OpenRaiser/Intern-Atlas huggingface dataset:
Distributed Query Processing Plans generation using Teacher Learner Based Optimization
Venue: arXiv cs.DB 2016
Year: 2016

With the growing popularity, the number of data sources and the amount of data has been growing very fast in recent years. The distribution of operational data on disperse data sources impose a challenge on processing user queries. In such database systems, the database relations required by a query to answer may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent or alternatives of a user query. Though it is not computationally reasonable to explore exhaustively all possible query plans in a large search space, thus a strategy is requisite to produce optimal query plans in distributed database systems. The query plan with most cost-effective option for query processing is measured necessary and must be generated for a given query. This paper attempts to generate such optimal query plans using a parameter less optimization technique Teaching-Learner Based Optimization(TLBO). The TLBO algorithm was experiential to go one better than the other optimization algorithms for the multi objective unconstrained and constrained benchmark problems. Experimental comparisons of TLBO based optimal plan generation with the multiobjective genetic algorithm based distributed query plan generation algorithm shows that for higher number of relations, the TLBO based algorithm is able to generate comparatively better quality Top K query plans.
Aperiodic Crosscorrelation of Sequences Derived from Characters
Venue: arXiv cs.IT 2016
Year: 2016

It is shown that pairs of maximal linear recursive sequences (m-sequences) typically have mean square aperiodic crosscorrelation on par with that of random sequences, but that if one takes a pair of m-sequences where one is the reverse of the other, and shifts them appropriately, one can get significantly lower mean square aperiodic crosscorrelation. Sequence pairs with even lower mean square aperiodic crosscorrelation are constructed by taking a Legendre sequence, cyclically shifting it, and then cutting it (approximately) in half and using the halves as the sequences of the pair. In some of these constructions, the mean square aperiodic crosscorrelation can be lowered further if one truncates or periodically extends (appends) the sequences. Exact asymptotic formulae for mean squared aperiodic crosscorrelation are proved for sequences derived from additive characters (including m-sequences and modified versions thereof) and multiplicative characters (including Legendre sequences and their relatives). Data is presented that shows that sequences of modest length have performance that closely approximates the asymptotic formulae.
Convolutional Tables Ensemble: classification in microseconds
Venue: arXiv cs.CV 2016
Year: 2016

We study classifiers operating under severe classification time constraints, corresponding to 1-1000 CPU microseconds, using Convolutional Tables Ensemble (CTE), an inherently fast architecture for object category recognition. The architecture is based on convolutionally-applied sparse feature extraction, using trees or ferns, and a linear voting layer. Several structure and optimization variants are considered, including novel decision functions, tree learning algorithm, and distillation from CNN to CTE architecture. Accuracy improvements of 24-45% over related art of similar speed are demonstrated on standard object recognition benchmarks. Using Pareto speed-accuracy curves, we show that CTE can provide better accuracy than Convolutional Neural Networks (CNN) for a certain range of classification time constraints, or alternatively provide similar error rates with 5-200X speedup.
A 3D Sweep Hull Algorithm for computing Convex Hulls and Delaunay Triangulation
Venue: arXiv cs.CG 2016
Year: 2016

This paper presents a new O(nlog(n)) algorithm for computing the convex hull of a set of 3 dimensional points. The algorithm first sorts the point in (x,y,z) then incrementally adds sorted points to the convex hull using the constraint that each new point added to the hull can 'see' at least one facet touching the last point added. The reduces the search time for adding new points. The algorithm belongs to the family of swept hull algorithms. While slower than q-hull for the general case it significantly outperforms q-hull for the pathological case where all of the points are on the 3D hull (as is the case for Delaunay triangulation). The algorithm has been named the 'Newton Apple Wrapper algorithm' and has been released under GPL in C++. keywords: Delaunay triangulation, 3D convex hull.
Identifying Structures in Social Conversations in NSCLC Patients through the Semi-Automatic extraction of Topical Taxonomies
Venue: arXiv cs.IR 2016
Year: 2016

The exploration of social conversations for addressing patient's needs is an important analytical task in which many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in order to generate insight from social media data, which can be considered as one of the most challenging source of information available today due to its sheer volume and noise. This study is based on the work by Scott Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The mechanism for automatically identifying and generating a taxonomy from social conversations is developed and pressured tested using public data from media sites focused on the needs of cancer patients and their families. Moreover, a novel method for generating the category's label and the determination of an optimal number of categories is presented which extends Scott and Jeffrey's research in a meaningful way. We assume the reader is familiar with taxonomies, what they are and how they are used.
A review on locomotion robophysics: the study of movement at the intersection of robotics, soft matter and dynamical systems
Venue: arXiv cs.RO 2016
Year: 2016

