OpenRaiser/Intern-Atlas huggingface dataset:
Fish School Search Algorithm for Constrained Optimization
Venue: arXiv cs.NE 2017
Year: 2017

In this work we investigate the effectiveness of the application of niching able swarm metaheuristic approaches in order to solve constrained optimization problems. Sub-swarms are used in order to allow the achievement of many feasible regions to be exploited in terms of fitness function. The niching approach employed was wFSS, a version of the Fish School Search algorithm devised specifically to deal with multi-modal search spaces. A base technique referred as wrFSS was conceived and three variations applying different constraint handling procedures were also proposed. Tests were performed in seven problems from CEC 2010 and a comparison with other approaches was carried out. Results show that the search strategy proposed is able to handle some heavily constrained problems and achieve results comparable to the state-of-the-art algorithms. However, we also observed that the local search operator present in wFSS and inherited by wrFSS makes the fitness convergence difficult when the feasible region presents some specific geometrical features.
Deformable Part-based Fully Convolutional Network for Object Detection
Venue: arXiv cs.CV 2017
Year: 2017

Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular. In this paper we introduce DP-FCN, a deep model for object detection which explicitly adapts to shapes of objects with deformable parts. Without additional annotations, it learns to focus on discriminative elements and to align them, and simultaneously brings more invariance for classification and geometric information to refine localization. DP-FCN is composed of three main modules: a Fully Convolutional Network to efficiently maintain spatial resolution, a deformable part-based RoI pooling layer to optimize positions of parts and build invariance, and a deformation-aware localization module explicitly exploiting displacements of parts to improve accuracy of bounding box regression. We experimentally validate our model and show significant gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on PASCAL VOC 2007 and 2012 with VOC data only.
Crowdsourcing Multiple Choice Science Questions
Venue: arXiv cs.HC 2017
Year: 2017

We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.
Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons
Venue: arXiv cs.LG 2017
Year: 2017

Pairwise comparison data arises in many domains, including tournament rankings, web search, and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items, we study the problem of estimating the underlying comparison probabilities under the assumption of strong stochastic transitivity (SST). We also consider the noisy sorting subclass of the SST model. We show that when the assignment of items to the topology is arbitrary, these permutation-based models, unlike their parametric counterparts, do not admit consistent estimation for most comparison topologies used in practice. We then demonstrate that consistent estimation is possible when the assignment of items to the topology is randomized, thus establishing a dichotomy between worst-case and average-case designs. We propose two estimators in the average-case setting and analyze their risk, showing that it depends on the comparison topology only through the degree sequence of the topology. The rates achieved by these estimators are shown to be optimal for a large class of graphs. Our results are corroborated by simulations on multiple comparison topologies.
Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Venue: arXiv cs.CL 2017
Year: 2017

Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialogue length. In this work, we propose a structured method for finding a good balance between these components by searching for the optimal reward component weighting. To render this search feasible, we use multi-objective reinforcement learning to significantly reduce the number of training dialogues required. We apply our proposed method to find optimized component weights for six domains and compare them to a default baseline.
Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks
Venue: arXiv cs.CV 2017
Year: 2017

Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full $512\times512$ images at $\approx$9K images per minute. It ranks third in the Neurofinder competition ($F_1=0.569$) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model's simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.
Open Source Software for Digital Preservation Repositories: a Survey
Venue: arXiv cs.DL 2017
Year: 2017

In the digital age, the amount of data produced is growing exponentially. Governments and institutions can no longer rely on old methods for storing data and passing on the knowledge to future generations. Digital data preservation is a mandatory issue that needs proper strategies and tools. With this awareness, efforts are being made to create and perfect software solutions capable of responding to the challenge of properly preserving digital information. This paper focuses on the state-of-the-art in open-source software solutions for the digital preservation and curation field used to assimilate and disseminate information to designated audiences. Eleven open source projects for digital preservation are surveyed in areas such as supported standards and protocols, strategies for preservation, methodologies for reporting, dynamic of development, targeted operating systems, multilingual support and open source license. Furthermore, five of these open source projects, are further analysed, with focus on features deemed important for the area. Along open source solutions, the paper also briefly surveys the standards and protocols relevant for digital data preservation. The area of digital data preservation repositories has several open source solutions, which can form the base to overcome the challenges to reach mature and reliable digital data preservation.
Energy-dissipation Limits in Variance-based Computing
Venue: arXiv cs.ET 2017
Year: 2017

