OpenRaiser/Intern-Atlas huggingface dataset:
Non-rigid 3D shape retrieval based on multi-view metric learning
Venue: arXiv cs.CV 2019
Year: 2019

This study presents a novel multi-view metric learning algorithm, which aims to improve 3D non-rigid shape retrieval. With the development of non-rigid 3D shape analysis, there exist many shape descriptors. The intrinsic descriptors can be explored to construct various intrinsic representations for non-rigid 3D shape retrieval task. The different intrinsic representations (features) focus on different geometric properties to describe the same 3D shape, which makes the representations are related. Therefore, it is possible and necessary to learn multiple metrics for different representations jointly. We propose an effective multi-view metric learning algorithm by extending the Marginal Fisher Analysis (MFA) into the multi-view domain, and exploring Hilbert-Schmidt Independence Criteria (HSCI) as a diversity term to jointly learning the new metrics. The different classes can be separated by MFA in our method. Meanwhile, HSCI is exploited to make the multiple representations to be consensus. The learned metrics can reduce the redundancy between the multiple representations, and improve the accuracy of the retrieval results. Experiments are performed on SHREC'10 benchmarks, and the results show that the proposed method outperforms the state-of-the-art non-rigid 3D shape retrieval methods.
Concatenated Feature Pyramid Network for Instance Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Low level features like edges and textures play an important role in accurately localizing instances in neural networks. In this paper, we propose an architecture which improves feature pyramid networks commonly used instance segmentation networks by incorporating low level features in all layers of the pyramid in an optimal and efficient way. Specifically, we introduce a new layer which learns new correlations from feature maps of multiple feature pyramid levels holistically and enhances the semantic information of the feature pyramid to improve accuracy. Our architecture is simple to implement in instance segmentation or object detection frameworks to boost accuracy. Using this method in Mask RCNN, our model achieves consistent improvement in precision on COCO Dataset with the computational overhead compared to the original feature pyramid network.
CUSUM Filter for Brain Segmentation on DSC Perfusion MR Head Scans with Abnormal Brain Anatomy
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a new approach for relatively accurate brain region of interest (ROI) detection from dynamic susceptibility contrast (DSC) perfusion magnetic resonance (MR) images of a human head with abnormal brain anatomy. Such images produce problems for automatic brain segmentation algorithms, and as a result, poor perfusion ROI detection affects both quantitative measurements and visual assessment of perfusion data. In the proposed approach image segmentation is based on CUSUM filter usage that was adapted to be applicable to process DSC perfusion MR images. The result of segmentation is a binary mask of brain ROI that is generated via usage of brain boundary location. Each point of the boundary between the brain and surrounding tissues is detected as a change-point by CUSUM filter. Proposed adopted CUSUM filter operates by accumulating the deviations between the observed and expected intensities of image points at the time of moving on a trajectory. Motion trajectory is created by the iterative change of movement direction inside the background region in order to reach brain region, and vice versa after boundary crossing. Proposed segmentation approach was evaluated with Dice index comparing obtained results to the reference standard. Manually marked brain region pixels (reference standard), as well as visual inspection of detected with CUSUM filter usage brain ROI, were provided by experienced radiologists. The results showed that proposed approach is suitable to be used for brain ROI detection from DSC perfusion MR images of a human head with abnormal brain anatomy and can, therefore, be applied in the DSC perfusion data analysis.
Multi-contact Phase Change Toggle Logic Device Utilizing Thermal Crosstalk
Venue: arXiv cs.ET 2019
Year: 2019

Phase change memory (PCM) is an emerging high speed, high density, high endurance, and scalable non-volatile memory technology which utilizes the large resistivity contrast between the amorphous and crystalline phases of chalcogenide materials such as Ge2Sb2Te5 (GST). In addition to being used as a standalone memory, there has been a growing interest in integration of PCM devices on top of the CMOS layer for computation in memory and neuromorphic computing. The large CMOS overhead for memory controllers is a limiting factor for this purpose. Transferring functionality like routing, multiplexing, and logic to the memory layer can substantially reduce the CMOS overhead, making it possible to integrate 100s of GB of PCM storage on top of a conventional CPU. In this work, we present computational analysis of a phase change device concept that can perform toggle operations. The toggle functionality is achieved using two physical mechanisms: (i) isolation of different read contacts due to amorphization between different write contact pairs, and (ii) thermal cross-talk between a molten region and a previously amorphized region. Phase-change devices with six contacts can be implemented as toggle flip-flops, multiplexer, or demultiplexer when interfaced with CMOS transistors. Here, we demonstrate the operation of the device as a toggle flip-flop with 5 transistors, requiring ~50% of the footprint compared to conventional CMOS alternatives, with the added advantage of non-volatility.
Feasibility vs. Optimality in Distributed AC OPF - A Case Study Considering ADMM and ALADIN
Venue: arXiv cs.SY 2019
Year: 2019

