OpenRaiser/Intern-Atlas huggingface dataset:
Robust Conditional Probabilities
Venue: arXiv cs.LG 2017
Year: 2017

Conditional probabilities are a core concept in machine learning. For example, optimal prediction of a label $Y$ given an input $X$ corresponds to maximizing the conditional probability of $Y$ given $X$. A common approach to inference tasks is learning a model of conditional probabilities. However, these models are often based on strong assumptions (e.g., log-linear models), and hence their estimate of conditional probabilities is not robust and is highly dependent on the validity of their assumptions. Here we propose a framework for reasoning about conditional probabilities without assuming anything about the underlying distributions, except knowledge of their second order marginals, which can be estimated from data. We show how this setting leads to guaranteed bounds on conditional probabilities, which can be calculated efficiently in a variety of settings, including structured-prediction. Finally, we apply them to semi-supervised deep learning, obtaining results competitive with variational autoencoders.
Modelling of a Permanent Magnet Synchronous Machine Using Isogeometric Analysis
Venue: arXiv cs.CE 2017
Year: 2017

Isogeometric analysis (IGA) is used to simulate a permanent magnet synchronous machine. IGA uses non-uniform rational B-splines to parametrise the domain and to approximate the solution space, thus allowing for the exact description of the geometries even on the coarsest level of mesh refinement. Given the properties of the isogeometric basis functions, this choice guarantees a higher accuracy than the classical finite element method. For dealing with the different stator and rotor topologies, the domain is split into two non-overlapping parts on which Maxwell's equations are solved independently in the context of a classical Dirichlet-to-Neumann domain decomposition scheme. The results show good agreement with the ones obtained by the classical finite element approach.
Flexible Multiple Base Station Association and Activation for Downlink Heterogeneous Networks
Venue: arXiv cs.NI 2017
Year: 2017

This letter shows that the flexible association of possibly multiple base stations (BSs) to each user over multiple frequency bands, along with the joint optimization of BS transmit power that encourages the turning-off of the BSs at off-peak time, can significantly improve the performance of a downlink heterogeneous wireless cellular network. We propose a gradient projection algorithm for optimizing BS association and an iteratively reweighting scheme together with a novel proximal gradient method for optimizing power in order to find the optimal tradeoff between network utility and power consumption. Simulation results reveal significant performance improvement as compared to the conventional single-BS association.
Model Predictive Control Based Trajectory Generation for Autonomous Vehicles - An Architectural Approach
Venue: arXiv cs.SY 2017
Year: 2017

Research in the field of automated driving has created promising results in the last years. Some research groups have shown perception systems which are able to capture even complicated urban scenarios in great detail. Yet, what is often missing are general-purpose path- or trajectory planners which are not designed for a specific purpose. In this paper we look at path- and trajectory planning from an architectural point of view and show how model predictive frameworks can contribute to generalized path- and trajectory generation approaches for generating safe trajectories even in cases of system failures.
Virtualized 5G Air Interface Protocol Stack for Multi-Cell Coordination
Venue: arXiv cs.NI 2017
Year: 2017

This article proposes a novel virtualized air interface protocol stack for next-generation wireless networks, that natively supports multiple radio access technologies and multi-point transmissions. Leveraging upon the concepts of softwarization of the air interface and virtualization, this design provides flexibility and scalability towards future advances in the radio access, whilst at the same time being backwards compatible with legacy technologies. This proposal enables the aggregation of multiple frequency bands and multiple technologies, without the need of modifying the operating procedures or the protocol of each supported technology. In the end, some challenges that have still to be addressed in future works are provided.
An Empirical Comparison of Developer Retention in the RubyGems and npm Software Ecosystems
Venue: arXiv cs.SE 2017
Year: 2017

