OpenRaiser/Intern-Atlas huggingface dataset:
Wireless Energy Harvesting Cooperative Communications with Direct Link and Energy Accumulation
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates a wireless energy harvesting cooperative network (WEHCN) consisting of a source, a decode-and-forward (DF) relay and a destination. We consider the relay as an energy harvesting (EH) node equipped with EH circuit and a rechargeable battery. Moreover, the direct link between source and destination is assumed to exist. The relay can thus harvest and accumulate energy from radio-frequency signals ejected by the source and assist its information transmission opportunistically. We develop an incremental accumulate-then-forward (IATF) relaying protocol for the considered WEHCN. In the IATF protocol, the source sends its information to destination via the direct link and requests the relay to cooperate only when it is necessary such that the relay has more chances to accumulate the harvested energy. By modeling the charging/discharging behaviors of the relay battery as a finite-state Markov chain, we derive a closed-form expression for the outage probability of the proposed IATF. Numerical results validate our theoretical analysis and show that the IATF scheme can significantly outperform the direct transmission scheme without cooperation.
Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field
Venue: arXiv cs.CV 2016
Year: 2016

Occlusion is one of the most challenging problems in depth estimation. Previous work has modeled the single-occluder occlusion in light field and get good results, however it is still difficult to obtain accurate depth for multi-occluder occlusion. In this paper, we explore the multi-occluder occlusion model in light field, and derive the occluder-consistency between the spatial and angular space which is used as a guidance to select the un-occluded views for each candidate occlusion point. Then an anti-occlusion energy function is built to regularize depth map. The experimental results on public light field datasets have demonstrated the advantages of the proposed algorithm compared with other state-of-the-art light field depth estimation algorithms, especially in multi-occluder areas.
Face Alignment In-the-Wild: A Survey
Venue: arXiv cs.CV 2016
Year: 2016

Over the last two decades, face alignment or localizing fiducial facial points has received increasing attention owing to its comprehensive applications in automatic face analysis. However, such a task has proven extremely challenging in unconstrained environments due to many confounding factors, such as pose, occlusions, expression and illumination. While numerous techniques have been developed to address these challenges, this problem is still far away from being solved. In this survey, we present an up-to-date critical review of the existing literatures on face alignment, focusing on those methods addressing overall difficulties and challenges of this topic under uncontrolled conditions. Specifically, we categorize existing face alignment techniques, present detailed descriptions of the prominent algorithms within each category, and discuss their advantages and disadvantages. Furthermore, we organize special discussions on the practical aspects of face alignment in-the-wild, towards the development of a robust face alignment system. In addition, we show performance statistics of the state of the art, and conclude this paper with several promising directions for future research.
Using Machine Learning to Decide When to Precondition Cylindrical Algebraic Decomposition With Groebner Bases
Venue: arXiv cs.SC 2016
Year: 2016

Cylindrical Algebraic Decomposition (CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. However, it can be expensive, with worst case complexity doubly exponential in the size of the input. Hence it is important to formulate the problem in the best manner for the CAD algorithm. One possibility is to precondition the input polynomials using Groebner Basis (GB) theory. Previous experiments have shown that while this can often be very beneficial to the CAD algorithm, for some problems it can significantly worsen the CAD performance. In the present paper we investigate whether machine learning, specifically a support vector machine (SVM), may be used to identify those CAD problems which benefit from GB preconditioning. We run experiments with over 1000 problems (many times larger than previous studies) and find that the machine learned choice does better than the human-made heuristic.
A Riemannian Network for SPD Matrix Learning
Venue: arXiv cs.CV 2016
Year: 2016

Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds. In this paper we build a Riemannian network architecture to open up a new direction of SPD matrix non-linear learning in a deep model. In particular, we devise bilinear mapping layers to transform input SPD matrices to more desirable SPD matrices, exploit eigenvalue rectification layers to apply a non-linear activation function to the new SPD matrices, and design an eigenvalue logarithm layer to perform Riemannian computing on the resulting SPD matrices for regular output layers. For training the proposed deep network, we exploit a new backpropagation with a variant of stochastic gradient descent on Stiefel manifolds to update the structured connection weights and the involved SPD matrix data. We show through experiments that the proposed SPD matrix network can be simply trained and outperform existing SPD matrix learning and state-of-the-art methods in three typical visual classification tasks.
The Scalability-Efficiency/Maintainability-Portability Trade-off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review
Venue: arXiv cs.SE 2016
Year: 2016

