Machines learning techniques plays a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners. For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as autoML. In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.
To the best of our knowledge, the only approach that uses a non-predetermined sequence of operators is @cite_36 , but it is not possible to add additional constraints.
@cite_36
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A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5 @math 3.5 ) for a previously published four class card pip recognition task and an accuracy of 84.9 @math 1.9 for a new more difficult 36 class character recognition task. Describing a video sequence in terms of a small number of coherently moving segments is useful for tasks ranging from video compression to event perception. A promising approach is to view the motion segmentation problem in a mixture estimation framework. However, existing formulations generally use only the motion, data and thus fail to make use of static cues when segmenting the sequence. Furthermore, the number of models is either specified in advance or estimated outside the mixture model framework. In this work we address both of these issues. We show how to add spatial constraints to the mixture formulations and present a variant of the EM algorithm that males use of both the form and the motion constraints. Moreover this algorithm estimates the number of segments given knowledge about the level of model failure expected in the sequence. The algorithm's performance is illustrated on synthetic and real image sequences. The goal of Complex Event Processing (CEP) systems is to efficiently detect complex patterns over a stream of primitive events. A pattern of particular significance is a sequence, where we are interested in identifying that a number of primitive events have arrived on the stream in a predefined order. Many popular CEP systems employ Non-deterministic Finite Automata (NFA) arranged in a chain topology to detect such sequences. Existing NFA-based mechanisms incrementally extend previously observed prefixes of a sequence until a match is found. Consequently, each newly arriving event needs to be processed to determine whether a new prefix is to be initiated or an existing one extended. This approach may be very inefficient when events at the beginning of the sequence are very frequent. We address the problem by introducing a lazy evaluation mechanism that is able to process events in descending order of selectivity. We employ this mechanism in a chain topology NFA, which waits until the most selective event in the sequence arrives and then adds events to partial matches according to a predetermined order of selectivity. In addition, we propose a tree topology NFA that does not require the selectivity order to be defined in advance. Finally, we experimentally evaluate our mechanism on real-world stock trading data, demonstrating a performance gain of two orders of magnitude, with significantly reduced memory resource requirements. In quantum zero knowledge, the assumption was made that the verifier is only using unitary operations. Under this assumption, many nice properties have been shown about quantum zero knowledge, including the fact that Honest-Verifier Quantum Statistical Zero Knowledge ( @math ) is equal to Cheating-Verifier Quantum Statistical Zero Knowledge ( @math ) (see Wat02,Wat06 ). In this paper, we study what happens when we allow an honest verifier to flip some coins in addition to using unitary operations. Flipping a coin is a non-unitary operation but doesn 't seem at first to enhance the cheating possibilities of the verifier since a classical honest verifier can flip coins. In this setting, we show an unexpected result: any classical Interactive Proof has an Honest-Verifier Quantum Statistical Zero Knowledge proof with coins. Note that in the classical case, honest verifier @math is no more powerful than @math and hence it is not believed to contain even @math . On the other hand, in the case of cheating verifiers, we show that Quantum Statistical Zero Knowledge where the verifier applies any non-unitary operation is equal to Quantum Zero-Knowledge where the verifier uses only unitaries. One can think of our results in two complementary ways. If we would like to use the honest verifier model as a means to study the general model by taking advantage of their equivalence, then it is imperative to use the unitary definition without coins, since with the general one this equivalence is most probably not true. On the other hand, if we would like to use quantum zero knowledge protocols in a cryptographic scenario where the honest-but-curious model is sufficient, then adding the unitary constraint severely decreases the power of quantum zero knowledge protocols. The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Deja Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information. Our long-term goal is to develop a general solution to the lifelong robotic object discovery (LROD) problem: to discover new objects in the environment while the robot operates, for as long