OpenRaiser/Intern-Atlas huggingface dataset:
Segmenting a Surface Mesh into Pants Using Morse Theory
Venue: arXiv cs.GR 2016
Year: 2016

A pair of pants is a genus zero orientable surface with three boundary components. A pants decomposition of a surface is a finite collection of unordered pairwise disjoint simple closed curves embedded in the surface that decompose the surface into pants. In this paper we present two Morse theory based algorithms for pants decomposition of a surface mesh. Both algorithms operates on a choice of an appropriate Morse function on the surface. The first algorithm uses this Morse function to identify handles that are glued systematically to obtain a pant decomposition. The second algorithm uses the Reeb graph of the Morse function to obtain a pant decomposition. Both algorithms work for surfaces with or without boundaries. Our preliminary implementation of the two algorithms shows that both algorithms run in much less time than an existing state-of-the-art method, and the Reeb graph based algorithm achieves the best time efficiency. Finally, we demonstrate the robustness of our algorithms against noise.
Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But Also Parameters
Venue: arXiv cs.LG 2016
Year: 2016

This paper emphasizes the significance to jointly exploit the problem structure and the parameter structure, in the context of deep modeling. As a specific and interesting example, we describe the deep double sparsity encoder (DDSE), which is inspired by the double sparsity model for dictionary learning. DDSE simultaneously sparsities the output features and the learned model parameters, under one unified framework. In addition to its intuitive model interpretation, DDSE also possesses compact model size and low complexity. Extensive simulations compare DDSE with several carefully-designed baselines, and verify the consistently superior performance of DDSE. We further apply DDSE to the novel application domain of brain encoding, with promising preliminary results achieved.
Stack and Queue Layouts via Layered Separators
Venue: arXiv cs.CG 2016
Year: 2016

It is known that every proper minor-closed class of graphs has bounded stack-number (a.k.a. book thickness and page number). While this includes notable graph families such as planar graphs and graphs of bounded genus, many other graph families are not closed under taking minors. For fixed $g$ and $k$, we show that every $n$-vertex graph that can be embedded on a surface of genus $g$ with at most $k$ crossings per edge has stack-number $\mathcal{O}(\log n)$; this includes $k$-planar graphs. The previously best known bound for the stack-number of these families was $\mathcal{O}(\sqrt{n})$, except in the case of $1$-planar graphs. Analogous results are proved for map graphs that can be embedded on a surface of fixed genus. None of these families is closed under taking minors. The main ingredient in the proof of these results is a construction proving that $n$-vertex graphs that admit constant layered separators have $\mathcal{O}(\log n)$ stack-number.
Searching Action Proposals via Spatial Actionness Estimation and Temporal Path Inference and Tracking
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we address the problem of searching action proposals in unconstrained video clips. Our approach starts from actionness estimation on frame-level bounding boxes, and then aggregates the bounding boxes belonging to the same actor across frames via linking, associating, tracking to generate spatial-temporal continuous action paths. To achieve the target, a novel actionness estimation method is firstly proposed by utilizing both human appearance and motion cues. Then, the association of the action paths is formulated as a maximum set coverage problem with the results of actionness estimation as a priori. To further promote the performance, we design an improved optimization objective for the problem and provide a greedy search algorithm to solve it. Finally, a tracking-by-detection scheme is designed to further refine the searched action paths. Extensive experiments on two challenging datasets, UCF-Sports and UCF-101, show that the proposed approach advances state-of-the-art proposal generation performance in terms of both accuracy and proposal quantity.
A Non-Local Conventional Approach for Noise Removal in 3D MRI
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, a filtering approach for the 3D magnetic resonance imaging (MRI) assuming a Rician model for noise is addressed. Our denoising method is based on the Conventional Approach (CA) proposed to deal with the noise issue in the squared domain of the acquired magnitude MRI, where the noise distribution follows a Chi-square model rather than the Rician one. In the CA filtering method, the local samples around each voxel is used to estimate the unknown signal value. Intrinsically, such a method fails to achieve the best results where the underlying signal values have different statistical properties. On the contrary, our proposal takes advantage of the data redundancy and self-similarity properties of real MR images to improve the noise removal performance. In other words, in our approach, the statistical momentums of the given 3D MR volume are first calculated to explore the similar patches inside a defined search volume. Then, these patches are put together to obtain the noise-free value for each voxel under processing. The experimental results on the synthetic as well as the clinical MR data show our proposed method outperforms the other compared denoising filters.
Improving FPGA resilience through Partial Dynamic Reconfiguration
Venue: arXiv cs.DC 2016
Year: 2016