Large-scale simulations play a central role in science and the industry. Several challenges occur when building simulation software, because simulations require complex software developed in a dynamic construction process. That is why simulation software engineering (SSE) is emerging lately as a research focus. The dichotomous trade-off between scalability and efficiency (SE) on the one hand and maintainability and portability (MP) on the other hand is one of the core challenges. We report on the SE/MP trade-off in the context of an ongoing systematic literature review (SLR). After characterizing the issue of the SE/MP trade-off using two examples from our own research, we (1) review the 33 identified articles that assess the trade-off, (2) summarize the proposed solutions for the trade-off, and (3) discuss the findings for SSE and future work. Overall, we see evidence for the SE/MP trade-off and first solution approaches. However, a strong empirical foundation has yet to be established; general quantitative metrics and methods supporting software developers in addressing the trade-off have to be developed. We foresee considerable future work in SSE across scientific communities.
Multi-way Monte Carlo Method for Linear Systems
Venue: arXiv cs.NA 2016
Year: 2016

We study the Monte Carlo method for solving a linear system of the form $x = H x + b$. A sufficient condition for the method to work is $\| H \| < 1$, which greatly limits the usability of this method. We improve this condition by proposing a new multi-way Markov random walk, which is a generalization of the standard Markov random walk. Under our new framework we prove that the necessary and sufficient condition for our method to work is the spectral radius $ρ(H^{+}) < 1$, which is a weaker requirement than $\| H \| < 1$. In addition to solving more problems, our new method can work faster than the standard algorithm. In numerical experiments on both synthetic and real world matrices, we demonstrate the effectiveness of our new method.
Distributed Space-Time Coding Techniques with Dynamic Buffers for Cooperative DS-CDMA Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we propose a dynamic buffer-aided distributed space-time coding (DSTC) scheme for cooperative direct-sequence code-division multiple access systems. We first devise a relay selection algorithm that can automatically select the optimum set of relays among both the source-relay phase and the relay-destination phase for DSTC transmission according to the signal-to-interference-plus-noise ratio (SINR) criterion. Multiple relays equipped with dynamic buffers are introduced in the network, which allows the relays to store data received from the sources and wait until the most appropriate time for transmission. The proposed technique improves the quality of the transmission with an acceptable delay as the buffer size is adjustable. Simulation results show that the proposed dynamic buffer-aided DSTC scheme and algorithm outperforms prior art.
Application of Microgrids in Addressing Distribution Network Net-Load Ramping
Venue: arXiv cs.SY 2016
Year: 2016

In spite of all advantages of solar energy, its deployment will significantly change the typical electric load profile, thus necessitating a change in traditional distribution grid management practices. In particular, the net load ramping, created as a result of simultaneous solar generation drop and load increase at early evening hours, is one of the major operational issues that needs to be carefully addressed. In this paper, microgrids are utilized to offer a viable and localized solution to this challenge while removing the need for costly investments by the electric utility. In this regard, first the microgrid ramping capability is determined via a min-max optimization, and second, the microgrid optimal scheduling model is developed to coordinate the microgrid net load with the distribution grid net load for addressing the ramping issue. Numerical simulations on a test distribution feeder with one microgrid exhibit the effectiveness of the proposed model.
Leveraging Microgrids for Capturing Uncertain Distribution Network Net Load Ramping
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, a flexibility-oriented microgrid optimal scheduling model is proposed to mitigate distribution network net load variability caused by large penetration distributed solar generation. The distributed solar generation variability, which is caused by increasing adoption of this technology by end-use consumers, is mainly addressed by electric utilities using grid reinforcement. Microgrids, however, provide viable and local solutions to this pressing challenge. The proposed model, which is developed using mixed-integer programming and employs robust optimization, not only can efficiently capture distribution network net load variations, mainly in terms of ramping, but also accounts for possible uncertainties in forecasting. Numerical simulations on a test distribution feeder with one microgrid and several consumers/prosumers indicate the effectiveness of the proposed model.
Authorship clustering using multi-headed recurrent neural networks
Venue: arXiv cs.CL 2016
Year: 2016

A recurrent neural network that has been trained to separately model the language of several documents by unknown authors is used to measure similarity between the documents. It is able to find clues of common authorship even when the documents are very short and about disparate topics. While it is easy to make statistically significant predictions regarding authorship, it is difficult to group documents into definite clusters with high accuracy.
SenTion: A framework for Sensing Facial Expressions
Venue: arXiv cs.CV 2016
Year: 2016

