OpenRaiser/Intern-Atlas huggingface dataset:
Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems
Venue: arXiv cs.IT 2019
Year: 2019

We study the joint unicast and multi-group multicast transmission in massive multiple-input-multiple-output (MIMO) systems. We consider a system model that accounts for channel estimation and pilot contamination, and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs), under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto optimal point describes a particular efficient trade-off between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, etc.) to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource. Finally, we validate our results using numerical simulations.
An Accuracy-Enhanced Stemming Algorithm for Arabic Information Retrieval
Venue: arXiv cs.CL 2019
Year: 2019

This paper provides a method for indexing and retrieving Arabic texts, based on natural language processing. Our approach exploits the notion of template in word stemming and replaces the words by their stems. This technique has proven to be effective since it has returned significant relevant retrieval results by decreasing silence during the retrieval phase. Series of experiments have been conducted to test the performance of the proposed algorithm ESAIR (Enhanced Stemmer for Arabic Information Retrieval). The results obtained indicate that the algorithm extracts the exact root with an accuracy rate up to 96% and hence, improving information retrieval.
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Venue: arXiv cs.CV 2019
Year: 2019

Bounding box regression is the crucial step in object detection. In existing methods, while $\ell_n$-norm loss is widely adopted for bounding box regression, it is not tailored to the evaluation metric, i.e., Intersection over Union (IoU). Recently, IoU loss and generalized IoU (GIoU) loss have been proposed to benefit the IoU metric, but still suffer from the problems of slow convergence and inaccurate regression. In this paper, we propose a Distance-IoU (DIoU) loss by incorporating the normalized distance between the predicted box and the target box, which converges much faster in training than IoU and GIoU losses. Furthermore, this paper summarizes three geometric factors in bounding box regression, \ie, overlap area, central point distance and aspect ratio, based on which a Complete IoU (CIoU) loss is proposed, thereby leading to faster convergence and better performance. By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e.g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric. Moreover, DIoU can be easily adopted into non-maximum suppression (NMS) to act as the criterion, further boosting performance improvement. The source code and trained models are available at https://github.com/Zzh-tju/DIoU.
Eliminating artefacts in Polarimetric Images using Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

Polarization measurements done using Imaging Polarimeters such as the Robotic Polarimeter are very sensitive to the presence of artefacts in images. Artefacts can range from internal reflections in a telescope to satellite trails that could contaminate an area of interest in the image. With the advent of wide-field polarimetry surveys, it is imperative to develop methods that automatically flag artefacts in images. In this paper, we implement a Convolutional Neural Network to identify the most dominant artefacts in the images. We find that our model can successfully classify sources with 98\% true positive and 97\% true negative rates. Such models, combined with transfer learning, will give us a running start in artefact elimination for near-future surveys like WALOP.
On the Upper Bound of the Kullback-Leibler Divergence and Cross Entropy
Venue: arXiv cs.IT 2019
Year: 2019

This archiving article consists of several short reports on the discussions between the two authors over the past two years at Oxford and Madrid, and their work carried out during that period on the upper bound of the Kullback-Leibler divergence and cross entropy. The work was motivated by the cost-benefit ratio proposed by Chen and Golan [1], and the less desirable property that the Kullback-Leibler (KL) divergence used in the measure is unbounded. The work subsequently (i) confirmed that the KL-divergence used in the cost-benefit ratio should exhibit a bounded property, (ii) proposed a new divergence measure, and (iii) compared this new divergence measure with a few other bounded measures.
Cichlid: Explicit physical memory management for large machines
Venue: arXiv cs.OS 2019
Year: 2019

In this paper, we rethink how an OS supports virtual memory. Classical VM is an opaque abstraction of RAM, backed by demand paging. However, most systems today (from phones to data-centers) do not page, and indeed may require the performance benefits of non-paged physical memory, precise NUMA allocation, etc. Moreover, MMU hardware is now useful for other purposes, such as detecting page access or providing large page translation. Accordingly, the venerable VM abstraction in OSes like Windows and Linux has acquired a plethora of extra APIs to poke at the policy behind the illusion of a virtual address space. Instead, we present Cichlid, a memory system which inverts this model. Applications explicitly manage their physical RAM of different types, and directly (though safely) program the translation hardware. Cichlid is implemented in Barrelfish, requires no virtualization support, and outperforms VMM-based approaches for all but the smallest working sets. We show that Cichlid enables use-cases for virtual memory not possible in Linux today, and other use-cases are simple to program and significantly faster.
Evaluation of performance portability frameworks for the implementation of a particle-in-cell code
Venue: arXiv cs.DC 2019
Year: 2019

