pwc-archive/papers-with-abstracts huggingface dataset:
Metabolic fluxes and value production
Authors: Wolfram Liebermeister
Date: 2014-04-20 00:00:00

Metabolic fluxes in cells are governed by physical, physiological, and economic principles. Here I assume an optimal allocation of enzyme resources and postulate a general principle for metabolism: each enzyme must convert less valuable into more valuable metabolites to justify its own cost. The "values", called economic potentials, describe the individual contributions of metabolites to cell fitness. Local value production implies that the cost of an enzyme must be balanced by a benefit, given by the economic potential difference the catalysed reaction multiplied by the flux. Flux profiles that satisfy this principle - i.e. for which consistent potentials can be found - are called economical. Economical fluxes must lead from lower to higher economic potentials, so certain flux cycles are incompatible with any choice of economic potentials and can be excluded. To obtain economical flux profiles, non-beneficial local patterns, called futile motifs, can be systematically removed from a given flux distribution. The principle of local value production resembles thermodynamic principles and complements them in models. Here I describe a modelling framework called Value Balance Analysis (VBA) that uses the two principles and yields the same solution as enzyme cost minimisation (in kinetic models) and flux cost minimisation (in FBA). Given an economical flux distribution, kinetic models in enzyme-optimal states and with these fluxes can be constructed systematically. VBA justifies the principle of minimal fluxes and the exclusion of futile cycles, predicts enzymes that could be plausible targets for regulation, provides criteria for the usage of enzymes and pathways, and explains the choice between high-yield and low-yield flux modes.
Chi-square simulation of the CIR process and the Heston model
Authors: Simon J. A. Malham, Anke Wiese
Date: 2008-02-29 00:00:00

The transition probability of a Cox-Ingersoll-Ross process can be represented by a non-central chi-square density. First we prove a new representation for the central chi-square density based on sums of powers of generalized Gaussian random variables. Second we prove Marsaglia's polar method extends to this distribution, providing a simple, exact, robust and efficient acceptance-rejection method for generalized Gaussian sampling and thus central chi-square sampling. Third we derive a simple, high-accuracy, robust and efficient direct inversion method for generalized Gaussian sampling based on the Beasley-Springer-Moro method. Indeed the accuracy of the approximation to the inverse cumulative distribution function is to the tenth decimal place. We then apply our methods to non-central chi-square variance sampling in the Heston model. We focus on the case when the number of degrees of freedom is small and the zero boundary is attracting and attainable, typical in foreign exchange markets. Using the additivity property of the chi-square distribution, our methods apply in all parameter regimes.
Robust speaker recognition using unsupervised adversarial invariance
Authors: Raghuveer Peri, Monisankha Pal, Arindam Jati, Krishna Somandepalli, Shrikanth Narayanan
Date: 2019-11-03 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speaker Recognition, Speaker Verification

In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations. We adopt a recently proposed unsupervised adversarial invariance architecture to train a network that maps speaker embeddings extracted using a pre-trained model onto two lower dimensional embedding spaces. The embedding spaces are learnt to disentangle speaker-discriminative information from all other information present in the audio recordings, without supervision about the acoustic conditions. We analyze the robustness of the proposed embeddings to various sources of variability present in the signal for speaker verification and unsupervised clustering tasks on a large-scale speaker recognition corpus. Our analyses show that the proposed system substantially outperforms the baseline in a variety of challenging acoustic scenarios. Furthermore, for the task of speaker diarization on a real-world meeting corpus, our system shows a relative improvement of 36\% in the diarization error rate compared to the state-of-the-art baseline.
Hybrid Feature Learning for Handwriting Verification
Authors: Mohammad Abuzar Shaikh, Mihir Chauhan, Jun Chu, Sargur Srihari
Date: 2018-11-19 00:00:00
Tasks: Handwriting Verification

