more than one susceptible host. Here we describe multi-host contact networks to elucidate the potential of disease spread among farms with multiple species. Four years of between-farm animal movement data of bovine, swine, small ruminants, and multi-host, were described through both static and time-series networks; the in-going and out-going contact chains were also calculated. We use the proposed stochastic multilevel model to simulate scenarios in which infection was seeded into a single host and multi-hosts farms, to estimate epidemic trajectories and simulate network-based control actions to assess the reduction of secondarily infected farms. Our analysis showed that the swine network was more connected than cattle and small ruminants in the temporal network view. The small ruminants network was shown disconnected, however, allowing the interaction among networks with different hosts enabling the spread of disease throughout the network. Independently from the initial infected host, secondary infections were observed crossing overall species. We showed that targeting the top 3.25% of the farms ranked by degree could reduce the total number of infected farms below 70% at the end of the simulation period. In conclusion, we demonstrated the potential of the multi-host network in disease propagation, therefore, it becomes important to consider the observed multi-host movement dynamics while designing surveillance and preparedness control strategies.
Forecasting performance of workforce reskilling programmes
Authors: Evan Hurwitz, George Cevora
Date: 2021-07-21 00:00:00

Estimating success rates for programmes aiming to reintegrate theunemployed into the workforce is essential for good stewardship of publicfinances. At the current moment, the methods used for this task arebased on the historical performance of comparable programmes. In lightof Brexit and Covid-19 simultaneously causing a shock to the labourmarket in the UK we developed an estimation method that is basedon fundamental factors involved - workforce demand and supply - asopposed to the historical values which are quickly becoming irrelevant.With an average error of 3.9% of the re-integration success rate, ourmodel outperforms the best benchmark known to us by 53%
Explainable AI Enabled Inspection of Business Process Prediction Models
Authors: Chun Ouyang, Renuka Sindhgatta, Catarina Moreira
Date: 2021-07-16 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Interpretable Machine Learning, Prediction

Modern data analytics underpinned by machine learning techniques has become a key enabler to the automation of data-led decision making. As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models. With the development of interpretable machine learning techniques, explanations can be generated for a black-box model, making it possible for (human) users to access the reasoning behind machine learned predictions. In this paper, we aim to present an approach that allows us to use model explanations to investigate certain reasoning applied by machine learned predictions and detect potential issues with the underlying methods thus enhancing trust in business process prediction models. A novel contribution of our approach is the proposal of model inspection that leverages both the explanations generated by interpretable machine learning mechanisms and the contextual or domain knowledge extracted from event logs that record historical process execution. Findings drawn from this work are expected to serve as a key input to developing model reliability metrics and evaluation in the context of business process predictions.
Two-phase Optimization of Binary Sequences with Low Peak Sidelobe Level Value
Authors: Borko Bošković, Janez Brest
Date: 2021-06-30 00:00:00

The search for binary sequences with low peak sidelobe level value represents a formidable computational problem. To locate better sequences for this problem, we designed a stochastic algorithm that uses two fitness functions. In these fitness functions, the value of the autocorrelation function has a different impact on the final fitness value. It is defined with the value of the exponent over the autocorrelation function values. Each function is used in the corresponding optimization phase, and the optimization process switches between these two phases until the stopping condition is satisfied. The proposed algorithm was implemented using the compute unified device architecture and therefore allowed us to exploit the computational power of graphics processing units. This algorithm was tested on sequences with lengths $L = 2^m - 1$, for $14 \le m \le 20$. From the obtained results it is evident that the usage of two fitness functions improved the efficiency of the algorithm significantly, new-best known solutions were achieved, and the achieved PSL values were significantly less than $\sqrt{L}$.
Graph Regularized Nonnegative Matrix Factorization for Data Representation
Authors: Deng Cai, Xiaofei He, Jiawei Han, Thomas S. Huang
Date: 2011-08-01 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2011 8
Tasks: Information Retrieval, Retrieval

Matrix factorization techniques have been frequently applied in information retrieval, computer vision, and pattern
recognition. Among them, Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and
physiological interpretation of naturally occurring data whose representation may be parts based in the human brain. On the other
hand, from the geometric perspective, the data is usually sampled from a low-dimensional manifold embedded in a high-dimensional
ambient space. One then hopes to find a compact representation,which uncovers the hidden semantics and simultaneously respects
the intrinsic geometric structure. In this paper, we propose a novel algorithm, called Graph Regularized Nonnegative Matrix
Factorization (GNMF), for this purpose. In GNMF, an affinity graph is constructed to encode the geometrical information and we seek a
matrix factorization, which respects the graph structure. Our empirical study shows encouraging results of the proposed algorithm in
comparison to the state-of-the-art algorithms on real-world problems.
RailSem19: A Dataset for Semantic Rail Scene Understanding
Authors: Oliver Zendel, Markus Murschitz, Marcel Zeilinger, Daniel Steininger, Sara Abbasi, Csaba Beleznai
Date: 2019-06-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Scene Understanding, Semantic Segmentation, Transfer Learning

