OpenRaiser/Intern-Atlas huggingface dataset:
Data-dependent Generalization Bounds for Multi-class Classification
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we study data-dependent generalization error bounds exhibiting a mild dependency on the number of classes, making them suitable for multi-class learning with a large number of label classes. The bounds generally hold for empirical multi-class risk minimization algorithms using an arbitrary norm as regularizer. Key to our analysis are new structural results for multi-class Gaussian complexities and empirical $\ell_\infty$-norm covering numbers, which exploit the Lipschitz continuity of the loss function with respect to the $\ell_2$- and $\ell_\infty$-norm, respectively. We establish data-dependent error bounds in terms of complexities of a linear function class defined on a finite set induced by training examples, for which we show tight lower and upper bounds. We apply the results to several prominent multi-class learning machines, exhibiting a tighter dependency on the number of classes than the state of the art. For instance, for the multi-class SVM by Crammer and Singer (2002), we obtain a data-dependent bound with a logarithmic dependency which significantly improves the previous square-root dependency. Experimental results are reported to verify the effectiveness of our theoretical findings.
Structural Analysis and Optimal Design of Distributed System Throttlers
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we investigate the performance analysis and synthesis of distributed system throttlers (DST). A throttler is a mechanism that limits the flow rate of incoming metrics, e.g., byte per second, network bandwidth usage, capacity, traffic, etc. This can be used to protect a service's backend/clients from getting overloaded, or to reduce the effects of uncertainties in demand for shared services. We study performance deterioration of DSTs subject to demand uncertainty. We then consider network synthesis problems that aim to improve the performance of noisy DSTs via communication link modifications as well as server update cycle modifications.
New Fairness Metrics for Recommendation that Embrace Differences
Venue: arXiv cs.CY 2017
Year: 2017

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative filtering methods to make unfair predictions against minority groups of users. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.
Optimal Control for Multi-Mode Systems with Discrete Costs
Venue: arXiv cs.LO 2017
Year: 2017

This paper studies optimal time-bounded control in multi-mode systems with discrete costs. Multi-mode systems are an important subclass of linear hybrid systems, in which there are no guards on transitions and all invariants are global. Each state has a continuous cost attached to it, which is linear in the sojourn time, while a discrete cost is attached to each transition taken. We show that an optimal control for this model can be computed in NEXPTIME and approximated in PSPACE. We also show that the one-dimensional case is simpler: although the problem is NP-complete (and in LOGSPACE for an infinite time horizon), we develop an FPTAS for finding an approximate solution.
Scalable Asymptotically-Optimal Multi-Robot Motion Planning
Venue: arXiv cs.MA 2017
Year: 2017

Finding asymptotically-optimal paths in multi-robot motion planning problems could be achieved, in principle, using sampling-based planners in the composite configuration space of all of the robots in the space. The dimensionality of this space increases with the number of robots, rendering this approach impractical. This work focuses on a scalable sampling-based planner for coupled multi-robot problems that provides asymptotic optimality. It extends the dRRT approach, which proposed building roadmaps for each robot and searching an implicit roadmap in the composite configuration space. This work presents a new method, dRRT* , and develops theory for scalable convergence to optimal paths in multi-robot problems. Simulated experiments indicate dRRT* converges to high-quality paths while scaling to higher numbers of robots where the naive approach fails. Furthermore, dRRT* is applicable to high-dimensional problems, such as planning for robot manipulators
Fast Processing of Large Graph Applications Using Asynchronous Architecture
Venue: arXiv cs.AR 2017
Year: 2017

Graph algorithms and techniques are increasingly being used in scientific and commercial applications to express relations and explore large data sets. Although conventional or commodity computer architectures, like CPU or GPU, can compute fairly well dense graph algorithms, they are often inadequate in processing large sparse graph applications. Memory access patterns, memory bandwidth requirements and on-chip network communications in these applications do not fit in the conventional program execution flow. In this work, we propose and design a new architecture for fast processing of large graph applications. To leverage the lack of the spatial and temporal localities in these applications and to support scalable computational models, we design the architecture around two key concepts. (1) The architecture is a multicore processor of independently clocked processing elements. These elements communicate in a self-timed manner and use handshaking to perform synchronization, communication, and sequencing of operations. By being asynchronous, the operating speed at each processing element is determined by actual local latencies rather than global worst-case latencies. We create a specialized ISA to support these operations. (2) The application compilation and mapping process uses a graph clustering algorithm to optimize parallel computing of graph operations and load balancing. Through the clustering process, we make scalability an inherent property of the architecture where task-to-element mapping can be done at the graph node level or at node cluster level. A prototyped version of the architecture outperforms a comparable CPU by 10~20x across all benchmarks and provides 2~5x better power efficiency when compared to a GPU.
Hamiltonicity is Hard in Thin or Polygonal Grid Graphs, but Easy in Thin Polygonal Grid Graphs
Venue: arXiv cs.CC 2017
Year: 2017

