OpenRaiser/Intern-Atlas huggingface dataset:
DynGEM: Deep Embedding Method for Dynamic Graphs
Venue: arXiv cs.SI 2018
Year: 2018

Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node classification. Existing methods focus on computing the embedding for static graphs. However, many graphs in practical applications are dynamic and evolve constantly over time. Naively applying existing embedding algorithms to each snapshot of dynamic graphs independently usually leads to unsatisfactory performance in terms of stability, flexibility and efficiency. In this work, we present an efficient algorithm DynGEM based on recent advances in deep autoencoders for graph embeddings, to address this problem. The major advantages of DynGEM include: (1) the embedding is stable over time, (2) it can handle growing dynamic graphs, and (3) it has better running time than using static embedding methods on each snapshot of a dynamic graph. We test DynGEM on a variety of tasks including graph visualization, graph reconstruction, link prediction and anomaly detection (on both synthetic and real datasets). Experimental results demonstrate the superior stability and scalability of our approach.
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

There is a common belief that the successful training of deep neural networks requires many annotated training samples, which are often expensive and difficult to obtain especially in the biomedical imaging field. While it is often easy for researchers to use data augmentation to expand the size of training sets, constructing and generating generic augmented data that is able to teach the network the desired invariance and robustness properties using traditional data augmentation techniques is challenging in practice. In this paper, we propose a novel automatic data augmentation method that uses generative adversarial networks to learn augmentations that enable machine learning based method to learn the available annotated samples more efficiently. The architecture consists of a coarse-to-fine generator to capture the manifold of the training sets and generate generic augmented data. In our experiments, we show the efficacy of our approach on a Magnetic Resonance Imaging (MRI) image, achieving improvements of 3.5% Dice coefficient on the BRATS15 Challenge dataset as compared to traditional augmentation approaches. Also, our proposed method successfully boosts a common segmentation network to reach the state-of-the-art performance on the BRATS15 Challenge.
In the IP of the Beholder: Strategies for Active IPv6 Topology Discovery
Venue: arXiv cs.NI 2018
Year: 2018

Existing methods for active topology discovery within the IPv6 Internet largely mirror those of IPv4. In light of the large and sparsely populated address space, in conjunction with aggressive ICMPv6 rate limiting by routers, this work develops a different approach to Internet-wide IPv6 topology mapping. We adopt randomized probing techniques in order to distribute probing load, minimize the effects of rate limiting, and probe at higher rates. Second, we extensively analyze the efficiency and efficacy of various IPv6 hitlists and target generation methods when used for topology discovery, and synthesize new target lists based on our empirical results to provide both breadth (coverage across networks) and depth (to find potential subnetting). Employing our probing strategy, we discover more than 1.3M IPv6 router interface addresses from a single vantage point. Finally, we share our prober implementation, synthesized target lists, and discovered IPv6 topology results.
Characteristics and Motivations of Players with Disabilities in Digital Games
Venue: arXiv cs.HC 2018
Year: 2018

In research and practice into the accessibility of digital games, much of the work has focused on how to make games accessible to people with disa- bilities. With an increasing number of people with disabilities playing main- stream commercial games, it is important that we understand who they are and how they play in order to take a more user-centered approach as this field grows. We conducted a demographic survey of 230 players with disabilities and found that they play mainstream digital games using a variety of assistive tech- nologies, use accessibility options such as key remapping and subtitles, and they identify themselves as gamers who play digital games as their primary hobby. This gives us a richer picture of players with disabilities and indicates that there are opportunities to begin to look at accessible player experiences (APX) in games.
"How to rate a video game?" - A prediction system for video games based on multimodal information
Venue: arXiv cs.CV 2018
Year: 2018

Video games have become an integral part of most people's lives in recent times. This led to an abundance of data related to video games being shared online. However, this comes with issues such as incorrect ratings, reviews or anything that is being shared. Recommendation systems are powerful tools that help users by providing them with meaningful recommendations. A straightforward approach would be to predict the scores of video games based on other information related to the game. It could be used as a means to validate user-submitted ratings as well as provide recommendations. This work provides a method to predict the G-Score, that defines how good a video game is, from its trailer (video) and summary (text). We first propose models to predict the G-Score based on the trailer alone (unimodal). Later on, we show that considering information from multiple modalities helps the models perform better compared to using information from videos alone. Since we couldn't find any suitable multimodal video game dataset, we created our own dataset named VGD (Video Game Dataset) and provide it along with this work. The approach mentioned here can be generalized to other multimodal datasets such as movie trailers and summaries etc. Towards the end, we talk about the shortcomings of the work and some methods to overcome them.
Standard Cell Library Evaluation with Multiple lithography-compliant verification and Improved Synopsys Pin Access Checking Utility
Venue: arXiv cs.OH 2018
Year: 2018