Solving tasks for autonomous road vehicles using com-puter vision is a dynamic and active research field. How-ever, one aspect of autonomous transportation has receivedlittle contributions: the rail domain. In this paper, we intro-duce the first public dataset for semantic scene understand-ing for trains and trams: RailSem19. This dataset consistsof 8500 annotated short sequences from the ego-perspectiveof trains, including over 1000 examples with railway cross-ings and 1200 tram scenes. Since it is the first image datasettargeting the rail domain, a novel label policy has been de-signed from scratch. It focuses on rail-specific labels notcovered by any other datasets. In addition to manual an-notations in the form of geometric shapes, we also supplydense pixel-wise semantic labeling. The dense labeling isa semantic-aware combination of (a) the geometric shapesand (b) weakly supervised annotations generated by exist-ing semantic segmentation networks from the road domain.Finally, multiple experiments give a first impression on howthe new dataset can be used to improve semantic sceneunderstanding in the rail environment. We present proto-types for the image-based classification of trains, switches,switch states, platforms, buffer stops, rail traffic signs andrail traffic lights. Applying transfer learning, we presentan early prototype for pixel-wise semantic segmentation onrail scenes. The resulting predictions show that this newdata also significantly improves scene understanding in sit-uations where cars and trains interact
Pricing Exchange Option Based on Copulas by MCMC Algorithm
Authors: Wen Su
Date: 2021-07-21 00:00:00

This paper focus on pricing exchange option based on copulas by MCMC algorithm. Initially, we introduce the methodologies concerned about risk-netural pricing, copulas and MCMC algorithm. After the basic knowledge, we compare the option prices given by different models, the results show except Gumbel copula, the other model provide similar estimation.
Default Distances Based on the CEV-KMV Model
Authors: Wen Su
Date: 2021-07-21 00:00:00
Tasks: model

This paper presents a new method to assess default risk based on applying the CEV process to the KMV model. We find that the volatility of the firm asset value may not be a constant, so we assume the firm's asset value dynamics are given by the CEV process $\frac{dV_A}{V_A} = \mu_A dt + \delta V_A^{\beta-1}dB$ and use the equivalent volatility method to estimate parameters. Focus on the distances to default, our CEV-KMV model fits the market better when forecasting the credit risk compared to the classical KMV model. Besides, The estimation results show the $\beta>1$ for non ST companies while $\beta<1$ for ST companies, which means their difference in the local volatility structure: ST volatility is decreasing with respect to the firm's asset while non ST volatility is increasing.
Shedding some light on Light Up with Artificial Intelligence
Authors: Libo Sun, James Browning, Roberto Perera
Date: 2021-07-22 00:00:00

The Light-Up puzzle, also known as the AKARI puzzle, has never been solved using modern artificial intelligence (AI) methods. Currently, the most widely used computational technique to autonomously develop solutions involve evolution theory algorithms. This project is an effort to apply new AI techniques for solving the Light-up puzzle faster and more computationally efficient. The algorithms explored for producing optimal solutions include hill climbing, simulated annealing, feed-forward neural network (FNN), and convolutional neural network (CNN). Two algorithms were developed for hill climbing and simulated annealing using 2 actions (add and remove light bulb) versus 3 actions(add, remove, or move light-bulb to a different cell). Both hill climbing and simulated annealing algorithms showed a higher accuracy for the case of 3 actions. The simulated annealing showed to significantly outperform hill climbing, FNN, CNN, and an evolutionary theory algorithm achieving 100% accuracy in 30 unique board configurations. Lastly, while FNN and CNN algorithms showed low accuracies, computational times were significantly faster compared to the remaining algorithms. The GitHub repository for this project can be found at https://github.com/rperera12/AKARI-LightUp-GameSolver-with-DeepNeuralNetworks-and-HillClimb-or-SimulatedAnnealing.
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Authors: Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan
Date: 2021-07-22 00:00:00
Tasks: Blind Super-Resolution, Super-Resolution, Video Super-Resolution

Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. In this work, we extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. Specifically, a high-order degradation modeling process is introduced to better simulate complex real-world degradations. We also consider the common ringing and overshoot artifacts in the synthesis process. In addition, we employ a U-Net discriminator with spectral normalization to increase discriminator capability and stabilize the training dynamics. Extensive comparisons have shown its superior visual performance than prior works on various real datasets. We also provide efficient implementations to synthesize training pairs on the fly.
RankDistil: Knowledge Distillation for Ranking
Authors: Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat Felix Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar
Date: 2021-04-13 00:00:00
Proceeding: AISTATS 2021 4
Tasks: Document Ranking, Knowledge Distillation

Knowledge distillation is an approach to improve the performance of a student model by using the knowledge of a complex teacher.Despite its success in several deep learning applications, the study of distillation is mostly confined to classification settings. In particular, the use of distillation in top-k ranking settings, where the goal is to rank k most relevant items correctly, remains largely unexplored. In this paper, we study such ranking problems through the lens of distillation.
We present a distillation framework for top-k ranking and draw connections with the existing ranking methods. The core idea of this framework is to preserve the ranking at the top by matching the order of items of student and teacher, while penalizing large scores for items ranked low by the teacher. Building on this, we develop a novel distillation approach, RankDistil, specifically catered towards ranking problems with a large number of items to rank, and establish statistical basis
for the method. Finally, we conduct experiments which demonstrate that RankDistil yields benefits over commonly used baselines for ranking problems.
Learning Sparse Fixed-Structure Gaussian Bayesian Networks
Authors: Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, Yuhao Wang
Date: 2021-07-22 00:00:00

Gaussian Bayesian networks (a.k.a. linear Gaussian structural equation models) are widely used to model causal interactions among continuous variables. In this work, we study the problem of learning a fixed-structure Gaussian Bayesian network up to a bounded error in total variation distance. We analyze the commonly used node-wise least squares regression (LeastSquares) and prove that it has a near-optimal sample complexity. We also study a couple of new algorithms for the problem: - BatchAvgLeastSquares takes the average of several batches of least squares solutions at each node, so that one can interpolate between the batch size and the number of batches. We show that BatchAvgLeastSquares also has near-optimal sample complexity. - CauchyEst takes the median of solutions to several batches of linear systems at each node. We show that the algorithm specialized to polytrees, CauchyEstTree, has near-optimal sample complexity. Experimentally, we show that for uncontaminated, realizable data, the LeastSquares algorithm performs best, but in the presence of contamination or DAG misspecification, CauchyEst/CauchyEstTree and BatchAvgLeastSquares respectively perform better.
Predicting Power Electronics Device Reliability under Extreme Conditions with Machine Learning Algorithms
Authors: Carlos Olivares, Raziur Rahman, Christopher Stankus, Jade Hampton, Andrew Zedwick, Moinuddin Ahmed
Date: 2021-07-21 00:00:00
Tasks: BIG-bench Machine Learning, Decoder

Power device reliability is a major concern during operation under extreme environments, as doing so reduces the operational lifetime of any power system or sensing infrastructure. Due to a potential for system failure, devices must be experimentally validated before implementation, which is expensive and time-consuming. In this paper, we have utilized machine learning algorithms to predict device reliability, significantly reducing the need for conducting experiments. To train the models, we have tested 224 power devices from 10 different manufacturers. First, we describe a method to process the data for modeling purposes. Based on the in-house testing data, we implemented various ML models and observed that computational models such as Gradient Boosting and LSTM encoder-decoder networks can predict power device failure with high accuracy.
A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks
Authors: Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan
Date: 2021-07-21 00:00:00

We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.
Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II
Authors: Yossi Arjevani, Michael Field
Date: 2021-07-21 00:00:00
Proceeding: NeurIPS 2021 12

We study the optimization problem associated with fitting two-layer ReLU neural networks with respect to the squared loss, where labels are generated by a target network. We make use of the rich symmetry structure to develop a novel set of tools for studying families of spurious minima. In contrast to existing approaches which operate in limiting regimes, our technique directly addresses the nonconvex loss landscape for a finite number of inputs $d$ and neurons $k$, and provides analytic, rather than heuristic, information. In particular, we derive analytic estimates for the loss at different minima, and prove that modulo $O(d^{-1/2})$-terms the Hessian spectrum concentrates near small positive constants, with the exception of $\Theta(d)$ eigenvalues which grow linearly with~$d$. We further show that the Hessian spectrum at global and spurious minima coincide to $O(d^{-1/2})$-order, thus challenging our ability to argue about statistical generalization through local curvature. Lastly, our technique provides the exact \emph{fractional} dimensionality at which families of critical points turn from saddles into spurious minima. This makes possible the study of the creation and the annihilation of spurious minima using powerful tools from equivariant bifurcation theory.