Facial expressions are an integral part of human cognition and communication, and can be applied in various real life applications. A vital precursor to accurate expression recognition is feature extraction. In this paper, we propose SenTion: A framework for sensing facial expressions. We propose a novel person independent and scale invariant method of extracting Inter Vector Angles (IVA) as geometric features, which proves to be robust and reliable across databases. SenTion employs a novel framework of combining geometric (IVA's) and appearance based features (Histogram of Gradients) to create a hybrid model, that achieves state of the art recognition accuracy. We evaluate the performance of SenTion on two famous face expression data set, namely: CK+ and JAFFE; and subsequently evaluate the viability of facial expression systems by a user study. Extensive experiments showed that SenTion framework yielded dramatic improvements in facial expression recognition and could be employed in real-world applications with low resolution imaging and minimal computational resources in real-time, achieving 15-18 fps on a 2.4 GHz CPU with no GPU.
Endpoint-transparent Multipath Transport with Software-defined Networks
Venue: arXiv cs.NI 2016
Year: 2016

Multipath forwarding consists of using multiple paths simultaneously to transport data over the network. While most such techniques require endpoint modifications, we investigate how multipath forwarding can be done inside the network, transparently to endpoint hosts. With such a network-centric approach, packet reordering becomes a critical issue as it may cause critical performance degradation. We present a Software Defined Network architecture which automatically sets up multipath forwarding, including solutions for reordering and performance improvement, both at the sending side through multipath scheduling algorithms, and the receiver side, by resequencing out-of-order packets in a dedicated in-network buffer. We implemented a prototype with commonly available technology and evaluated it in both emulated and real networks. Our results show consistent throughput improvements, thanks to the use of aggregated path capacity. We give comparisons to Multipath TCP, where we show our approach can achieve a similar performance while offering the advantage of endpoint transparency.
A novel transfer learning method based on common space mapping and weighted domain matching
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we propose a novel learning framework for the problem of domain transfer learning. We map the data of two domains to one single common space, and learn a classifier in this common space. Then we adapt the common classifier to the two domains by adding two adaptive functions to it respectively. In the common space, the target domain data points are weighted and matched to the target domain in term of distributions. The weighting terms of source domain data points and the target domain classification responses are also regularized by the local reconstruction coefficients. The novel transfer learning framework is evaluated over some benchmark cross-domain data sets, and it outperforms the existing state-of-the-art transfer learning methods.
Attribute Extraction from Product Titles in eCommerce
Venue: arXiv cs.CL 2016
Year: 2016

This paper presents a named entity extraction system for detecting attributes in product titles of eCommerce retailers like Walmart. The absence of syntactic structure in such short pieces of text makes extracting attribute values a challenging problem. We find that combining sequence labeling algorithms such as Conditional Random Fields and Structured Perceptron with a curated normalization scheme produces an effective system for the task of extracting product attribute values from titles. To keep the discussion concrete, we will illustrate the mechanics of the system from the point of view of a particular attribute - brand. We also discuss the importance of an attribute extraction system in the context of retail websites with large product catalogs, compare our approach to other potential approaches to this problem and end the paper with a discussion of the performance of our system for extracting attributes.
A Shallow High-Order Parametric Approach to Data Visualization and Compression
Venue: arXiv cs.AI 2016
Year: 2016

Explicit high-order feature interactions efficiently capture essential structural knowledge about the data of interest and have been used for constructing generative models. We present a supervised discriminative High-Order Parametric Embedding (HOPE) approach to data visualization and compression. Compared to deep embedding models with complicated deep architectures, HOPE generates more effective high-order feature mapping through an embarrassingly simple shallow model. Furthermore, two approaches to generating a small number of exemplars conveying high-order interactions to represent large-scale data sets are proposed. These exemplars in combination with the feature mapping learned by HOPE effectively capture essential data variations. Moreover, through HOPE, these exemplars are employed to increase the computational efficiency of kNN classification for fast information retrieval by thousands of times. For classification in two-dimensional embedding space on MNIST and USPS datasets, our shallow method HOPE with simple Sigmoid transformations significantly outperforms state-of-the-art supervised deep embedding models based on deep neural networks, and even achieved historically low test error rate of 0.65% in two-dimensional space on MNIST, which demonstrates the representational efficiency and power of supervised shallow models with high-order feature interactions.
Adaptive Restart and CEGAR-based Solver for Inverting Cryptographic Hash Functions
Venue: arXiv cs.CR 2016
Year: 2016

