OpenRaiser/Intern-Atlas huggingface dataset:
Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
Venue: arXiv cs.CL 2017
Year: 2017

Temporal gates play a significant role in modern recurrent-based neural encoders, enabling fine-grained control over recursive compositional operations over time. In recurrent models such as the long short-term memory (LSTM), temporal gates control the amount of information retained or discarded over time, not only playing an important role in influencing the learned representations but also serving as a protection against vanishing gradients. This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner. In our approach, temporal gates are learned via 1D convolutional layers and then subsequently cross applied across question and answer for joint learning. Empirically, we show that this conceptually simple sharing of temporal gates can lead to competitive performance across multiple benchmarks. Intuitively, what our network achieves can be interpreted as learning representations of question and answer pairs that are aware of what each other is remembering or forgetting, i.e., pairwise temporal gating. Via extensive experiments, we show that our proposed model achieves state-of-the-art performance on two community-based QA datasets and competitive performance on one factoid-based QA dataset.
Transferring Agent Behaviors from Videos via Motion GANs
Venue: arXiv cs.LG 2017
Year: 2017

A major bottleneck for developing general reinforcement learning agents is determining rewards that will yield desirable behaviors under various circumstances. We introduce a general mechanism for automatically specifying meaningful behaviors from raw pixels. In particular, we train a generative adversarial network to produce short sub-goals represented through motion templates. We demonstrate that this approach generates visually meaningful behaviors in unknown environments with novel agents and describe how these motions can be used to train reinforcement learning agents.
Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the hope of discovering more efficient methods for training artificial networks. In this paper, we propose a new algorithm named Variational Probably Flow (VPF), an extension of minimum probability flow for training binary Deep Boltzmann Machines (DBMs). We show that weight updates in VPF are local, depending only on the states and firing rates of the adjacent neurons. Unlike contrastive divergence, there is no need for Gibbs confabulations; and unlike backpropagation, alternating feedforward and feedback phases are not required. Moreover, the learning algorithm is effective for training DBMs with intra-layer connections between the hidden nodes. Experiments with MNIST and Fashion MNIST demonstrate that VPF learns reasonable features quickly, reconstructs corrupted images more accurately, and generates samples with a high estimated log-likelihood. Lastly, we note that, interestingly, if an asymmetric version of VPF exists, the weight updates directly explain experimental results in Spike-Timing-Dependent Plasticity (STDP).
Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations
Venue: arXiv cs.IR 2017
Year: 2017

In this work, we address the problem of recommending jobs to university students. For this, we explore the utilization of neural item embeddings for the task of content-based recommendation, and we propose to integrate the factors of frequency and recency of interactions with job postings to combine these item embeddings. We evaluate our job recommendation system on a dataset of the Austrian student job portal Studo using prediction accuracy, diversity and an adapted novelty metric. This paper demonstrates that utilizing frequency and recency of interactions with job postings for combining item embeddings results in a robust model with respect to accuracy and diversity, which also provides the best adapted novelty results.
Ultra-Reliable Low Latency Communication (URLLC) using Interface Diversity
Venue: arXiv cs.IT 2017
Year: 2017

An important ingredient of the future 5G systems will be Ultra-Reliable Low-Latency Communication (URLLC). A way to offer URLLC without intervention in the baseband/PHY layer design is to use interface diversity and integrate multiple communication interfaces, each interface based on a different technology. In this work, we propose to use coding to seamlessly distribute coded payload and redundancy data across multiple available communication interfaces. We formulate an optimization problem to find the payload allocation weights that maximize the reliability at specific target latency values. In order to estimate the performance in terms of latency and reliability of such an integrated communication system, we propose an analysis framework that combines traditional reliability models with technology-specific latency probability distributions. Our model is capable to account for failure correlation among interfaces/technologies. By considering different scenarios, we find that optimized strategies can in some cases significantly outperform strategies based on $k$-out-of-$n$ erasure codes, where the latter do not account for the characteristics of the different interfaces. The model has been validated through simulation and is supported by experimental results.
Reflection-Aware Sound Source Localization
Venue: arXiv cs.SD 2017
Year: 2017