In 2007, Arkin et al. initiated a systematic study of the complexity of the Hamiltonian cycle problem on square, triangular, or hexagonal grid graphs, restricted to polygonal, thin, superthin, degree-bounded, or solid grid graphs. They solved many combinations of these problems, proving them either polynomially solvable or NP-complete, but left three combinations open. In this paper, we prove two of these unsolved combinations to be NP-complete: Hamiltonicity of Square Polygonal Grid Graphs and Hamiltonicity of Hexagonal Thin Grid Graphs. We also consider a new restriction, where the grid graph is both thin and polygonal, and prove that Hamiltonicity then becomes polynomially solvable for square, triangular, and hexagonal grid graphs.
Emotions make cities live. Towards mapping emotions of older adults on urban space
Venue: arXiv cs.CY 2017
Year: 2017

Understanding of interaction between people and urban spaces is crucial for inclusive decision making process. Smartphones and social media can be a rich source of behavioral and declarative data about urban space, but it threatens to exclude voice of older adults. The platform proposed in the paper attempts to address this issue. A universal tagging mechanism based on the Pluchik Wheel of Emotion is proposed. Usability of the platform was tested and prospect studies are proposed.
On the Effects of Distributed Electric Vehicle Network Utility Maximization in Low Voltage Feeders
Venue: arXiv cs.SY 2017
Year: 2017

The fast charging of Electric Vehicles (EVs) in distribution networks requires real-time EV charging control to avoid the overloading of grid components. Recent studies have proposed congestion control protocols, which result from distributed optimization solutions of the Network Utility Maximization (NUM) problem. While the NUM formulation allows the definition of distributed computations with closed form solutions, its simple model does not account for many of the feeders operational constraints. This puts the resulting control algorithms effectiveness into question. In this paper, we investigate the impact of implementing such algorithms for congestion control in low voltage feeders. We review the latest NUM based algorithms for real-time EV charging control, and evaluate their behavior and impact on the comprehensive IEEE European Low Voltage Test Feeder. Our results show that the EV NUM problem can effectively capture the relevant operational constraints, as long as ampacity violations are the main bottleneck. Moreover, the results demonstrate an advantage of the primal NUM solution over the more conventional dual NUM solution in preventing a system overload.
From Big Data to Big Displays: High-Performance Visualization at Blue Brain
Venue: arXiv cs.GR 2017
Year: 2017

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, which integrates C++ visualization applications with novel collaborative display systems. We motivate how our strategy of transforming visualization engines into services enables a variety of use cases, not only for the integration with high-fidelity displays, but also to build service oriented architectures, to link into web applications and to provide remote services to Python applications.
On the Complexity of Polytopes in $LI(2)$
Venue: arXiv cs.CC 2017
Year: 2017

In this paper we consider polytopes given by systems of $n$ inequalities in $d$ variables, where every inequality has at most two variables with nonzero coefficient. We denote this family by $LI(2)$. We show that despite of the easy algebraic structure, polytopes in $LI(2)$ can have high complexity. We construct a polytope in $LI(2)$, whose number of vertices is almost the number of vertices of the dual cyclic polytope, the difference is a multiplicative factor of depending on $d$ and in particular independent of $n$. Moreover we show that the dual cyclic polytope can not be realized in $LI(2)$.
Restricted Causal Inference Algorithm
Venue: arXiv cs.AI 2017
Year: 2017

This paper proposes a new algorithm for recovery of belief network structure from data handling hidden variables. It consists essentially in an extension of the CI algorithm of Spirtes et al. by restricting the number of conditional dependencies checked up to k variables and in an extension of the original CI by additional steps transforming so called partial including path graph into a belief network. Its correctness is demonstrated.
What Happens to Intentional Concepts in Requirements Engineering If Intentional States Cannot Be Known?
Venue: arXiv cs.SE 2017
Year: 2017