Variance-based logic (VBL) uses the fluctuations or the variance in the state of a particle or a physical quantity to represent different logic levels. In this letter we show that compared to the traditional bi-stable logic representation the variance-based representation can theoretically achieve a superior performance trade-off (in terms of energy dissipation and information capacity) when operating at fundamental limits imposed by thermal-noise. We show that for a bi-stable logic device the lower limit on energy dissipated per bit is 4.35KT/bit, whereas under similar operating conditions, a VBL device could achieve a lower limit of sub-KT/bit. These theoretical results are general enough to be applicable to different instantiations and variants of VBL ranging from digital processors based on energy-scavenging or to processors based on the emerging valleytronic devices.
Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices
Venue: arXiv cs.NE 2017
Year: 2017

In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron network, we evaluate the learning performance according to various conductance responses of electronic synapse devices and weight-updating methods. It is shown that the learning accuracy is comparable to that obtained when using a software-based BP algorithm when the electronic synapse device has a linear conductance response with a high dynamic range. Furthermore, the proposed unidirectional weight-updating method is suitable for electronic synapse devices which have nonlinear and finite conductance responses. Because this weight-updating method can compensate the demerit of asymmetric weight updates, we can obtain better accuracy compared to other methods. This adaptive learning rule, which can be applied to full hardware implementation, can also compensate the degradation of learning accuracy due to the probable device-to-device variation in an actual electronic synapse device.
Scalable Full Flow with Learned Binary Descriptors
Venue: arXiv cs.CV 2017
Year: 2017

We propose a method for large displacement optical flow in which local matching costs are learned by a convolutional neural network (CNN) and a smoothness prior is imposed by a conditional random field (CRF). We tackle the computation- and memory-intensive operations on the 4D cost volume by a min-projection which reduces memory complexity from quadratic to linear and binary descriptors for efficient matching. This enables evaluation of the cost on the fly and allows to perform learning and CRF inference on high resolution images without ever storing the 4D cost volume. To address the problem of learning binary descriptors we propose a new hybrid learning scheme. In contrast to current state of the art approaches for learning binary CNNs we can compute the exact non-zero gradient within our model. We compare several methods for training binary descriptors and show results on public available benchmarks.
Consistent Tomography under Partial Observations over Adaptive Networks
Venue: arXiv cs.MA 2017
Year: 2017

This work studies the problem of inferring whether an agent is directly influenced by another agent over an adaptive diffusion network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update its online statistic. The solution of this inference task is challenging for two main reasons. First, only the output of the diffusion learning algorithm is available to the external observer that must perform the inference based on these indirect measurements. Second, only output measurements from a fraction of the network agents is available, with the total number of agents itself being also unknown. The main focus of this article is ascertaining under these demanding conditions whether consistent tomography is possible, namely, whether it is possible to reconstruct the interaction profile of the observable portion of the network, with negligible error as the network size increases. We establish a critical achievability result, namely, that for symmetric combination policies and for any given fraction of observable agents, the interacting and non-interacting agent pairs split into two separate clusters as the network size increases. This remarkable property then enables the application of clustering algorithms to identify the interacting agents influencing the observations. We provide a set of numerical experiments that verify the results for finite network sizes and time horizons. The numerical experiments show that the results hold for asymmetric combination policies as well, which is particularly relevant in the context of causation.
Effects of Network Structure on the Performance of a Modeled Traffic Network under Drivers' Bounded Rationality
Venue: arXiv cs.MA 2017
Year: 2017

