pwc-archive/papers-with-abstracts huggingface dataset:
Blockchain Technology: Bitcoins, Cryptocurrency and Applications
Authors: Bosubabu Sambana
Date: 2021-07-16 00:00:00

Blockchain is a decentralized ledger used to securely exchange digital currency, perform deals and transactions efficient manner, each user of the network has access to the least copy of the encrypted ledger so that they can validate a new transaction. The blockchain ledger is a collection of all Bitcoin transactions executed in the past. Basically, it's distributed database that maintains continuously growing tamper-proof data structure blocks that holds batches of individual transactions. The completed blocks are added in a linear and chronological order. Each block contains a timestamp and information link which points to a previous block. Bitcoin is a peer-to-peer permissionless network that allows every user to connect to the network and send new transactions to verify and create new blocks. Satoshi Nakamoto described the design of Bitcoin digital currency in his research paper posted to a cryptography listserv 2008. Nakamoto's suggestion has solved the long-pending problem of cryptography and laid the foundation stone for digital currency. This paper explains the concept of bitcoin, its characteristics, the need for Blockchain, and how Bitcoin works. It attempts to highlight the role of Blockchain in shaping the future of banking , financial services, and the adoption of the Internet of Thinks and future Technologies.
Coarse-grained Stochastic Model of Myosin-Driven Vesicles into Dendritic Spines
Authors: Youngmin Park, Prashant Singh, Thomas G. Fai
Date: 2021-07-15 00:00:00
Tasks: Math

We study the dynamics of membrane vesicle motor transport into dendritic spines, which are bulbous intracellular compartments in neurons that play a key role in transmitting signals between neurons. We consider the stochastic analog of the vesicle transport model in [Park and Fai, The Dynamics of Vesicles Driven Into Closed Constrictions by Molecular Motors. Bull. Math. Biol. 82, 141 (2020)]. The stochastic version, which may be considered as an agent-based model, relies mostly on the action of individual myosin motors to produce vesicle motion. To aid in our analysis, we coarse-grain this agent-based model using a master equation combined with a partial differential equation describing the probability of local motor positions. We confirm through convergence studies that the coarse-graining captures the essential features of bistability in velocity (observed in experiments) and waiting-time distributions to switch between steady-state velocities. Interestingly, these results allow us to reformulate the translocation problem in terms of the mean first passage time for a run-and-tumble particle moving on a finite domain with absorbing boundaries at the two ends. We conclude by presenting numerical and analytical calculations of vesicle translocation.
Auto-differentiable Ensemble Kalman Filters
Authors: Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
Date: 2021-07-16 00:00:00
Tasks: BIG-bench Machine Learning

Data assimilation is concerned with sequentially estimating a temporally-evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown. This paper introduces a machine learning framework for learning dynamical systems in data assimilation. Our auto-differentiable ensemble Kalman filters (AD-EnKFs) blend ensemble Kalman filters for state recovery with machine learning tools for learning the dynamics. In doing so, AD-EnKFs leverage the ability of ensemble Kalman filters to scale to high-dimensional states and the power of automatic differentiation to train high-dimensional surrogate models for the dynamics. Numerical results using the Lorenz-96 model show that AD-EnKFs outperform existing methods that use expectation-maximization or particle filters to merge data assimilation and machine learning. In addition, AD-EnKFs are easy to implement and require minimal tuning.
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
Authors: Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
Date: 2021-07-16 00:00:00

Algorithmic recourse explanations inform stakeholders on how to act to revert unfavorable predictions. However, in general ML models do not predict well in interventional distributions. Thus, an action that changes the prediction in the desired way may not lead to an improvement of the underlying target. Such recourse is neither meaningful nor robust to model refits. Extending the work of Karimi et al. (2021), we propose meaningful algorithmic recourse (MAR) that only recommends actions that improve both prediction and target. We justify this selection constraint by highlighting the differences between model audit and meaningful, actionable recourse explanations. Additionally, we introduce a relaxation of MAR called effective algorithmic recourse (EAR), which, under certain assumptions, yields meaningful recourse by only allowing interventions on causes of the target.
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens
Authors: Palash Ghosh, Xinru Wang, Trikay Nalamada, Shruti Agarwal, Maria Jahja, Bibhas Chakraborty
Date: 2021-07-13 00:00:00
Tasks: Q-Learning

A dynamic treatment regimen (DTR) is a set of decision rules to personalize treatments for an individual using their medical history. The Q-learning-based Q-shared algorithm has been used to develop DTRs that involve decision rules shared across multiple stages of intervention. We show that the existing Q-shared algorithm can suffer from non-convergence due to the use of linear models in the Q-learning setup, and identify the condition under which Q-shared fails. We develop a penalized Q-shared algorithm that not only converges in settings that violate the condition, but can outperform the original Q-shared algorithm even when the condition is satisfied. We give evidence for the proposed method in a real-world application and several synthetic simulations.
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Authors: Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Date: 2021-07-16 00:00:00
Proceeding: ICML Workshop INNF 2021 7

Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling posteriors. Since no data set from the target posterior distribution is available beforehand, the flow is typically trained using the reverse Kullback-Leibler (KL) divergence that only requires samples from a base distribution. This strategy may perform poorly when the posterior is complicated and hard to sample with an untrained normalizing flow. Here we explore a distinct training strategy, using the direct KL divergence as loss, in which samples from the posterior are generated by (i) assisting a local MCMC algorithm on the posterior with a normalizing flow to accelerate its mixing rate and (ii) using the data generated this way to train the flow. The method only requires a limited amount of \textit{a~priori} input about the posterior, and can be used to estimate the evidence required for model validation, as we illustrate on examples.
Online Graph Topology Learning from Matrix-valued Time Series
Authors: Yiye Jiang, Jérémie Bigot, Sofian Maabout
Date: 2021-07-16 00:00:00
Tasks: Graph Learning, Time Series, Time Series Analysis

The focus is on the statistical analysis of matrix-valued time series, where data is collected over a network of sensors, typically at spatial locations, over time. Each sensor records a vector of features at each time point, creating a vectorial time series for each sensor. The goal is to identify the dependency structure among these sensors and represent it with a graph. When only one feature per sensor is observed, vector auto-regressive (VAR) models are commonly used to infer Granger causality, resulting in a causal graph. The first contribution extends VAR models to matrix-variate models for the purpose of graph learning. Additionally, two online procedures are proposed for both low and high dimensions, enabling rapid updates of coefficient estimates as new samples arrive. In the high-dimensional setting, a novel Lasso-type approach is introduced, and homotopy algorithms are developed for online learning. An adaptive tuning procedure for the regularization parameter is also provided. Given that the application of auto-regressive models to data typically requires detrending, which is not feasible in an online context, the proposed AR models are augmented by incorporating trend as an additional parameter, with a particular focus on periodic trends. The online algorithms are adapted to these augmented data models, allowing for simultaneous learning of the graph and trend from streaming samples. Numerical experiments using both synthetic and real data demonstrate the effectiveness of the proposed methods.
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks
Authors: Ismail Alarab, Simant Prakoonwit
Date: 2021-07-15 00:00:00
Tasks: Adversarial Attack

We propose a novel method to capture data points near decision boundary in neural network that are often referred to a specific type of uncertainty. In our approach, we sought to perform uncertainty estimation based on the idea of adversarial attack method. In this paper, uncertainty estimates are derived from the input perturbations, unlike previous studies that provide perturbations on the model's parameters as in Bayesian approach. We are able to produce uncertainty with couple of perturbations on the inputs. Interestingly, we apply the proposed method to datasets derived from blockchain. We compare the performance of model uncertainty with the most recent uncertainty methods. We show that the proposed method has revealed a significant outperformance over other methods and provided less risk to capture model uncertainty in machine learning.
Correlation detection in trees for planted graph alignment
Authors: Luca Ganassali, Laurent Massoulié, Marc Lelarge
Date: 2021-07-15 00:00:00

Motivated by alignment of correlated sparse random graphs, we introduce a hypothesis testing problem of deciding whether or not two random trees are correlated. We obtain sufficient conditions under which this testing is impossible or feasible. We propose MPAlign, a message-passing algorithm for graph alignment inspired by the tree correlation detection problem. We prove MPAlign to succeed in polynomial time at partial alignment whenever tree detection is feasible. As a result our analysis of tree detection reveals new ranges of parameters for which partial alignment of sparse random graphs is feasible in polynomial time. We then conjecture that graph alignment is not feasible in polynomial time when the associated tree detection problem is impossible. If true, this conjecture together with our sufficient conditions on tree detection impossibility would imply the existence of a hard phase for graph alignment, i.e. a parameter range where alignment cannot be done in polynomial time even though it is known to be feasible in non-polynomial time.
Obtaining Causal Information by Merging Datasets with MAXENT
Authors: Sergio Hernan Garrido Mejia, Elke Kirschbaum, Dominik Janzing
Date: 2021-07-15 00:00:00