I assume in this paper that the proposition "I cannot know your intentional states" is true. I consider its consequences on the use of so-called "intentional concepts" for Requirements Engineering. I argue that if you take this proposition to be true, then intentional concepts (e.g., goal, belief, desire, intention, etc.) start to look less relevant (though not irrelevant), despite being the focus of significant research attention over the past three decades. I identify substantial problems that arise if you use instances of intentional concepts to reflect intentional states. I sketch an approach to address these problems. In it, intentional concepts have a less prominent role, while notions of time, uncertainty, prediction, observability, evidence, and learning are at the forefront.
Prepaid or Postpaid? That is the question. Novel Methods of Subscription Type Prediction in Mobile Phone Services
Venue: arXiv cs.SI 2017
Year: 2017

In this paper we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections between customers of the same subscription type are much more frequent than those between customers of different subscription types. Based on these observations we provide methods to detect the subscription type of customers by using information about their personal call statistics, and also their egocentric networks simultaneously. The key of our first approach is to cast this classification problem as a problem of graph labelling, which can be solved by max-flow min-cut algorithms. Our experiments show that, by using both user attributes and relationships, the proposed graph labelling approach is able to achieve a classification accuracy of $\sim 87\%$, which outperforms by $\sim 7\%$ supervised learning methods using only user attributes. In our second problem we aim to infer the subscription type of customers of external operators. We propose via approximate methods to solve this problem by using node attributes, and a two-ways indirect inference method based on observed homophilic structural correlations. Our results have straightforward applications in behavioural prediction and personal marketing.
Joint Optimization of User Association, Data Delivery Rate and Precoding for Cache-Enabled F-RANs
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers the downlink of a cache-enabled fog radio access network (F-RAN) with limited fronthaul capacity, where user association (UA), data delivery rate (DDR) and signal precoding are jointly optimized. We formulate a mixed-integer nonlinear programming problem in which the weighted difference of network throughput and total power consumption is maximized, subject to the predefined DDR requirements and the maximum transmit power at each eRRH. To address this challenging problem, we first apply the l0-norm approximation and l1-norm minimization techniques to deal with the UA. After this key step, we arrive at an approximated problem that only involves the joint optimization of DDR and precoding. By using the alternating descent method, we further decompose this problem into a convex subproblem for DDR allocation and a nonconvex subproblem for precoding design. While the former is globally solved by the interior-point method, the latter is solved by a specifically tailored successive convex quadratic programming method. Finally, we propose an iterative algorithm for the original joint optimization that is guaranteed to converge. Importantly, each iteration of the developed algorithm only involves solving simple convex problems. Numerical examples demonstrate that the proposed design significantly improves both throughput and power performances, especially in practical F-RANs with limited fronthaul capacity. Compared to the sole precoder design for a given cache placement, our joint design is shown to improve the throughput by 50% while saving at least half of the total power consumption in the considered examples.
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes
Venue: arXiv cs.LG 2017
Year: 2017

It is widely observed that deep learning models with learned parameters generalize well, even with much more model parameters than the number of training samples. We systematically investigate the underlying reasons why deep neural networks often generalize well, and reveal the difference between the minima (with the same training error) that generalize well and those they don't. We show that it is the characteristics the landscape of the loss function that explains the good generalization capability. For the landscape of loss function for deep networks, the volume of basin of attraction of good minima dominates over that of poor minima, which guarantees optimization methods with random initialization to converge to good minima. We theoretically justify our findings through analyzing 2-layer neural networks; and show that the low-complexity solutions have a small norm of Hessian matrix with respect to model parameters. For deeper networks, extensive numerical evidence helps to support our arguments.
Signal Reconstruction from Interferometric Measurements under Sensing Constraints
Venue: arXiv cs.IT 2017
Year: 2017

