OpenRaiser/Intern-Atlas huggingface dataset:
XferNAS: Transfer Neural Architecture Search
Venue: arXiv cs.LG 2019
Year: 2019

The term Neural Architecture Search (NAS) refers to the automatic optimization of network architectures for a new, previously unknown task. Since testing an architecture is computationally very expensive, many optimizers need days or even weeks to find suitable architectures. However, this search time can be significantly reduced if knowledge from previous searches on different tasks is reused. In this work, we propose a generally applicable framework that introduces only minor changes to existing optimizers to leverage this feature. As an example, we select an existing optimizer and demonstrate the complexity of the integration of the framework as well as its impact. In experiments on CIFAR-10 and CIFAR-100, we observe a reduction in the search time from 200 to only 6 GPU days, a speed up by a factor of 33. In addition, we observe new records of 1.99 and 14.06 for NAS optimizers on the CIFAR benchmarks, respectively. In a separate study, we analyze the impact of the amount of source and target data. Empirically, we demonstrate that the proposed framework generally gives better results and, in the worst case, is just as good as the unmodified optimizer.
Learning High-Level Planning Symbols from Intrinsically Motivated Experience
Venue: arXiv cs.AI 2019
Year: 2019

In symbolic planning systems, the knowledge on the domain is commonly provided by an expert. Recently, an automatic abstraction procedure has been proposed in the literature to create a Planning Domain Definition Language (PDDL) representation, which is the most widely used input format for most off-the-shelf automated planners, starting from `options', a data structure used to represent actions within the hierarchical reinforcement learning framework. We propose an architecture that potentially removes the need for human intervention. In particular, the architecture first acquires options in a fully autonomous fashion on the basis of open-ended learning, then builds a PDDL domain based on symbols and operators that can be used to accomplish user-defined goals through a standard PDDL planner. We start from an implementation of the above mentioned procedure tested on a set of benchmark domains in which a humanoid robot can change the state of some objects through direct interaction with the environment. We then investigate some critical aspects of the information abstraction process that have been observed, and propose an extension that mitigates such criticalities, in particular by analysing the type of classifiers that allow a suitable grounding of symbols.
Greedy Optimized Multileaving for Personalization
Venue: arXiv cs.IR 2019
Year: 2019

Personalization plays an important role in many services. To evaluate personalized rankings, online evaluation, such as A/B testing, is widely used today. Recently, multileaving has been found to be an efficient method for evaluating rankings in information retrieval fields. This paper describes the first attempt to optimize the multileaving method for personalization settings. We clarify the challenges of applying this method to personalized rankings. Then, to solve these challenges, we propose greedy optimized multileaving (GOM) with a new credit feedback function. The empirical results showed that GOM was stable for increasing ranking lengths and the number of rankers. We implemented GOM on our actual news recommender systems, and compared its online performance. The results showed that GOM evaluated the personalized rankings precisely, with significantly smaller sample sizes (< 1/10) than A/B testing.
Optimizing Validation Phase of Hyperledger Fabric
Venue: arXiv cs.DC 2019
Year: 2019

Blockchain technologies are on the rise, and Hyperledger Fabric is one of the most popular permissioned blockchain platforms. In this paper, we re-architect the validation phase of Fabric based on our analysis from fine-grained breakdown of the validation phase's latency. Our optimized validation phase uses a chaincode cache during validation of transactions, initiates state database reads in parallel with validation of transactions, and writes to the ledger and databases in parallel. Our experiments reveal performance improvements of 2x for CouchDB and 1.3x for LevelDB. Notably, our optimizations can be adopted in a future release of Hyperledger Fabric.
Accurate IMU Preintegration Using Switched Linear Systems For Autonomous Systems
Venue: arXiv cs.RO 2019
Year: 2019

Employing an inertial measurement unit (IMU) as an additional sensor can dramatically improve both reliability and accuracy of visual/Lidar odometry (VO/LO). Different IMU integration models are introduced using different assumptions on the linear acceleration from the IMU. In this paper, a novel IMU integration model is proposed by using switched linear systems. The proposed approach assumes that both the linear acceleration and the angular velocity in the body frame are constant between two consecutive IMU measurements. This is more realistic in real world situation compared to existing approaches which assume that linear acceleration is constant in the world frame while angular velocity is constant in the body frame between two successive IMU measurements. Experimental results show that the proposed approach outperforms the state-of-the-art IMU integration model. The proposed model is thus important for localization of high speed autonomous vehicles in GPS denied environments.
Neural Cross-Domain Collaborative Filtering with Shared Entities
Venue: arXiv cs.IR 2019
Year: 2019

Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factorization or deep neural networks. Either of the techniques in isolation may result in suboptimal performance for the prediction task. Also, most of the existing models face challenges particularly in handling diversity between domains and learning complex non-linear relationships that exist amongst entities (users/items) within and across domains. In this work, we propose an end-to-end neural network model -- NeuCDCF, to address these challenges in a cross-domain setting. More importantly, NeuCDCF follows a wide and deep framework and it learns the representations combinedly from both matrix factorization and deep neural networks. We perform experiments on four real-world datasets and demonstrate that our model performs better than state-of-the-art CDCF models.
Post-synaptic potential regularization has potential
Venue: arXiv cs.LG 2019
Year: 2019

Improving generalization is one of the main challenges for training deep neural networks on classification tasks. In particular, a number of techniques have been proposed, aiming to boost the performance on unseen data: from standard data augmentation techniques to the $\ell_2$ regularization, dropout, batch normalization, entropy-driven SGD and many more.\\ In this work we propose an elegant, simple and principled approach: post-synaptic potential regularization (PSP). We tested this regularization on a number of different state-of-the-art scenarios. Empirical results show that PSP achieves a classification error comparable to more sophisticated learning strategies in the MNIST scenario, while improves the generalization compared to $\ell_2$ regularization in deep architectures trained on CIFAR-10.
Language Support for Adaptation: Intent-Driven Programming in FAST
Venue: arXiv cs.PL 2019
Year: 2019

Historically, programming language semantics has focused on assigning a precise mathematical meaning to programs. That meaning is a function from the program's input domain to its output domain determined solely by its syntactic structure. Such a semantics, fosters the development of portable applications which are oblivious to the performance characteristics and limitations (such as a maximum memory footprint) of particular hardware and software platforms. This paper introduces the idea of intent-driven programming where the meaning of a program additionally depends on an accompanying intent specification expressing how the ordinary program meaning is dynamically modified during execution to satisfy additional properties expressed by the intent. These include both intensional properties---e.g., resource usage---and extensional properties---e.g., accuracy of the computed answer. To demonstrate the intent-driven programming model's value, this paper presents a general-purpose intent-driven programming language---called FAST---implemented as an extension of Swift. FAST consists of an intent compiler, a profiler, a general controller interface and a runtime module which supports interoperation with legacy C/C++ codes. Compared to existing frameworks for adaptive computing, \FAST{} supports dynamic adaptation to changes both in the operating environment and in the intent itself, and enables the mixing of procedural control and control based on feedback and optimization.
Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests
Venue: arXiv cs.AI 2019
Year: 2019

Transportation service providers that dispatch drivers and vehicles to riders start to support both on-demand ride requests posted in real time and rides scheduled in advance, leading to new challenges which, to the best of our knowledge, have not been addressed by existing works. To fill the gap, we design novel trip-vehicle dispatch algorithms to handle both types of requests while taking into account an estimated request distribution of on-demand requests. At the core of the algorithms is the newly proposed Constrained Spatio-Temporal value function (CST-function), which is polynomial-time computable and represents the expected value a vehicle could gain with the constraint that it needs to arrive at a specific location at a given time. Built upon CST-function, we design a randomized best-fit algorithm for scheduled requests and an online planning algorithm for on-demand requests given the scheduled requests as constraints. We evaluate the algorithms through extensive experiments on a real-world dataset of an online ride-hailing platform.
Pan-tilt-zoom SLAM for Sports Videos
Venue: arXiv cs.CV 2019
Year: 2019

We present an online SLAM system specifically designed to track pan-tilt-zoom (PTZ) cameras in highly dynamic sports such as basketball and soccer games. In these games, PTZ cameras rotate very fast and players cover large image areas. To overcome these challenges, we propose to use a novel camera model for tracking and to use rays as landmarks in mapping. Rays overcome the missing depth in pure-rotation cameras. We also develop an online pan-tilt forest for mapping and introduce moving objects (players) detection to mitigate negative impacts from foreground objects. We test our method on both synthetic and real datasets. The experimental results show the superior performance of our method over previous methods for online PTZ camera pose estimation.
Hierarchical Coded Matrix Multiplication
Venue: arXiv cs.IT 2019
Year: 2019

