OpenRaiser/Intern-Atlas huggingface dataset:
The Unreasonable Effectiveness of Address Clustering
Venue: arXiv cs.CR 2018
Year: 2018

Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on the micro-structure of transactions have proved very effective in practice. In this paper we describe the primary reasons behind this effectiveness: address reuse, avoidable merging, super-clusters with high centrality, and the incremental growth of address clusters. We quantify their impact during Bitcoin's first seven years of existence.
Active Nearest-Neighbor Learning in Metric Spaces
Venue: arXiv cs.LG 2018
Year: 2018

We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a nearest-neighbor classifier. We give prediction error guarantees that depend on the noisy-margin properties of the input sample, and are competitive with those obtained by previously proposed passive learners. We prove that the label complexity of MARMANN is significantly lower than that of any passive learner with similar error guarantees. MARMANN is based on a generalized sample compression scheme, and a new label-efficient active model-selection procedure.
Matrix Completion has No Spurious Local Minimum
Venue: arXiv cs.LG 2018
Year: 2018

Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algorithms are popular and effective in practice. Despite recent progress in proving various non-convex algorithms converge from a good initial point, it remains unclear why random or arbitrary initialization suffices in practice. We prove that the commonly used non-convex objective function for \textit{positive semidefinite} matrix completion has no spurious local minima --- all local minima must also be global. Therefore, many popular optimization algorithms such as (stochastic) gradient descent can provably solve positive semidefinite matrix completion with \textit{arbitrary} initialization in polynomial time. The result can be generalized to the setting when the observed entries contain noise. We believe that our main proof strategy can be useful for understanding geometric properties of other statistical problems involving partial or noisy observations.
A novel algorithm for solving the Decision Boolean Satisfiability Problem without algebra
Venue: arXiv cs.DS 2018
Year: 2018

This paper depicts an algorithm for solving the Decision Boolean Satisfiability Problem using the binary numerical properties of a Special Decision Satisfiability Problem, parallel execution, object oriented, and short termination. The two operations: expansion and simplification are used to explains why using algebra grows the resolution steps. It is proved that its complexity has an upper bound of $2^{n-1}$ where $n$ is the number of logical variables of the given problem.
A counterexample to Thiagarajan's conjecture on regular event structures
Venue: arXiv cs.FL 2018
Year: 2018

We provide a counterexample to a conjecture by Thiagarajan (1996 and 2002) that regular event structures correspond exactly to event structures obtained as unfoldings of finite 1-safe Petri nets. The same counterexample is used to disprove a closely related conjecture by Badouel, Darondeau, and Raoult (1999) that domains of regular event structures with bounded $\natural$-cliques are recognizable by finite trace automata. Event structures, trace automata, and Petri nets are fundamental models in concurrency theory. There exist nice interpretations of these structures as combinatorial and geometric objects. Namely, from a graph theoretical point of view, the domains of prime event structures correspond exactly to median graphs; from a geometric point of view, these domains are in bijection with CAT(0) cube complexes. A necessary condition for both conjectures to be true is that domains of regular event structures (with bounded $\natural$-cliques) admit a regular nice labeling. To disprove these conjectures, we describe a regular event domain (with bounded $\natural$-cliques) that does not admit a regular nice labeling. Our counterexample is derived from an example by Wise (1996 and 2007) of a nonpositively curved square complex whose universal cover is a CAT(0) square complex containing a particular plane with an aperiodic tiling. We prove that other counterexamples to Thiagarajan's conjecture arise from aperiodic 4-way deterministic tile sets of Kari and Papasoglu (1999) and Lukkarila (2009). On the positive side, using breakthrough results by Agol (2013) and Haglund and Wise (2008, 2012) from geometric group theory, we prove that Thiagarajan's conjecture is true for regular event structures whose domains occur as principal filters of hyperbolic CAT(0) cube complexes which are universal covers of finite nonpositively curved cube complexes.
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
Venue: arXiv cs.CV 2018
Year: 2018

Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision algorithms has facilitated rapid progress in this area over the last few years. However, directly comparing re-id algorithms reported in the literature has become difficult since a wide variety of features, experimental protocols, and evaluation metrics are employed. In order to address this need, we present an extensive review and performance evaluation of single- and multi-shot re-id algorithms. The experimental protocol incorporates the most recent advances in both feature extraction and metric learning. To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques. All approaches were evaluated using a new large-scale dataset that closely mimics a real-world problem setting, in addition to 16 other publicly available datasets: VIPeR, GRID, CAVIAR, DukeMTMC4ReID, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, CUHK01, CHUK02, CUHK03, RAiD, iLIDSVID, HDA+ and Market1501. The evaluation codebase and results will be made publicly available for community use.
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling
Venue: arXiv cs.NE 2018
Year: 2018

