snu-aidas/HVMap-dataset huggingface dataset:
Higher Lower Bounds from the 3SUM Conjecture

The 3SUM conjecture has proven to be a valuable tool for proving conditional lower bounds on dynamic data structures and graph problems. This line of work was initiated by Pǎtraşcu (STOC 2010) who reduced 3SUM to an offline SetDisjointness problem. However, the reduction introduced by Pǎtraşcu suffers from several inefficiencies, making it difficult to obtain tight conditional lower bounds from the 3SUM conjecture. In this paper we address many of the deficiencies of Pǎtraşcu’s framework. We give new and efficient reductions from 3SUM to offline SetDisjointness and offline SetIntersection (the reporting version of SetDisjointness) which leads to polynomially higher lower bounds on several problems. Using our reductions, we are able to show the essential optimality of several algorithms, assuming the 3SUM conjecture. • Chiba and Nishizeki’s O(mα)-time algorithm (SICOMP 1985) for enumerating all triangles in a graph with arboricity/degeneracy α is essentially optimal, for any α. • Bjørklund, Pagh, Williams, and Zwick’s algorithm (ICALP 2014) for listing t triangles is essentially optimal (assuming the matrix multiplication exponent is ω = 2). • Any static data structure for SetDisjointness that answers queries in constant time must spend Ω(N2−o(1)) time in preprocessing, where N is the size of the set system. These statements were unattainable via Pǎtraşcu’s reductions. We also introduce several new reductions from 3SUM to pattern matching problems and dynamic graph problems. Of particular interest are new conditional lower bounds for dynamic versions of Maximum Cardinality Matching, which introduce a new technique for obtaining amortized lower bounds.
Rigid-Body Dynamics with Friction and Impact

Rigid-body dynamics with unilateral contact is a good approximation for a wide range of everyday phenomena, from the operation of car brakes to walking to rock slides. It is also of vital importance for simulating robots, virtual reality, and realistic animation. However, correctly modeling rigid-body dynamics with friction is difficult due to a number of discontinuities in the behavior of rigid bodies and the discontinuities inherent in the Coulomb friction law. This is particularly crucial for handling situations with large coefficients of friction, which can result in paradoxical results known at least since Painlevé [C. R. Acad. Sci. Paris, 121 (1895), pp. 112–115]. This single example has been a counterexample and cause of controversy ever since, and only recently have there been rigorous mathematical results that show the existence of solutions to his example. The new mathematical developments in rigid-body dynamics have come from several sources: “sweeping processes” and the measure differential inclusions of Moreau in the 1970s and 1980s, the variational inequality approaches of Duvaut and J.-L. Lions in the 1970s, and the use of complementarity problems to formulate frictional contact problems by Lötstedt in the early 1980s. However, it wasn’t until much more recently that these tools were finally able to produce rigorous results about rigid-body dynamics with Coulomb friction and impulses.
Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions

K.M. Hornik, M. Stinchcombe, and H. White (Univ. of California at San Diego, Dept. of Economics Discussion Paper, June 1988; to appear in Neural Networks) showed that multilayer feedforward networks with as few as one hidden layer, no squashing at the output layer, and arbitrary sigmoid activation function at the hidden layer are universal approximators: they are capable of arbitrarily accurate approximation to arbitrary mappings, provided sufficiently many hidden units are available. The present authors obtain identical conclusions but do not require the hidden-unit activation to be sigmoid. Instead, it can be a rather general nonlinear function. Thus, multilayer feedforward networks possess universal approximation capabilities by virtue of the presence of intermediate layers with sufficiently many parallel processors; the properties of the intermediate-layer activation function are not so crucial. In particular, sigmoid activation functions are not necessary for universal approximation.<<ETX>>
Control methodologies in networked control systems

The use of a data network in a control loop has gained increasing attentions in recent years due to its cost effective and flexible applications. One of the major challenges in this so-called networked control system (NCS) is the network-induced delay effect in the control loop. Network delays degrade the NCS control performance and destabilize the system. A significant emphasis has been on developing control methodologies to handle the network delay effect in NCS. This survey paper presents recent NCS control methodologies. The overview on NCS structures and description of network delays including characteristics and effects are also covered. r 2003 Published by Elsevier Ltd.
Nonparametric Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel or neural network structure), and are computationally demanding. Interestingly, the theory of nonlinear CCA, without functional restrictions, had been studied in the population setting by Lancaster already in the 1950s, but these results have not inspired practical algorithms. We revisit Lancaster’s theory to devise a practical algorithm for nonparametric CCA (NCCA). Specifically, we show that the solution can be expressed in terms of the singular value decomposition of a certain operator associated with the joint density of the views. Thus, by estimating the population density from data, NCCA reduces to solving an eigenvalue system, superficially like kernel CCA but, importantly, without requiring the inversion of any kernel matrix. We also derive a partially linear CCA (PLCCA) variant in which one of the views undergoes a linear projection while the other is nonparametric. Using a kernel density estimate based on a small number of nearest neighbors, our NCCA and PLCCA algorithms are memory-efficient, often run much faster, and perform better than kernel CCA and comparable to deep CCA. Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 48. Copyright 2016 by the author(s).
A near-optimal algorithm for computing the entropy of a stream

