OpenRaiser/Intern-Atlas huggingface dataset:
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
Venue: arXiv cs.CV 2018
Year: 2018

Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of embeddings. In this work, we show how to improve the robustness of such embeddings by exploiting the independence within ensembles. To this end, we divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. Further, we propose two loss functions which increase the diversity in our ensemble. These loss functions can be applied either for weight initialization or during training. Together, our contributions leverage large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increase retrieval accuracy of the embedding. Our method works with any differentiable loss function and does not introduce any additional parameters during test time. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-of-the-art methods on the CUB 200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets.
Disseminacao de mensagens DTN com base em grupos de interesses
Venue: arXiv cs.NI 2018
Year: 2018

Recent works explore social characteristics of nodes to improve message delivery rate in Delay Tolerant Networks (DTN). This work uses machine learning techniques to create node groups organized by common interests. Messages are sent to target groups, and from there to the final destination. Simulation results using The ONE simulator show that the larger the group size the higher the message delivery rate, that reaches 100% in some cases. The paper also presents results related to the groups of interest such as message delivery rat, delivery delay and an average number of hops to deliver messages. The overall results indicate that group-based routing is a promising research filed.
System-Aware Compression
Venue: arXiv cs.IT 2018
Year: 2018

Many information systems employ lossy compression as a crucial intermediate stage among other processing components. While the important distortion is defined by the system's input and output signals, the compression usually ignores the system structure, therefore, leading to an overall sub-optimal rate-distortion performance. In this paper we propose a compression methodology for an operational rate-distortion optimization considering a known system layout, modeled using linear operators and noise. Using the alternating direction method of multipliers (ADMM) technique, we show that the design of the new globally-optimized compression reduces to a standard compression of a "system adjusted" signal. Essentially, the proposed framework leverages standard compression techniques to address practical settings of the noisy source coding problem. We further explain the main ideas of our method by theoretically studying the case of a cyclo-stationary Gaussian signal. We present experimental results for coding of one-dimensional signals and for video compression using the HEVC standard, showing significant gains by the adjustment to an acquisition-rendering system.
Secrecy Amplification for Distributed Encrypted Sources with Correlated Keys using Affine Encoders
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposed the application of post-encryption-compression (PEC) to strengthen the secrecy in the case of distributed encryption where the encryption keys are correlated to each other. We derive the universal code construction for the compression and the rate region where codes with achievability and secrecy are obtainable. Our main technique is to use affine encoders which are constructed from certain linear encoders to encode the ciphertexts before sending them to public communication channels. We show that if the rates of linear codes are within a certain rate region:(1) information leakage on the original sources from the encoded ciphertexts without the keys is negligible, while (2) one who has legitimate keys is able to retrieve the original source data with negligible error probability.
Dynamic compensation and homeostasis: a feedback control perspective
Venue: arXiv cs.SY 2018
Year: 2018

"Dynamic compensation" is a robustness property where a perturbed biological circuit maintains a suitable output [Karin O., Swisa A., Glaser B., Dor Y., Alon U. (2016). Mol. Syst. Biol., 12: 886]. In spite of several attempts, no fully convincing analysis seems now to be on hand. This communication suggests an explanation via "model-free control" and the corresponding "intelligent" controllers [Fliess M., Join C. (2013). Int. J. Contr., 86, 2228-2252], which are already successfully applied in many concrete situations. As a byproduct this setting provides also a slightly different presentation of homeostasis, or "exact adaptation," where the working conditions are assumed to be "mild." Several convincing, but academic, computer simulations are provided and discussed.
DKVF: A Framework for Rapid Prototyping and Evaluating Distributed Key-value Stores
Venue: arXiv cs.DC 2018
Year: 2018

We present our framework DKVF that enables one to quickly prototype and evaluate new protocols for key-value stores and compare them with existing protocols based on selected benchmarks. Due to limitations of CAP theorem, new protocols must be developed that achieve the desired trade-off between consistency and availability for the given application at hand. Hence, both academic and industrial communities focus on developing new protocols that identify a different (and hopefully better in one or more aspect) point on this trade-off curve. While these protocols are often based on a simple intuition, evaluating them to ensure that they indeed provide increased availability, consistency, or performance is a tedious task. Our framework, DKVF, enables one to quickly prototype a new protocol as well as identify how it performs compared to existing protocols for pre-specified benchmarks. Our framework relies on YCSB (Yahoo! Cloud Servicing Benchmark) for benchmarking. We demonstrate DKVF by implementing four existing protocols --eventual consistency, COPS, GentleRain and CausalSpartan-- with it. We compare the performance of these protocols against different loading conditions. We find that the performance is similar to our implementation of these protocols from scratch. And, the comparison of these protocols is consistent with what has been reported in the literature. Moreover, implementation of these protocols was much more natural as we only needed to translate the pseudocode into Java (and add the necessary error handling). Hence, it was possible to achieve this in just 1-2 days per protocol. Finally, our framework is extensible. It is possible to replace individual components in the framework (e.g., the storage component).
Autonomous UAV Navigation Using Reinforcement Learning
Venue: arXiv cs.RO 2018
Year: 2018

Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper provides a framework for using reinforcement learning to allow the UAV to navigate successfully in such environments. We conducted our simulation and real implementation to show how the UAVs can successfully learn to navigate through an unknown environment. Technical aspects regarding to applying reinforcement learning algorithm to a UAV system and UAV flight control were also addressed. This will enable continuing research using a UAV with learning capabilities in more important applications, such as wildfire monitoring, or search and rescue missions.
On the Analysis of Puncturing for Finite-Length Polar Codes: Boolean Function Approach
Venue: arXiv cs.IT 2018
Year: 2018

This paper investigates the impact of puncturing on finite-length polar codes in which a puncturing pattern $\pv^{N}=(p_0,...,p_N)$ is applied to a length-$N$ polar code.. We first introduce two virtual channels to stochastically model the punctured (untransmitted) bits, which are respectively called {\em useless channel model} (UCM) and {\em deterministic channel model} (DCM). Under each model, we derive boolean functions in variables $p_0,...,p_{N-1}$ that can indicate which polarized channels should carry frozen bits. Based on this, we present an efficient method to jointly optimize a puncturing pattern and an information set. Focusing on a fixed information set, we show that there exist the so-called {\em catastrophic} puncturing patterns that will surely lead to a block error and derive their weight distributions recursively. We then propose the two construction methods of a rate-compatible (RC) polar code which ensures that each puncturing pattern in the family is non-catastrophic. Simulation results demonstrate that the proposed RC polar code outperform the RC Turbo code adopted in LTE.
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers
Venue: arXiv cs.CV 2018
Year: 2018

Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel highly parameter and memory efficient FCN based architecture for medical image analysis. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in FCN like architectures. In order to processes the input images at multiple scales and view points simultaneously, we propose to incorporate Inception module's parallel structures. We also propose a novel dual loss function whose weighting scheme allows to combine advantages of cross-entropy and dice loss. We have validated our proposed network architecture on two publicly available datasets, namely: (i) Automated Cardiac Disease Diagnosis Challenge (ACDC-2017), (ii) Left Ventricular Segmentation Challenge (LV-2011). Our approach in ACDC-2017 challenge stands second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100%. In the LV-2011 challenge our approach attained 0.74 Jaccard index, which is so far the highest published result in fully automated algorithms. From the segmentation we extracted clinically relevant cardiac parameters and hand-crafted features which reflected the clinical diagnostic analysis to train an ensemble system for cardiac disease classification. Our approach combined both cardiac segmentation and disease diagnosis into a fully automated framework which is computational efficient and hence has the potential to be incorporated in computer-aided diagnosis (CAD) tools for clinical application.
Why and How Java Developers Break APIs
Venue: arXiv cs.SE 2018
Year: 2018

Modern software development depends on APIs to reuse code and increase productivity. As most software systems, these libraries and frameworks also evolve, which may break existing clients. However, the main reasons to introduce breaking changes in APIs are unclear. Therefore, in this paper, we report the results of an almost 4-month long field study with the developers of 400 popular Java libraries and frameworks. We configured an infrastructure to observe all changes in these libraries and to detect breaking changes shortly after their introduction in the code. After identifying breaking changes, we asked the developers to explain the reasons behind their decision to change the APIs. During the study, we identified 59 breaking changes, confirmed by the developers of 19 projects. By analyzing the developers' answers, we report that breaking changes are mostly motivated by the need to implement new features, by the desire to make the APIs simpler and with fewer elements, and to improve maintainability. We conclude by providing suggestions to language designers, tool builders, software engineering researchers and API developers.
A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks
Venue: arXiv cs.NI 2018
Year: 2018