Synaptic plasticity is implemented and controlled through over thousand different types of molecules in the postsynaptic density and presynaptic boutons that assume a staggering array of different states through phosporylation and other mechanisms. One of the most prominent molecule in the postsynaptic density is CaMKII, that is described in molecular biology as a "memory molecule" that can integrate through auto-phosporylation Ca-influx signals on a relatively large time scale of dozens of seconds. The functional impact of this memory mechanism is largely unknown. We show that the experimental data on the specific role of CaMKII activation in dopamine-gated spine consolidation suggest a general functional role in speeding up reward-guided search for network configurations that maximize reward expectation. Our theoretical analysis shows that stochastic search could in principle even attain optimal network configurations by emulating one of the most well-known nonlinear optimization methods, simulated annealing. But this optimization is usually impeded by slowness of stochastic search at a given temperature. We propose that CaMKII contributes a momentum term that substantially speeds up this search. In particular, it allows the network to overcome saddle points of the fitness function. The resulting improved stochastic policy search can be understood on a more abstract level as Hamiltonian sampling, which is known to be one of the most efficient stochastic search methods.
Anti-Powers in Infinite Words
Venue: arXiv cs.DM 2018
Year: 2018

In combinatorics of words, a concatenation of $k$ consecutive equal blocks is called a power of order $k$. In this paper we take a different point of view and define an anti-power of order $k$ as a concatenation of $k$ consecutive pairwise distinct blocks of the same length. As a main result, we show that every infinite word contains powers of any order or anti-powers of any order. That is, the existence of powers or anti-powers is an unavoidable regularity. Indeed, we prove a stronger result, which relates the density of anti-powers to the existence of a factor that occurs with arbitrary exponent. As a consequence, we show that in every aperiodic uniformly recurrent word, anti-powers of every order begin at every position. We further show that every infinite word avoiding anti-powers of order $3$ is ultimately periodic, while there exist aperiodic words avoiding anti-powers of order $4$. We also show that there exist aperiodic recurrent words avoiding anti-powers of order $6$.
Decidable Characterization of FO2(<,+1) and locality of DA
Venue: arXiv cs.FL 2018
Year: 2018

Several years ago Thérien and Wilke exhibited a decidable characterization of the languages of words that are definable in FO2(<,+1). Their proof relies on three separate ingredients. The first one is the characterization of the languages that are definable in FO2(<) as those whose syntactic semigroup belongs to the variety DA. Then, this result is combined with a wreath product argument showing that being definable in FO2(<,+1) corresponds to having a syntactic semigroup in DA*D. Finally, proving that membership of a semigroup in DA*D is decidable requires a third ingredient: the "locality" of DA, a result proved by Almeida. In this note we present a new self-contained and simple proof that definability in FO2(<,+1) is decidable. We obtain the locality of DA as a corollary.
Learning overcomplete, low coherence dictionaries with linear inference
Venue: arXiv cs.LG 2018
Year: 2018

Finding overcomplete latent representations of data has applications in data analysis, signal processing, machine learning, theoretical neuroscience and many other fields. In an overcomplete representation, the number of latent features exceeds the data dimensionality, which is useful when the data is undersampled by the measurements (compressed sensing, information bottlenecks in neural systems) or composed from multiple complete sets of linear features, each spanning the data space. Independent Components Analysis (ICA) is a linear technique for learning sparse latent representations, which typically has a lower computational cost than sparse coding, its nonlinear, recurrent counterpart. While well suited for finding complete representations, we show that overcompleteness poses a challenge to existing ICA algorithms. Specifically, the coherence control in existing ICA algorithms, necessary to prevent the formation of duplicate dictionary features, is ill-suited in the overcomplete case. We show that in this case several existing ICA algorithms have undesirable global minima that maximize coherence. Further, by comparing ICA algorithms on synthetic data and natural images to the computationally more expensive sparse coding solution, we show that the coherence control biases the exploration of the data manifold, sometimes yielding suboptimal solutions. We provide a theoretical explanation of these failures and, based on the theory, propose improved overcomplete ICA algorithms. All told, this study contributes new insights into and methods for coherence control for linear ICA, some of which are applicable to many other, potentially nonlinear, unsupervised learning methods.
Tropical Geometry and Mechanism Design
Venue: arXiv cs.GT 2018
Year: 2018