Slow working nodes, known as stragglers, can greatly reduce the speed of distributed computation. Coded matrix multiplication is a recently introduced technique that enables straggler-resistant distributed multiplication of large matrices. A key property is that the finishing time depends only on the work completed by a set of the fastest workers, while the work done by the slowest workers is ignored completely. This paper is motivated by the observation that in real-world commercial cloud computing systems such as Amazon's Elastic Compute Cloud (EC2) the distinction between fast and slow nodes is often a soft one. Thus, if we could also exploit the work completed by stragglers we may realize substantial performance gains. To realize such gains, in this paper we use the idea of hierarchical coding (Ferdinand and Draper, IEEE Int. Symp. Inf. Theory, 2018). We decompose the overall matrix multiplication task into a hierarchy of heterogeneously sized subtasks. The duty to complete each subtask is shared amongst all workers and each subtask is (generally) of a different complexity. The motivation for the hierarchical decomposition is the recognition that more workers will finish the first subtask than the second (or third, forth, etc.). Connecting to error correction coding, earlier subtasks can therefore be designed to be of a higher rate than later subtasks. Through this hierarchical design our scheme exploits the work completed by stragglers, rather than ignoring it, even if that amount is much less than that completed by the fastest workers. We numerically show that our method realizes a 60% improvement in the expected finishing time for a widely studied statistical model of the speed of computation and, on Amazon EC2, the gain is 35%.
Early Acceptance Matching Game for User Association in 5G Cellular HetNet
Venue: arXiv cs.IT 2019
Year: 2019

In this paper we examine the use of matching theory for user association in millimeter wave (mmWave)-enabled cellular heterogeneous networks (HetNets). In a mmWave system, the channel variations can be fast and unpredictable, rendering centralized user association potentially inefficient. We propose an efficient distributed matching algorithm, called early acceptance (EA), tailored for user association in 5G HetNets. The effectiveness of the proposed algorithm is assessed by comparing its performance with the well-known deferred acceptance (DA) matching algorithm, in which user association is delayed until the algorithm finishes. Numerical results show that the proposed EA matching algorithm reaches a near-optimal solution when compared with a centralized user association, and leads to a more power-efficient and much faster user association process when compared to the distributed DA algorithm.
Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
Venue: arXiv cs.CV 2019
Year: 2019

We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition. The key idea is to first construct a fully-connected graph from a hand skeleton, where the node features and edges are then automatically learned via a self-attention mechanism that performs in both spatial and temporal domains. We further propose to leverage the spatial-temporal cues of joint positions to guarantee robust recognition in challenging conditions. In addition, a novel spatial-temporal mask is applied to significantly cut down the computational cost by 99%. We carry out extensive experiments on benchmarks (DHG-14/28 and SHREC'17) and prove the superior performance of our method compared with the state-of-the-art methods. The source code can be found at https://github.com/yuxiaochen1103/DG-STA.
Open Problems in a Logic of Gossips
Venue: arXiv cs.AI 2019
Year: 2019

Gossip protocols are programs used in a setting in which each agent holds a secret and the aim is to reach a situation in which all agents know all secrets. Such protocols rely on a point-to-point or group communication. Distributed epistemic gossip protocols use epistemic formulas in the component programs for the agents. The advantage of the use of epistemic logic is that the resulting protocols are very concise and amenable for a simple verification. Recently, we introduced a natural modal logic that allows one to express distributed epistemic gossip protocols and to reason about their correctness. We proved that the resulting protocols are implementable and that all aspects of their correctness, including termination, are decidable. To establish these results we showed that both the definition of semantics and of truth of the underlying logic are decidable. We also showed that the analogous results hold for an extension of this logic with the 'common knowledge' operator. However, several, often deceptively simple, questions about this logic and the corresponding gossip protocols remain open. The purpose of this paper is to list and elucidate these questions and provide for them an appropriate background information in the form of partial of related results.
Uncertainty About Evidence
Venue: arXiv cs.LO 2019
Year: 2019

We develop a logical framework for reasoning about knowledge and evidence in which the agent may be uncertain about how to interpret their evidence. Rather than representing an evidential state as a fixed subset of the state space, our models allow the set of possible worlds that a piece of evidence corresponds to to vary from one possible world to another, and therefore itself be the subject of uncertainty. Such structures can be viewed as (epistemically motivated) generalizations of topological spaces. In this context, there arises a natural distinction between what is actually entailed by the evidence and what the agent knows is entailed by the evidence -- with the latter, in general, being much weaker. We provide a sound and complete axiomatization of the corresponding bi-modal logic of knowledge and evidence entailment, and investigate some natural extensions of this core system, including the addition of a belief modality and its interaction with evidence interpretation and entailment, and the addition of a "knowability" modality interpreted via a (generalized) interior operator.
On the Consistency among Prior, Posteriors, and Information Sets (Extended Abstract)
Venue: arXiv cs.GT 2019
Year: 2019