We present a novel, reflection-aware method for 3D sound localization in indoor environments. Unlike prior approaches, which are mainly based on continuous sound signals from a stationary source, our formulation is designed to localize the position instantaneously from signals within a single frame. We consider direct sound and indirect sound signals that reach the microphones after reflecting off surfaces such as ceilings or walls. We then generate and trace direct and reflected acoustic paths using inverse acoustic ray tracing and utilize these paths with Monte Carlo localization to estimate a 3D sound source position. We have implemented our method on a robot with a cube-shaped microphone array and tested it against different settings with continuous and intermittent sound signals with a stationary or a mobile source. Across different settings, our approach can localize the sound with an average distance error of 0.8m tested in a room of 7m by 7m area with 3m height, including a mobile and non-line-of-sight sound source. We also reveal that the modeling of indirect rays increases the localization accuracy by 40% compared to only using direct acoustic rays.
Efficient Implementation of a Recognition System Using the Cortex Ventral Stream Model
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, an efficient implementation for a recognition system based on the original HMAX model of the visual cortex is proposed. Various optimizations targeted to increase accuracy at the so-called layers S1, C1, and S2 of the HMAX model are proposed. At layer S1, all unimportant information such as illumination and expression variations are eliminated from the images. Each image is then convolved with 64 separable Gabor filters in the spatial domain. At layer C1, the minimum scales values are exploited to be embedded into the maximum ones using the additive embedding space. At layer S2, the prototypes are generated in a more efficient way using Partitioning Around Medoid (PAM) clustering algorithm. The impact of these optimizations in terms of accuracy and computational complexity was evaluated on the Caltech101 database, and compared with the baseline performance using support vector machine (SVM) and nearest neighbor (NN) classifiers. The results show that our model provides significant improvement in accuracy at the S1 layer by more than 10% where the computational complexity is also reduced. The accuracy is slightly increased for both approximations at the C1 and S2 layers.
Universality of Egoless Behavior of Software Engineering Students
Venue: arXiv cs.SE 2017
Year: 2017

Software organizations have relied on process and technology initiatives to compete in a highly globalized world. Unfortunately, that has led to little or no success. We propose that the organizations start working on people initiatives, such as inspiring egoless behavior among software developers. This paper proposes a multi-stage approach to develop egoless behavior and discusses the universality of the egoless behavior by studying cohorts from three different countries, i.e., Japan, India, and Canada. The three stages in the approach are self-assessment, peer validation, and action plan development. The paper covers the first stage of self-assssment using an instrument based on Lamont Adams Ten commandments (factors) of egoless programming, seven of the factors are general, whereas three are related to coding behavior. We found traces of universality in the egoless behavior among the three cohorts such as there was no difference in egoless behaviours between Indian and Canadian cohorts and both Indian and Japanese cohorts had difficulties in behaving in egoless manner in coding activities than in general activities.
Mobility and Popularity-Aware Coded Small-Cell Caching
Venue: arXiv cs.IT 2017
Year: 2017

In heterogeneous cellular networks with caching capability, due to mobility of users and storage constraints of small-cell base stations (SBSs), users may not be able to download all of their requested content from the SBSs within the delay deadline of the content. In that case, the users are directed to the macro-cell base station (MBS) in order to satisfy the service quality requirement. Coded caching is exploited here to minimize the amount of data downloaded from the MBS taking into account the mobility of the users as well as the popularity of the contents. An optimal distributed caching policy is presented when the delay deadline is below a certain threshold, and a distributed greedy caching policy is proposed when the delay deadline is relaxed.
Soft Sides of Software
Venue: arXiv cs.SE 2017
Year: 2017

Software is a field of rapid changes: the best technology today becomes obsolete in the near future. If we review the graduate attributes of any of the software engineering programs across the world, life-long learning is one of them. The social and psychological aspects of professional development is linked with rewards. In organizations, where people are provided with learning opportunities and there is a culture that rewards learning, people embrace changes easily. However, the software industry tends to be short-sighted and its primary focus is more on current project success; it usually ignores the capacity building of the individual or team. It is hoped that our software engineering colleagues will be motivated to conduct more research into the area of software psychology so as to understand more completely the possibilities for increased effectiveness and personal fulfillment among software engineers working alone and in teams.
Direct and mediating influences of user-developer perception gaps in requirements understanding on user participation
Venue: arXiv cs.SE 2017
Year: 2017

