OpenRaiser/Intern-Atlas huggingface dataset:
Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment
Venue: arXiv cs.CE 2017
Year: 2017

On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundamental PE ratios are often estimated only by subjective expert opinions. The purpose of this research is to formalize a process of fundamental PE estimation by employing advanced dynamic Bayesian network (DBN) methodology. The estimated PE ratio from our model can be used either as a information support for an expert to make investment decisions, or as an automatic trading system illustrated in experiments. Forward-backward inference and EM parameter estimation algorithms are derived with respect to the proposed DBN structure. Unlike existing works in literatures, the economic interpretation of our DBN model is well-justified by behavioral finance evidences of volatility. A simple but practical trading strategy is invented based on the result of Bayesian inference. Extensive experiments show that our trading strategy equipped with the inferenced PE ratios consistently outperforms standard investment benchmarks.
Securing Application with Software Partitioning: A case study using SGX
Venue: arXiv cs.CR 2017
Year: 2017

Application size and complexity are the underlying cause of numerous security vulnerabilities in code. In order to mitigate the risks arising from such vulnerabilities, various techniques have been proposed to isolate the execution of sensitive code from the rest of the application and from other software on the platform (e.g. the operating system). However, even with these partitioning techniques, it is not immediately clear exactly how they can and should be used to partition applications. What overall partitioning scheme should be followed; what granularity of the partitions should be. To some extent, this is dependent on the capabilities and performance of the partitioning technology in use. For this work, we focus on the upcoming Intel Software Guard Extensions (SGX) technology as the state-of-the-art in this field. SGX provides a trusted execution environment, called an enclave, that protects the integrity of the code and the confidentiality of the data inside it from other software, including the operating system. We present a novel framework consisting of four possible schemes under which an application can be partitioned. These schemes range from coarse-grained partitioning, in which the full application is included in a single enclave, through ultra-fine partitioning, in which each application secret is protected in an individual enclave. We explain the specific security benefits provided by each of the partitioning schemes and discuss how the performance of the application would be affected. To compare the different partitioning schemes, we have partitioned OpenSSL using four different schemes. We discuss SGX properties together with the implications of our design choices in this paper.
Min-Max Regret Scheduling To Minimize the Total Weight of Late Jobs With Interval Uncertainty
Venue: arXiv cs.DM 2017
Year: 2017

We study the single machine scheduling problem with the objective to minimize the total weight of late jobs. It is assumed that the processing times of jobs are not exactly known at the time when a complete schedule must be dispatched. Instead, only interval bounds for these parameters are given. In contrast to the stochastic optimization approach, we consider the problem of finding a robust schedule, which minimizes the maximum regret of a solution. Heuristic algorithm based on mixed-integer linear programming is presented and examined through computational experiments.
Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control
Venue: arXiv cs.AI 2017
Year: 2017

MOBAs represent a huge segment of online gaming and are growing as both an eSport and a casual genre. The natural starting point for AI researchers interested in MOBAs is to develop an AI to play the game better than a human - but MOBAs have many more challenges besides adversarial AI. In this paper we introduce the reader to the wider context of MOBA culture, propose a range of challenges faced by the community today, and posit concrete AI projects that can be undertaken to begin solving them.
Rethinking Skip-thought: A Neighborhood based Approach
Venue: arXiv cs.CL 2017
Year: 2017

We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn't aid our model to perform better, while it hurts the performance of the skip-thought model.
IJIT: An API for Boolean Program Analysis with Just-in-Time Translation (Extended Technical Report)
Venue: arXiv cs.SE 2017
Year: 2017

Exploration algorithms for explicit-state transition systems are a core back-end technology in program verification. They can be applied to programs by generating the transition system on the fly, avoiding an expensive up-front translation. An on-the-fly strategy requires significant modifications to the implementation, into a form that stores states directly as valuations of program variables. Performed manually on a per-algorithm basis, such modifications are laborious and error-prone. In this paper we present the IJIT Application Programming Interface (API), which allows users to automatically transform a given transition system exploration algorithm to one that operates on Boolean programs. The API converts system states temporarily to program states just in time for expansion via image computations, forward or backward. Using our API, we have effortlessly extended various non-trivial (e.g. infinite-state) model checking algorithms to operate on multi-threaded Boolean programs. We demonstrate the ease of use of the API, and present a case study on the impact of the just-in-time translation on these algorithms.
Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals
Venue: arXiv cs.NE 2017
Year: 2017

