pwc-archive/papers-with-abstracts huggingface dataset:
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners
Authors: Shike Mei, Xiaojin Zhu
Date: 2015-01-25 00:00:00
Tasks: Bilevel Optimization, regression

We investigate a problem at the intersection of machine learning and security: training-set attacks on machine learners. In such attacks an attacker contaminates the training data so that a specific learning algorithm would produce a model profitable to the attacker. Understanding training-set attacks is important as more intelligent agents (e.g. spam filters and robots) are equipped with learning capability and can potentially be hacked via data they receive from the environment. This paper identifies the optimal training-set attack on a broad family of machine learners. First we show that optimal training-set attack can be formulated as a bilevel optimization problem. Then we show that for machine learners with certain Karush-Kuhn-Tucker conditions we can solve the bilevel problem efficiently using gradient methods on an implicit function. As examples, we demonstrate optimal training-set attacks on Support Vector Machines, logistic regression, and linear regression with extensive experiments. Finally, we discuss potential defenses against such attacks.
Improved K-means Algorithm Based on optimizing Initial Cluster Centers and Its Application
Authors: Xue Linyao 、 Wang Jianguo
Date: 2017-02-01 00:00:00
Tasks: Clustering

Data mining is a process of data grouping or partitioning from the large and complex data, and the clustering analysis is an important research field in data mining. The K-means algorithm is considered to be the most important unsupervised machine learning method in clustering, which can divide all the data into k subclasses that are very different from each other. By constantly iterating, the distance between each data object and the center of its subclass is minimized. Because K-means algorithm is simple and efficient, it is applied to data mining, knowledge discovery and other fields. However, the algorithm has its inherent shortcomings, such as the K value in the K-means algorithm needs to be given in advance; clustering results are highly dependent on the selection of initial clustering centers and so on. In order to adapt to the historical data clustering of the geological disaster monitoring system, this paper presents a method to optimize the initial clustering center and the method of isolating points. The experimental results show that the improved k-means algorithm is better than the traditional clustering in terms of accuracy and stability, and the experimental results are closer to the actual data distribution.
MultiDendrograms: Variable-Group Agglomerative Hierarchical Clusterings
Authors: Sergio Gomez, Justo Montiel, David Torres, Alberto Fernandez
Date: 2012-01-08 00:00:00
Tasks: Clustering

MultiDendrograms is a Java-written application that computes agglomerative hierarchical clusterings of data. Starting from a distances (or weights) matrix, MultiDendrograms is able to calculate its dendrograms using the most common agglomerative hierarchical clustering methods. The application implements a variable-group algorithm that solves the non-uniqueness problem found in the standard pair-group algorithm. This problem arises when two or more minimum distances between different clusters are equal during the agglomerative process, because then different output clusterings are possible depending on the criterion used to break ties between distances. MultiDendrograms solves this problem implementing a variable-group algorithm that groups more than two clusters at the same time when ties occur.
Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms
Authors: Alberto Fernandez, Sergio Gomez
Date: 2006-08-08 00:00:00
Tasks: Clustering

In agglomerative hierarchical clustering, pair-group methods suffer from a problem of non-uniqueness when two or more distances between different clusters coincide during the amalgamation process. The traditional approach for solving this drawback has been to take any arbitrary criterion in order to break ties between distances, which results in different hierarchical classifications depending on the criterion followed. In this article we propose a variable-group algorithm that consists in grouping more than two clusters at the same time when ties occur. We give a tree representation for the results of the algorithm, which we call a multidendrogram, as well as a generalization of the Lance and Williams' formula which enables the implementation of the algorithm in a recursive way.
The Softwarised Network Data Zoo
Authors: Manuel Peuster, Stefan Schneider, Holger Karl
Date: 2019-10-21 00:00:00
Tasks: BIG-bench Machine Learning, Management