Fractional Frequency Reuse techniques can be employed to address interference in mobile networks, improving throughput for edge users. There is a tradeoff between the coverage and overall throughput achievable, as interference avoidance techniques lead to a loss in a cell's overall throughput, with spectrum efficiency decreasing with the fencing off of orthogonal resources. In this paper we propose MANN, a dynamic multiagent frequency reuse scheme, where individual agents in charge of cells control their configurations based on input from neural networks. The agents' decisions are partially influenced by a coordinator agent, which attempts to maximise a global metric of the network (e.g., cell-edge performance). Each agent uses a neural network to estimate the best action (i.e., cell configuration) for its current environment setup, and attempts to maximise in turn a local metric, subject to the constraint imposed by the coordinator agent. Results show that our solution provides improved performance for edge users, increasing the throughput of the bottom 5% of users by 22%, while retaining 95% of a network's overall throughput from the full frequency reuse case. Furthermore, we show how our method improves on static fractional frequency reuse schemes.
Unsupervised Representation Learning with Laplacian Pyramid Auto-encoders
Venue: arXiv cs.CV 2018
Year: 2018

Scale-space representation has been popular in computer vision community due to its theoretical foundation. The motivation for generating a scale-space representation of a given data set originates from the basic observation that real-world objects are composed of different structures at different scales. Hence, it's reasonable to consider learning features with image pyramids generated by smoothing and down-sampling operations. In this paper we propose Laplacian pyramid auto-encoders, a straightforward modification of the deep convolutional auto-encoder architecture, for unsupervised representation learning. The method uses multiple encoding-decoding sub-networks within a Laplacian pyramid framework to reconstruct the original image and the low pass filtered images. The last layer of each encoding sub-network also connects to an encoding layer of the sub-network in the next level, which aims to reverse the process of Laplacian pyramid generation. Experimental results showed that Laplacian pyramid benefited the classification and reconstruction performance of deep auto-encoder approaches, and batch normalization is critical to get deep auto-encoders approaches to begin learning.
Bounds on the Effective-length of Optimal Codes for Interference Channel with Feedback
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate the necessity of finite blocklength codes in distributed transmission of independent message sets over channels with feedback. Previously, it was shown that finite effective length codes are necessary in distributed transmission and compression of sources. We provide two examples of three user interference channels with feedback where codes with asymptotically large effective lengths are sub-optimal. As a result, we conclude that coded transmission using finite effective length codes is necessary to achieve optimality. We argue that the sub-optimal performance of large effective length codes is due to their inefficiency in preserving the correlation between the inputs to the distributed terminals in the communication system. This correlation is made available by the presence of feedback at the terminals and is used as a means for coordination between the terminals when using finite effective length coding strategies.
Joint CSI Estimation, Beamforming and Scheduling Design for Wideband Massive MIMO System
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes a novel approach for designing channel estimation, beamforming and scheduling jointly for wideband massive multiple input multiple output (MIMO) systems. With the proposed approach, we first quantify the maximum number of user equipments (UEs) that can send pilots which may or may not be orthogonal. Specifically, when the channel has a maximum of $L$ multipath taps, and we allocate $\tilde{M}$ sub-carriers for the channel state information (CSI) estimation, a maximum of $\tilde{M}$ UEs CSI can be estimated ($L$ times compared to the conventional CSI estimation approach) in a massive MIMO regime. Then, we propose to schedule a subset of these UEs using greedy based scheduling to transmit their data on each sub-carrier with the proposed joint beamforming and scheduling design. We employ the well known maximum ratio combiner (MRC) beamforming approach in the uplink channel data transmission. All the analytical expressions are validated via numerical results, and the superiority of the proposed design over the conventional orthogonal frequency division multiplexing (OFDM) transmission approach is demonstrated using extensive numerical simulations in the long term evolution (LTE) channel environment. The proposed channel estimation and beamforming design is linear and simple to implement.
Minimax Optimal Additive Functional Estimation with Discrete Distribution: Slow Divergence Speed Case
Venue: arXiv cs.IT 2018
Year: 2018

This paper addresses an estimation problem of an additive functional of $ϕ$, which is defined as $θ(P;ϕ)=\sum_{i=1}^kϕ(p_i)$, given $n$ i.i.d. random samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with alphabet size $k$. We have revealed in the previous paper that the minimax optimal rate of this problem is characterized by the divergence speed of the fourth derivative of $ϕ$ in a range of fast divergence speed. In this paper, we prove this fact for a more general range of the divergence speed. As a result, we show the minimax optimal rate of the additive functional estimation for each range of the parameter $α$ of the divergence speed. For $α\in (1,3/2)$, we show that the minimax rate is $\frac{1}{n}+\frac{k^2}{(n\ln n)^{2α}}$. Besides, we show that the minimax rate is $\frac{1}{n}$ for $α\in [3/2,2]$.
Debugging Framework Applications: Benefits and Challenges
Venue: arXiv cs.SE 2018
Year: 2018