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN.
Serverless Computing: Current Trends and Open Problems
Venue: arXiv cs.DC 2017
Year: 2017

Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programming models, abstractions, and platforms, and is a testament to the maturity and wide adoption of cloud technologies. In this chapter, we survey existing serverless platforms from industry, academia, and open source projects, identify key characteristics and use cases, and describe technical challenges and open problems.
Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm
Venue: arXiv cs.CV 2017
Year: 2017

Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management. In this study, we propose an end-to-end deep-learning algorithm framework (OF-RNN ) to accurately detect the MI area at the pixel level. Our OF-RNN consists of three different function layers: the heart localization layers, which can accurately and automatically crop the region-of-interest (ROI) sequences, including the left ventricle, using the whole cardiac magnetic resonance image sequences; the motion statistical layers, which are used to build a time-series architecture to capture two types of motion features (at the pixel-level) by integrating the local motion features generated by long short-term memory-recurrent neural networks and the global motion features generated by deep optical flows from the whole ROI sequence, which can effectively characterize myocardial physiologic function; and the fully connected discriminate layers, which use stacked auto-encoders to further learn these features, and they use a softmax classifier to build the correspondences from the motion features to the tissue identities (infarction or not) for each pixel. Through the seamless connection of each layer, our OF-RNN can obtain the area, position, and shape of the MI for each patient. Our proposed framework yielded an overall classification accuracy of 94.35% at the pixel level, from 114 clinical subjects. These results indicate the potential of our proposed method in aiding standardized MI assessments.
Two-Scale Topology Optimization with Microstructures
Venue: arXiv cs.CE 2017
Year: 2017

In this paper we present a novel two-scale framework to optimize the structure and the material distribution of an object given its functional specifications. Our approach utilizes multi-material microstructures as low-level building blocks of the object. We start by precomputing the material property gamut -- the set of bulk material properties that can be achieved with all material microstructures of a given size. We represent the boundary of this material property gamut using a level set field. Next, we propose an efficient and general topology optimization algorithm that simultaneously computes an optimal object topology and spatially-varying material properties constrained by the precomputed gamut. Finally, we map the optimal spatially-varying material properties onto the microstructures with the corresponding properties in order to generate a high-resolution printable structure. We demonstrate the efficacy of our framework by designing, optimizing, and fabricating objects in different material property spaces on the level of a trillion voxels, i.e several orders of magnitude higher than what can be achieved with current systems.
A Grid-Based Approximation Algorithm for the Minimum Weight Triangulation Problem
Venue: arXiv cs.CG 2017
Year: 2017

Given a set of $n$ points on a plane, in the Minimum Weight Triangulation problem, we wish to find a triangulation that minimizes the sum of Euclidean length of its edges. This incredibly challenging problem has been studied for more than four decades and has been only recently shown to be NP-Hard. In this paper we present a novel polynomial-time algorithm that computes a $14$-approximation of the minimum weight triangulation -- a constant that is significantly smaller than what has been previously known. In our algorithm, we use grids to partition the edges into levels where shorter edges appear at smaller levels and edges with similar lengths appear at the same level. We then triangulate the point set incrementally by introducing edges in increasing order of their levels. We introduce the edges of any level $i+1$ in two steps. In the first step, we add edges using a variant of the well-known ring heuristic to generate a partial triangulation $\hat{\mathcal{A}}_i$. In the second step, we greedily add non-intersecting level $i+1$ edges to $\hat{\mathcal{A}}_i$ in increasing order of their length and obtain a partial triangulation $\mathcal{A}_{ i+1}$. The ring heuristic is known to yield only an $O(\log n)$-approximation even for a convex polygon and the greedy heuristic achieves only a $Θ(\sqrt{n})$-approximation. Therefore, it is surprising that their combination leads to an improved approximation ratio of $14$. For the proof, we identify several useful properties of $\hat{\mathcal{A}}_i$ and combine it with a new Euler characteristic based technique to show that $\hat{\mathcal{A}}_i$ has more edges than $\mathcal{T}_i$; here $\mathcal{T}_i$ is the partial triangulation consisting of level $\le i$ edges of some minimum weight triangulation. We then use a simple greedy stays ahead proof strategy to bound the approximation ratio.
Learning Large-Scale Topological Maps Using Sum-Product Networks
Venue: arXiv cs.RO 2017
Year: 2017