as the robot operates. In this paper, we consider the first step towards LROD: we automatically process the raw data stream of an entire workday of a robotic agent to discover objects. Our key contribution to achieve this goal is to incorporate domain knowledge (robotic metadata) in the discovery process, in addition to visual data. We propose a general graph-based formulation for LROD in which generic domain knowledge is encoded as constraints. To make long-term object discovery feasible, we encode into our formulation the natural constraints and non-visual sensory information in service robotics. A key advantage of our generic formulation is that we can add, modify, or remove sources of domain knowledge dynamically, as they become available or as conditions change. In our experiments, we show that by adding domain knowledge we discover 2.7A more objects and decrease processing time 190 times. With our optimized implementation, HerbDisc, we show for the first time a system that processes a video stream of 6 h 20 min of continuous exploration in cluttered human environments (and over half a million images) in 18 min 34 s, to discover 206 new objects with their 3D models. Online matching has received significant attention over the last 15 years due to its close connection to Internet advertising. As the seminal work of Karp, Vazirani, and Vazirani has an optimal (1 - 1 epsilon) competitive ratio in the standard adversarial online model, much effort has gone into developing useful online models that incorporate some stochasticity in the arrival process. One such popular model is the "known I.I.D. model" where different customer-types arrive online from a known distribution. We develop algorithms with improved competitive ratios for some basic variants of this model with integral arrival rates, including: (a) the case of general weighted edges, where we improve the best-known ratio of 0.667 due to [Haeupler, Mirrokni and Zadimoghaddam WINE 2011] to 0.705; and (b) the vertex-weighted case, where we improve the 0.7250 ratio of [Jaillet and Lu Math. Oper. Res 2013] to 0.7299. We also consider two extensions, one is "known I.I.D." with non-integral arrival rate and stochastic rewards; the other is "known I.I.D." b-matching with non-integral arrival rate and stochastic rewards. We present a simple non-adaptive algorithm which works well simultaneously on the two extensions. One of the key ingredients of our improvement is the following (offline) approach to bipartite-matching polytopes with additional constraints. We first add several valid constraints in order to get a good fractional solution f; however, these give us less control over the structure of f. We next remove all these additional constraints and randomly move from f to a feasible point on the matching polytope with all coordinates being from the set 0, 1 k, 2 k,..., 1 for a chosen integer k. The structure of this solution is inspired by [Jaillet and Lu Math. Oper. Res 2013] and is a tractable structure for algorithm design and analysis. The appropriate random move preserves many of the removed constraints (approximately [exactly] with high probability [in expectation]). This underlies some of our improvements, and, we hope, could be of independent interest. Knowledge acquisition is the essential process of extracting and encoding knowledge, both domain specific and commonsense, to be used in intelligent systems. While many large knowledge bases have been constructed, none is close to complete. This paper presents an approach to improving a knowledge base efficiently under resource constraints. Using a guiding knowledge base, questions are generated from a weak form of similarity-based inference given the glossary mapping between two knowledge bases. The candidate questions are prioritized in terms of the concept coverage of the target knowledge. Experiments were conducted to find questions to grow the Chinese ConceptNet using the English ConceptNet as a guide. The results were evaluated by online users to verify that 94.17 of the questions and 85.77 of the answers are good. In addition, the answers collected in a six-week period showed consistent improvement to a 36.33 increase in concept coverage of the Chinese commonsense knowledge base against the English ConceptNet. We describe a new approach to transfer knowledge across views for action recognition by using examples from a large collection of unlabelled mocap data. We achieve this by directly matching purely motion based features from videos to mocap. Our approach recovers 3D pose sequences without performing any body part tracking. We use these matches to generate multiple motion projections and thus add view invariance to our action recognition model. We also introduce a closed form solution for approximate non-linear Circulant Temporal Encoding (nCTE), which allows us to efficiently perform the matches in the frequency domain. We test our approach on the challenging unsupervised modality of the IXMAS dataset, and use publicly available motion capture data for matching. Without any additional annotation effort, we are able to significantly outperform the