User participation is considered an effective way to conduct requirements engineering, but user-developer perception gaps in requirements understanding occur frequently. Since user participation in practice is not as active as we expect and the requirements perception gap has been recognized as a risk that negatively affects projects, exploring whether user-developer perception gaps in requirements understanding will hinder user participation is worthwhile. This will help develop a greater comprehension of the intertwined relationship between user participation and perception gap, a topic that has not yet been extensively examined. This study investigates the direct and mediating influences of user-developer requirements perception gaps on user participation by integrating requirements uncertainty and top management support. Survey data collected from 140 subjects were examined and analyzed using structural equation modeling. The results indicate that perception gaps have a direct negative effect on user participation and negate completely the positive effect of top management support on user participation. Additionally, perception gaps do not have a mediating effect between requirements uncertainty and user participation because requirements uncertainty does not significantly and directly affect user participation, but requirements uncertainty indirectly influences user participation due to its significant direct effect on perception gaps. The theoretical and practical implications are discussed, and limitations and possible future research areas are identified.
Cellular Automata Simulation on FPGA for Training Neural Networks with Virtual World Imagery
Venue: arXiv cs.MA 2017
Year: 2017

We present ongoing work on a tool that consists of two parts: (i) A raw micro-level abstract world simulator with an interface to (ii) a 3D game engine, translator of raw abstract simulator data to photorealistic graphics. Part (i) implements a dedicated cellular automata (CA) on reconfigurable hardware (FPGA) and part (ii) interfaces with a deep learning framework for training neural networks. The bottleneck of such an architecture usually lies in the fact that transferring the state of the whole CA significantly slows down the simulation. We bypass this by sending only a small subset of the general state, which we call a 'locus of visibility', akin to a torchlight in a darkened 3D space, into the simulation. The torchlight concept exists in many games but these games generally only simulate what is in or near the locus. Our chosen architecture will enable us to simulate on a micro level outside the locus. This will give us the advantage of being able to create a larger and more fine-grained simulation which can be used to train neural networks for use in games.
Fine-Grained I/O Complexity via Reductions: New lower bounds, faster algorithms, and a time hierarchy
Venue: arXiv cs.DS 2017
Year: 2017

This paper initiates the study of I/O algorithms (minimizing cache misses) from the perspective of fine-grained complexity (conditional polynomial lower bounds). Specifically, we aim to answer why sparse graph problems are so hard, and why the Longest Common Subsequence problem gets a savings of a factor of the size of cache times the length of a cache line, but no more. We take the reductions and techniques from complexity and fine-grained complexity and apply them to the I/O model to generate new (conditional) lower bounds as well as faster algorithms. We also prove the existence of a time hierarchy for the I/O model, which motivates the fine-grained reductions. Using fine-grained reductions, we give an algorithm for distinguishing 2 vs. 3 diameter and radius that runs in $O(|E|^2/(MB))$ cache misses, which for sparse graphs improves over the previous $O(|V|^2/B)$ running time. We give new reductions from radius and diameter to Wiener index and median. We show meaningful reductions between problems that have linear-time solutions in the RAM model. The reductions use low I/O complexity (typically $O(n/B)$), and thus help to finely capture the relationship between "I/O linear time" $Θ(n/B)$ and RAM linear time $Θ(n)$. We generate new I/O assumptions based on the difficulty of improving sparse graph problem running times in the I/O model. We create conjectures that the current best known algorithms for Single Source Shortest Paths (SSSP), diameter, and radius are optimal. From these I/O-model assumptions, we show that many of the known reductions in the word-RAM model can naturally extend to hold in the I/O model as well (e.g., a lower bound on the I/O complexity of Longest Common Subsequence that matches the best known running time). Finally, we prove an analog of the Time Hierarchy Theorem in the I/O model.
WAYLA - Generating Images from Eye Movements
Venue: arXiv cs.CV 2017
Year: 2017

We present a method for reconstructing images viewed by observers based only on their eye movements. By exploring the relationships between gaze patterns and image stimuli, the "What Are You Looking At?" (WAYLA) system learns to synthesize photo-realistic images that are similar to the original pictures being viewed. The WAYLA approach is based on the Conditional Generative Adversarial Network (Conditional GAN) image-to-image translation technique of Isola et al. We consider two specific applications - the first, of reconstructing newspaper images from gaze heat maps, and the second, of detailed reconstruction of images containing only text. The newspaper image reconstruction process is divided into two image-to-image translation operations, the first mapping gaze heat maps into image segmentations, and the second mapping the generated segmentation into a newspaper image. We validate the performance of our approach using various evaluation metrics, along with human visual inspection. All results confirm the ability of our network to perform image generation tasks using eye tracking data.
Personalization of Saliency Estimation
Venue: arXiv cs.CV 2017
Year: 2017