While standard cell layouts are drawn with minimum design rules to maximize the benefit of design area shrinkage, the complicated design rules have caused difficulties with signal routes accessing the pins in standard cell layouts. As a result, it has become a great challenge for physical layout designers to design a standard cell layout that is optimized for area, power, timing, signal integrity, and printability. Multiple design iterations are required to consider pin accessibility during standard cells layout to increase the number of feasible solutions available to the router. In this work, we will demonstrate several improvements with the Synopsys PAC methodology, such as reducing the number of cells required for each Synopsys 'testcell' with the same cell abutment condition, increasing the complexity of the pin connection for better pin accessibility evaluation. We also recommend additional constraints to improve the probability of detecting pin accessibility issues. We also integrate other physical verification methods to access the design rule compliance and the printability of standard cells. We hope that the easy to use utility enables layout engineers to perform the verification, simplifying the verification methodology.
Model-based Pricing for Machine Learning in a Data Marketplace
Venue: arXiv cs.DB 2018
Year: 2018

Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potentially leads to a loss of sellers' revenue and buyers' affordability and efficiency. In this paper, we propose a model-based pricing (MBP) framework, which instead of pricing the data, directly prices ML model instances. We first formally describe the desired properties of the MBP framework, with a focus on avoiding arbitrage. Next, we show a concrete realization of the MBP framework via a noise injection approach, which provably satisfies the desired formal properties. Based on the proposed framework, we then provide algorithmic solutions on how the seller can assign prices to models under different market scenarios (such as to maximize revenue). Finally, we conduct extensive experiments, which validate that the MBP framework can provide high revenue to the seller, high affordability to the buyer, and also operate on low runtime cost.
Technical Report: Optimistic Execution in Key-Value Store
Venue: arXiv cs.DC 2018
Year: 2018

Limitations of the CAP theorem imply that if availability is desired in the presence of network partitions, one must sacrifice sequential consistency, a consistency model that is more natural for system design. We focus on the problem of what a designer should do if he/she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker (e.g., eventual or causal) consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say $P$, and continues to run the protocol, as our system monitors $P$. If $P$ is violated (because the underlying key-value store provides a weaker consistency), the system rolls back and resumes the computation at a state where $P$ holds. We evaluate this approach with practical graph applications running on the Voldemort key-value store. Our experiments with deployment on Amazon AWS EC2 instances shows that using eventual consistency with monitoring can provide a $50-80\%$ increase in throughput when compared with sequential consistency. We also show that the overhead of the monitoring itself is low (typically less than 4\%) and the latency of detecting violations is small. In particular, more than $99.9\%$ of violations are detected in less than $50$ milliseconds in regional AWS networks, and in less than $5$ seconds in global AWS networks.
OpenNMT: Neural Machine Translation Toolkit
Venue: arXiv cs.CL 2018
Year: 2018

OpenNMT is an open-source toolkit for neural machine translation (NMT). The system prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques. OpenNMT has been used in several production MT systems, modified for numerous research papers, and is implemented across several deep learning frameworks.
Quantum Adiabatic Evolution for Global Optimization in Big Data
Venue: arXiv cs.OH 2018
Year: 2018