We propose an effective Hybrid Deep Learning (HDL) architecture for the task of determining the probability that a questioned handwritten word has been written by a known writer. HDL is an amalgamation of Auto-Learned Features (ALF) and Human-Engineered Features (HEF). To extract auto-learned features we use two methods: First, Two Channel Convolutional Neural Network (TC-CNN); Second, Two Channel Autoencoder (TC-AE). Furthermore, human-engineered features are extracted by using two methods: First, Gradient Structural Concavity (GSC); Second, Scale Invariant Feature Transform (SIFT). Experiments are performed by complementing one of the HEF methods with one ALF method on 150000 pairs of samples of the word "AND" cropped from handwritten notes written by 1500 writers. Our results indicate that HDL architecture with AE-GSC achieves 99.7% accuracy on seen writer dataset and 92.16% accuracy on shuffled writer dataset which out performs CEDAR-FOX, as for unseen writer dataset, AE-SIFT performs comparable to this sophisticated handwriting comparison tool.
Block Coordinate Descent for Sparse NMF
Authors: Vamsi K. Potluru, Sergey M. Plis, Jonathan Le Roux, Barak A. Pearlmutter, Vince D. Calhoun, Thomas P. Hayes
Date: 2013-01-15 00:00:00
Tasks: 3D Object Classification

Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L$_0$ norm, however its optimization is NP-hard. Mixed norms, such as L$_1$/L$_2$ measure, have been shown to model sparsity robustly, based on intuitive attributes that such measures need to satisfy. This is in contrast to computationally cheaper alternatives such as the plain L$_1$ norm. However, present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow and other formulations for sparse NMF have been proposed such as those based on L$_1$ and L$_0$ norms. Our proposed algorithm allows us to solve the mixed norm sparsity constraints while not sacrificing computation time. We present experimental evidence on real-world datasets that shows our new algorithm performs an order of magnitude faster compared to the current state-of-the-art solvers optimizing the mixed norm and is suitable for large-scale datasets.
Searching for Spaceships
Authors: David Eppstein
Date: 2000-04-10 00:00:00

We describe software that searches for spaceships in Conway's Game of Life and related two-dimensional cellular automata. Our program searches through a state space related to the de Bruijn graph of the automaton, using a method that combines features of breadth first and iterative deepening search, and includes fast bit-parallel graph reachability and path enumeration algorithms for finding the successors of each state. Successful results include a new 2c/7 spaceship in Life, found by searching a space with 2^126 states.
The Author-Topic Model for Authors and Documents
Authors: Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth
Date: 2012-07-11 00:00:00
Tasks: model

We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors. We apply the model to a collection of 1,700 NIPS conference papers and 160,000 CiteSeer abstracts. Exact inference is intractable for these datasets and we use Gibbs sampling to estimate the topic and author distributions. We compare the performance with two other generative models for documents, which are special cases of the author-topic model: LDA (a topic model) and a simple author model in which each author is associated with a distribution over words rather than a distribution over topics. We show topics recovered by the author-topic model, and demonstrate applications to computing similarity between authors and entropy of author output.
On Critical Sampling of Time-Vertex Graph Signals
Authors: Junhao Yu, Xuan Xie, Hui Feng, Bo Hu
Date: 2019-09-06 00:00:00

Joint time-vertex graph signals are pervasive in real-world. This paper focuses on the fundamental problem of sampling and reconstruction of joint time-vertex graph signals. We prove the existence and the necessary condition of a critical sampling set using minimum number of samples in time and graph domain respectively. The theory proposed in this paper suggests to assign heterogeneous sampling pattern for each node in a network under the constraint of minimum resources. An efficient algorithm is also provided to construct a critical sampling set.
Rule Extraction using Artificial Neural Networks
Authors: S. M. Kamruzzaman, Ahmed Ryadh Hasan
Date: 2010-09-25 00:00:00

Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The pruning process attempts to eliminate as many connections as possible from the network. Relevant and irrelevant attributes of the data are distinguished during the training process. Those that are relevant will be kept and others will be automatically discarded. From the simplified networks having small number of connections and nodes we may easily able to extract symbolic rules using the proposed algorithm. Extensive experimental results on several benchmarks problems in neural networks demonstrate the effectiveness of the proposed approach with good generalization ability.
Inferring deterministic causal relations
Authors: Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schoelkopf
Date: 2012-03-15 00:00:00
Tasks: Causal Inference

We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.
Greedy Algorithms for Hybrid Compressed Sensing
Authors: Ching-Lun Tai, Sung-Hsien Hsieh, Chun-Shien Lu
Date: 2019-08-18 00:00:00
Tasks: compressed sensing