Software ecosystems can be viewed as socio-technical networks consisting of technical components (software packages) and social components (communities of developers) that maintain the technical components. Ecosystems evolve over time through socio-technical changes that may greatly impact the ecosystem's sustainability. Social changes like developer turnover may lead to technical degradation. This motivates the need to identify those factors leading to developer abandonment, in order to automate the process of identifying developers with high abandonment risk. This paper compares such factors for two software package ecosystems, RubyGems and npm. We analyse the evolution of their packages hosted on GitHub, considering development activity in terms of commits, and social interaction with other developers in terms of comments associated to commits, issues or pull requests. We analyse this socio-technical activity for more than 30k and 60k developers for RubyGems and npm respectively. We use survival analysis to identify which factors coincide with a lower survival probability. Our results reveal that developers with a higher probability to abandon an ecosystem: do not engage in discussions with other developers; do not have strong social and technical activity intensity; communicate or commit less frequently; and do not participate to both technical and social activities for long periods of time. Such observations could be used to automate the identification of developers with a high probability of abandoning the ecosystem and, as such, reduce the risks associated to knowledge loss.
Kinematic interpretation of the Study quadric's ambient space
Venue: arXiv cs.CG 2017
Year: 2017

It is well known that real points of the Study quadric (sliced along a 3-dimensional generator space) correspond to displacements of the Euclidean 3-space. But we still lack of a kinematic meaning for the points of the ambient 7-dimensional projective space $P^7$. This paper gives one possible interpretation in terms of displacements of the Euclidean 4-space. From this point of view we also discuss the extended inverse kinematic map, motions corresponding to straight lines in $P^7$ and linear complexes of SE(3)-displacements. Moreover we present an application of this interpretation in the context of interactive motion design.
Cheryl's Birthday
Venue: arXiv cs.AI 2017
Year: 2017

We present four logic puzzles and after that their solutions. Joseph Yeo designed 'Cheryl's Birthday'. Mike Hartley came up with a novel solution for 'One Hundred Prisoners and a Light Bulb'. Jonathan Welton designed 'A Blind Guess' and 'Abby's Birthday'. Hans van Ditmarsch and Barteld Kooi authored the puzzlebook 'One Hundred Prisoners and a Light Bulb' that contains other knowledge puzzles, and that can also be found on the webpage http://personal.us.es/hvd/lightbulb.html dedicated to the book.
Human Skin Detection Using RGB, HSV and YCbCr Color Models
Venue: arXiv cs.CV 2017
Year: 2017

Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image. Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process. A new human skin detection algorithm is proposed in this paper. The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The objective of proposed algorithm is to improve the recognition of skin pixels in given images. The algorithm not only considers individual ranges of the three color parameters but also takes into ac- count combinational ranges which provide greater accuracy in recognizing the skin area in a given image.
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for research in artificial intelligence, given its multimodal nature, clear evaluation protocol, and potential real-world applications. The performance of deep neural networks for VQA is very dependent on choices of architectures and hyperparameters. To help further research in the area, we describe in detail our high-performing, though relatively simple model. Through a massive exploration of architectures and hyperparameters representing more than 3,000 GPU-hours, we identified tips and tricks that lead to its success, namely: sigmoid outputs, soft training targets, image features from bottom-up attention, gated tanh activations, output embeddings initialized using GloVe and Google Images, large mini-batches, and smart shuffling of training data. We provide a detailed analysis of their impact on performance to assist others in making an appropriate selection.
Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Recognizing freehand sketches with high arbitrariness is greatly challenging. Most existing methods either ignore the geometric characteristics or treat sketches as handwritten characters with fixed structural ordering. Consequently, they can hardly yield high recognition performance even though sophisticated learning techniques are employed. In this paper, we propose a sequential deep learning strategy that combines both shape and texture features. A coded shape descriptor is exploited to characterize the geometry of sketch strokes with high flexibility, while the outputs of constitutional neural networks (CNN) are taken as the abstract texture feature. We develop dual deep networks with memorable gated recurrent units (GRUs), and sequentially feed these two types of features into the dual networks, respectively. These dual networks enable the feature fusion by another gated recurrent unit (GRU), and thus accurately recognize sketches invariant to stroke ordering. The experiments on the TU-Berlin data set show that our method outperforms the average of human and state-of-the-art algorithms even when significant shape and appearance variations occur.
Multi-dimensional Gated Recurrent Units for Automated Anatomical Landmark Localization
Venue: arXiv cs.CV 2017
Year: 2017