This paper studies implications of the consistency conditions among prior, posteriors, and information sets on introspective properties of qualitative belief induced from information sets. The main result reformulates the consistency conditions as: (i) the information sets, without any assumption, almost surely form a partition; and (ii) the posterior at a state is equal to the Bayes conditional probability given the corresponding information set. Implications are as follows. First, each posterior is uniquely determined. Second, qualitative belief reduces to fully introspective knowledge in a ``standard'' environment. Thus, a care must be taken when one studies non-veridical belief or non-introspective knowledge. Third, an information partition compatible with the consistency conditions is uniquely determined by the posteriors. Fourth, qualitative and probability-one beliefs satisfy truth axiom almost surely. The paper also sheds light on how the additivity of the posteriors yields negative introspective properties of beliefs.
Aggregation in Value-Based Argumentation Frameworks
Venue: arXiv cs.MA 2019
Year: 2019

Value-based argumentation enhances a classical abstract argumentation graph - in which arguments are modelled as nodes connected by directed arrows called attacks - with labels on arguments, called values, and an ordering on values, called audience, to provide a more fine-grained justification of the attack relation. With more than one agent facing such an argumentation problem, agents may differ in their ranking of values. When needing to reach a collective view, such agents face a dilemma between two equally justifiable approaches: aggregating their views at the level of values, or aggregating their attack relations, remaining therefore at the level of the graphs. We explore the strenghts and limitations of both approaches, employing techniques from preference aggregation and graph aggregation, and propose a third possibility aggregating rankings extracted from given attack relations.
Measuring Belief and Risk Attitude
Venue: arXiv cs.GT 2019
Year: 2019

Ramsey (1926) sketches a proposal for measuring the subjective probabilities of an agent by their observable preferences, assuming that the agent is an expected utility maximizer. I show how to extend the spirit of Ramsey's method to a strictly wider class of agents: risk-weighted expected utility maximizers (Buchak 2013). In particular, I show how we can measure the risk attitudes of an agent by their observable preferences, assuming that the agent is a risk-weighted expected utility maximizer. Further, we can leverage this method to measure the subjective probabilities of a risk-weighted expected utility maximizer.
Motion Browser: Visualizing and Understanding Complex Upper Limb Movement Under Obstetrical Brachial Plexus Injuries
Venue: arXiv cs.GR 2019
Year: 2019

The brachial plexus is a complex network of peripheral nerves that enables sensing from and control of the movements of the arms and hand. Nowadays, the coordination between the muscles to generate simple movements is still not well understood, hindering the knowledge of how to best treat patients with this type of peripheral nerve injury. To acquire enough information for medical data analysis, physicians conduct motion analysis assessments with patients to produce a rich dataset of electromyographic signals from multiple muscles recorded with joint movements during real-world tasks. However, tools for the analysis and visualization of the data in a succinct and interpretable manner are currently not available. Without the ability to integrate, compare, and compute multiple data sources in one platform, physicians can only compute simple statistical values to describe patient's behavior vaguely, which limits the possibility to answer clinical questions and generate hypotheses for research. To address this challenge, we have developed \systemname, an interactive visual analytics system which provides an efficient framework to extract and compare muscle activity patterns from the patient's limbs and coordinated views to help users analyze muscle signals, motion data, and video information to address different tasks. The system was developed as a result of a collaborative endeavor between computer scientists and orthopedic surgery and rehabilitation physicians. We present case studies showing physicians can utilize the information displayed to understand how individuals coordinate their muscles to initiate appropriate treatment and generate new hypotheses for future research.
Learning dynamic word embeddings with drift regularisation
Venue: arXiv cs.CL 2019
Year: 2019

Word usage, meaning and connotation change throughout time. Diachronic word embeddings are used to grasp these changes in an unsupervised way. In this paper, we use variants of the Dynamic Bernoulli Embeddings model to learn dynamic word embeddings, in order to identify notable properties of the model. The comparison is made on the New York Times Annotated Corpus in English and a set of articles from the French newspaper Le Monde covering the same period. This allows us to define a pipeline to analyse the evolution of words use across two languages.
ParaFIS:A new online fuzzy inference system based on parallel drift anticipation
Venue: arXiv cs.AI 2019
Year: 2019

This paper proposes a new architecture of incremen-tal fuzzy inference system (also called Evolving Fuzzy System-EFS). In the context of classifying data stream in non stationary environment, concept drifts problems must be addressed. Several studies have shown that EFS can deal with such environment thanks to their high structural flexibility. These EFS perform well with smooth drift (or incremental drift). The new architecture we propose is focused on improving the processing of brutal changes in the data distribution (often called brutal concept drift). More precisely, a generalized EFS is paired with a module of anticipation to improve the adaptation of new rules after a brutal drift. The proposed architecture is evaluated on three datasets from UCI repository where artificial brutal drifts have been applied. A fit model is also proposed to get a "reactivity time" needed to converge to the steady-state and the score at end. Both characteristics are compared between the same system with and without anticipation and with a similar EFS from state-of-the-art. The experiments demonstrates improvements in both cases.