This paper investigates the role of feasible initial guesses and large consensus-violation penalization in distributed optimization for Optimal Power Flow (OPF) problems. Specifically, we discuss the behavior of the Alternating Direction of Multipliers Method (ADMM). We show that in case of large consensus-violation penalization ADMM might exhibit slow progress. We support this observation by an analysis of the algorithmic properties of ADMM. Furthermore, we illustrate our findings considering the IEEE 57 bus system and we draw upon a comparison of ADMM and the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method.
The Historical Perspective of Botnet tools
Venue: arXiv cs.CY 2019
Year: 2019

Bot as it is popularly called is an inherent attributes of botnet tool. Botnet is a group of malicious tools acting as an entity. Furthermore, history has it that the aim of what gave rise to botnet was the idea to simplify the method of message exchange within networking platform. However, this has led to several botnet tools ravaging the server environments in recent times. The working principle of these botnet tools is to get client systems that are vulnerable and thereafter, steal valuable credentials. This work is part of a comprehensive research work into botnet detection mechanism but, on this paper it primarily look at how botnet as threat tool began, the trend since inception and as well as few approaches that have been used to curb it.
Recognising and evaluating the effectiveness of extortion in the Iterated Prisoner's Dilemma
Venue: arXiv cs.GT 2019
Year: 2019

Since the introduction of zero-determinant strategies, extortionate strategies have received considerable interest. While an interesting class of strategies, the definitions of extortionate strategies are algebraically rigid, apply only to memory-one strategies, and require complete knowledge of a strategy (memory-one cooperation probabilities). We describe a method to detect extortionate behaviour from the history of play of a strategy. When applied to a corpus of 204 strategies this method detects extortionate behaviour in well-known extortionate strategies as well others that do not fit the algebraic definition. The highest performing strategies in this corpus are able to exhibit selectively extortionate behavior, cooperating with strong strategies while exploiting weaker strategies, which no memory-one strategy can do. These strategies emerged from an evolutionary selection process and their existence contradicts widely-repeated folklore in the evolutionary game theory literature: complex strategies can be extraordinarily effective, zero-determinant strategies can be outperformed by non-zero determinant strategies, and longer memory strategies are able to outperform short memory strategies. Moreover, while resistance to extortion is critical for the evolution of cooperation, the extortion of weak opponents need not prevent cooperation between stronger opponents, and this adaptability may be crucial to maintaining cooperation in the long run.
Sentiment analysis with genetically evolved Gaussian kernels
Venue: arXiv cs.CL 2019
Year: 2019

Sentiment analysis consists of evaluating opinions or statements from the analysis of text. Among the methods used to estimate the degree in which a text expresses a given sentiment, are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a predefined kernel with hyperparameters that can be tuned but whose structure can not be adapted. In this paper, we propose the application of Genetic Programming for evolving Gaussian Process kernels that are more precise for sentiment analysis. We use use a very flexible representation of kernels combined with a multi-objective approach that simultaneously considers two quality metrics and the computational time spent by the kernels. Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.
Algorithms Clearly Beat Gamers at Quantum Moves. A Verification
Venue: arXiv cs.OH 2019
Year: 2019

The paper [Sørensen et al., Nature 532] considers how human players compare to algorithms for solving the Quantum Moves game BringHomeWater and design new algorithms based on the intuition extracted from players. The claim by [Sørensen et al., Nature 532] is that players outperform widely used algorithms, in particular the KASS algorithm, based on the Krotov algorithm, and that player intuition is crucial to develop improved methods. However, as initially discussed by D. Sels [D. Sels, Phys. Rev. A 97], a standard Coordinate Ascent algorithm outperforms all players by a large margin. Albeit D. Sels only compare to player solutions, the simple algorithm outperforms all algorithms based on player solutions and Krotov, and it does so using much less time and iterations. In this paper we elaborate on the methods discussed by D. Sels and verify that the presented algorithm, solves the problem better than all players and algorithms derived from player solutions in [Sørensen et al., Nature 532]. We also verify the theoretical analysis presented by D. Sels, that gives a theoretically derived protocol that outperforms all players. We add a comparison with gradient ascent or GRAPE. Starting from uniform random values, GRAPE outperforms all players by a large margin. GRAPE works at least as well as the methods from [Sørensen et al., Nature 532] initialized with player solutions. A standard analysis of the results from GRAPE provides a starting point for GRAPE, that outperform all algorithms from [Sørensen et al., Nature 532]. We compare with a basic Krotov algorithm, and get results similar to GRAPE, clearly outperforming players and the KASS algorithm. These experiments verify and underline the result in [D. Sels, Phys. Rev. A 97] that the conclusions from [Sørensen et al., Nature 532] regarding algorithms are untenable. In fact the opposite conclusions are true.
A benchmark for C program verification
Venue: arXiv cs.PL 2019
Year: 2019