Big Data is characterized by Volume, Velocity, Veracity and Complexity. The interaction between this huge data is complex with an associated free will having dynamic and non linear nature. We reduced big data based on its characteristics, conceptually driven by quantum field theory and utilizing the physics of condensed matter theory in a complex nonlinear dynamic system: Quantum Topological Field Theory of Data. The model is formulated from the dynamics and evolution of single datum, eventually defining the global properties and evolution of collective data space via action, partition function, green propagators in almost polynomially solvable O(nlogn) complexity. The simulated results show that the time complexity of our algorithm for global optimization via quantum adiabatic evolution is almost in O(logn) Our algorithm first mines the space via greedy approach and makes a list of all ground state Hamiltonians, then utilizing the tunnelling property of quantum mechanics optimizes the algorithm unlike up hill and iterative techniques and doesnot let algorithm to get localized in local minima or sharp valley due to adiabatic evolution of the system. The loss in quantumness, non realizable, no clone, noise, decoherence, splitting of energy states due to electric and magnetic fields, variant to perturbations and less lifetime makes it inefficient for practical implementation. The inefficiencies of qubit can be overcome via property that remains invariant to perturbation and Cartesian independent having well defined mathematical structure. It can be well addressed via topological field theory of data.
The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making
Venue: arXiv cs.AI 2018
Year: 2018

Off-policy reinforcement learning enables near-optimal policy from suboptimal experience, thereby provisions opportunity for artificial intelligence applications in healthcare. Previous works have mainly framed patient-clinician interactions as Markov decision processes, while true physiological states are not necessarily fully observable from clinical data. We capture this situation with partially observable Markov decision process, in which an agent optimises its actions in a belief represented as a distribution of patient states inferred from individual history trajectories. A Gaussian mixture model is fitted for the observed data. Moreover, we take into account the fact that nuance in pharmaceutical dosage could presumably result in significantly different effect by modelling a continuous policy through a Gaussian approximator directly in the policy space, i.e. the actor. To address the challenge of infinite number of possible belief states which renders exact value iteration intractable, we evaluate and plan for only every encountered belief, through heuristic search tree by tightly maintaining lower and upper bounds of the true value of belief. We further resort to function approximations to update value bounds estimation, i.e. the critic, so that the tree search can be improved through more compact bounds at the fringe nodes that will be back-propagated to the root. Both actor and critic parameters are learned via gradient-based approaches. Our proposed policy trained from real intensive care unit data is capable of dictating dosing on vasopressors and intravenous fluids for sepsis patients that lead to the best patient outcomes.
Optimisation and Illumination of a Real-world Workforce Scheduling and Routing Application via Map-Elites
Venue: arXiv cs.AI 2018
Year: 2018

Workforce Scheduling and Routing Problems (WSRP) are very common in many practical domains, and usually, have a number of objectives. Illumination algorithms such as Map-Elites (ME) have recently gained traction in application to {\em design} problems, in providing multiple diverse solutions as well as illuminating the solution space in terms of user-defined characteristics, but typically require significant computational effort to produce the solution archive. We investigate whether ME can provide an effective approach to solving WSRP, a {\em repetitive} problem in which solutions have to be produced quickly and often. The goals of the paper are two-fold. The first is to evaluate whether ME can provide solutions of competitive quality to an Evolutionary Algorithm (EA) in terms of a single objective function, and the second to examine its ability to provide a repertoire of solutions that maximise user choice. We find that very small computational budgets favour the EA in terms of quality, but ME outperforms the EA at larger budgets, provides a more diverse array of solutions, and lends insight to the end-user.
Beyond admissibility: Dominance between chains of strategies
Venue: arXiv cs.GT 2018
Year: 2018

Admissible strategies, i.e. those that are not dominated by any other strategy, are a typical rationality notion in game theory. In many classes of games this is justified by results showing that any strategy is admissible or dominated by an admissible strategy. However, in games played on finite graphs with quantitative objectives (as used for reactive synthesis), this is not the case. We consider increasing chains of strategies instead to recover a satisfactory rationality notion based on dominance in such games. We start with some order-theoretic considerations establishing sufficient criteria for this to work. We then turn our attention to generalised safety/reachability games as a particular application. We propose the notion of maximal uniform chain as the desired dominance-based rationality concept in these games. Decidability of some fundamental questions about uniform chains is established.
Teaching Meaningful Explanations
Venue: arXiv cs.AI 2018
Year: 2018