In this review we argue for the creation of a physics of moving systems -- a locomotion "robophysics" -- which we define as the pursuit of the discovery of principles of self generated motion. Robophysics can provide an important intellectual complement to the discipline of robotics, largely the domain of researchers from engineering and computer science. The essential idea is that we must complement study of complex robots in complex situations with systematic study of simplified robophysical devices in controlled laboratory settings and simplified theoretical models. We must thus use the methods of physics to examine successful and failed locomotion in simplified (abstracted) devices using parameter space exploration, systematic control, and techniques from dynamical systems. Using examples from our and other's research, we will discuss how such robophysical studies have begun to aid engineers in the creation of devices that begin to achieve life-like locomotor abilities on and within complex environments, have inspired interesting physics questions in low dimensional dynamical systems, geometric mechanics and soft matter physics, and have been useful to develop models for biological locomotion in complex terrain. The rapidly decreasing cost of constructing sophisticated robot models with easy access to significant computational power bodes well for scientists and engineers to engage in a discipline which can readily integrate experiment, theory and computation.
Towards Robot Task Planning From Probabilistic Models of Human Skills
Venue: arXiv cs.RO 2016
Year: 2016

We describe an algorithm for motion planning based on expert demonstrations of a skill. In order to teach robots to perform complex object manipulation tasks that can generalize robustly to new environments, we must (1) learn a representation of the effects of a task and (2) find an optimal trajectory that will reproduce these effects in a new environment. We represent robot skills in terms of a probability distribution over features learned from multiple expert demonstrations. When utilizing a skill in a new environment, we compute feature expectations over trajectory samples in order to stochastically optimize the likelihood of a trajectory in the new environment. The purpose of this method is to enable execution of complex tasks based on a library of probabilistic skill models. Motions can be combined to accomplish complex tasks in hybrid domains. Our approach is validated in a variety of case studies, including an Android game, simulated assembly task, and real robot experiment with a UR5.
Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs
Venue: arXiv cs.SI 2016
Year: 2016

Given a stream of heterogeneous graphs containing different types of nodes and edges, how can we spot anomalous ones in real-time while consuming bounded memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. We propose StreamSpot, a clustering based anomaly detection approach that addresses challenges in two key fronts: (1) heterogeneity, and (2) streaming nature. We introduce a new similarity function for heterogeneous graphs that compares two graphs based on their relative frequency of local substructures, represented as short strings. This function lends itself to a vector representation of a graph, which is (a) fast to compute, and (b) amenable to a sketched version with bounded size that preserves similarity. StreamSpot exhibits desirable properties that a streaming application requires---it is (i) fully-streaming; processing the stream one edge at a time as it arrives, (ii) memory-efficient; requiring constant space for the sketches and the clustering, (iii) fast; taking constant time to update the graph sketches and the cluster summaries that can process over 100K edges per second, and (iv) online; scoring and flagging anomalies in real time. Experiments on datasets containing simulated system-call flow graphs from normal browser activity and various attack scenarios (ground truth) show that our proposed StreamSpot is high-performance; achieving above 95% detection accuracy with small delay, as well as competitive time and memory usage.
Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation
Venue: arXiv cs.LG 2016
Year: 2016

Recurrent neural network(RNN) has been broadly applied to natural language processing(NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory(LSTM) units for Chinese word segmentation, which is a crucial preprocess task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft features from context, whereas bi-directional LSTM network(BLSTM) does not need any prior knowledge or pre-designing, and it is expert in keeping the contextual information in both directions. Experiment result shows that our approach gets state-of-the-art performance in word segmentation on both traditional Chinese datasets and simplified Chinese datasets.
Segmentation Rectification for Video Cutout via One-Class Structured Learning
Venue: arXiv cs.CV 2016
Year: 2016