We present twenty-five C programs, as a benchmark for C program verification using formal methods. This benchmark can be used for system demonstration, for comparison of verification effort between systems, and as a friendly competition. For this last purpose, we give a scoring formula that allows a verification system to score up to a hundred points.
Modular Synthesis of Divide-and-Conquer Parallelism for Nested Loops (Extended Version)
Venue: arXiv cs.PL 2019
Year: 2019

We propose a methodology for automatic generation of divide-and-conquer parallel implementations of sequential nested loops. We focus on a class of loops that traverse read-only multidimensional collections (lists or arrays) and compute a function over these collections. Our approach is modular, in that, the inner loop nest is abstracted away to produce a simpler loop nest for parallelization. Then, the summarized version of the loop nest is parallelized. The main challenge addressed by this paper is that to perform the code transformations necessary in each step, the loop nest may have to be augmented (automatically) with extra computation to make possible the abstraction and/or the parallelization tasks. We present theoretical results to justify the correctness of our modular approach, and algorithmic solutions for automation. Experimental results demonstrate that our approach can parallelize highly non-trivial loop nests efficiently.
Infant-Prints: Fingerprints for Reducing Infant Mortality
Venue: arXiv cs.CV 2019
Year: 2019

In developing countries around the world, a multitude of infants continue to suffer and die from vaccine-preventable diseases, and malnutrition. Lamentably, the lack of any official identification documentation makes it exceedingly difficult to prevent these infant deaths. To solve this global crisis, we propose Infant-Prints which is comprised of (i) a custom, compact, low-cost (85 USD), high-resolution (1,900 ppi) fingerprint reader, (ii) a high-resolution fingerprint matcher, and (iii) a mobile application for search and verification for the infant fingerprint. Using Infant-Prints, we have collected a longitudinal database of infant fingerprints and demonstrate its ability to perform accurate and reliable recognition of infants enrolled at the ages 0-3 months, in time for effective delivery of critical vaccinations and nutritional supplements (TAR=90% @ FAR = 0.1% for infants older than 8 weeks).
ScriptNet: Neural Static Analysis for Malicious JavaScript Detection
Venue: arXiv cs.CR 2019
Year: 2019

Malicious scripts are an important computer infection threat vector in the wild. For web-scale processing, static analysis offers substantial computing efficiencies. We propose the ScriptNet system for neural malicious JavaScript detection which is based on static analysis. We use the Convoluted Partitioning of Long Sequences (CPoLS) model, which processes Javascript files as byte sequences. Lower layers capture the sequential nature of these byte sequences while higher layers classify the resulting embedding as malicious or benign. Unlike previously proposed solutions, our model variants are trained in an end-to-end fashion allowing discriminative training even for the sequential processing layers. Evaluating this model on a large corpus of 212,408 JavaScript files indicates that the best performing CPoLS model offers a 97.20% true positive rate (TPR) for the first 60K byte subsequence at a false positive rate (FPR) of 0.50%. The best performing CPoLS model significantly outperform several baseline models.
Semi-supervised graph labelling reveals increasing partisanship in the United States Congress
Venue: arXiv cs.SI 2019
Year: 2019

Graph labelling is a key activity of network science, with broad practical applications, and close relations to other network science tasks, such as community detection and clustering. While a large body of work exists on both unsupervised and supervised labelling algorithms, the class of random walk-based supervised algorithms requires further exploration, particularly given their relevance to social and political networks. This work refines and expands upon a new semi-supervised graph labelling method, the GLaSS method, that exactly calculates absorption probabilities for random walks on connected graphs. The method models graphs exactly as discrete-time Markov chains, treating labelled nodes as absorbing states. The method is applied to roll call voting data for 42 meetings of the United States House of Representatives and Senate, from 1935 to 2019. Analysis of the 84 resultant political networks demonstrates strong and consistent performance of GLaSS when estimating labels for unlabelled nodes in graphs, and reveals a significant trend of increasing partisanship within the United States Congress.
Distributed Mechanism Design for Network Resource Allocation Problems
Venue: arXiv cs.GT 2019
Year: 2019