We describe a simple algorithm for approximating the empirical entropy of a stream of <i>m</i> values in a single pass, using <i>O</i>(ε<sup>-2</sup> log(Δ<sup>-1</sup>) log <i>m</i>) words of space. Our algorithm is based upon a novel extension of a method introduced by Alon, Matias, and Szegedy [1]. We show a space lower bound of Ω(ε<sup>-2</sup> / log(ε<sup>-1</sup>)), meaning that our algorithm is near-optimal in terms of its dependency on ε. This improves over previous work on this problem [8, 13, 17, 5]. We show that generalizing to <i>k</i>th order entropy requires close to linear space for all <i>k</i> ≥ 1, and give additive approximations using our algorithm. Lastly, we show how to compute a multiplicative approximation to the entropy of a random walk on an undirected graph.
Agile Flight Control Techniques for a Fixed-Wing Aircraft by Frantisek Michal Sobolic

As unmanned aerial vehicles (UAVs) become more involved in challenging mission objectives, the need for agility controlled flight becomes more of a necessity. The ability to navigate through constrained environments as well as quickly maneuver to each mission target is essential. Currently, individual vehicles are developed with a particular mission objective, whether it be persistent surveillance or fly-by reconnaissance. Fixed-wing vehicles with a high thrust-to-weight ratio are capable of performing maneuvers such as take-off or perch style landing and switch between hover and conventional flight modes. Agile flight controllers enable a single vehicle to achieve multiple mission objectives. By utilizing the knowledge of the flight dynamics through all flight regimes, nonlinear controllers can be developed that control the aircraft in a single design. This thesis develops a full six-degree-of-freedom model for a fixed-wing propellerdriven aircraft along with methods of control through nonconventional flight regimes. In particular, these controllers focus on transitioning into and out of hover to level flight modes. This maneuver poses hardships for conventional linear control architectures because these flights involve regions of the post-stall regime, which is highly nonlinear due to separation of flow over the lifting surfaces. Using Lyapunov backstepping control stability theory as well as quaternion-based control methods, control strategies are developed that stabilize the aircraft through these flight regimes without the need to switch control schemes. The effectiveness of each control strategy is demonstrated in both simulation and flight experiments. Thesis Supervisor: Jonathan P. How Title: Professor
Simple to complex cross-modal learning to rank

The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model’s robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the stateof-the-arts in this literature. © 2017 Elsevier Inc. All rights reserved.
Can Twitter be used to predict county excessive alcohol consumption rates?

OBJECTIVES
The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county.


METHODS
Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis.


RESULTS
Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption.


CONCLUSIONS
Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.
Reproducible Experiments for Comparing Apache Flink and Apache Spark on Public Clouds

Big data processing is a hot topic in today’s computer science world. There is a significant demand for analysing big data to satisfy many requirements of many industries. Emergence of the Kappa architecture created a strong requirement for a highly capable and efficient data processing engine. Therefore data processing engines such as Apache Flink and Apache Spark emerged in open source world to fulfill that efficient and high performing data processing requirement. There are many available benchmarks to evaluate those two data processing engines. But complex deployment patterns and dependencies make those benchmarks very difficult to reproduce by our own. This project has two main goals. They are making few of community accepted benchmarks easily reproducible on cloud and validate the performance claimed by those studies. Keywords– Data Processing, Apache Flink, Apache Spark, Batch processing, Stream processing, Reproducible experiments, Cloud
Anusaaraka: Machine Translation in Stages

Fully-automatic general-purpose high-quality machine translation systems (FGH-MT) are extremely difficult to build. In fact, there is no system in the world for any pair of languages which qualifies to be called FGH-MT. The reasons are not far to seek. Translation is a creative process which involves interpretation of the given text by the translator. Translation would also vary depending on the audience and the purpose for which it is meant. This would explain the difficulty of building a machine translation system. Since, the machine is not capable of interpreting a general text with sufficient accuracy automatically at present let alone re-expressing it for a given audience, it fails to perform as FGH-MT. FOOTNOTE{The major difficulty that the machine facesin interpreting a given text is the lack of general world knowledge or common sense knowledge.}
LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK

In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves stateof-art results in terms of the false positive rate at a 95% recall rate on standard benchmark datasets. * Corresponding author
Risk Management for Service-Oriented Systems