current state of the art. A perfect straight-line matching @math on a finite set @math of points in the plane is a set of segments such that each point in @math is an endpoint of exactly one segment. @math is non-crossing if no two segments in @math cross each other. Given a perfect straight-line matching @math with at least one crossing, we can remove this crossing by a flip operation. The flip operation removes two crossing segments on a point set @math and adds two non-crossing segments to attain a new perfect matching @math . It is well known that after a finite number of flips, a non-crossing matching is attained and no further flip is possible. However, prior to this work, no non-trivial upper bound on the number of flips was known. If @math (resp. @math ) is the maximum length of the longest (resp. shortest) sequence of flips starting from any matching of size @math , we show that @math and @math (resp. @math and @math ). In the first 36 pages of this paper, we provide polynomial quantum algorithms for additive approximations of the Tutte polynomial, at any point in the Tutte plane, for any planar graph. This includes as special cases the AJL algorithm for the Jones polynomial, the partition function of the Potts model for any weighted planer graph at any temperature, and many other combinatorial graph properties. In the second part of the paper we prove the quantum universality of many of the problems for which we provide an algorithm, thus providing a large set of new quantum-complete problems. Unfortunately, we do not know that this holds for the Potts model case; this is left as an important open problem. The main progress in this work is in our ability to handle non-unitary representations of the Temperley Lieb algebra, both when applying them in the algorithm, and, more importantly, in the proof of universality, when encoding quantum circuits using non-unitary operators. To this end we develop many new tools, that allow proving density and applying the Solovay Kitaev theorem in the case of non-unitary matrices. We hope that these tools will open up new possibilities of using non-unitary reps in other quantum computation contexts. Transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple-input multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector; both are hard to realize. In this article, a quantized maximum signal-to-noise ratio (SNR) beamforming technique is proposed where the receiver only sends the label of the best beamforming vector in a predetermined codebook to the transmitter. By using the distribution of the optimal beamforming vector in independent and identically distributed Rayleigh fading matrix channels, the codebook design problem is solved and related to the problem of Grassmannian line packing. The proposed design criterion is flexible enough to allow for side constraints on the codebook vectors. Bounds on the codebook size are derived to guarantee full diversity order. Results on the density of Grassmannian line packings are derived and used to develop bounds on the codebook size given a capacity or SNR loss. Monte Carlo simulations are presented that compare the probability of error for different quantization strategies. In this paper we investigate the problem of counting the size of a network where processes are anonymous (i.e., they share the same identifier) and the network topology constantly changes controlled by an adversary able to look internal process states and add and remove edges in order to contrast the convergence of the algorithm to the correct count. It is easy to show that, if the adversary can generate graphs without any constraint on the connectivity (i.e. it can generate topologies where there exist nodes not able to influence the others), counting is impossible. In this paper we consider a synchronous round based computation and the dynamicity is governed by a worst-case adversary that generates a sequence of graphs, one for each round, with the only constraint that each graph must be connected (1-interval connectivity property). It has been conjectured that counting in a finite time against such adversary is impossible and the existing solutions consider that each process has some knowledge about network topologies generated by the adversary, i.e. at each round, each node has a degree lesser than D. Along the path of proving the validity (or not) of the conjecture, this paper presents an algorithm that counts in a finite time against the worst-case adversary assuming each process is equipped with an oracle. The latter provides a process at each round r with an estimation of the process degree in the graph generated by the adversary at round r. To the best of our knowledge, this is the first counting algorithm (terminating in a finite time) where processes exploit the minimal knowledge about the behavior of the adversary. Interestingly, such oracle can be implemented in a wide range of real systems. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... This paper presents a novel solution for flow-based tracking and 3D reconstruction of deforming objects in monocular image sequences. A non-rigid 3D object undergoing rotation and deformation can be effectively approximated using a linear combination of 3D basis shapes. This puts a bound on the rank of the tracking matrix. The rank constraint is used to achieve robust and precise low-level optical flow estimation without prior knowledge of the 3D shape of the object. The bound on the rank is also exploited to handle occlusion at the tracking level leading to the possibility of recovering the complete trajectories of occluded disoccluded points. Following the same low-rank principle, the resulting flow matrix can be factored to get the 3D pose, configuration coefficients, and 3D basis shapes. The flow matrix is factored in an iterative manner, looping between solving for pose, configuration, and basis shapes. The flow-based tracking is applied to several video sequences and provides the input to the 3D non-rigid reconstruction task. Additional results on synthetic data and comparisons to ground truth complete the experiments. We introduce a novel algorithm, TOQR, for relaxing failed queries over databases, that is, over-constrained DNF queries that return an empty result. TOQR uses a small dataset to discover the implicit relationships among the domain attributes, and then it exploits this domain knowledge to relax the failed query. TOQR starts with a relaxed query that does not include any constraint, and it tries to add to it as many as possible of the original constraints or their relaxations. The order in which the constraints are added is derived from the domain's causal structure, which is learned by applying the TAN algorithm to the small training dataset. Our experiments show that TOQR clearly outperforms other approaches: even when trained on a handful of examples, it successfully relaxes more that 97 of the failed queries; furthermore, TOQR'S relaxed queries are highly similar to the original failed query. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . We propose a new proximal, path-following framework for a class of---possibly non-smooth---constrained convex problems. We consider settings where the non-smooth part is endowed with a proximity operator, and the constraint set is equipped with a self-concordant barrier. Our main contribution is a new re-parametrization of the optimality condition of the barrier problem, that allows us to process the objective function with its proximal operator within a new path following scheme. In particular, our approach relies on the following two main ideas. First, we re-parameterize the optimality condition as an auxiliary problem, such that a "good" initial point is available. Second, we combine the proximal operator of the objective and path-following ideas to design a single phase, proximal, path-following algorithm. Our method has several advantages. First, it allows handling non-smooth objectives via proximal operators, this avoids lifting the problem dimension via slack variables and additional constraints. Second, it consists of only a single phase as compared to a two-phase algorithm in [43] In this work, we show how to overcome this difficulty in the proximal setting and prove that our scheme has the same O(ν√log(1 e)) worst-case iteration-complexity with standard approaches [30, 33], but our method can handle nonsmooth objectives, where ν is the barrier parameter and e is a desired accuracy. Finally, our framework allows errors in the calculation of proximal-Newton search directions, without sacrificing the worst-case iteration complexity. We demonstrate the merits of our algorithm via three numerical examples, where proximal operators play a key role to improve the performance over off-the-shelf interior-point solvers. We develop two new proximal alternating penalty algorithms to solve a wide range class of constrained convex optimization problems. Our approach mainly relies on a novel combination of the classical quadratic penalty, alternating minimization, Nesterov’s acceleration, adaptive strategy for parameters. The first algorithm is designed to solve generic and possibly nonsmooth constrained convex problems without requiring any Lipschitz gradient continuity or strong convexity, while achieving the best-known ( O ( 1 k ) )-convergence rate in a non-ergodic sense, where k is the iteration counter. The second algorithm is also designed to solve non-strongly convex, but semi-strongly convex problems. This algorithm can achieve the best-known ( O ( 1 k^2 ) )-convergence rate on the primal constrained problem. Such a rate is obtained in two cases: (1) averaging only on the iterate sequence of the strongly convex term, or (2) using two proximal operators of this term without averaging. In both algorithms, we allow one to linearize the second subproblem to use the proximal operator of the corresponding objective term. Then, we customize our methods to solve different convex problems, and lead to new variants. As a byproduct, these algorithms preserve the same convergence guarantees as in our main algorithms. We verify our theoretical development via different numerical examples and compare our methods with some existing state-of-the-art algorithms.