The investigation of the question "which treatment has a causal effect on a target variable?" is of particular relevance in a large number of scientific disciplines. This challenging task becomes even more difficult if not all treatment variables were or even cannot be observed jointly with the target variable. Another similarly important and challenging task is to quantify the causal influence of a treatment on a target in the presence of confounders. In this paper, we discuss how causal knowledge can be obtained without having observed all variables jointly, but by merging the statistical information from different datasets. We show how the maximum entropy principle can be used to identify edges among random variables when assuming causal sufficiency and an extended version of faithfulness, and when only subsets of the variables have been observed jointly.
Active learning for imbalanced data under cold start
Authors: Ricardo Barata, Miguel Leite, Ricardo Pacheco, Marco O. P. Sampaio, João Tiago Ascensão, Pedro Bizarro
Date: 2021-07-16 00:00:00
Tasks: Active Learning, Fraud Detection

Modern systems that rely on Machine Learning (ML) for predictive modelling, may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain. This problem is even worse in imbalanced data scenarios, where labels of the positive class take longer to accumulate. We propose an Active Learning (AL) system for datasets with orders of magnitude of class imbalance, in a cold start streaming scenario. We present a computationally efficient Outlier-based Discriminative AL approach (ODAL) and design a novel 3-stage sequence of AL labeling policies where ODAL is used as warm-up. Then, we perform empirical studies in four real world datasets, with various magnitudes of class imbalance. The results show that our method can more quickly reach a high performance model than standard AL policies without ODAL warm-up. Its observed gains over random sampling can reach 80% and be competitive with policies with an unlimited annotation budget or additional historical data (using just 2% to 10% of the labels).
Efficient proximal gradient algorithms for joint graphical lasso
Authors: Jie Chen, Ryosuke Shimmura, Joe Suzuki
Date: 2021-07-16 00:00:00

We consider learning an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction method of multipliers (ADMM) is the main approach taken concerning for joint graphical lasso (JGL). We propose proximal gradient procedures with and without a backtracking option for the JGL. These procedures are first-order and relatively simple, and the subproblems are solved efficiently in closed form. We further show the boundedness for the solution of the JGL problem and the iterations in the algorithms. The numerical results indicate that the proposed algorithms can achieve high accuracy and precision, and their efficiency is competitive with state-of-the-art algorithms.
Chi-square and normal inference in high-dimensional multi-task regression
Authors: Pierre C Bellec, Gabriel Romon
Date: 2021-07-16 00:00:00
Tasks: regression, valid, Vocal Bursts Intensity Prediction

The paper proposes chi-square and normal inference methodologies for the unknown coefficient matrix $B^*$ of size $p\times T$ in a Multi-Task (MT) linear model with $p$ covariates, $T$ tasks and $n$ observations under a row-sparse assumption on $B^*$. The row-sparsity $s$, dimension $p$ and number of tasks $T$ are allowed to grow with $n$. In the high-dimensional regime $p\ggg n$, in order to leverage row-sparsity, the MT Lasso is considered. We build upon the MT Lasso with a de-biasing scheme to correct for the bias induced by the penalty. This scheme requires the introduction of a new data-driven object, coined the interaction matrix, that captures effective correlations between noise vector and residuals on different tasks. This matrix is psd, of size $T\times T$ and can be computed efficiently. The interaction matrix lets us derive asymptotic normal and $\chi^2_T$ results under Gaussian design and $\frac{sT+s\log(p/s)}{n}\to0$ which corresponds to consistency in Frobenius norm. These asymptotic distribution results yield valid confidence intervals for single entries of $B^*$ and valid confidence ellipsoids for single rows of $B^*$, for both known and unknown design covariance $\Sigma$. While previous proposals in grouped-variables regression require row-sparsity $s\lesssim\sqrt n$ up to constants depending on $T$ and logarithmic factors in $n,p$, the de-biasing scheme using the interaction matrix provides confidence intervals and $\chi^2_T$ confidence ellipsoids under the conditions ${\min(T^2,\log^8p)}/{n}\to 0$ and $$ \frac{sT+s\log(p/s)+\|\Sigma^{-1}e_j\|_0\log p}{n}\to0, \quad \frac{\min(s,\|\Sigma^{-1}e_j\|_0)}{\sqrt n} \sqrt{[T+\log(p/s)]\log p}\to 0, $$ allowing row-sparsity $s\ggg\sqrt n$ when $\|\Sigma^{-1}e_j\|_0 \sqrt T\lll \sqrt{n}$ up to logarithmic factors.
POS tagging, lemmatization and dependency parsing of West Frisian
Authors: Wilbert Heeringa, Gosse Bouma, Martha Hofman, Eduard Drenth, Jan Wijffels, Hans Van de Velde
Date: 2021-07-16 00:00:00
Proceeding: LREC 2022 6
Tasks: Dependency Parsing, LEMMA, Lemmatization, POS, POS Tagging, Translation, Word Translation