This paper explores advances in reconfiguration properties of SRAM-based FPGAs, namely Partial Dynamic Reconfiguration, to improve the resilience of critical systems that take advantage of this technology. Commercial of-the-shelf state-of-the-art FPGA devices use SRAM cells for the configuration memory, which allow an increase in both performance and capacity. The fast access times and unlimited number of writes of this technology, reduces reconfiguration delays and extends the device lifetime but, at the same time, makes them more sensitive to radiation effects, in the form of Single Event Upsets. To overcome this limitation, manufacturers have proposed a few fault tolerant approaches, which rely on space/time redundancy and configuration memory content recovery - scrubbing. In this paper, we first present radiation effects on these devices and investigate the applicability of the most commonly used fault tolerant approaches, and then propose an approach to improve FPGA resilience, through the use of a less intrusive failure prediction scrubbing. It is expected that this approach relieves the system designer from dependability concerns and reduces both time intrusiveness and overall power consumption.
Syntax and analytic semantics of LISA
Venue: arXiv cs.PL 2016
Year: 2016

We provide the syntax and semantics of the LISA (for "Litmus Instruction Set Architecture") language. The parallel assembly language LISA is implemented in the herd7 tool (http://virginia.cs.ucl.ac.uk/herd/) for simulating weak consistency models.
Conversion Methods, Block Triangularization, and Structural Analysis of Differential-Algebraic Equation Systems
Venue: arXiv cs.SC 2016
Year: 2016

In a previous article, the authors developed two conversion methods to improve the $Σ$-method for structural analysis (SA) of differential-algebraic equations (DAEs). These methods reformulate a DAE on which the $Σ$-method fails into an equivalent problem on which this SA is more likely to succeed with a generically nonsingular Jacobian. The basic version of these methods processes the DAE as a whole. This article presents the block version that exploits block triangularization of a DAE. Using a block triangular form of a Jacobian sparsity pattern, we identify which diagonal blocks of the Jacobian are identically singular and then perform a conversion on each such block. This approach improves the efficiency of finding a suitable conversion for fixing SA's failures. All of our conversion methods can be implemented in a computer algebra system so that every conversion can be automated.
Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we report results for recognizing colorectal NBI endoscopic images by using features extracted from convolutional neural network (CNN). In this comparative study, we extract features from different layers from different CNN models, and then train linear SVM classifiers. Experimental results with 10-fold cross validations show that features from first few convolution layers are enough to achieve similar performance (i.e., recognition rate of 95%) with non-CNN local features such as Bag-of-Visual words, Fisher vector, and VLAD.
A Novel Approach for Shot Boundary Detection in Videos
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a novel approach for video shot boundary detection. The proposed approach is based on split and merge concept. A fisher linear discriminant criterion is used to guide the process of both splitting and merging. For the purpose of capturing the between class and within class scatter we employ 2D2 FLD method which works on texture feature of regions in each frame of a video. Further to reduce the complexity of the process we propose to employ spectral clustering to group related regions together to a single there by achieving reduction in dimension. The proposed method is experimentally also validated on a cricket video. It is revealed that shots obtained by the proposed approach are highly cohesive and loosely coupled
A Study of Vision based Human Motion Recognition and Analysis
Venue: arXiv cs.CV 2016
Year: 2016

Vision based human motion recognition has fascinated many researchers due to its critical challenges and a variety of applications. The applications range from simple gesture recognition to complicated behaviour understanding in surveillance system. This leads to major development in the techniques related to human motion representation and recognition. This paper discusses applications, general framework of human motion recognition, and the details of each of its components. The paper emphasizes on human motion representation and the recognition methods along with their advantages and disadvantages. This study also discusses the selected literature, popular datasets, and concludes with the challenges in the domain along with a future direction. The human motion recognition domain has been active for more than two decades, and has provided a large amount of literature. A bird's eye view for new researchers in the domain is presented in the paper.
Expressibility of norms in temporal logic
Venue: arXiv cs.AI 2016
Year: 2016