We develop a novel framework to construct and analyze finite valued, multidimensional mechanisms using tropical convex geometry. We geometrically characterize incentive compatibility using cells in the tropical convex hull of the type set. These cells are the sets of incentive compatible payments and form tropical simplices, spanned by generating payments whose number equals the dimension of the simplex. The analysis of the collection of incentive compatible mechanisms via tropical simplices and their generating payments facilitates the use of geometric techniques. We use this view to derive a new geometric characterization of revenue equivalence but also show how to handle multidimensional mechanisms in the absence of revenue equivalence.
Open Connect Everywhere: A Glimpse at the Internet Ecosystem through the Lens of the Netflix CDN
Venue: arXiv cs.NI 2018
Year: 2018

The importance of IXPs to interconnect different networks and exchange traffic locally has been well studied over the last few years. However, far less is known about the role IXPs play as a platform to enable large-scale content delivery and to reach a world-wide customer base. In this paper, we study the infrastructure deployment of a content hypergiant, Netflix, and show that the combined worldwide IXP substrate is the major corner stone of its Content Delivery Network. To meet its worldwide demand for high-quality video delivery, Netflix has built a dedicated CDN. Its scale allows us to study a major part of the Internet ecosystem, by observing how Netflix takes advantage of the combined capabilities of IXPs and ISPs present in different regions. We find wide disparities in the regional Netflix deployment and traffic levels at IXPs and ISPs across various local ecosystems. This highlights the complexity of large-scale content delivery as well as differences in the capabilities of IXPs in specific regions. On a global scale we find that the footprint provided by IXPs allows Netflix to deliver most of its traffic directly from them. This highlights the additional role that IXPs play in the Internet ecosystem, not just in terms of interconnection, but also allowing players such as Netflix to deliver significant amounts of traffic.
Multi-Party Protocols, Information Complexity and Privacy
Venue: arXiv cs.CC 2018
Year: 2018

We introduce a new information theoretic measure that we call Public Information Complexity (PIC), as a tool for the study of multi-party computation protocols, and of quantities such as their communication complexity, or the amount of randomness they require in the context of information-theoretic private computations. We are able to use this measure directly in the natural asynchronous message-passing peer-to-peer model and show a number of interesting properties and applications of our new notion: the Public Information Complexity is a lower bound on the Communication Complexity and an upper bound on the Information Complexity; the difference between the Public Information Complexity and the Information Complexity provides a lower bound on the amount of randomness used in a protocol; any communication protocol can be compressed to its Public Information Cost; an explicit calculation of the zero-error Public Information Complexity of the $k$-party, $n$-bit Parity function, where a player outputs the bit-wise parity of the inputs. The latter result also establishes that the amount of randomness needed by a private protocol that computes this function is $Ω(n)$.
Hiring Doctors in E-Healthcare With Zero Budget
Venue: arXiv cs.GT 2018
Year: 2018

The doctors (or expert consultants) are the critical resources on which the success of critical medical cases are heavily dependent. With the emerging technologies (such as video conferencing, smartphone, etc.) this is no longer a dream but a fact, that for critical medical cases in a hospital, expert consultants from around the world could be hired, who may be present physically or virtually. Earlier, this interesting situation by taking the expert consultancies from outside the hospital had been studied, but under monetary perspective. In this paper, for the first time, to the best of our knowledge, we investigate the situation, where the below income group (BIG) people of the society may be served efficiently through the expert consultancy by the renowned doctors from outside of the hospital under zero budget. This will help us saving many lives which will fulfil the present day need of biomedical research. We propose three mechanisms: Random pick-assign mechanism (RanPAM), Truthful optimal allocation mechanism (TOAM), and Truthful optimal allocation mechanism for incomplete preferences (TOAM-IComP) to allocate the doctor to the patient. With theoretical analysis, we demonstrate that the TOAM is strategy-proof, and exhibits a unique core property. The mechanisms are also validated with exhaustive experiments.
Programming Patterns in Dataflow Matrix Machines and Generalized Recurrent Neural Nets
Venue: arXiv cs.PL 2018
Year: 2018