Web service technology can be used for integrating heterogeneous and autonomous applications into cross-organizational systems. A key problem is to support a high quality of service-oriented systems despite vulnerabilities caused by the use of external web services. One important aspect that has received little attention so far is risk management for such systems. This paper discusses risks peculiar for servicebased systems, their impact and ways of mitigation. In the context of service-oriented design, risks can be reduced by selection of appropriate business partners, web service discovery, service composition and Quality of Service (QoS) management. Advisors. Vincenzo D’Andrea.
Tabu Search - Part II

Full terms and conditions of use: http://pubsonline.informs.org/page/terms-and-conditions This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. © 1990 INFORMS
Facebook® and academic performance

There is much talk of a change in modern youth – often referred to as digital natives or Homo Zappiens – with respect to their ability to simultaneously process multiple channels of information. In other words, kids today can multitask. Unfortunately for proponents of this position, there is much empirical documentation concerning the negative effects of attempting to simultaneously process different streams of information showing that such behavior leads to both increased study time to achieve learning parity and an increase in mistakes while processing information than those who are sequentially or serially processing that same information. This article presents the preliminary results of a descriptive and exploratory survey study involving Facebook use, often carried out simultaneously with other study activities, and its relation to academic performance as measured by self-reported Grade Point Average (GPA) and hours spent studying per week. Results show that Facebook users reported having lower GPAs and spend fewer hours per week studying than nonusers. 2010 Elsevier Ltd. All rights reserved.
Efficient mining of weighted association rules (WAR)

ABSTRACT In this paper, we extend the tradition association rule problem by allowing a weight to be associated with each item in a transaction, to re ect interest/intensity of the item within the transaction. This provides us in turn with an opportunity to associate a weight parameter with each item in the resulting association rule. We call it weighted association rule (WAR). WAR not only improves the con dence of the rules, but also provides a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty or volume of purchases. Our approach mines WARs by rst ignoring the weight and nding the frequent itemsets (via a traditional frequent itemset discovery algorithm), and is followed by introducing the weight during the rule generation. It is shown by experimental results that our approach not only results in shorter average execution times, but also produces higher quality results than the generalization of previous known methods on quantitative association rules.
Logic and Databases: A Deductive Approach

ion, Databases and Conceptual Modeling (Pingree Park, Colo., June), pp. 112-114; ACM SZGMOD Rec. 11, 2 (Feb.). CODD, E. F. 1982. Relational database: A practical foundation for productivity. Commun. ACM 25, 2 (Feb.), 109-117. COLMERAUER, A. 1973. Un systeme de communication homme-machine en francais. Rapport, Groupe Intelligence Artificielle, Universite d’AixMarseille-Luminy, Marseilles, France. COLMERAUER, A., AND PIQUE, J. F. 1981. About natural logic. In Advances in Database Theory vol. 1, H. Gallaire, J. Minker, and J.-M. Nicolas, Eds. Plenum, New York, pp. 343-365. COOPER, E. C. 1980. On the expressive power of query languages for relational databases. Tech. Rep. 14-80, Computer Science Dept., Harvard Univ., Cambridge, Mass. DAHL, V. 1982. On database systems development through logic. ACM Trans. Database Syst. 7, 1 (Mar.), 102-123. DATE, C. J. 1977. An Introduction to Database Systems. Addison-Wesley, Reading, Mass. DATE, C. J. 1981. An Introduction to Database Systems, 3rd ed. Addison-Wesley, Reading, Mass. DELIYANNI, A., AND KOWALSKI, R. A. 1979. Logic and semantic networks. Commun. ACM 22, 3
A Rule Based Approach to Discourse Parsing

In this paper we present an overview of recent developments in discourse theory and parsing under the Linguistic Discourse Model (LDM) framework, a semantic theory of discourse structure. We give a novel approach to the problem of discourse segmentation based on discourse semantics and sketch a limited but robust approach to symbolic discourse parsing based on syntactic, semantic and lexical rules. To demonstrate the utility of the system in a real application, we briefly describe the architecture of the PALSUMM system, a symbolic summarization system being developed at FX Palo Alto Laboratory that uses discourse structures constructed using the theory outlined to summarize written English prose texts. 1
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

OBJECTIVE
Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible.


APPROACH
In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR).


MAIN RESULTS
We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms. In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features.


SIGNIFICANCE
Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.
NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING

WHAT IS DATA MINING? WHY DATA MINING? NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING WHAT TASKS CAN DATA MINING ACCOMPLISH? CASE STUDY 2: PREDICTING ABNORMAL STOCK MARKET RETURNS USING NEURAL NETWORKS CASE STUDY 3: MINING ASSOCIATION RULES FROM LEGAL DATABASES CASE STUDY 4: PREDICTING CORPORATE BANKRUPTCIES USING DECISION TREES CASE STUDY 5: PROFILING THE TOURISM MARKET USING k-MEANS CLUSTERING ANALYSIS