SAT solvers are increasingly being used for cryptanalysis of hash functions and symmetric encryption schemes. Inspired by this trend, we present MapleCrypt which is a SAT solver-based cryptanalysis tool for inverting hash functions. We reduce the hash function inversion problem for fixed targets into the satisfiability problem for Boolean logic, and use MapleCrypt to construct preimages for these targets. MapleCrypt has two key features, namely, a multi-armed bandit based adaptive restart (MABR) policy and a counterexample-guided abstraction refinement (CEGAR) technique. The MABR technique uses reinforcement learning to adaptively choose between different restart policies during the run of the solver. The CEGAR technique abstracts away certain steps of the input hash function, replacing them with the identity function, and verifies whether the solution constructed by MapleCrypt indeed hashes to the previously fixed targets. If it is determined that the solution produced is spurious, the abstraction is refined until a correct inversion to the input hash target is produced. We show that the resultant system is faster for inverting the SHA-1 hash function than state-of-the-art inversion tools.
Faster Sublinear Algorithms using Conditional Sampling
Venue: arXiv cs.DS 2016
Year: 2016

A conditional sampling oracle for a probability distribution D returns samples from the conditional distribution of D restricted to a specified subset of the domain. A recent line of work (Chakraborty et al. 2013 and Cannone et al. 2014) has shown that having access to such a conditional sampling oracle requires only polylogarithmic or even constant number of samples to solve distribution testing problems like identity and uniformity. This significantly improves over the standard sampling model where polynomially many samples are necessary. Inspired by these results, we introduce a computational model based on conditional sampling to develop sublinear algorithms with exponentially faster runtimes compared to standard sublinear algorithms. We focus on geometric optimization problems over points in high dimensional Euclidean space. Access to these points is provided via a conditional sampling oracle that takes as input a succinct representation of a subset of the domain and outputs a uniformly random point in that subset. We study two well studied problems: k-means clustering and estimating the weight of the minimum spanning tree. In contrast to prior algorithms for the classic model, our algorithms have time, space and sample complexity that is polynomial in the dimension and polylogarithmic in the number of points. Finally, we comment on the applicability of the model and compare with existing ones like streaming, parallel and distributed computational models.
Dynamic Collaborative Filtering with Compound Poisson Factorization
Venue: arXiv cs.LG 2016
Year: 2016

Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferences and item perceptions drift over time. In this paper, we propose a conjugate and numerically stable dynamic matrix factorization (DCPF) based on compound Poisson matrix factorization that models the smoothly drifting latent factors using Gamma-Markov chains. We propose a numerically stable Gamma chain construction, and then present a stochastic variational inference approach to estimate the parameters of our model. We apply our model to time-stamped ratings data sets: Netflix, Yelp, and Last.fm, where DCPF achieves a higher predictive accuracy than state-of-the-art static and dynamic factorization models.
Mollifying Networks
Venue: arXiv cs.LG 2016
Year: 2016

The optimization of deep neural networks can be more challenging than traditional convex optimization problems due to the highly non-convex nature of the loss function, e.g. it can involve pathological landscapes such as saddle-surfaces that can be difficult to escape for algorithms based on simple gradient descent. In this paper, we attack the problem of optimization of highly non-convex neural networks by starting with a smoothed -- or \textit{mollified} -- objective function that gradually has a more non-convex energy landscape during the training. Our proposition is inspired by the recent studies in continuation methods: similar to curriculum methods, we begin learning an easier (possibly convex) objective function and let it evolve during the training, until it eventually goes back to being the original, difficult to optimize, objective function. The complexity of the mollified networks is controlled by a single hyperparameter which is annealed during the training. We show improvements on various difficult optimization tasks and establish a relationship with recent works on continuation methods for neural networks and mollifiers.
Ensemble of Jointly Trained Deep Neural Network-Based Acoustic Models for Reverberant Speech Recognition
Venue: arXiv cs.CL 2016
Year: 2016

Distant speech recognition is a challenge, particularly due to the corruption of speech signals by reverberation caused by large distances between the speaker and microphone. In order to cope with a wide range of reverberations in real-world situations, we present novel approaches for acoustic modeling including an ensemble of deep neural networks (DNNs) and an ensemble of jointly trained DNNs. First, multiple DNNs are established, each of which corresponds to a different reverberation time 60 (RT60) in a setup step. Also, each model in the ensemble of DNN acoustic models is further jointly trained, including both feature mapping and acoustic modeling, where the feature mapping is designed for the dereverberation as a front-end. In a testing phase, the two most likely DNNs are chosen from the DNN ensemble using maximum a posteriori (MAP) probabilities, computed in an online fashion by using maximum likelihood (ML)-based blind RT60 estimation and then the posterior probability outputs from two DNNs are combined using the ML-based weights as a simple average. Extensive experiments demonstrate that the proposed approach leads to substantial improvements in speech recognition accuracy over the conventional DNN baseline systems under diverse reverberant conditions.