The adoption of machine learning in high-stakes applications such as healthcare and law has lagged in part because predictions are not accompanied by explanations comprehensible to the domain user, who often holds the ultimate responsibility for decisions and outcomes. In this paper, we propose an approach to generate such explanations in which training data is augmented to include, in addition to features and labels, explanations elicited from domain users. A joint model is then learned to produce both labels and explanations from the input features. This simple idea ensures that explanations are tailored to the complexity expectations and domain knowledge of the consumer. Evaluation spans multiple modeling techniques on a game dataset, a (visual) aesthetics dataset, a chemical odor dataset and a Melanoma dataset showing that our approach is generalizable across domains and algorithms. Results demonstrate that meaningful explanations can be reliably taught to machine learning algorithms, and in some cases, also improve modeling accuracy.
The Future of Virtual Classroom: Using Existing Features to Move Beyond Traditional Classroom Limitations
Venue: arXiv cs.CY 2018
Year: 2018

This paper argues that the true potential of virtual classrooms in education is not fully exploited yet. The features available in most environments that have been incorporated as virtual classrooms are classified into two groups. The first group includes common features, related only to the emulation of a traditional classroom. In this group, the practical differences between traditional and virtual classroom are discussed. In addition, best practices that could aid the professors to make students feel like participating in a typical classroom are presented. The second group comprises of advanced features and practices, which extend the traditional classroom. In this group, examples of successful practices which could not be performed in a traditional classroom are introduced. Finally, a qualitative study with interviews of 21 experts from 15 countries is presented, showing that even these experts are not fully exploiting the advanced features that contemporary virtual classroom environments are offering.
Biologically Motivated Algorithms for Propagating Local Target Representations
Venue: arXiv cs.NE 2018
Year: 2018

Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E. We compare our procedures to several other biologically-motivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation. In two benchmarks, we find that both of our proposed algorithms yield stable performance and strong generalization compared to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
Depth and nonlinearity induce implicit exploration for RL
Venue: arXiv cs.LG 2018
Year: 2018

The question of how to explore, i.e., take actions with uncertain outcomes to learn about possible future rewards, is a key question in reinforcement learning (RL). Here, we show a surprising result: We show that Q-learning with nonlinear Q-function and no explicit exploration (i.e., a purely greedy policy) can learn several standard benchmark tasks, including mountain car, equally well as, or better than, the most commonly-used $ε$-greedy exploration. We carefully examine this result and show that both the depth of the Q-network and the type of nonlinearity are important to induce such deterministic exploration.
Deep Video Portraits
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. In contrast to existing approaches that are restricted to manipulations of facial expressions only, we are the first to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor. The core of our approach is a generative neural network with a novel space-time architecture. The network takes as input synthetic renderings of a parametric face model, based on which it predicts photo-realistic video frames for a given target actor. The realism in this rendering-to-video transfer is achieved by careful adversarial training, and as a result, we can create modified target videos that mimic the behavior of the synthetically-created input. In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network -- thus taking full control of the target. With the ability to freely recombine source and target parameters, we are able to demonstrate a large variety of video rewrite applications without explicitly modeling hair, body or background. For instance, we can reenact the full head using interactive user-controlled editing, and realize high-fidelity visual dubbing. To demonstrate the high quality of our output, we conduct an extensive series of experiments and evaluations, where for instance a user study shows that our video edits are hard to detect.
The Age of Updates in a Simple Relay Network
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we examine a system where status updates are generated by a source and are forwarded in a First-Come-First-Served (FCFS) manner to the monitor. We consider the case where the server has other tasks to fulfill, a simple example being relaying the packets of another stream. Due to the server's necessity to go on vacations, the age process of the stream of interest becomes complicated to evaluate. By leveraging specific queuing theory tools, we provide a closed form of the average age for both streams which enables us to optimize the generation rate of packets belonging to each stream to achieve the minimum possible average age. The tools used can be further adopted to provide insights on more general multi-hop scenarios. Numerical results are provided to corroborate the theoretical findings and highlight the interaction between the two streams.
A Geometric Property of Relative Entropy and the Universal Threshold Phenomenon for Binary-Input Channels with Noisy State Information at the Encoder
Venue: arXiv cs.IT 2018
Year: 2018

Tight lower and upper bounds on the ratio of relative entropies of two probability distributions with respect to a common third one are established, where the three distributions are collinear in the standard $(n-1)$-simplex. These bounds are leveraged to analyze the capacity of an arbitrary binary-input channel with noisy causal state information (provided by a side channel) at the encoder and perfect state information at the decoder, and in particular to determine the exact universal threshold on the noise measure of the side channel, above which the capacity is the same as that with no encoder side information.