More and more management and orchestration approaches for (software) networks are based on machine learning paradigms and solutions. These approaches depend not only on their program code to operate properly, but also require enough input data to train their internal models. However, such training data is barely available for the software networking domain and most presented solutions rely on their own, sometimes not even published, data sets. This makes it hard, or even infeasible, to reproduce and compare many of the existing solutions. As a result, it ultimately slows down the adoption of machine learning approaches in softwarised networks. To this end, we introduce the "softwarised network data zoo" (SNDZoo), an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. We present a general methodology to collect, archive, and publish those data sets for use by other researches and, as an example, eight initial data sets, focusing on the performance of virtualised network functions.
Self-Driving Network and Service Coordination Using Deep Reinforcement Learning
Authors: Stefan Schneider, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, Ramin Khalili, Artur Hecker
Date: 2020-11-02 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.
Distributed Online Service Coordination Using Deep Reinforcement Learning
Authors: Stefan Schneider, Haydar Qarawlus, Holger Karl
Date: 2021-07-07 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling, Self-Learning

Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).
Are energy savings the only reason for the emergence of bird echelon formation?
Authors: Mingming Shi, Julien M. Hendrickx
Date: 2021-03-24 00:00:00
Tasks: Position

We analyze the conditions under which the emergence of frequently observed echelon formation can be explained solely by the maximization of energy savings. We consider a two-dimensional multi-agent echelon formation, where each agent receives a benefit that depends on its position relative to the others, and adjusts its position to increase this benefit. We analyze the selfish case where each agent maximizes its own benefit, leading to a Nash-equilibrium problem, and the collaborative case in which agents maximize the global benefit of the group. We provide conditions on the benefit function under which the frequently observed echelon formations cannot be Nash equilbriums or group optimums. We then show that these conditions are satisfied by the conventionally used fixed-wing wake benefit model. This implies that energy saving alone is not sufficient to explain the emergence of the migratory formations observed, based on the fixed-wing model. Hence, either non-aerodynamic aspects or a more accurate model of bird dynamics should be considered to construct such formations.
Continuous-Domain Formulation of Inverse Problems for Composite Sparse-Plus-Smooth Signals
Authors: Thomas Debarre, Shayan Aziznejad, Michael Unser
Date: 2021-03-24 00:00:00

We present a novel framework for the reconstruction of 1D composite signals assumed to be a mixture of two additive components, one sparse and the other smooth, given a finite number of linear measurements. We formulate the reconstruction problem as a continuous-domain regularized inverse problem with multiple penalties. We prove that these penalties induce reconstructed signals that indeed take the desired form of the sum of a sparse and a smooth component. We then discretize this problem using Riesz bases, which yields a discrete problem that can be solved by standard algorithms. Our discretization is exact in the sense that we are solving the continuous-domain problem over the search space specified by our bases without any discretization error. We propose a complete algorithmic pipeline and demonstrate its feasibility on simulated data.
A Behavioral Input-Output Parametrization of Control Policies with Suboptimality Guarantees
Authors: Luca Furieri, Baiwei Guo, Andrea Martin, Giancarlo Ferrari-Trecate
Date: 2021-02-26 00:00:00

Recent work in data-driven control has revived behavioral theory to perform a variety of complex control tasks, by directly plugging libraries of past input-output trajectories into optimal control problems. Despite recent advances, a key aspect remains unclear: how and to what extent do noise-corrupted data impact control performance? In this work, we provide a quantitative answer to this question. We formulate a Behavioral version of the Input-Output Parametrization (BIOP) for the optimal predictive control of unknown systems using output-feedback dynamic control policies. The main advantages of the proposed framework are that 1) the state-space parameters and the initial state need not be specified for controller synthesis, 2) it can be used in combination with state-of-the-art impulse response estimators, and 3) it allows to recover suboptimality results on learning the Linear Quadratic Gaussian (LQG) controller, therefore revealing, in a quantitative way, how the level of noise in the data affects the performance of behavioral methods. Specifically, it is shown that the performance degrades linearly with the prediction error of the behavioral model. We conclude the paper with numerical experiments to validate our results.
Phase transition of the monotonicity assumption in learning local average treatment effects
Authors: Yinchu Zhu
Date: 2021-03-24 00:00:00