We present an automated method for localizing an anatomical landmark in three-dimensional medical images. The method combines two recurrent neural networks in a coarse-to-fine approach: The first network determines a candidate neighborhood by analyzing the complete given image volume. The second network localizes the actual landmark precisely and accurately in the candidate neighborhood. Both networks take advantage of multi-dimensional gated recurrent units in their main layers, which allow for high model complexity with a comparatively small set of parameters. We localize the medullopontine sulcus in 3D magnetic resonance images of the head and neck. We show that the proposed approach outperforms similar localization techniques both in terms of mean distance in millimeters and voxels w.r.t. manual labelings of the data. With a mean localization error of 1.7 mm, the proposed approach performs on par with neurological experts, as we demonstrate in an interrater comparison.
Non-Adaptive Randomized Algorithm for Group Testing
Venue: arXiv cs.LG 2017
Year: 2017

We study the problem of group testing with a non-adaptive randomized algorithm in the random incidence design (RID) model where each entry in the test is chosen randomly independently from $\{0,1\}$ with a fixed probability $p$. The property that is sufficient and necessary for a unique decoding is the separability of the tests, but unfortunately no linear time algorithm is known for such tests. In order to achieve linear-time decodable tests, the algorithms in the literature use the disjunction property that gives almost optimal number of tests. We define a new property for the tests which we call semi-disjunction property. We show that there is a linear time decoding for such test and for $d\to \infty$ the number of tests converges to the number of tests with the separability property and is therefore optimal (in the RID model). Our analysis shows that, in the RID model, the number of tests in our algorithm is better than the one with the disjunction property even for small $d$.
Mutual Visibility by Robots with Persistent Memory
Venue: arXiv cs.DC 2017
Year: 2017

This paper addresses the mutual visibility problem for a set of semi-synchronous, opaque robots occupying distinct positions in the Euclidean plane. Since robots are opaque, if three robots lie on a line, the middle robot obstructs the visions of the two other robots. The mutual visibility problem asks the robots to coordinate their movements to form a configuration, within finite time and without collision, in which no three robots are collinear. Robots are endowed with a constant bits of persistent memory. In this work, we consider the FSTATE computational model in which the persistent memory is used by the robots only to remember their previous internal states. Except from this persistent memory, robots are oblivious i.e., they do not carry forward any other information from their previous computational cycles. The paper presents a distributed algorithm to solve the mutual visibility problem for a set of semi-synchronous robots using only 1 bit of persistent memory. The proposed algorithm does not impose any other restriction on the capability of the robots and guarantees collision-free movements for the robots.
Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering
Venue: arXiv cs.LG 2017
Year: 2017

Matrix factorization is one of the best approaches for collaborative filtering, because of its high accuracy in presenting users and items latent factors. The main disadvantages of matrix factorization are its complexity, and being very hard to be parallelized, specially with very large matrices. In this paper, we introduce a new method for collaborative filtering based on Matrix Factorization by combining simulated annealing with levy distribution. By using this method, good solutions are achieved in acceptable time with low computations, compared to other methods like stochastic gradient descent, alternating least squares, and weighted non-negative matrix factorization.
An evaluation of large-scale methods for image instance and class discovery
Venue: arXiv cs.CV 2017
Year: 2017

This paper aims at discovering meaningful subsets of related images from large image collections without annotations. We search groups of images related at different levels of semantic, i.e., either instances or visual classes. While k-means is usually considered as the gold standard for this task, we evaluate and show the interest of diffusion methods that have been neglected by the state of the art, such as the Markov Clustering algorithm. We report results on the ImageNet and the Paris500k instance dataset, both enlarged with images from YFCC100M. We evaluate our methods with a labelling cost that reflects how much effort a human would require to correct the generated clusters. Our analysis highlights several properties. First, when powered with an efficient GPU implementation, the cost of the discovery process is small compared to computing the image descriptors, even for collections as large as 100 million images. Second, we show that descriptions selected for instance search improve the discovery of object classes. Third, the Markov Clustering technique consistently outperforms other methods; to our knowledge it has never been considered in this large scale scenario.
A Novel Formal Agent-based Simulation Modeling Framework of an AIDS Complex Adaptive System
Venue: arXiv cs.MA 2017
Year: 2017