In this short note we address the issue of expressing norms (such as obligations and prohibitions) in temporal logic. In particular, we address the argument from [Governatori 2015] that norms cannot be expressed in Linear Time Temporal Logic (LTL).
$E^2$-MAC: Energy Efficient Medium Access for Massive M2M Communications
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate energy-efficient clustering and medium access control (MAC) for cellular-based M2M networks to minimize device energy consumption and prolong network battery lifetime. First, we present an accurate energy consumption model that considers both static and dynamic energy consumptions, and utilize this model to derive the network lifetime. Second, we find the cluster size to maximize the network lifetime and develop an energy-efficient cluster-head selection scheme. Furthermore, we find feasible regions where clustering is beneficial in enhancing network lifetime. We further investigate communications protocols for both intra- and inter-cluster communications. While inter-cluster communications use conventional cellular access schemes, we develop an energy-efficient and load-adaptive multiple access scheme, called n-phase CSMA/CA, which provides a tunable tradeoff between energy efficiency, delay, and spectral efficiency of the network. The simulation results show that the proposed clustering, cluster-head selection, and communications protocol design outperform the others in energy saving and significantly prolong the lifetimes of both individual nodes and the whole M2M network.
Effect of Incomplete Meta-dataset on Average Ranking Method
Venue: arXiv cs.AI 2016
Year: 2016

One of the simplest metalearning methods is the average ranking method. This method uses metadata in the form of test results of a given set of algorithms on given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. We investigate the problem of how the process of generating the average ranking is affected by incomplete metadata including fewer test results. This issue is relevant, because if we could show that incomplete metadata does not affect the final results much, we could explore it in future design. We could simply conduct fewer tests and save thus computation time. In this paper we describe an upgraded average ranking method that is capable of dealing with incomplete metadata. Our results show that the proposed method is relatively robust to omission in test results in the meta datasets.
Kullback-Leibler Penalized Sparse Discriminant Analysis for Event-Related Potential Classification
Venue: arXiv cs.CV 2016
Year: 2016

A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve this type of classification problems, but it fails when the number of features is large relative to the number of observations. In this work we propose a penalized version of the sparse discriminant analysis (SDA), called Kullback-Leibler penalized sparse discriminant analysis (KLSDA). This method inherits both the discriminative feature selection and classification properties of SDA and it also improves SDA performance through the addition of Kullback-Leibler class discrepancy information. The KLSDA method is design to automatically select the optimal regularization parameters. Numerical experiments with two real ERP-EEG datasets show that this new method outperforms standard SDA.
Multi-objective Compositions for Collision-Free Connectivity Maintenance in Teams of Mobile Robots
Venue: arXiv cs.RO 2016
Year: 2016

Compositional barrier functions are proposed in this paper to systematically compose multiple objectives for teams of mobile robots. The objectives are first encoded as barrier functions, and then composed using AND and OR logical operators. The advantage of this approach is that compositional barrier functions can provably guarantee the simultaneous satisfaction of all composed objectives. The compositional barrier functions are applied to the example of ensuring collision avoidance and static/dynamical graph connectivity of teams of mobile robots. The resulting composite safety and connectivity barrier certificates are verified experimentally on a team of four mobile robots.
A Parallel Memory-efficient Epistemic Logic Program Solver: Harder, Better, Faster
Venue: arXiv cs.AI 2016
Year: 2016

As the practical use of answer set programming (ASP) has grown with the development of efficient solvers, we expect a growing interest in extensions of ASP as their semantics stabilize and solvers supporting them mature. Epistemic Specifications, which adds modal operators K and M to the language of ASP, is one such extension. We call a program in this language an epistemic logic program (ELP). Solvers have thus far been practical for only the simplest ELPs due to exponential growth of the search space. We describe a solver that is able to solve harder problems better (e.g., without exponentially-growing memory needs w.r.t. K and M occurrences) and faster than any other known ELP solver.
Multi-View Fuzzy Clustering with Minimax Optimization for Effective Clustering of Data from Multiple Sources
Venue: arXiv cs.AI 2016
Year: 2016

Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many approaches have been proposed to improve clustering performance by exploring and integrating heterogeneous information underlying different views. In this paper, we propose a new multi-view fuzzy clustering approach called MinimaxFCM by using minimax optimization based on well-known Fuzzy c means. In MinimaxFCM the consensus clustering results are generated based on minimax optimization in which the maximum disagreements of different weighted views are minimized. Moreover, the weight of each view can be learned automatically in the clustering process. In addition, there is only one parameter to be set besides the fuzzifier. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed MinimaxFCM are provided here. Experimental studies on nine multi-view data sets including real world image and document data sets have been conducted. We observed that MinimaxFCM outperforms related multi-view clustering approaches in terms of clustering accuracy, demonstrating the great potential of MinimaxFCM for multi-view data analysis.
Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study
Venue: arXiv cs.CV 2016
Year: 2016

