OpenRaiser/Intern-Atlas huggingface dataset:
A Perceptual Aesthetics Measure for 3D Shapes
Venue: arXiv cs.GR 2016
Year: 2016

While the problem of image aesthetics has been well explored, the study of 3D shape aesthetics has focused on specific manually defined features. In this paper, we learn an aesthetics measure for 3D shapes autonomously from raw voxel data and without manually-crafted features by leveraging the strength of deep learning. We collect data from humans on their aesthetics preferences for various 3D shape classes. We take a deep convolutional 3D shape ranking approach to compute a measure that gives an aesthetics score for a 3D shape. We demonstrate our approach with various types of shapes and for applications such as aesthetics-based visualization, search, and scene composition.
Secret Sharing With Trusted Third Parties Using Piggy Bank Protocol
Venue: arXiv cs.CR 2016
Year: 2016

This paper presents a new scheme to distribute secret shares using two trusted third parties to increase security and eliminate the dependency on single trusted third party. This protocol for communication between a device and two trusted third parties uses the piggy bank cryptographic paradigm. We also present a protocol to give law enforcing agencies access to sensitive information present on a cell phone or a device using secret sharing scheme. The ideas for classical systems may also be applied to quantum schemes.
Complex Correntropy: Probabilistic Interpretation and Optimization
Venue: arXiv cs.IT 2016
Year: 2016

Recent studies have demonstrated that correntropy is an efficient tool for analyzing higher-order statistical moments in nonGaussian noise environments. Although correntropy has been used with complex data, no theoretical study was pursued to elucidate its properties, nor how to best use it for optimization. This paper presents a probabilistic interpretation for correntropy using complex-valued data called complex correntropy. A recursive solution for the maximum complex correntropy criterion (MCCC) is introduced based on a fixed point solution. This technique is applied to a simple system identification case study, and the results demonstrate prominent advantages when compared to the complex recursive least squares (RLS) algorithm. By using such probabilistic interpretation, correntropy can be applied to solve several problems involving complex data in a more straightforward way. Keywords: complex-valued data correntropy, maximum complex correntropy criterion, fixed-point algorithm.
Scene Labeling Through Knowledge-Based Rules Employing Constrained Integer Linear Programing
Venue: arXiv cs.CV 2016
Year: 2016

Scene labeling task is to segment the image into meaningful regions and categorize them into classes of objects which comprised the image. Commonly used methods typically find the local features for each segment and label them using classifiers. Afterward, labeling is smoothed in order to make sure that neighboring regions receive similar labels. However, they ignore expressive and non-local dependencies among regions due to expensive training and inference. In this paper, we propose to use high-level knowledge regarding rules in the inference to incorporate dependencies among regions in the image to improve scores of classification. Towards this aim, we extract these rules from data and transform them into constraints for Integer Programming to optimize the structured problem of assigning labels to super-pixels (consequently pixels) of an image. In addition, we propose to use soft-constraints in some scenarios, allowing violating the constraint by imposing a penalty, to make the model more flexible. We assessed our approach on three datasets and obtained promising results.
An Investigation of Randomized Controlled Trial (RCT) Method as a Customer Baseline Load (CBL) Calculation for Residential Customers
Venue: arXiv cs.SY 2016
Year: 2016

FERC Order 745 allows demand response owners to sell their load reduction in the wholesale market. However, in order to be able to sell the load reduction, some implementation challenges must be addressed, one of which is to establish Customer Baseline Load (CBL) calculation methods with acceptable error performance, which has proven to be very challenging so far. In this paper, the error and financial performance of Randomized Controlled Trial (RCT) method, applied to both granular and aggregated forms of the consumption load, are investigated for a hypothetical demand response program offered to a real dataset of residential customers .
Effective Multi-step Temporal-Difference Learning for Non-Linear Function Approximation
Venue: arXiv cs.AI 2016
Year: 2016

Multi-step temporal-difference (TD) learning, where the update targets contain information from multiple time steps ahead, is one of the most popular forms of TD learning for linear function approximation. The reason is that multi-step methods often yield substantially better performance than their single-step counter-parts, due to a lower bias of the update targets. For non-linear function approximation, however, single-step methods appear to be the norm. Part of the reason could be that on many domains the popular multi-step methods TD($λ$) and Sarsa($λ$) do not perform well when combined with non-linear function approximation. In particular, they are very susceptible to divergence of value estimates. In this paper, we identify the reason behind this. Furthermore, based on our analysis, we propose a new multi-step TD method for non-linear function approximation that addresses this issue. We confirm the effectiveness of our method using two benchmark tasks with neural networks as function approximation.
Multi-stage Object Detection with Group Recursive Learning
Venue: arXiv cs.CV 2016
Year: 2016

Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately. However, the important semantic and spatial layout correlations among proposals are often ignored, which are actually useful for more accurate object detection. In this work, we propose a new EM-like group recursive learning approach to iteratively refine object proposals by incorporating such context of surrounding proposals and provide an optimal spatial configuration of object detections. In addition, we propose to incorporate the weakly-supervised object segmentation cues and region-based object detection into a multi-stage architecture in order to fully exploit the learned segmentation features for better object detection in an end-to-end way. The proposed architecture consists of three cascaded networks which respectively learn to perform weakly-supervised object segmentation, object proposal generation and recursive detection refinement. Combining the group recursive learning and the multi-stage architecture provides competitive mAPs of 78.6% and 74.9% on the PASCAL VOC2007 and VOC2012 datasets respectively, which outperforms many well-established baselines [10] [20] significantly.
Optical quorum cycles for efficient communication
Venue: arXiv cs.NI 2016
Year: 2016

Many optical networks face heterogeneous communication requests requiring topologies to be efficient and fault tolerant. For efficiency and distributed control, it is common in distributed systems and algorithms to group nodes into intersecting sets referred to as quorum sets. We show efficiency and distributed control can also be accomplished in optical network routing by applying the same established quorum set theory. Cycle-based optical network routing, whether using SONET rings or p-cycles, provides the sufficient reliability in the network. Light-trails forming a cycle allow broadcasts within a cycle to be used for efficient multicasts. Cyclic quorum sets also have all pairs of nodes occurring in one or more quorums, so efficient, arbitrary unicast communication can occur between any two nodes. Efficient broadcasts to all network nodes are possible by a node broadcasting to all quorum cycles to which it belongs (O(sqrt(N))). In this paper, we propose applying the distributed efficiency of the quorum sets to routing optical cycles based on light-trails. With this new method of topology construction, unicast and multicast communication requests do not need to be known or even modeled a priori. Additionally, in the presence of network link faults, greater than 99 % average coverage enables the continued operation of nearly all arbitrary unicast and multicast requests in the network. Finally, to further improve the fault coverage, an augmentation to the ECBRA cycle finding algorithm is proposed.
Resource efficient redundancy using quorum-based cycle routing in optical networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper we propose a cycle redundancy technique that provides optical networks almost fault-tolerant point-to-point and multipoint-to-multipoint communications. The technique more importantly is shown to approximately halve the necessary light-trail resources in the network while maintaining the fault-tolerance and dependability expected from cycle-based routing. For efficiency and distributed control, it is common in distributed systems and algorithms to group nodes into intersecting sets referred to as quorum sets. Optimal communication quorum sets forming optical cycles based on light-trails have been shown to flexibly and efficiently route both point-to-point and multipoint-to-multipoint traffic requests. Commonly cycle routing techniques will use pairs of cycles to achieve both routing and fault-tolerance, which uses substantial resources and creates the potential for underutilization. Instead, we intentionally utilize redundancy within the quorum cycles for fault-tolerance such that almost every point-to-point communication occurs in more than one cycle. The result is a set of cycles with 96.60% - 99.37% fault coverage, while using 42.9% - 47.18% fewer resources.
Scaling Distributed All-Pairs Algorithms: Manage Computation and Limit Data Replication with Quorums
Venue: arXiv cs.DC 2016
Year: 2016

In this paper we propose and prove that cyclic quorum sets can efficiently manage all-pairs computations and data replication. The quorums are O(N/sqrt(P)) in size, up to 50% smaller than the dual N/sqrt(P) array implementations, and significantly smaller than solutions requiring all data. Implementation evaluation demonstrated scalability on real datasets with a 7x speed up on 8 nodes with 1/3rd the memory usage per process. The all-pairs problem requires all data elements to be paired with all other data elements. These all-pair problems occur in many science fields, which has led to their continued interest. Additionally, as datasets grow in size, new methods like these that can reduce memory footprints and distribute work equally across compute nodes will be demanded.
Refining Geometry from Depth Sensors using IR Shading Images
Venue: arXiv cs.CV 2016
Year: 2016

We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight (ToF) technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements.
Multi-Period Do-Not-Exceed Limit for Variable Renewable Generation Dispatch Considering Discrete Recourse Controls
Venue: arXiv cs.SY 2016
Year: 2016