We present a lemmatizer/POS-tagger/dependency parser for West Frisian using a corpus of 44,714 words in 3,126 sentences that were annotated according to the guidelines of Universal Dependency version 2. POS tags were assigned to words by using a Dutch POS tagger that was applied to a literal word-by-word translation, or to sentences of a Dutch parallel text. Best results were obtained when using literal translations that were created by using the Frisian translation program Oersetter. Morphologic and syntactic annotations were generated on the basis of a literal Dutch translation as well. The performance of the lemmatizer/tagger/annotator when it was trained using default parameters was compared to the performance that was obtained when using the parameter values that were used for training the LassySmall UD 2.5 corpus. A significant improvement was found for `lemma'. The Frisian lemmatizer/PoS tagger/dependency parser is released as a web app and as a web service.
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization
Authors: Yongxin Zhang, Jiahai Wang, Zizhen Zhang, Yalan Zhou
Date: 2021-07-16 00:00:00
Tasks: Combinatorial Optimization, Deep Reinforcement Learning, Multiobjective Optimization, reinforcement-learning, Reinforcement Learning (RL)

Learning-based heuristics for solving combinatorial optimization problems has recently attracted much academic attention. While most of the existing works only consider the single objective problem with simple constraints, many real-world problems have the multiobjective perspective and contain a rich set of constraints. This paper proposes a multiobjective deep reinforcement learning with evolutionary learning algorithm for a typical complex problem called the multiobjective vehicle routing problem with time windows (MO-VRPTW). In the proposed algorithm, the decomposition strategy is applied to generate subproblems for a set of attention models. The comprehensive context information is introduced to further enhance the attention models. The evolutionary learning is also employed to fine-tune the parameters of the models. The experimental results on MO-VRPTW instances demonstrate the superiority of the proposed algorithm over other learning-based and iterative-based approaches.
Controlling Recurrent Neural Networks by Diagonal Conceptors
Authors: J. P. de Jong
Date: 2021-07-16 00:00:00

The human brain is capable of learning, memorizing, and regenerating a panoply of temporal patterns. A neuro-dynamical mechanism called conceptors offers a method for controlling the dynamics of a recurrent neural network by which a variety of temporal patterns can be learned and recalled. However, conceptors are matrices whose size scales quadratically with the number of neurons in the recurrent neural network, hence they quickly become impractical. In the work reported in this thesis, a variation of conceptors is introduced, called diagonal conceptors, which are diagonal matrices, thus reducing the computational cost drastically. It will be shown that diagonal conceptors achieve the same accuracy as conceptors, but are slightly more unstable. This instability can be improved, but requires further research. Nevertheless, diagonal conceptors show to be a promising practical alternative to the standard full matrix conceptors.
Ranking labs-of-origin for genetically engineered DNA using Metric Learning
Authors: I. Muniz, F. H. F. Camargo, A. Marques
Date: 2021-07-16 00:00:00
Tasks: Clustering, Metric Learning

With the constant advancements of genetic engineering, a common concern is to be able to identify the lab-of-origin of genetically engineered DNA sequences. For that reason, AltLabs has hosted the genetic Engineering Attribution Challenge to gather many teams to propose new tools to solve this problem. Here we show our proposed method to rank the most likely labs-of-origin and generate embeddings for DNA sequences and labs. These embeddings can also perform various other tasks, like clustering both DNA sequences and labs and using them as features for Machine Learning models applied to solve other problems. This work demonstrates that our method outperforms the classic training method for this task while generating other helpful information.
Testing Surrogate-Based Optimization with the Fortified Branin-Hoo Extended to Four Dimensions
Authors: Charles F. Jekel, Raphael T. Haftka
Date: 2021-07-16 00:00:00
Tasks: global-optimization

Some popular functions used to test global optimization algorithms have multiple local optima, all with the same value, making them all global optima. It is easy to make them more challenging by fortifying them via adding a localized bump at the location of one of the optima. In previous work the authors illustrated this for the Branin-Hoo function and the popular differential evolution algorithm, showing that the fortified Branin-Hoo required an order of magnitude more function evaluations. This paper examines the effect of fortifying the Branin-Hoo function on surrogate-based optimization, which usually proceeds by adaptive sampling. Two algorithms are considered. The EGO algorithm, which is based on a Gaussian process (GP) and an algorithm based on radial basis functions (RBF). EGO is found to be more frugal in terms of the number of required function evaluations required to identify the correct basin, but it is expensive to run on a desktop, limiting the number of times the runs could be repeated to establish sound statistics on the number of required function evaluations. The RBF algorithm was cheaper to run, providing more sound statistics on performance. A four-dimensional version of the Branin-Hoo function was introduced in order to assess the effect of dimensionality. It was found that the difference between the ordinary function and the fortified one was much more pronounced for the four-dimensional function compared to the two dimensional one.