We consider the setting in which a strong binary instrument is available for a binary treatment. The traditional LATE approach assumes the monotonicity condition stating that there are no defiers (or compliers). Since this condition is not always obvious, we investigate the sensitivity and testability of this condition. In particular, we focus on the question: does a slight violation of monotonicity lead to a small problem or a big problem? We find a phase transition for the monotonicity condition. On one of the boundary of the phase transition, it is easy to learn the sign of LATE and on the other side of the boundary, it is impossible to learn the sign of LATE. Unfortunately, the impossible side of the phase transition includes data-generating processes under which the proportion of defiers tends to zero. This boundary of phase transition is explicitly characterized in the case of binary outcomes. Outside a special case, it is impossible to test whether the data-generating process is on the nice side of the boundary. However, in the special case that the non-compliance is almost one-sided, such a test is possible. We also provide simple alternatives to monotonicity.
Mechanical models of pattern and form in biological tissues: the role of stress-strain constitutive equations
Authors: Chiara Villa, Mark A. J. Chaplain, Alf Gerisch, Tommaso Lorenzi
Date: 2020-09-23 00:00:00
Tasks: Form, Stress-Strain Relation

Mechanochemical models of pattern formation in biological tissues have been used to study a variety of biomedical systems and describe the physical interactions between cells and their local surroundings. These models generally consist of a balance equation for the cell density, one for the density of the extracellular matrix (ECM), and a force-balance equation describing the mechanical equilibrium of the cell-ECM system. Assuming this system can be regarded as an isotropic linear viscoelastic material, the force-balance equation is often defined using the Kelvin-Voigt model of linear viscoelasticity to represent the stress-strain relation of the ECM. However, due to the multifaceted bio-physical nature of the ECM constituents, there are rheological aspects that cannot be effectively captured by this model and, therefore, depending on the type of biological tissue considered, other constitutive models of linear viscoelasticity may be better suited. In this work, we systematically assess the pattern formation potential of different stress-strain constitutive equations for the ECM within a mechanical model of pattern formation in biological tissues. The results obtained through linear stability analysis support the idea that constitutive equations capturing viscous flow and permanent set (Maxwell model, Jeffrey model) have a pattern formation potential much higher than the others (Kelvin-Voigt model, standard linear solid model), further confirmed by the results of our numerical simulations. Our findings suggest that further empirical work is required to acquire detailed quantitative information on the mechanical properties of components of the ECM in different biological tissues in order to furnish mechanochemical models of pattern formation with stress-strain constitutive equations for the ECM that provide a more faithful representation of the underlying tissue rheology.
Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance
Authors: Nadia Tahiri, Bernard Fichet, Vladimir Makarenkov
Date: 2021-03-24 00:00:00
Tasks: Clustering

Each gene has its own evolutionary history which can substantially differ from the evolutionary histories of other genes. For example, some individual genes or operons can be affected by specific horizontal gene transfer and recombination events. Thus, the evolutionary history of each gene should be represented by its own phylogenetic tree which may display different evolutionary patterns from the species tree that accounts for the main patterns of vertical descent. The output of traditional consensus tree or supertree inference methods is a unique consensus tree or supertree. We describe a new efficient method for inferring multiple alternative consensus trees and supertrees to best represent the most important evolutionary patterns of a given set of gene phylogenies. We show how an adapted version of the popular k-means clustering algorithm, based on some interesting properties of the Robinson and Foulds distance, can be used to partition a given set of trees into one (for homogeneous data) or multiple (for heterogeneous data) cluster(s) of trees. Moreover, we adapt the popular Cali\'nski-Harabasz, Silhouette, Ball and Hall, and Gap cluster validity indices to tree clustering with k-means. A special attention is given to the relevant but very challenging problem of inferring alternative supertrees. The use of the Euclidean property of the objective function of the method makes it faster than the existing tree clustering techniques, and thus perfectly suitable for analyzing large evolutionary datasets. We apply the new method to discover alternative supertrees characterizing the main patterns of evolution of SARS-CoV-2 and genetically related betacoronaviruses.
Incremental Nonlinear Stability Analysis of Stochastic Systems Perturbed by Lévy Noise
Authors: SooJean Han, Soon-Jo Chung
Date: 2021-03-24 00:00:00