It is well known that clothing fashion is a distinctive and often habitual trend in the style in which a person dresses. Clothing fashions are usually expressed with visual stimuli such as style, color, and texture. However, it is not clear which visual stimulus places higher/lower influence on the updating of clothing fashion. In this study, computer vision and machine learning techniques are employed to analyze the influence of different visual stimuli on clothing-fashion updates. Specifically, a classification-based model is proposed to quantify the influence of different visual stimuli, in which each visual stimulus's influence is quantified by its corresponding accuracy in fashion classification. Experimental results demonstrate that, on clothing-fashion updates, the style holds a higher influence than the color, and the color holds a higher influence than the texture.
When to use 3D Die-Stacked Memory for Bandwidth-Constrained Big Data Workloads
Venue: arXiv cs.AR 2016
Year: 2016

Response time requirements for big data processing systems are shrinking. To meet this strict response time requirement, many big data systems store all or most of their data in main memory to reduce the access latency. Main memory capacities have grown, and systems with 2 TB of main memory capacity available today. However, the rate at which processors can access this data--the memory bandwidth--has not grown at the same rate. In fact, some of these big-memory systems can access less than 10% of their main memory capacity in one second (billions of processor cycles). 3D die-stacking is one promising solution to this bandwidth problem, and industry is investing significantly in 3D die-stacking. We use a simple back-of-the-envelope-style model to characterize if and when the 3D die-stacked architecture is more cost-effective than current architectures for in-memory big data workloads. We find that die-stacking has much higher performance than current systems (up to 256x lower response times), and it does not require expensive memory over provisioning to meet real-time (10 ms) response time service-level agreements. However, the power requirements of the die-stacked systems are significantly higher (up to 50x) than current systems, and its memory capacity is lower in many cases. Even in this limited case study, we find 3D die-stacking is not a panacea. Today, die-stacking is the most cost-effective solution for strict SLAs and by reducing the power of the compute chip and increasing memory densities die-stacking can be cost-effective under other constraints in the future.
Leveraging over intact priors for boosting control and dexterity of prosthetic hands by amputees
Venue: arXiv cs.LG 2016
Year: 2016

Non-invasive myoelectric prostheses require a long training time to obtain satisfactory control dexterity. These training times could possibly be reduced by leveraging over training efforts by previous subjects. So-called domain adaptation algorithms formalize this strategy and have indeed been shown to significantly reduce the amount of required training data for intact subjects for myoelectric movements classification. It is not clear, however, whether these results extend also to amputees and, if so, whether prior information from amputees and intact subjects is equally useful. To overcome this problem, we evaluated several domain adaptation algorithms on data coming from both amputees and intact subjects. Our findings indicate that: (1) the use of previous experience from other subjects allows us to reduce the training time by about an order of magnitude; (2) this improvement holds regardless of whether an amputee exploits previous information from other amputees or from intact subjects.
A dynamic system characterization of road network node models
Venue: arXiv cs.SY 2016
Year: 2016

The propagation of traffic congestion along roads is a commonplace nonlinear phenomenon. When many roads are connected in a network, congestion can spill from one road to others as drivers queue to enter a congested road, creating further nonlinearities in the network dynamics. This paper considers the node model problem, which refers to methods for solving for cross-flows when roads meet at a junction. We present a simple hybrid dynamic system that, given a macroscopic snapshot of the roads entering and exiting a node, intuitively models the node's throughflows over time. This dynamic system produces solutions to the node model problem that are equal to those produced by many popular node models without intuitive physical meanings. We also show how the earlier node models can be rederived as executions of our dynamic system. The intuitive physical description supplied by our system provides a base for control of the road junction system dynamics, as well as the emergent network dynamics.
Resource Allocation for Machine-to-Machine Communications with Unmanned Aerial Vehicles
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, a novel framework for power-efficient, cluster-based machine-to-machine (M2M) communications is proposed. In the studied model, a number of unmanned aerial vehicles (UAVs) are used as aerial base stations to collect data from the cluster heads (CHs) of a set of M2M clusters. To minimize the CHs' transmit power while satisfying the rate requirements of M2M devices, an optimal scheduling and resource allocation mechanism for CH-UAV communications is proposed. First, using the queue rate stability concept, the minimum number of UAVs as well as the dwelling time that each UAV must spend for servicing the CHs are computed. Next, the optimal resource allocation for the CH-UAV communication links is determined such that M2M devices rate requirements are satisfied with a minimum transmit power. Simulation results show that, as the packet transmission probability of machines increases, the minimum number of UAVs required to guarantee the queue rate stability of CHs will also significantly increase. Our results also show that, compared to a case with pre-deployed terrestrial base stations, the average transmit power of CHs will decrease by 68% when UAVs are used.