The do-not-exceed (DNE) limit method was proposed to accommodate more variable renewable generation (VRG) securely. However, the lack of involving discrete recourse control precludes this method from gaining more flexibility for better VRG integration. This letter formulates a multi-period DNE limit model considering continuous and discrete recourse controls. This model belongs to two-stage robust optimization with mixed integer recourse. A nested column-and-constraint generation approach is employed to solve this model. Case studies show the effectiveness of the proposed method.
Scaling Bounded Model Checking By Transforming Programs With Arrays
Venue: arXiv cs.PL 2016
Year: 2016

Bounded Model Checking is one the most successful techniques for finding bugs in program. However, for programs with loops iterating over large-sized arrays, bounded model checkers often exceed the limit of resources available to them. We present a transformation that enables bounded model checkers to verify a certain class of array properties. Our technique transforms an array-manipulating program in ANSI-C to an array-free and loop-free program. The transformed program can efficiently be verified by an off-the-shelf bounded model checker. Though the transformed program is, in general, an abstraction of the original program, we formally characterize the properties for which the transformation is precise. We demonstrate the applicability and usefulness of our technique on both industry code as well as academic benchmarks.
Algorithms to solve coupled systems of differential equations in terms of power series
Venue: arXiv cs.SC 2016
Year: 2016

Using integration by parts relations, Feynman integrals can be represented in terms of coupled systems of differential equations. In the following we suppose that the unknown Feynman integrals can be given in power series representations, and that sufficiently many initial values of the integrals are given. Then there exist algorithms that decide constructively if the coefficients of their power series representations can be given within the class of nested sums over hypergeometric products. In this article we will work out the calculation steps that solve this problem. First, we will present a successful tactic that has been applied recently to challenging problems coming from massive 3-loop Feynman integrals. Here our main tool is to solve scalar linear recurrences within the class of nested sums over hypergeometric products. Second, we will present a new variation of this tactic which relies on more involved summation technologies but succeeds in reducing the problem to solve scalar recurrences with lower recurrence orders. The article will work out the different challenges of this new tactic and demonstrates how they can be treated efficiently with our existing summation technologies.
Distributed Optimization of Convex Sum of Non-Convex Functions
Venue: arXiv cs.DC 2016
Year: 2016

We present a distributed solution to optimizing a convex function composed of several non-convex functions. Each non-convex function is privately stored with an agent while the agents communicate with neighbors to form a network. We show that coupled consensus and projected gradient descent algorithm proposed in [1] can optimize convex sum of non-convex functions under an additional assumption on gradient Lipschitzness. We further discuss the applications of this analysis in improving privacy in distributed optimization.
Links between the Logarithmic Sobolev Inequality and the convolution inequalities for Entropy and Fisher Information
Venue: arXiv cs.IT 2016
Year: 2016

Relative to the Gaussian measure on $\mathbb{R}^d$, entropy and Fisher information are famously related via Gross' logarithmic Sobolev inequality (LSI). These same functionals also separately satisfy convolution inequalities, as proved by Stam. We establish a dimension-free inequality that interpolates among these relations. Several interesting corollaries follow: (i) the deficit in the LSI satisfies a convolution inequality itself; (ii) the deficit in the LSI controls convergence in the entropic and Fisher information central limit theorems; and (iii) the LSI is stable with respect to HWI jumps (i.e., a jump in any of the convolution inequalities associated to the HWI functionals). Another consequence is that the convolution inequalities for Fisher information and entropy powers are reversible in general, up to a factor depending on the Stam defect. An improved form of Nelson's hypercontractivity estimate also follows. Finally, we speculate on the possibility of an analogous reverse Brunn-Minkowski inequality and a related upper bound on surface area associated to Minkowski sums.
A heuristic scheme for the Cooperative Team Orienteering Problem with Time Windows
Venue: arXiv cs.AI 2016
Year: 2016

The Cooperative Orienteering Problem with Time Windows (COPTW)is a class of problems with some important applications and yet has received relatively little attention. In the COPTW a certain number of team members are required to collect the associated reward from each customer simultaneously and cooperatively. This requirement to have one or more team members simultaneously available at a vertex to collect the reward, poses a challenging OR task. Exact methods are not able to handle large scale instances of the COPTW and no heuristic schemes have been developed for this problem so far. In this paper, a new modification to the classical Clarke and Wright saving heuristic is proposed to handle this problem. A new benchmark set generated by adding the resource requirement attribute to the existing benchmarks. The heuristic algorithm followed by boosting operators achieves optimal solutions for 64.5% of instances for which the optimal results are known. The proposed solution approach attains an optimality gap of 2.61% for the same instances and solves benchmarks with realistic size within short computational times.
End of Publication? Open access and a new scholarly communication technology
Venue: arXiv cs.DL 2016
Year: 2016