This paper reports on an in-depth evaluation of the performance portability frameworks Kokkos and RAJA with respect to their suitability for the implementation of complex particle-in-cell (PIC) simulation codes, extending previous studies based on codes from other domains. At the example of a particle-in-cell model, we implemented the hotspot of the code in C++ and parallelized it using OpenMP, OpenACC, CUDA, Kokkos, and RAJA, targeting multi-core (CPU) and graphics (GPU) processors. Both, Kokkos and RAJA appear mature, are usable for complex codes, and keep their promise to provide performance portability across different architectures. Comparing the obtainable performance on state-of-the art hardware, but also considering aspects such as code complexity, feature availability, and overall productivity, we finally draw the conclusion that the Kokkos framework would be suited best to tackle the massively parallel implementation of the full PIC model.
Time-varying constrained proximal type dynamics in multi-agent network games
Venue: arXiv cs.GT 2019
Year: 2019

In this paper, we study multi-agent network games subject to affine time-varying coupling constraints and a time-varying communication network. We focus on the class of games adopting proximal dynamics and study their convergence to a persistent equilibrium. The assumptions considered to solve the problem are discussed and motivated. We develop an iterative equilibrium seeking algorithm, using only local information, that converges to a special class of game equilibria. Its derivation is motivated by several examples, showing that the original game dynamics fail to converge. Finally, we apply the designed algorithm to solve a constrained consensus problem, validating the theoretical results.
The Future Time Traveller Project: Career Guidance on Future Skills, Jobs and Career Prospects of Generation Z through a Game-Based Virtual World Environment
Venue: arXiv cs.HC 2019
Year: 2019

Future Time Traveller is a European project that aims at transforming career guidance of generation Z through an innovative, games-based scenario approach and to prepare the next generation for the jobs of the future. The pro-ject objective is to foster innovative thinking and future-oriented mindset of young people, through an innovative game-based virtual world environment. This environment helps them explore the future world, understand the trends that shape the future world of work, the emerging jobs, and the skills they will require. The Future Time Traveller project is implemented by a team of experts in 7 European countries (Bulgaria, Germany, Greece, Italy, Poland, Portugal, and United Kingdom). The project target groups include young people (genera-tion Z), career guidance practitioners and experts, and policymakers. This paper presents, in brief, the Future Time Traveller project and introduces the reader to the main features and functionalities of the 3-dimensional virtual world and the games developed in this environment.
Deep Motion Blur Removal Using Noisy/Blurry Image Pairs
Venue: arXiv cs.CV 2019
Year: 2019

Removing spatially variant motion blur from a blurry image is a challenging problem as blur sources are complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel free single image deblurring can be efficiently performed, but questions about deblurring performance persist. Thus, we propose to restore a sharp image by fusing a pair of noisy/blurry images captured in a burst. Two neural network structures, DeblurRNN and DeblurMerger, are presented to exploit the pair of images in a sequential manner or parallel manner. To boost the training, gradient loss, adversarial loss and spectral normalization are leveraged. The training dataset that consists of pairs of noisy/blurry images and the corresponding ground truth sharp image is synthesized based on the benchmark dataset GOPRO. We evaluated the trained networks on a variety of synthetic datasets and real image pairs. The results demonstrate that the proposed approach outperforms the state-of-the-art both qualitatively and quantitatively.
Classification as Decoder: Trading Flexibility for Control in Medical Dialogue
Venue: arXiv cs.CL 2019
Year: 2019

Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a deeper understanding of conversational context, and generate a wide variety of responses. This flexibility comes at the cost of control, a concerning tradeoff in doctor/patient interactions. Inaccuracies, typos, or undesirable content in the training data will be reproduced by the model at inference time. We trade a small amount of labeling effort and some loss of response variety in exchange for quality control. More specifically, a pretrained language model encodes the conversational context, and we finetune a classification head to map an encoded conversational context to a response class, where each class is a noisily labeled group of interchangeable responses. Experts can update these exemplar responses over time as best practices change without retraining the classifier or invalidating old training data. Expert evaluation of 775 unseen doctor/patient conversations shows that only 12% of the discriminative model's responses are worse than the what the doctor ended up writing, compared to 18% for the generative model.
Towards Reducing Bias in Gender Classification
Venue: arXiv cs.LG 2019
Year: 2019

Societal bias towards certain communities is a big problem that affects a lot of machine learning systems. This work aims at addressing the racial bias present in many modern gender recognition systems. We learn race invariant representations of human faces with an adversarially trained autoencoder model. We show that such representations help us achieve less biased performance in gender classification. We use variance in classification accuracy across different races as a surrogate for the racial bias of the model and achieve a drop of over 40% in variance with race invariant representations.
Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation
Venue: arXiv cs.LG 2019
Year: 2019