Dataflow matrix machines arise naturally in the context of synchronous dataflow programming with linear streams. They can be viewed as a rather powerful generalization of recurrent neural networks. Similarly to recurrent neural networks, large classes of dataflow matrix machines are described by matrices of numbers, and therefore dataflow matrix machines can be synthesized by computing their matrices. At the same time, the evidence is fairly strong that dataflow matrix machines have sufficient expressive power to be a convenient general-purpose programming platform. Because of the network nature of this platform, programming patterns often correspond to patterns of connectivity in the generalized recurrent neural networks understood as programs. This paper explores a variety of such programming patterns.
Matched Metrics to the Binary Asymmetric Channels
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we establish some criteria to decide when a discrete memoryless channel admits a metric in such a way that the maximum likelihood decoding coincides with the nearest neighbour decoding. In particular we prove a conjecture presented by M. Firer and J. L. Walker establishing that every binary asymmetric channel admits a matched metric.
Scenario-based decision-making for power systems investment planning
Venue: arXiv cs.GT 2018
Year: 2018

The optimization of power systems involves complex uncertainties, such as technological progress, political context, geopolitical constraints. Negotiations at COP21 are complicated by the huge number of scenarios that various people want to consider; these scenarios correspond to many uncertainties. These uncertainties are difficult to modelize as probabilities, due to the lack of data for future technologies and due to partially adversarial geopolitical decision makers. Tools for such difficult decision making problems include Wald and Savage criteria, possibilistic reasoning and Nash equilibria. We investigate the rationale behind the use of a two-player Nash equilibrium approach in such a difficult context; we show that the computational cost is indeed smaller than for simpler criteria. Moreover, it naturally provides a selection of decisions and scenarios, and it has a natural interpretation in the sense that Nature does not make decisions taking into account our own decisions. The algorithm naturally provides a matrix of results, namely the matrix of outcomes in the most interesting decisions and for the most critical scenarios. These decisions and scenarios are also equipped with a ranking.
A Survey of Cellular Automata: Types, Dynamics, Non-uniformity and Applications
Venue: arXiv cs.FL 2018
Year: 2018

Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation. Since the inception of cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modelling different physical, natural as well as real-life phenomena. Classically, CAs are uniform. However, non-uniformity has also been introduced in update pattern, lattice structure, neighborhood dependency and local rule. In this survey, we tour to the various types of CAs introduced till date, the different characterization tools, the global behaviors of CAs, like universality, reversibility, dynamics etc. Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.
A Factor-Graph Approach to Algebraic Topology, With Applications to Kramers--Wannier Duality
Venue: arXiv cs.IT 2018
Year: 2018

Algebraic topology studies topological spaces with the help of tools from abstract algebra. The main focus of this paper is to show that many concepts from algebraic topology can be conveniently expressed in terms of (normal) factor graphs. As an application, we give an alternative proof of a classical duality result of Kramers and Wannier, which expresses the partition function of the two-dimensional Ising model at a low temperature in terms of the partition function of the two-dimensional Ising model at a high temperature. Moreover, we discuss analogous results for the three-dimensional Ising model and the Potts model.
AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis
Venue: arXiv cs.SD 2018
Year: 2018

Recently, sound recognition has been used to identify sounds, such as car and river. However, sounds have nuances that may be better described by adjective-noun pairs such as slow car, and verb-noun pairs such as flying insects, which are under explored. Therefore, in this work we investigate the relation between audio content and both adjective-noun pairs and verb-noun pairs. Due to the lack of datasets with these kinds of annotations, we collected and processed the AudioPairBank corpus consisting of a combined total of 1,123 pairs and over 33,000 audio files. One contribution is the previously unavailable documentation of the challenges and implications of collecting audio recordings with these type of labels. A second contribution is to show the degree of correlation between the audio content and the labels through sound recognition experiments, which yielded results of 70% accuracy, hence also providing a performance benchmark. The results and study in this paper encourage further exploration of the nuances in audio and are meant to complement similar research performed on images and text in multimedia analysis.
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation
Venue: arXiv cs.RO 2018
Year: 2018

This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system. The main contributions of this paper are as follows: a novel and efficient Deep Learning person detection and a standardization of human-aware constraints. In the first stage of the approach, we propose to cascade the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) to achieve fast and accurate Pedestrian Detection (PD). Regarding the human awareness (that can be defined as constraints associated with the robot's motion), we use a mixture of asymmetric Gaussian functions, to define the cost functions associated to each constraint. Both methods proposed herein are evaluated individually to measure the impact of each of the components. The final solution (including both the proposed pedestrian detection and the human-aware constraints) is tested in a typical domestic indoor scenario, in four distinct experiments. The results show that the robot is able to cope with human-aware constraints, defined after common proxemics and social rules.