Aspects of frameworks, such as inversion of control and the structure of framework applications, require developers to adjust their debugging strategies as compared to debugging sequential programs. However, the benefits and challenges of framework debugging are not fully understood, and gaining this knowledge could provide guidance in debugging strategies and framework tool design. To gain insight into the process developers use to fix problems in framework applications, we performed two human studies investigating how developers fix applications that use a framework API incorrectly. These studies focused on the Android Fragment class and the ROS framework. We analyzed the results of the studies using a mixed-methods approach, consisting of techniques from grounded theory, qualitative content analysis, and case studies. From our analysis, we produced a theory of the benefits and challenges of framework debugging. This theory states that developers find inversion of control challenging when debugging but find the structure of framework applications helpful. This theory could be used to guide strategies for debugging framework applications and framework tool designs.
Using Sat solvers for synchronization issues in non-deterministic automata
Venue: arXiv cs.FL 2018
Year: 2018

We approach the problem of computing a $D_{3}$-synchronizing word of minimum length for a given nondeterministic automaton via its encoding as an instance of SAT and invoking a SAT solver. We also present some experimental results.
A Comparative Study of Rule Extraction for Recurrent Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Understanding recurrent networks through rule extraction has a long history. This has taken on new interests due to the need for interpreting or verifying neural networks. One basic form for representing stateful rules is deterministic finite automata (DFA). Previous research shows that extracting DFAs from trained second-order recurrent networks is not only possible but also relatively stable. Recently, several new types of recurrent networks with more complicated architectures have been introduced. These handle challenging learning tasks usually involving sequential data. However, it remains an open problem whether DFAs can be adequately extracted from these models. Specifically, it is not clear how DFA extraction will be affected when applied to different recurrent networks trained on data sets with different levels of complexity. Here, we investigate DFA extraction on several widely adopted recurrent networks that are trained to learn a set of seven regular Tomita grammars. We first formally analyze the complexity of Tomita grammars and categorize these grammars according to that complexity. Then we empirically evaluate different recurrent networks for their performance of DFA extraction on all Tomita grammars. Our experiments show that for most recurrent networks, their extraction performance decreases as the complexity of the underlying grammar increases. On grammars of lower complexity, most recurrent networks obtain desirable extraction performance. As for grammars with the highest level of complexity, while several complicated models fail with only certain recurrent networks having satisfactory extraction performance.
ProvThreads: Analytic Provenance Visualization and Segmentation
Venue: arXiv cs.HC 2018
Year: 2018

Our work aims to generate visualizations to enable meta-analysis of analytic provenance and aid better understanding of analysts' strategies during exploratory text analysis. We introduce ProvThreads, a visual analytics approach that incorporates interactive topic modeling outcomes to illustrate relationships between user interactions and the data topics under investigation. ProvThreads uses a series of continuous analysis paths called topic threads to demonstrate both topic coverage and the progression of an investigation over time. As an analyst interacts with different pieces of data during the analysis, interactions are logged and used to track user interests in topics over time. A line chart shows different amounts of interest in multiple topics over the duration of the analysis. We discuss how different configurations of ProvThreads can be used to reveal changes in focus throughout an analysis.
Longest Processing Time rule for identical parallel machines revisited
Venue: arXiv cs.DS 2018
Year: 2018

We consider the Pm || Cmax scheduling problem where the goal is to schedule n jobs on m identical parallel machines to minimize makespan. We revisit the famous Longest Processing Time (LPT) rule proposed by Graham in 1969. LPT requires to sort jobs in non-ascending order of processing times and then to assign one job at a time to the machine whose load is smallest so far. We provide new insights on LPT and discuss the approximation ratio of a modification of LPT that improves Graham's bound from 4/3 - 1/(3m) to 4/3 - 1/(3(m-1)) for m >= 3 and from 7/6 to 9/8 for m = 2. We use Linear Programming (LP) to analyze the approximation ratio of our approach. This performance analysis can be seen as a valid alternative to formal proofs based on analytical derivation. Also, we derive from the proposed approach an O(n log n) heuristic. The heuristic splits the sorted jobset in tuples of m consecutive jobs (1,...,m; m+1,...,2m; etc.) and sorts the tuples in non-increasing order of the difference (slack) between largest job and smallest job in the tuple. Then, List Scheduling is applied to the set of sorted tuples. This approach strongly outperforms LPT on benchmark literature instances.