Most existing saliency models use low-level features or task descriptions when generating attention predictions. However, the link between observer characteristics and gaze patterns is rarely investigated. We present a novel saliency prediction technique which takes viewers' identities and personal traits into consideration when modeling human attention. Instead of only computing image salience for average observers, we consider the interpersonal variation in the viewing behaviors of observers with different personal traits and backgrounds. We present an enriched derivative of the GAN network, which is able to generate personalized saliency predictions when fed with image stimuli and specific information about the observer. Our model contains a generator which generates grayscale saliency heat maps based on the image and an observer label. The generator is paired with an adversarial discriminator which learns to distinguish generated salience from ground truth salience. The discriminator also has the observer label as an input, which contributes to the personalization ability of our approach. We evaluate the performance of our personalized salience model by comparison with a benchmark model along with other un-personalized predictions, and illustrate improvements in prediction accuracy for all tested observer groups.
Efficient image deployment in cloud environments
Venue: arXiv cs.DC 2017
Year: 2017

The biggest overhead for the instantiation of a virtual machine in a cloud infrastructure is the time spent in transferring the image of the virtual machine into the physical node that executes it. This overhead becomes larger for requests composed of several virtual machines to be started concurrently, and the illusion of flexibility and elasticity usually associated with the cloud computing model may vanish. This poses a problem for both the resource providers and the software developers, since tackling those overheads is not a trivial issue. In this work we implement and evaluate several improvements for virtual machine image distribution problem in a cloud infrastructure and propose a method based on BitTorrent and local caching of the virtual machine images that reduces the transfer time when large requests are made
Schur Number Five
Venue: arXiv cs.LO 2017
Year: 2017

We present the solution of a century-old problem known as Schur Number Five: What is the largest (natural) number $n$ such that there exists a five-coloring of the positive numbers up to $n$ without a monochromatic solution of the equation $a + b = c$? We obtained the solution, $n = 160$, by encoding the problem into propositional logic and applying massively parallel satisfiability solving techniques on the resulting formula. We constructed and validated a proof of the solution to increase trust in the correctness of the multi-CPU-year computations. The proof is two petabytes in size and was certified using a formally verified proof checker, demonstrating that any result by satisfiability solvers---no matter how large---can now be validated using highly trustworthy systems.
Photometric Stereo by UV-Induced Fluorescence to Detect Protrusions on Georgia O'Keeffe's Paintings
Venue: arXiv cs.GR 2017
Year: 2017

A significant number of oil paintings produced by Georgia O'Keeffe (1887-1986) show surface protrusions of varying width, up to several hundreds of microns. These protrusions are similar to those described in the art conservation literature as metallic soaps. Since the presence of these protrusions raises questions about the state of conservation and long-term prospects for deterioration of these artworks, a 3D-imaging technique, photometric stereo using ultraviolet illumination, was developed for the long-term monitoring of the surface-shape of the protrusions and the surrounding paint. Because the UV fluorescence response of painting materials is isotropic, errors typically caused by non-Lambertian (anisotropic) specularities when using visible reflected light can be avoided providing a more accurate estimation of shape. As an added benefit, fluorescence provides additional contrast information contributing to materials characterization. The developed methodology aims to detect, characterize, and quantify the distribution of micro-protrusions and their development over the surface of entire artworks. Combined with a set of analytical in-situ techniques, and computational tools, this approach constitutes a novel methodology to investigate the selective distribution of protrusions in correlation with the composition of painting materials at the macro-scale. While focused on O'Keeffe's paintings as a case study, we expect the proposed approach to have broader significance by providing a non-invasive protocol to the conservation community to probe topological changes for any relatively flat painted surface of an artwork, and more specifically to monitor the dynamic formation of protrusions, in relation to paint composition and modifications of environmental conditions, loans, exhibitions and storage over the long-term.
On the Feasibility of Full-duplex Large-scale MIMO Cellular Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper concerns the feasibility of full-duplex large-scale multiple-input-multiple-output (MIMO) cellular systems. We first propose a pilot transmission scheme and assess its performance, specifically the ergodic sum-rate. The proposed scheme -- the simultaneous pilot transmission (SPT) -- enables to reduce pilot overhead, where the pilot overhead depends on the number of antennas at the base station (BS), since the self-interference channel has to be estimated. We consider two multicell scenarios-- cooperative and non-cooperative multicell systems--, and derive the analytic model of the ergodic achievable sum-rate for cell-boundary users. The model is derived by applying a simple linear filter, i.e., matched filter or zero-forcing filter, to the BS. In the analytic model, we also consider large-scale fading, pilot contamination, transmitter noise and receiver distortion. Exploiting the derived analytic model, the feasibility of full-duplex large-scale MIMO systems is shown with respect to system parameters. In the end, we confirm that our analytic model matches well the numerical results and the SPT has advantages over other pilot transmission methods.