We present a theoretical framework for characterizing incremental stability of nonlinear stochastic systems perturbed by compound Poisson shot noise and finite-measure L\'{e}vy noise. For each noise type, we compare trajectories of the perturbed system with distinct noise sample paths against trajectories of the nominal, unperturbed system. We show that for a finite number of jumps arising from the noise process, the mean-squared error between the trajectories exponentially converge towards a bounded error ball across a finite interval of time under practical boundedness assumptions. The convergence rate for shot noise systems is the same as the exponentially-stable nominal system, but with a tradeoff between the parameters of the shot noise process and the size of the error ball. The convergence rate and the error ball for the L\'{e}vy noise system are shown to be nearly direct sums of the respective quantities for the shot and white noise systems separately, a result which is analogous to the L\'{e}vy-Khintchine theorem. We demonstrate our results using several numerical case studies.
Co-Creation of Innovative Gamification Based Learning: A Case of Synchronous Partnership
Authors: Nicholas Dacre, Vasilis Gkogkidis, Peter Jenkins
Date: 2021-03-24 00:00:00

In higher education, gamification offers the prospect of providing a pivotal shift from traditional asynchronous forms of engagement, to developing methods to foster greater levels of synchronous interactivity and partnership between and amongst teaching and learning stakeholders. The small vein of research that focuses on gamification in teaching and learning contexts, has mainly focused on the implementation of pre-determined game elements. This approach reflects a largely asynchronous approach to the development of learning practices in educational settings, thereby limiting stakeholder engagement in their design and adoption. Therefore, we draw on the theory of co-creation to examine the development process of gamification-based learning as a synchronous partnership between and amongst teaching and learning stakeholders. Empirical insights suggest that students gain a greater sense of partnership and inclusivity as part of a synchronous co-creation gamification-based learning development and implementation process.
Dynamic industry uncertainty networks and the business cycle
Authors: Jozef Barunik, Mattia Bevilacqua, Robert Faff
Date: 2021-01-18 00:00:00

We argue that uncertainty network structures extracted from option prices contain valuable information for business cycles. Classifying U.S. industries according to their contribution to system-related uncertainty across business cycles, we uncover an uncertainty hub role for the communications, industrials and information technology sectors, while shocks to materials, real estate and utilities do not create strong linkages in the network. Moreover, we find that this ex-ante network of uncertainty is a useful predictor of business cycles, especially when it is based on uncertainty hubs. The industry uncertainty network behaves counter-cyclically in that a tighter network tends to associate with future business cycle contractions.
PAT image reconstruction using augmented sparsity regularization with practicable tuning of regularization weight
Authors: Nadaparambil Aravindakshan Rejesh, Muthuvel Arigovindan
Date: 2021-03-24 00:00:00
Tasks: Image Reconstruction

Among all tissue imaging modalities, photo-acoustic tomography (PAT) has been getting increasing attention in the recent past due to the fact that it has high contrast, high penetrability, and has capability of retrieving high resolution. Model-based regularized reconstruction methods used in PAT plays a crucial role in the applicability of PAT because such methods can yield high quality images from measurements obtained using fewer number of transducers. A crucial factor that determines the quality of reconstruction in such methods is the choice of regularization weight. Unfortunately, an appropriately tuned value of regularization weight varies significantly with variation in the noise level, as well as, with the variation in the high resolution contents of the image, in a way that has not been well understood. There has been attempts to determine optimum regularization weight from the measured data in the context of using elementary and general purpose regularizations. In this paper, we develop a practicable method for semi-automated tuning of the regularization weight in the context of using a modern type of regularization that is specifically designed for PAT image reconstruction. As a first step, we introduce a relative smoothness constraint with a parameter; this parameter computationally maps into the actual regularization weight, but, its tuning does not vary significantly with variation in the noise level, and with the variation in the high resolution contents of the image. Next, we construct an algorithm that integrates the task of determining this mapping along with obtaining the reconstruction. Finally we demonstrate experimentally that we can run this algorithm with a nominal value of the relative smoothness parameter -- a value independent of the noise level and the structure of the underlying image -- to obtain good quality reconstructions.