Compressed sensing (CS) is a technique which uses fewer measurements than dictated by the Nyquist sampling theorem. The traditional CS with linear measurements achieves efficient recovery performances, but it suffers from the large bit consumption due to the huge storage occupied by those measurements. Then, the one-bit CS with binary measurements is proposed and saves the bit budget, but it is infeasible when the energy information of signals is not available as a prior knowledge. Subsequently, the hybrid CS which combines the traditional CS and one-bit CS appears, striking a balance between the pros and cons of both types of CS. Considering the fact that the one-bit CS is optimal for the direction estimation of signals under noise with a fixed bit budget and that the traditional CS is able to provide residue information and estimated signals, we focus on the design of greedy algorithms, which consist of the main steps of support detection and recovered signal update, for the hybrid CS in this paper. We first propose a theorem on the random uniform tessellations for sparse signals to further investigate the properties of one-bit CS. Afterwards, we propose two greedy algorithms for the hybrid CS, with the one-bit CS responsible for support detection and traditional CS offering updated residues and signal estimates. For each of the proposed algorithms, we provide the corresponding theorem with proof to analyze their capabilities theoretically. Simulation results have demonstrated the efficacy of the proposed greedy algorithms under a limited bit budget in noisy environments.
Poisson Multi-Bernoulli Mixtures for Sets of Trajectories
Authors: Karl Granström, Lennart Svensson, Yuxuan Xia, Jason Williams, Ángel F. García-Fernández
Date: 2019-12-17 00:00:00

The Poisson Multi-Bernoulli Mixture (PMBM) density is a conjugate multi-target density for the standard point target model with Poisson point process birth. This means that both the filtering and predicted densities for the set of targets are PMBM. In this paper, we first show that the PMBM density is also conjugate for sets of trajectories with the standard point target measurement model. Second, based on this theoretical foundation, we develop two trajectory PMBM filters that provide recursions to calculate the posterior density for the set of all trajectories that have ever been present in the surveillance area, and the posterior density of the set of trajectories present at the current time step in the surveillance area. These two filters therefore provide complete probabilistic information on the considered trajectories enabling optimal trajectory estimation. Third, we establish that the density of the set of trajectories in any time window, given the measurements in a possibly different time window, is also a PMBM. Finally, the trajectory PMBM filters are evaluated via simulations, and are shown to yield state-of-the-art performance compared to other multi-target tracking algorithms based on random finite sets and multiple hypothesis tracking.
Momentum and liquidity in cryptocurrencies
Authors: Stjepan Begušić, Zvonko Kostanjčar
Date: 2019-04-01 00:00:00

The goal of this paper is to explore the relationship between momentum effects and liquidity in cryptocurrency markets. Portfolios based on momentum-liquidity bivariate sorts are formed and rebalanced on a varying number of cryptocurrencies through time. We find a strong momentum effect in the most liquid cryptocurrencies, which supports the theories of investor herding behavior. Moreover, we propose two profitable long-only strategies: the illiquid losers and liquid winners, which exhibit improved risk adjusted performance over the market capitalization weighted portfolio.
Ranking with Fairness Constraints
Authors: L. Elisa Celis, Damian Straszak, Nisheeth K. Vishnoi
Date: 2017-04-22 00:00:00
Tasks: Attribute, Diversity, Fairness, Hypergraph Matching, Recommendation Systems

Ranking algorithms are deployed widely to order a set of items in applications such as search engines, news feeds, and recommendation systems. Recent studies, however, have shown that, left unchecked, the output of ranking algorithms can result in decreased diversity in the type of content presented, promote stereotypes, and polarize opinions. In order to address such issues, we study the following variant of the traditional ranking problem when, in addition, there are fairness or diversity constraints. Given a collection of items along with 1) the value of placing an item in a particular position in the ranking, 2) the collection of sensitive attributes (such as gender, race, political opinion) of each item and 3) a collection of constraints that, for each k, bound the number of items with each attribute that are allowed to appear in the top k positions of the ranking, the goal is to output a ranking that maximizes the value with respect to the original rank quality metric while respecting the constraints. This problem encapsulates various well-studied problems related to bipartite and hypergraph matching as special cases and turns out to be hard to approximate even with simple constraints. Our main technical contributions are fast exact and approximation algorithms along with complementary hardness results that, together, come close to settling the approximability of this constrained ranking maximization problem. Unlike prior work on the constrained matching problems, our algorithm runs in linear time, even when the number of constraints is large, its approximation ratio does not depend on the number of constraints, and it produces solutions with small constraint violations. Our results rely on insights about the constrained matching problem when the objective satisfies properties that appear in common ranking metrics such as Discounted Cumulative Gain, Spearman's rho or Bradley-Terry.
Deciding Morality of Graphs is NP-complete
Authors: Tom S. Verma, Judea Pearl
Date: 2013-03-06 00:00:00