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and employ annotation schemes with limited generalizability to conversations spanning multiple domains. To address these gaps, we created a new Response Quality annotation scheme, introduced five new domain-independent feature sets and experimented with six machine learning models to estimate User Satisfaction at both turn and dialogue level. Response Quality ratings achieved significantly high correlation (0.76) with explicit turn-level user ratings. Using the new feature sets we introduced, Gradient Boosting Regression model achieved best (rating [1-5]) prediction performance on 26 seen (linear correlation ~0.79) and one new multi-turn domain (linear correlation 0.67). We observed a 16% relative improvement (68% -> 79%) in binary ("satisfactory/dissatisfactory") class prediction accuracy of a domain-independent dialogue-level satisfaction estimation model after including predicted turn-level satisfaction ratings as features.
Localizing Occluders with Compositional Convolutional Networks
Venue: arXiv cs.CV 2019
Year: 2019

Compositional convolutional networks are generative compositional models of neural network features, that achieve state of the art results when classifying partially occluded objects, even when they have not been exposed to occluded objects during training. In this work, we study the performance of CompositionalNets at localizing occluders in images. We show that the original model is not able to localize occluders well. We propose to overcome this limitation by modeling the feature activations as a mixture of von-Mises-Fisher distributions, which also allows for an end-to-end training of CompositionalNets. Our experimental results demonstrate that the proposed extensions increase the model's performance at localizing occluders as well as at classifying partially occluded objects.
Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems
Venue: arXiv cs.NE 2019
Year: 2019

Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real-world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This paper proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (\emph{route failure}), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm.
Individual-Based Stability in Hedonic Diversity Games
Venue: arXiv cs.GT 2019
Year: 2019

In hedonic diversity games (HDGs), recently introduced by Bredereck, Elkind and Igarashi (2019), each agent belongs to one of two classes (men and women, vegetarians and meat-eaters, junior and senior researchers), and agents' preferences over coalitions are determined by the fraction of agents from their class in each coalition. Bredereck et al. show that while an HDG may fail to have a Nash stable (NS) or a core stable (CS) outcome, every HDG in which all agents have single-peaked preferences admits an individually stable (IS) outcome, which can be computed in polynomial time. In this work, we extend and strengthen these results in several ways. First, we establish that the problem of deciding if an HDG has an NS outcome is NP-complete, but admits an XP algorithm with respect to the size of the smaller class. Second, we show that, in fact, all HDGs admit IS outcomes that can be computed in polynomial time; our algorithm for finding such outcomes is considerably simpler than that of Bredereck et al. We also consider two ways of generalizing the model of Bredereck et al. to k > 2 classes. We complement our theoretical results by empirical analysis, comparing the IS outcomes found by our algorithm, the algorithm of Bredereck et al. and a natural better-response dynamics.
Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification
Venue: arXiv cs.LG 2019
Year: 2019

The graph-based semi-supervised label propagation algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, available methods typically calculate the weights and estimate the labels in the original input space, which typically contains noise and corruption. Thus, the en-coded similarities and manifold smoothness may be inaccurate for label estimation. In this paper, we present effective schemes for resolving these issues and propose a novel and robust semi-supervised classification algorithm, namely, the tri-ple-matrix-recovery-based robust auto-weighted label propa-gation framework (ALP-TMR). Our ALP-TMR introduces a triple matrix recovery mechanism to remove noise or mixed signs from the estimated soft labels and improve the robustness to noise and outliers in the steps of assigning weights and pre-dicting the labels simultaneously. Our method can jointly re-cover the underlying clean data, clean labels and clean weighting spaces by decomposing the original data, predicted soft labels or weights into a clean part plus an error part by fitting noise. In addition, ALP-TMR integrates the au-to-weighting process by minimizing reconstruction errors over the recovered clean data and clean soft labels, which can en-code the weights more accurately to improve both data rep-resentation and classification. By classifying samples in the recovered clean label and weight spaces, one can potentially improve the label prediction results. The results of extensive experiments demonstrated the satisfactory performance of our ALP-TMR.
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation
Venue: arXiv cs.LG 2019
Year: 2019

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than $1$ BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than $10$ times) the inference process over AT baselines.
CleanQ: a lightweight, uniform, formally specified interface for intra-machine data transfer
Venue: arXiv cs.OS 2019
Year: 2019

We present CleanQ, a high-performance operating-system interface for descriptor-based data transfer with rigorous formal semantics, based on a simple, formally-verified notion of ownership transfer, with a fast reference implementation. CleanQ aims to replace the current proliferation of similar, but subtly diverse, and loosely specified, descriptor-based interfaces in OS kernels and device drivers. CleanQ has strict semantics that not only clarify both the implementation of the interface for different hardware devices and software usecases, but also enable composition of modules as in more heavyweight frameworks like Unix streams. We motivate CleanQ by showing that loose specifications derived from implementation lead to security and correctness bugs in production systems that a clean, formal, and easilyunderstandable abstraction helps eliminate. We further demonstrate by experiment that there is negligible performance cost for a clean design: we show overheads in the tens of cycles for operations, and comparable end-to-end performance to the highly-tuned Virtio and DPDK implementations on Linux.
Joint Embedding Learning of Educational Knowledge Graphs
Venue: arXiv cs.CL 2019
Year: 2019

As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.