In the standard Mechanism Design framework, agents' messages are gathered at a central point and allocation/tax functions are calculated in a centralized manner, i.e., as functions of all network agents' messages. This requirement may cause communication and computation overhead and necessitates the design of mechanisms that alleviate this bottleneck. We consider a scenario where message transmission can only be performed locally so that the mechanism allocation/tax functions can be calculated in a decentralized manner. Each agent transmits messages to her local neighborhood, as defined by a given message-exchange network, and her allocation/tax functions are only functions of the available neighborhood messages. This scenario gives rise to a novel research problem that we call "Distributed Mechanism Design". In this paper, we propose two distributed mechanisms for network utility maximization problems that involve private and public goods with competition and cooperation between agents. As a concrete example, we use the problems of rate allocation in networks with either unicast or multirate multicast transmission protocols. The proposed mechanism for each of the protocols fully implements the optimal allocation in Nash equilibria and its message space dimensionality scales linearly with respect to the number of agents in the network.
Pricing Traffic Networks with Mixed Vehicle Autonomy
Venue: arXiv cs.GT 2019
Year: 2019

In a traffic network, vehicles normally select their routes selfishly. Consequently, traffic networks normally operate at an equilibrium characterized by Wardrop conditions. However, it is well known that equilibria are inefficient in general. In addition to the intrinsic inefficiency of equilibria, the authors recently showed that, in mixed-autonomy networks in which autonomous vehicles maintain a shorter headway than human-driven cars, increasing the fraction of autonomous vehicles in the network may increase the inefficiency of equilibria. In this work, we study the possibility of obviating the inefficiency of equilibria in mixed-autonomy traffic networks via pricing mechanisms. In particular, we study assigning prices to network links such that the overall or social delay of the resulting equilibria is minimum. First, we study the possibility of inducing such optimal equilibria by imposing a set of undifferentiated prices, i.e. a set of prices that treat both human-driven and autonomous vehicles similarly at each link. We provide an example which demonstrates that undifferentiated pricing is not sufficient for achieving minimum social delay. Then, we study differentiated pricing where the price of traversing each link may depend on whether vehicles are human-driven or autonomous. Under differentiated pricing, we prove that link prices obtained from the marginal cost taxation of links will induce equilibria with minimum social delay if the degree of road capacity asymmetry (i.e. the ratio between the road capacity when all vehicles are human-driven and the road capacity when all vehicles are autonomous) is homogeneous among network links.
A PCA-like Autoencoder
Venue: arXiv cs.CV 2019
Year: 2019

An autoencoder is a neural network which data projects to and from a lower dimensional latent space, where this data is easier to understand and model. The autoencoder consists of two sub-networks, the encoder and the decoder, which carry out these transformations. The neural network is trained such that the output is as close to the input as possible, the data having gone through an information bottleneck : the latent space. This tool bears significant ressemblance to Principal Component Analysis (PCA), with two main differences. Firstly, the autoencoder is a non-linear transformation, contrary to PCA, which makes the autoencoder more flexible and powerful. Secondly, the axes found by a PCA are orthogonal, and are ordered in terms of the amount of variability which the data presents along these axes. This makes the interpretability of the PCA much greater than that of the autoencoder, which does not have these attributes. Ideally, then, we would like an autoencoder whose latent space consists of independent components, ordered by decreasing importance to the data. In this paper, we propose an algorithm to create such a network. We create an iterative algorithm which progressively increases the size of the latent space, learning a new dimension at each step. Secondly, we propose a covariance loss term to add to the standard autoencoder loss function, as well as a normalisation layer just before the latent space, which encourages the latent space components to be statistically independent. We demonstrate the results of this autoencoder on simple geometric shapes, and find that the algorithm indeed finds a meaningful representation in the latent space. This means that subsequent interpolation in the latent space has meaning with respect to the geometric properties of the images.
Concurrent Typestate-Oriented Programming in Java
Venue: arXiv cs.PL 2019
Year: 2019

We describe a generative approach that enables concurrent typestate-oriented programming in Java and other mainstream languages. The approach allows programmers to implement objects exposing a state-sensitive interface using a high-level synchronization abstraction that synchronizes methods with the states of the receiver object in which those methods have an effect. An external tool takes care of generating all the boilerplate code that implements the synchronization logic. Behavioral types are used to specify object protocols. The tool integrates protocol conformance verification with the synchronization logic so that protocol violations are promptly detected at runtime.
Value-Dependent Session Design in a Dependently Typed Language
Venue: arXiv cs.PL 2019
Year: 2019

Session Types offer a typing discipline that allows protocol specifications to be used during type-checking, ensuring that implementations adhere to a given specification. When looking to realise global session types in a dependently typed language care must be taken that values introduced in the description are used by roles that know about the value. We present Sessions, a Resource Dependent EDSL for describing global session descriptions in the dependently typed language Idris. As we construct session descriptions the values parameterising the EDSLs' type keeps track of roles and messages they have encountered. We can use this knowledge to ensure that message values are only used by those who know the value. Sessions supports protocol descriptions that are computable, composable, higher-order, and value-dependent. We demonstrate Sessions expressiveness by describing the TCP Handshake, a multi-modal server providing echo and basic arithmetic operations, and a Higher-Order protocol that supports an authentication interaction step.