HIV/AIDS spread depends upon complex patterns of interaction among various sub-sets emerging at population level. This added complexity makes it difficult to study and model AIDS and its dynamics. AIDS is therefore a natural candidate to be modeled using agent-based modeling, a paradigm well-known for modeling Complex Adaptive Systems (CAS). While agent-based models are also well-known to effectively model CAS, often times models can tend to be ambiguous and the use of purely text-based specifications (such as ODD) can make models difficult to be replicated. Previous work has shown how formal specification may be used in conjunction with agent-based modeling to develop models of various CAS. However, to the best of our knowledge, no such model has been developed in conjunction with AIDS. In this paper, we present a Formal Agent-Based Simulation modeling framework (FABS-AIDS) for an AIDS-based CAS. FABS-AIDS employs the use of a formal specification model in conjunction with an agent-based model to reduce ambiguity as well as improve clarity in the model definition. The proposed model demonstrates the effectiveness of using formal specification in conjunction with agent-based simulation for developing models of CAS in general and, social network-based agent-based models, in particular.
Tikhonov Regularization for Long Short-Term Memory Networks
Venue: arXiv cs.LG 2017
Year: 2017

It is a well-known fact that adding noise to the input data often improves network performance. While the dropout technique may be a cause of memory loss, when it is applied to recurrent connections, Tikhonov regularization, which can be regarded as the training with additive noise, avoids this issue naturally, though it implies regularizer derivation for different architectures. In case of feedforward neural networks this is straightforward, while for networks with recurrent connections and complicated layers it leads to some difficulties. In this paper, a Tikhonov regularizer is derived for Long-Short Term Memory (LSTM) networks. Although it is independent of time for simplicity, it considers interaction between weights of the LSTM unit, which in theory makes it possible to regularize the unit with complicated dependences by using only one parameter that measures the input data perturbation. The regularizer that is proposed in this paper has three parameters: one to control the regularization process, and other two to maintain computation stability while the network is being trained. The theory developed in this paper can be applied to get such regularizers for different recurrent neural networks with Hadamard products and Lipschitz continuous functions.
Robust Video Watermarking against H.264 and H.265 Compression Attacks
Venue: arXiv cs.MM 2017
Year: 2017

This paper proposes a robust watermarking method for uncompressed video data against H.264/AVC and H.265/HEVC compression standards. We embed the watermark data in the mid-range transform coefficients of a block that is less similar to its corresponding block in the previous and next frames. This idea makes the watermark robust against the compression standards that use the inter prediction technique. The last two video compression standards also use inter prediction for motion compensation like previous video compression standards. Therefore, the proposed method is also well suited with these standards. Simulation results show the adequate robustness and transparency of our watermarking scheme.
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.
A Simple and Realistic Pedestrian Model for Crowd Simulation and Application
Venue: arXiv cs.MA 2017
Year: 2017

The simulation of pedestrian crowd that reflects reality is a major challenge for researches. Several crowd simulation models have been proposed such as cellular automata model, agent-based model, fluid dynamic model, etc. It is important to note that agent-based model is able, over others approaches, to provide a natural description of the system and then to capture complex human behaviors. In this paper, we propose a multi-agent simulation model in which pedestrian positions are updated at discrete time intervals. It takes into account the major normal conditions of a simple pedestrian situated in a crowd such as preferences, realistic perception of environment, etc. Our objective is to simulate the pedestrian crowd realistically towards a simulation of believable pedestrian behaviors. Typical pedestrian phenomena, including the unidirectional and bidirectional movement in a corridor as well as the flow through bottleneck, are simulated. The conducted simulations show that our model is able to produce realistic pedestrian behaviors. The obtained fundamental diagram and flow rate at bottleneck agree very well with classic conclusions and empirical study results. It is hoped that the idea of this study may be helpful in promoting the modeling and simulation of pedestrian crowd in a simple way.
TensorFlow Enabled Genetic Programming
Venue: arXiv cs.DC 2017
Year: 2017

Genetic Programming, a kind of evolutionary computation and machine learning algorithm, is shown to benefit significantly from the application of vectorized data and the TensorFlow numerical computation library on both CPU and GPU architectures. The open source, Python Karoo GP is employed for a series of 190 tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data points. This body of tests demonstrates that datasets measured in tens and hundreds of data points see 2-15x improvement when moving from the scalar/SymPy configuration to the vector/TensorFlow configuration, with a single core performing on par or better than multiple CPU cores and GPUs. A dataset composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing 5.5M data points sees GPU configurations out-performing CPU configurations on average by 1.3x.