Recent works on interactive video object cutout mainly focus on designing dynamic foreground-background (FB) classifiers for segmentation propagation. However, the research on optimally removing errors from the FB classification is sparse, and the errors often accumulate rapidly, causing significant errors in the propagated frames. In this work, we take the initial steps to addressing this problem, and we call this new task \emph{segmentation rectification}. Our key observation is that the possibly asymmetrically distributed false positive and false negative errors were handled equally in the conventional methods. We, alternatively, propose to optimally remove these two types of errors. To this effect, we propose a novel bilayer Markov Random Field (MRF) model for this new task. We also adopt the well-established structured learning framework to learn the optimal model from data. Additionally, we propose a novel one-class structured SVM (OSSVM) which greatly speeds up the structured learning process. Our method naturally extends to RGB-D videos as well. Comprehensive experiments on both RGB and RGB-D data demonstrate that our simple and effective method significantly outperforms the segmentation propagation methods adopted in the state-of-the-art video cutout systems, and the results also suggest the potential usefulness of our method in image cutout system.
Reinforcement Learning approach for Real Time Strategy Games Battle city and S3
Venue: arXiv cs.AI 2016
Year: 2016

In this paper we proposed reinforcement learning algorithms with the generalized reward function. In our proposed method we use Q-learning and SARSA algorithms with generalised reward function to train the reinforcement learning agent. We evaluated the performance of our proposed algorithms on two real-time strategy games called BattleCity and S3. There are two main advantages of having such an approach as compared to other works in RTS. (1) We can ignore the concept of a simulator which is often game specific and is usually hard coded in any type of RTS games (2) our system can learn from interaction with any opponents and quickly change the strategy according to the opponents and do not need any human traces as used in previous works. Keywords : Reinforcement learning, Machine learning, Real time strategy, Artificial intelligence.
Strongly Universal Reversible Gate Sets
Venue: arXiv cs.ET 2016
Year: 2016

It is well-known that the Toffoli gate and the negation gate together yield a universal gate set, in the sense that every permutation of $\{0,1\}^n$ can be implemented as a composition of these gates. Since every bit operation that does not use all of the bits performs an even permutation, we need to use at least one auxiliary bit to perform every permutation, and it is known that one bit is indeed enough. Without auxiliary bits, all even permutations can be implemented. We generalize these results to non-binary logic: If $A$ is a finite set of odd cardinality then a finite gate set can generate all permutations of $A^n$ for all $n$, without any auxiliary symbols. If the cardinality of $A$ is even then, by the same argument as above, only even permutations of $A^n$ can be implemented for large $n$, and we show that indeed all even permutations can be obtained from a finite universal gate set. We also consider the conservative case, that is, those permutations of $A^n$ that preserve the weight of the input word. The weight is the vector that records how many times each symbol occurs in the word. It turns out that no finite conservative gate set can, for all $n$, implement all conservative even permutations of $A^n$ without auxiliary bits. But we provide a finite gate set that can implement all those conservative permutations that are even within each weight class of $A^n$.
Contextual Media Retrieval Using Natural Language Queries
Venue: arXiv cs.IR 2016
Year: 2016

The widespread integration of cameras in hand-held and head-worn devices as well as the ability to share content online enables a large and diverse visual capture of the world that millions of users build up collectively every day. We envision these images as well as associated meta information, such as GPS coordinates and timestamps, to form a collective visual memory that can be queried while automatically taking the ever-changing context of mobile users into account. As a first step towards this vision, in this work we present Xplore-M-Ego: a novel media retrieval system that allows users to query a dynamic database of images and videos using spatio-temporal natural language queries. We evaluate our system using a new dataset of real user queries as well as through a usability study. One key finding is that there is a considerable amount of inter-user variability, for example in the resolution of spatial relations in natural language utterances. We show that our retrieval system can cope with this variability using personalisation through an online learning-based retrieval formulation.
Tight Lower Bounds on Graph Embedding Problems
Venue: arXiv cs.DS 2016
Year: 2016

We prove that unless the Exponential Time Hypothesis (ETH) fails, deciding if there is a homomorphism from graph $G$ to graph $H$ cannot be done in time $|V(H)|^{o(|V(G)|)}$. We also show an exponential-time reduction from Graph Homomorphism to Subgraph Isomorphism. This rules out (subject to ETH) a possibility of $|V(H)|^{o(|V(H)|)}$-time algorithm deciding if graph $G$ is a subgraph of $H$. For both problems our lower bounds asymptotically match the running time of brute-force algorithms trying all possible mappings of one graph into another. Thus, our work closes the gap in the known complexity of these fundamental problems. Moreover, as a consequence of our reductions conditional lower bounds follow for other related problems such as Locally Injective Homomorphism, Graph Minors, Topological Graph Minors, Minimum Distortion Embedding and Quadratic Assignment Problem.
Enumerating all the Irreducible Polynomials over Finite Field
Venue: arXiv cs.DM 2016
Year: 2016