In order to perform complex actions in human environments, an autonomous robot needs the ability to understand the environment, that is, to gather and maintain spatial knowledge. Topological map is commonly used for representing large scale, global maps such as floor plans. Although much work has been done in topological map extraction, we have found little previous work on the problem of learning the topological map using a probabilistic model. Learning a topological map means learning the structure of the large-scale space and dependency between places, for example, how the evidence of a group of places influence the attributes of other places. This is an important step towards planning complex actions in the environment. In this thesis, we consider the problem of using probabilistic deep learning model to learn the topological map, which is essentially a sparse undirected graph where nodes represent places annotated with their semantic attributes (e.g. place category). We propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs), due to their unique properties. We present two methods for learning topological maps using SPNs: the place grid method and the template-based method. We contribute an algorithm that builds SPNs for graphs using template models. Our experiments evaluate the ability of our models to enable robots to infer semantic attributes and detect maps with novel semantic attribute arrangements. Our results demonstrate their understanding of the topological map structure and spatial relations between places.
Constraint Satisfaction Problem Dichotomy for Finite Templates: a Proof Via Consistency Checks
Venue: arXiv cs.CC 2017
Year: 2017

One of the central problems in the study of parametrized constraint satisfaction problems is the Dichotomy Conjecture by T. Feder and M. Vardi stating that the constraint satisfaction problem (CSP) over a fixed, finite constraint language is either solvable in polynomial time or \textsc{NP}-complete. The conjecture was verified in certain special cases (domains with a relatively small number of elements, constraint languages containing all unary relations, etc.) In this article, we present a proof of the Dichotomy Conjecture via local consistency and AF- consistency checks. In fact, we show that, for every Taylor domain, which is $(2\lceil\frac{K}{2}\rceil,3\lceil\frac{K}{2}\rceil)$-consistent, where $K$ is the largest arity of a relation in the constraint language, we can define polynomially many proper subinstances such that, the original instance of the CSP is solvable if, and only if, the problem has a solution in one of those subinstances.. Finally, a solution is constructed using the combination of SLAC (Singleton Linear Arc Consistency), introduced by M. Kozik, and AF-consistency.
Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results
Venue: arXiv cs.NE 2017
Year: 2017

In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTSOO research.
Asymptotic convergence of spectral inverse iterations for stochastic eigenvalue problems
Venue: arXiv cs.NA 2017
Year: 2017

We consider and analyze applying a spectral inverse iteration algorithm and its subspace iteration variant for computing eigenpairs of an elliptic operator with random coefficients. With these iterative algorithms the solution is sought from a finite dimensional space formed as the tensor product of the approximation space for the underlying stochastic function space, and the approximation space for the underlying spatial function space. Sparse polynomial approximation is employed to obtain the first one, while classical finite elements are employed to obtain the latter. An error analysis is presented for the asymptotic convergence of the spectral inverse iteration to the smallest eigenvalue and the associated eigenvector of the problem. A series of detailed numerical experiments supports the conclusions of this analysis. Numerical experiments are also presented for the spectral subspace iteration, and convergence of the algorithm is observed in an example case, where the eigenvalues cross within the parameter space. The outputs of both algorithms are verified by comparing to solutions obtained by a sparse stochastic collocation method.
Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition
Venue: arXiv cs.CV 2017
Year: 2017

We design an Enriched Deep Recurrent Visual Attention Model (EDRAM) - an improved attention-based architecture for multiple object recognition. The proposed model is a fully differentiable unit that can be optimized end-to-end by using Stochastic Gradient Descent (SGD). The Spatial Transformer (ST) was employed as visual attention mechanism which allows to learn the geometric transformation of objects within images. With the combination of the Spatial Transformer and the powerful recurrent architecture, the proposed EDRAM can localize and recognize objects simultaneously. EDRAM has been evaluated on two publicly available datasets including MNIST Cluttered (with 70K cluttered digits) and SVHN (with up to 250k real world images of house numbers). Experiments show that it obtains superior performance as compared with the state-of-the-art models.
Clustering Small Samples with Quality Guarantees: Adaptivity with One2all pps
Venue: arXiv cs.LG 2017
Year: 2017