This paper develops a unifying framework for signal reconstruction from interferometric measurements that is broadly applicable to various applications of interferometry. In this framework, the problem of signal reconstruction in interferometry amounts to one of basis analysis. Its applicability is shown to extend beyond conventional temporal interferometry, which leverages the relative delay between the two arms of an interferometer, to arbitrary degrees of freedom of the input signal. This allows for reconstruction of signals supported in other domains (e.g., spatial) with no modification to the underlying structure except for replacing the standard temporal delay with a generalized delay, that is, a practically realizable unitary transformation for which the basis elements are eigenfunctions. Under the proposed model, the interferometric measurements are shown to be linear in the basis coefficients, thereby enabling efficient and fast recovery of the desired information. While the corresponding linear transformation has only a limited number of degrees of freedom set by the structure of the interferometer giving rise to a highly constrained sensing structure, we show that the problem of signal recovery from such measurements can still be carried out compressively. This signifies significant reduction in sample complexity without introducing any additional randomization as is typically done in prior work leveraging compressive sensing techniques. We provide performance guarantees under constrained sensing by proving that the transformation satisfies sufficient conditions for successful reconstruction of sparse signals using concentration arguments. We showcase the effectiveness of the proposed approach using simulation results, as well as actual experimental results in the context of optical modal analysis of spatial beams.
LivingLab PJAIT: Towards Better Urban Participation of Seniors
Venue: arXiv cs.CY 2017
Year: 2017

In this paper we provide a brief summary of development LivingLab PJAIT as an attempt to establish a comprehensive and sustainable ICT-based solution for empowerment of elderly communities towards better urban participation of seniors. We report on our various endeavors for better involvement and participation of older adults in urban life by lowering ICT barriers, encouraging social inclusion, intergenerational interaction, physical activity and engaging older adults in the process of development of ICT solutions. We report on a model and assumptions of the LivingLab PJAIT as well as a number of activities created and implemented for LivingLab participants: from ICT courses, both traditional and e-learning, through on-line crowdsourcing tasks, to blended activities of different forms and complexity. We also provide conclusions on the lessons learned in the process and some future plans, including solutions for better senior urban participation and citizen science.
Synthetic Data for Neural Machine Translation of Spoken-Dialects
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach transforms a given parallel corpus between a written language and a target language to a parallel corpus between a spoken dialect variant and the target language. Our approach is language independent and can be used to generate data for any variant of the source language such as slang or spoken dialect or even for a different language that is closely related to the source language. The proposed approach is based on local embedding projection of distributed representations which utilizes monolingual embeddings to transform parallel data across language variants. We report experimental results on Levantine to English translation using Neural Machine Translation. We show that the generated data can improve a very large scale system by more than 2.8 Bleu points using synthetic spoken data which shows that it can be used to provide a reliable translation system for a spoken dialect that does not have sufficient parallel data.
Estimation of Miner Hash Rates and Consensus on Blockchains (draft)
Venue: arXiv cs.CR 2017
Year: 2017

We make several contributions that quantify the real-time hash rate and therefore the consensus of a blockchain. We show that by using only the hash value of blocks, we can estimate and measure the hash rate of all miners or individual miners, with quanti able accuracy. We apply our techniques to the Ethereum and Bitcoin blockchains; our solution applies to any proof-of-work-based blockchain that relies on a numeric target for the validation of blocks. We also show that if miners regularly broadcast status reports of their partial proof-of- work, the hash rate estimates are signi cantly more accurate at a cost of slightly higher bandwidth. Whether using only the blockchain, or the additional information in status reports, merchants can use our techniques to quantify in real-time the threat of double-spend attacks.
A criterion for "easiness" of certain SAT problems
Venue: arXiv cs.CC 2017
Year: 2017

A generalized 1-in-3SAT problem is defined and found to be in complexity class P when restricted to a certain subset of CNF expressions. In particular, 1-in-kSAT with no restrictions on the number of literals per clause can be decided in polynomial time when restricted to exact READ-3 formulas with equal number of clauses (m) and variables (n), and no pure literals. Also individual instances can be checked for easiness with respect to a given SAT problem. By identifying whole classes of formulas as being solvable efficiently the approach might be of interest also in the complementary search for hard instances.
Predicting User-Interactions on Reddit
Venue: arXiv cs.SI 2017
Year: 2017

In order to keep up with the demand of curating the deluge of crowd-sourced content, social media platforms leverage user interaction feedback to make decisions about which content to display, highlight, and hide. User interactions such as likes, votes, clicks, and views are assumed to be a proxy of a content's quality, popularity, or news-worthiness. In this paper we ask: how predictable are the interactions of a user on social media? To answer this question we recorded the clicking, browsing, and voting behavior of 186 Reddit users over a year. We present interesting descriptive statistics about their combined 339,270 interactions, and we find that relatively simple models are able to predict users' individual browse- or vote-interactions with reasonable accuracy.