We propose a minority route choice game to investigate the effect of the network structure on traffic network performance under the assumption of drivers' bounded rationality. We investigate ring-and-hub topologies to capture the nature of traffic networks in cities, and employ a minority game-based inductive learning process to model the characteristic behavior under the route choice scenario. Through numerical experiments, we find that topological changes in traffic networks induce a phase transition from an uncongested phase to a congested phase. Understanding this phase transition is helpful in planning new traffic networks.
Video Object Segmentation using Tracked Object Proposals
Venue: arXiv cs.CV 2017
Year: 2017

We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 challenge. Our approach combines category-based object detection, category-independent object appearance segmentation and temporal object tracking. We are motivated by the fact that the objects semantic category tends not to change throughout the video while its appearance and location can vary considerably. In order to capture the specific object appearance independent of its category, for each video we train a fully convolutional network using augmentations of the given annotated frame. We refine the appearance segmentation mask with the bounding boxes provided either by a semantic object detection network, when applicable, or by a previous frame prediction. By introducing a temporal continuity constraint on the detected boxes, we are able to improve the object segmentation mask of the appearance network and achieve competitive results on the DAVIS datasets.
leave a trace - A People Tracking System Meets Anomaly Detection
Venue: arXiv cs.CV 2017
Year: 2017

Video surveillance always had a negative connotation, among others because of the loss of privacy and because it may not automatically increase public safety. If it was able to detect atypical (i.e. dangerous) situations in real time, autonomously and anonymously, this could change. A prerequisite for this is a reliable automatic detection of possibly dangerous situations from video data. This is done classically by object extraction and tracking. From the derived trajectories, we then want to determine dangerous situations by detecting atypical trajectories. However, due to ethical considerations it is better to develop such a system on data without people being threatened or even harmed, plus with having them know that there is such a tracking system installed. Another important point is that these situations do not occur very often in real, public CCTV areas and may be captured properly even less. In the artistic project leave a trace the tracked objects, people in an atrium of a institutional building, become actor and thus part of the installation. Visualisation in real-time allows interaction by these actors, which in turn creates many atypical interaction situations on which we can develop our situation detection. The data set has evolved over three years and hence, is huge. In this article we describe the tracking system and several approaches for the detection of atypical trajectories.
Pictures of Combinatorial Cubes
Venue: arXiv cs.CV 2017
Year: 2017

We prove that the 8-point algorithm always fails to reconstruct a unique fundamental matrix $F$ independent on the camera positions, when its input are image point configurations that are perspective projections of the vertices of a combinatorial cube in $\mathbb{R}^3$. We give an algorithm that improves the 7- and 8-point algorithm in such a pathological situation. Additionally we analyze the regions of focal point positions where a reconstruction of $F$ is possible at all, when the world points are the vertices of a combinatorial cube in $\mathbb{R}^3$.
On the Design of Secure Full-Duplex Multiuser Systems under User Grouping Method
Venue: arXiv cs.IT 2017
Year: 2017

Consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to simultaneously serve both downlink users and uplink users in the presence of half-duplex eavesdroppers (Eves). Our problem is to maximize the minimum secrecy rate (SR) among all legitimate users by proposing a novel user grouping method, where information signals at the FD-BS are accompanied with artificial noise to degrade the Eves' channel. The SR problem has a highly nonconcave and nonsmooth objective, subject to nonconvex constraints due to coupling between the optimization variables. Nevertheless, we develop a path-following low-complexity algorithm, which invokes only a simple convex program of moderate dimensions at each iteration. We show that our path-following algorithm guarantees convergence at least to a local optima. The numerical results demonstrate the merit of our proposed approach compared to existing well-known ones, i.e., conventional FD and nonorthogonal multiple access.
Efficient Defenses Against Adversarial Attacks
Venue: arXiv cs.LG 2017
Year: 2017

Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the development of efficient defenses. In this paper, we propose a new defense method based on practical observations which is easy to integrate into models and performs better than state-of-the-art defenses. Our proposed solution is meant to reinforce the structure of a DNN, making its prediction more stable and less likely to be fooled by adversarial samples. We conduct an extensive experimental study proving the efficiency of our method against multiple attacks, comparing it to numerous defenses, both in white-box and black-box setups. Additionally, the implementation of our method brings almost no overhead to the training procedure, while maintaining the prediction performance of the original model on clean samples.
On the Orbits of Crossed Cubes
Venue: arXiv cs.DM 2017
Year: 2017

An orbit of $G$ is a subset $S$ of $V(G)$ such that $ϕ(u)=v$ for any two vertices $u,v\in S$, where $ϕ$ is an isomorphism of $G$. The orbit number of a graph $G$, denoted by $\text{Orb}(G)$, is the number of orbits of $G$. In [A Note on Path Embedding in Crossed Cubes with Faulty Vertices, Information Processing Letters 121 (2017) pp. 34--38], Chen et al. conjectured that $\text{Orb}(\text{CQ}_n)=2^{\lceil\frac{n}{2}\rceil-2}$ for $n\geqslant 3$, where $\text{CQ}_n$ denotes an $n$-dimensional crossed cube. In this paper, we settle the conjecture.
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
Venue: arXiv cs.CL 2017
Year: 2017

Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance. However, little is published which parameters and design choices should be evaluated or selected making the correct hyperparameter optimization often a "black art that requires expert experiences" (Snoek et al., 2012). In this paper, we evaluate the importance of different network design choices and hyperparameters for five common linguistic sequence tagging tasks (POS, Chunking, NER, Entity Recognition, and Event Detection). We evaluated over 50.000 different setups and found, that some parameters, like the pre-trained word embeddings or the last layer of the network, have a large impact on the performance, while other parameters, for example the number of LSTM layers or the number of recurrent units, are of minor importance. We give a recommendation on a configuration that performs well among different tasks.
Errors and secret data in the Italian research assessment exercise. A comment to a reply
Venue: arXiv cs.DL 2017
Year: 2017

Italy adopted a performance-based system for funding universities that is centered on the results of a national research assessment exercise, realized by a governmental agency (ANVUR). ANVUR evaluated papers by using 'a dual system of evaluation', that is by informed peer review or by bibliometrics. In view of validating that system, ANVUR performed an experiment for estimating the agreement between informed review and bibliometrics. Ancaiani et al. (2015) presents the main results of the experiment. Baccini and De Nicolao (2017) documented in a letter, among other critical issues, that the statistical analysis was not realized on a random sample of articles. A reply to the letter has been published by Research Evaluation (Benedetto et al. 2017). This note highlights that in the reply there are (1) errors in data, (2) problems with 'representativeness' of the sample, (3) unverifiable claims about weights used for calculating kappas, (4) undisclosed averaging procedures; (5) a statement about 'same protocol in all areas' contradicted by official reports. Last but not least: the data used by the authors continue to be undisclosed. A general warning concludes: many recently published papers use data originating from Italian research assessment exercise. These data are not accessible to the scientific community and consequently these papers are not reproducible. They can be hardly considered as containing sound evidence at least until authors or ANVUR disclose the data necessary for replication.
Neuron Pruning for Compressing Deep Networks using Maxout Architectures
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents an efficient and robust approach for reducing the size of deep neural networks by pruning entire neurons. It exploits maxout units for combining neurons into more complex convex functions and it makes use of a local relevance measurement that ranks neurons according to their activation on the training set for pruning them. Additionally, a parameter reduction comparison between neuron and weight pruning is shown. It will be empirically shown that the proposed neuron pruning reduces the number of parameters dramatically. The evaluation is performed on two tasks, the MNIST handwritten digit recognition and the LFW face verification, using a LeNet-5 and a VGG16 network architecture. The network size is reduced by up to $74\%$ and $61\%$, respectively, without affecting the network's performance. The main advantage of neuron pruning is its direct influence on the size of the network architecture. Furthermore, it will be shown that neuron pruning can be combined with subsequent weight pruning, reducing the size of the LeNet-5 and VGG16 up to $92\%$ and $80\%$ respectively.