In this paper we give a detailed analysis of deterministic and randomized algorithms that enumerate any number of irreducible polynomials of degree $n$ over a finite field and their roots in the extension field in quasilinear where $N=n^2$ is the size of the output.} time cost per element. Our algorithm is based on an improved algorithm for enumerating all the Lyndon words of length $n$ in linear delay time and the known reduction of Lyndon words to irreducible polynomials.
Comments on "the return of information theory"
Venue: arXiv cs.IT 2016
Year: 2016

We criticize the article "Quantifying sustainability: Resilience, efficiency and the return of information theory" (Ulanowicz et al., Ecological Complexity, 2009) for its presentation of information theory to an audience of ecologists. We find that the formulation and use of basic IT concepts are unnecessarily obscure, deliberately incompatible with standard terminology, and in places just mathematically wrong. We lift some of the confusion by correcting errors, dissecting ambiguities and using standard terminology wherever possible. We believe that clear and rigorous presentations are necessary in order to enable information theoreticians and ecologists to engage in a fruitful dialogue.
Skill Evaluation for Newly Graduated Students Via Online Test
Venue: arXiv cs.CY 2016
Year: 2016

Every year in each university many students are graduated holding a first university degree. For example Bachelor degree in Computer Science. Most of those students have a motivation to continue with further studies to get higher education level degree in universities. However, some other students may have enthusiasm towards working based on their skills that they got during their life of studies in university. In both cases, it is required that applicant must pass a test that comprise the entire subject that they learned before. For this reasons, this research is proposing a new technique to evaluate graduate students skills.
Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes
Venue: arXiv cs.LG 2016
Year: 2016

Over the past decade the rate of care unit (CU) use in the United States has been increasing. With an aging population and ever-growing demand for medical care, effective management of patients' transitions among different care facilities will prove indispensible for shortening the length of hospital stays, improving patient outcomes, allocating critical care resources, and reducing preventable re-admissions. In this paper, we focus on an important problem of predicting the so-called "patient flow" from longitudinal electronic health records (EHRs), which has not been explored via existing machine learning techniques. By treating a sequence of transition events as a point process, we develop a novel framework for modeling patient flow through various CUs and jointly predicting patients' destination CUs and duration days. Instead of learning a generative point process model via maximum likelihood estimation, we propose a novel discriminative learning algorithm aiming at improving the prediction of transition events in the case of sparse data. By parameterizing the proposed model as a mutually-correcting process, we formulate the estimation problem via generalized linear models, which lends itself to efficient learning based on alternating direction method of multipliers (ADMM). Furthermore, we achieve simultaneous feature selection and learning by adding a group-lasso regularizer to the ADMM algorithm. Additionally, for suppressing the negative influence of data imbalance on the learning of model, we synthesize auxiliary training data for the classes with extremely few samples, and improve the robustness of our learning method accordingly. Testing on real-world data, we show that our method obtains superior performance in terms of accuracy of predicting the destination CU transition and duration of each CU occupancy.
Trade-off between Communication and Cooperation in the Interference Channel
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of coding over the multi-user Interference Channel (IC). It is well-known that aligning the interfering signals results in improved achievable rates in certain setups involving more than two users. We argue that in the general interference problem, senders face a tradeoff between communicating their message to their corresponding decoder or cooperating with other users by aligning their signals. Traditionally, interference alignment is carried out using structured codes such as linear codes and group codes. We show through an example that the usual structured coding schemes used for interference neutralization lack the necessary flexibility to optimize this tradeoff. Based on this intuition, we propose a new class of codes for this problem. We use the example to show that the application of these codes gives strict improvements in terms of achievable rates. Finally, we derive a new achievable region for the three user IC which strictly improves upon the previously known inner bounds for this problem.
Large Scale Kernel Learning using Block Coordinate Descent
Venue: arXiv cs.LG 2016
Year: 2016

We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points. Armed with this capability, we conduct a thorough comparison between the full kernel, the Nyström method, and random features on three large classification tasks from various domains. Our results suggest that the Nyström method generally achieves better statistical accuracy than random features, but can require significantly more iterations of optimization. Lastly, we derive new rates for block coordinate descent which support our experimental findings when specialized to kernel methods.