Clustering of data points is a fundamental tool in data analysis. We consider points $X$ in a relaxed metric space, where the triangle inequality holds within a constant factor. The {\em cost} of clustering $X$ by $Q$ is $V(Q)=\sum_{x\in X} d_{xQ}$. Two basic tasks, parametrized by $k \geq 1$, are {\em cost estimation}, which returns (approximate) $V(Q)$ for queries $Q$ such that $|Q|=k$ and {\em clustering}, which returns an (approximate) minimizer of $V(Q)$ of size $|Q|=k$. With very large data sets $X$, we seek efficient constructions of small samples that act as surrogates to the full data for performing these tasks. Existing constructions that provide quality guarantees are either worst-case, and unable to benefit from structure of real data sets, or make explicit strong assumptions on the structure. We show here how to avoid both these pitfalls using adaptive designs. At the core of our design is the {\em one2all} construction of multi-objective probability-proportional-to-size (pps) samples: Given a set $M$ of centroids and $α\geq 1$, one2all efficiently assigns probabilities to points so that the clustering cost of {\em each} $Q$ with cost $V(Q) \geq V(M)/α$ can be estimated well from a sample of size $O(α|M|ε^{-2})$. For cost queries, we can obtain worst-case sample size $O(kε^{-2})$ by applying one2all to a bicriteria approximation $M$, but we adaptively balance $|M|$ and $α$ to further reduce sample size. For clustering, we design an adaptive wrapper that applies a base clustering algorithm to a sample $S$. Our wrapper uses the smallest sample that provides statistical guarantees that the quality of the clustering on the sample carries over to the full data set. We demonstrate experimentally the huge gains of using our adaptive instead of worst-case methods.
Improving Gravitational Search Algorithm Performance with Artificial Bee Colony Algorithm for Constrained Numerical Optimization
Venue: arXiv cs.NE 2017
Year: 2017

In this paper, we propose an improved gravitational search algorithm named GSABC. The algorithm improves gravitational search algorithm (GSA) results improved by using artificial bee colony algorithm (ABC) to solve constrained numerical optimization problems. In GSA, solutions are attracted towards each other by applying gravitational forces, which depending on the masses assigned to the solutions, to each other. The heaviest mass will move slower than other masses and gravitate others. Due to nature of gravitation, GSA may pass global minimum if some solutions stuck to local minimum. ABC updates the positions of the best solutions that has obtained from GSA, preventing the GSA from sticking to the local minimum by its strong searching ability. The proposed algorithm improves the performance of GSA. The proposed method tested on 23 well-known unimodal, multimodal and fixed-point multimodal benchmark test functions. Experimental results show that GSABC outperforms or performs similarly to five state-of-the-art optimization approaches.
Neural Domain Adaptation for Biomedical Question Answering
Venue: arXiv cs.CL 2017
Year: 2017

Factoid question answering (QA) has recently benefited from the development of deep learning (DL) systems. Neural network models outperform traditional approaches in domains where large datasets exist, such as SQuAD (ca. 100,000 questions) for Wikipedia articles. However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch. For example, the BioASQ dataset for biomedical QA comprises less then 900 factoid (single answer) and list (multiple answers) QA instances. In this work, we adapt a neural QA system trained on a large open-domain dataset (SQuAD, source) to a biomedical dataset (BioASQ, target) by employing various transfer learning techniques. Our network architecture is based on a state-of-the-art QA system, extended with biomedical word embeddings and a novel mechanism to answer list questions. In contrast to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create. Despite this fact, our systems achieve state-of-the-art results on factoid questions and competitive results on list questions.
Refined Holonomic Summation Algorithms in Particle Physics
Venue: arXiv cs.SC 2017
Year: 2017

An improved multi-summation approach is introduced and discussed that enables one to simultaneously handle indefinite nested sums and products in the setting of difference rings and holonomic sequences. Relevant mathematics is reviewed and the underlying advanced difference ring machinery is elaborated upon. The flexibility of this new toolbox contributed substantially to evaluating complicated multi-sums coming from particle physics. Illustrative examples of the functionality of the new software package RhoSum are given.