In order to find a causal explanation for data presented in the form of covariance and concentration matrices it is necessary to decide if the graph formed by such associations is a projection of a directed acyclic graph (dag). We show that the general problem of deciding whether such a dag exists is NP-complete.
Embedded Systems and Computer Vision Techniques utilized in Spray Painting Robots: A Review
Authors: Soham Shah, Siddhi Vinayak Pandey, Archit Sorathiya, Raj Sheth, Alok Kumar Singh, Jignesh Thaker
Date: 2020-10-02 00:00:00
Tasks: Edge Detection, object-detection, Object Detection

The advent of the era of machines has limited human interaction and this has increased their presence in the last decade. The requirement to increase the effectiveness, durability and reliability in the robots has also risen quite drastically too. Present paper covers the various embedded system and computer vision methodologies, techniques and innovations used in the field of spray painting robots. There have been many advancements in the sphere of painting robots utilized for high rise buildings, wall painting, road marking paintings, etc. Review focuses on image processing, computational and computer vision techniques that can be applied in the product to increase efficiency of the performance drastically. Image analysis, filtering, enhancement, object detection, edge detection methods, path and localization methods and fine tuning of parameters are being discussed in depth to use while developing such products. Dynamic system design is being deliberated by using which results in reduction of human interaction, environment sustainability and better quality of work in detail. Embedded systems involving the micro-controllers, processors, communicating devices, sensors and actuators, soft-ware to use them; is being explained for end-to-end development and enhancement of accuracy and precision in Spray Painting Robots.
Gaussian Process Molecule Property Prediction with FlowMO
Authors: Henry B. Moss, Ryan-Rhys Griffiths
Date: 2020-10-02 00:00:00
Tasks: Active Learning, Gaussian Processes, Molecular Property Prediction, Prediction, Property Prediction

We present FlowMO: an open-source Python library for molecular property prediction with Gaussian Processes. Built upon GPflow and RDKit, FlowMO enables the user to make predictions with well-calibrated uncertainty estimates, an output central to active learning and molecular design applications. Gaussian Processes are particularly attractive for modelling small molecular datasets, a characteristic of many real-world virtual screening campaigns where high-quality experimental data is scarce. Computational experiments across three small datasets demonstrate comparable predictive performance to deep learning methods but with superior uncertainty calibration.
Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation
Authors: Patrick Dendorfer, Aljoša Ošep, Laura Leal-Taixé
Date: 2020-10-02 00:00:00
Tasks: Position, Trajectory Prediction

In this paper, we present Goal-GAN, an interpretable and end-to-end trainable model for human trajectory prediction. Inspired by human navigation, we model the task of trajectory prediction as an intuitive two-stage process: (i) goal estimation, which predicts the most likely target positions of the agent, followed by a (ii) routing module which estimates a set of plausible trajectories that route towards the estimated goal. We leverage information about the past trajectory and visual context of the scene to estimate a multi-modal probability distribution over the possible goal positions, which is used to sample a potential goal during the inference. The routing is governed by a recurrent neural network that reacts to physical constraints in the nearby surroundings and generates feasible paths that route towards the sampled goal. Our extensive experimental evaluation shows that our method establishes a new state-of-the-art on several benchmarks while being able to generate a realistic and diverse set of trajectories that conform to physical constraints.
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
Authors: Lanqing Li, Rui Yang, Dijun Luo
Date: 2020-10-02 00:00:00
Proceeding: ICLR 2021 1
Tasks: Meta Reinforcement Learning, Metric Learning, Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks without any interactions with the environments, making RL truly practical in many real-world applications. This problem is still not fully understood, for which two major challenges need to be addressed. First, offline RL usually suffers from bootstrapping errors of out-of-distribution state-actions which leads to divergence of value functions. Second, meta-RL requires efficient and robust task inference learned jointly with control policy. In this work, we enforce behavior regularization on learned policy as a general approach to offline RL, combined with a deterministic context encoder for efficient task inference. We propose a novel negative-power distance metric on bounded context embedding space, whose gradients propagation is detached from the Bellman backup. We provide analysis and insight showing that some simple design choices can yield substantial improvements over recent approaches involving meta-RL and distance metric learning. To the best of our knowledge, our method is the first model-free and end-to-end OMRL algorithm, which is computationally efficient and demonstrated to outperform prior algorithms on several meta-RL benchmarks.