At this time, developers of research information systems are experimenting with new tools for research outputs usage that can expand the open access to research. These tools allow researchers to record research as annotations, nanopublications or other micro research outputs and link them by scientific relationships. If these micro outputs and relationships are shared by their creators publicly, these actions can initiate direct scholarly communication between the creators and the authors of the used research outputs. Such direct communication takes place while researchers are manipulating and organising their research results, e.g. as manuscripts. Thus, researchers come to communication before the manuscripts become traditional publications. In this paper, we discuss how this pre-publication communication can affect existing research practice. It can have important consequences for the research community like the end of publication as a communication instrument, the higher level of transparency in research, changes for the Open Access movement, academic publishers, peer-reviewing and research assessment systems. We analyse a background that exists in the economics discipline for experiments with the pre-publication communication. We propose a set of experiments with already existed and new tools, which can help with exploring the end of publication possible impacts on the research community.
Detecting and Tracking The Real-time Hot Topics: A Study on Computational Neuroscience
Venue: arXiv cs.DL 2016
Year: 2016

In this study, following the idea of our previous paper (Wang, et al., 2013a), we improve the method to detect and track hot topics in a specific field by using the real-time article usage data. With the "usage count" data provided by Web of Science, we take the field of computational neuroscience as an example to make analysis. About 10 thousand articles in the field of Computational Neuroscience are queried in Web of Science, when the records, including the usage count data of each paper, have been harvested and updated weekly from October 19, 2015 to March 21, 2016. The hot topics are defined by the most frequently used keywords aggregated from the articles. The analysis reveals that hot topics in Computational Neuroscience are related to the key technologies, like "fmri", "eeg", "erp", etc. Furthermore, using the weekly updated data, we track the dynamical changes of the topics. The characteristic of immediacy of usage data makes it possible to track the "heat" of hot topics timely and dynamically.
On Joining Graphs
Venue: arXiv cs.DB 2016
Year: 2016

In the graph database literature the term "join" does not refer to an operator used to merge two graphs. In particular, a counterpart of the relational join is not present in existing graph query languages, and consequently no efficient algorithms have been developed for this operator. This paper provides two main contributions. First, we define a binary graph join operator that acts on the vertices as a standard relational join and combines the edges according to a user-defined semantics. Then we propose the "CoGrouped Graph Conjunctive $θ$-Join" algorithm running over data indexed in secondary memory. Our implementation outperforms the execution of the same operation in Cypher and SPARQL on major existing graph database management systems by at least one order of magnitude, also including indexing and loading time.
Symbolic Abstract Contract Synthesis in a Rewriting Framework
Venue: arXiv cs.PL 2016
Year: 2016

We propose an automated technique for inferring software contracts from programs that are written in a non-trivial fragment of C, called KernelC, that supports pointer-based structures and heap manipulation. Starting from the semantic definition of KernelC in the K framework, we enrich the symbolic execution facilities recently provided by K with novel capabilities for assertion synthesis that are based on abstract subsumption. Roughly speaking, we define an abstract symbolic technique that explains the execution of a (modifier) C function by using other (observer) routines in the same program. We implemented our technique in the automated tool KindSpec 2.0, which generates logical axioms that express pre- and post-condition assertions by defining the precise input/output behaviour of the C routines.
Thrill: High-Performance Algorithmic Distributed Batch Data Processing with C++
Venue: arXiv cs.DC 2016
Year: 2016

We present the design and a first performance evaluation of Thrill -- a prototype of a general purpose big data processing framework with a convenient data-flow style programming interface. Thrill is somewhat similar to Apache Spark and Apache Flink with at least two main differences. First, Thrill is based on C++ which enables performance advantages due to direct native code compilation, a more cache-friendly memory layout, and explicit memory management. In particular, Thrill uses template meta-programming to compile chains of subsequent local operations into a single binary routine without intermediate buffering and with minimal indirections. Second, Thrill uses arrays rather than multisets as its primary data structure which enables additional operations like sorting, prefix sums, window scans, or combining corresponding fields of several arrays (zipping). We compare Thrill with Apache Spark and Apache Flink using five kernels from the HiBench suite. Thrill is consistently faster and often several times faster than the other frameworks. At the same time, the source codes have a similar level of simplicity and abstraction