New Embedded Representations and Evaluation Protocols for Inferring Transitive Relations

Beyond word embeddings, continuous representations of knowledge graph (KG) components, such as entities, types and relations, are widely used for entity mention disambiguation, relation inference and deep question answering. Great strides have been made in modeling general, asymmetric or antisymmetric KG relations using Gaussian, holographic, and complex embeddings. None of these directly enforce transitivity inherent in the is-instance-of and is-subtype-of relations. A recent proposal, called order embedding (OE), demands that the vector representing a subtype elementwise dominates the vector representing a supertype. However, the manner in which such constraints are asserted and evaluated have some limitations. In this short research note, we make three contributions specific to representing and inferring transitive relations. First, we propose and justify a significant improvement to the OE loss objective. Second, we propose a new representation of types as hyper-rectangular regions, that generalize and improve on OE. Third, we show that some current protocols to evaluate transitive relation inference can be misleading, and offer a sound alternative. Rather than use black-box deep learning modules off-the-shelf, we develop our training networks using elementary geometric considerations.Comment: Accepted at SIGIR 201

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.Comment: Accepted at ICLR 201

Adversarial Generation of Natural Language

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.Comment: 11 pages, 3 figures, 5 table

Sliding mode control method having terminal convergence in finite time

An object of this invention is to provide robust nonlinear controllers for robotic operations in unstructured environments based upon a new class of closed loop sliding control methods, sometimes denoted terminal sliders, where the new class will enforce closed-loop control convergence to equilibrium in finite time. Improved performance results from the elimination of high frequency control switching previously employed for robustness to parametric uncertainties. Improved performance also results from the dependence of terminal slider stability upon the rate of change of uncertainties over the sliding surface rather than the magnitude of the uncertainty itself for robust control. Terminal sliding mode control also yields improved convergence where convergence time is finite and is to be controlled. A further object is to apply terminal sliders to robot manipulator control and benchmark performance with the traditional computed torque control method and provide for design of control parameters

Viewing medium affects arm motor performance in 3D virtual environments

<p>Abstract</p> <p>Background</p> <p>2D and 3D virtual reality platforms are used for designing individualized training environments for post-stroke rehabilitation. Virtual environments (VEs) are viewed using media like head mounted displays (HMDs) and large screen projection systems (SPS) which can influence the quality of perception of the environment. We estimated if there were differences in arm pointing kinematics when subjects with and without stroke viewed a 3D VE through two different media: HMD and SPS.</p> <p>Methods</p> <p>Two groups of subjects participated (healthy control, n = 10, aged 53.6 ± 17.2 yrs; stroke, n = 20, 66.2 ± 11.3 yrs). Arm motor impairment and spasticity were assessed in the stroke group which was divided into mild (n = 10) and moderate-to-severe (n = 10) sub-groups based on Fugl-Meyer Scores. Subjects pointed (8 times each) to 6 randomly presented targets located at two heights in the ipsilateral, middle and contralateral arm workspaces. Movements were repeated in the same VE viewed using HMD (Kaiser XL50) and SPS. Movement kinematics were recorded using an Optotrak system (Certus, 6 markers, 100 Hz). Upper limb motor performance (precision, velocity, trajectory straightness) and movement pattern (elbow, shoulder ranges and trunk displacement) outcomes were analyzed using repeated measures ANOVAs.</p> <p>Results</p> <p>For all groups, there were no differences in endpoint trajectory straightness, shoulder flexion and shoulder horizontal adduction ranges and sagittal trunk displacement between the two media. All subjects, however, made larger errors in the vertical direction using HMD compared to SPS. Healthy subjects also made larger errors in the sagittal direction, slower movements overall and used less range of elbow extension for the lower central target using HMD compared to SPS. The mild and moderate-to-severe sub-groups made larger RMS errors with HMD. The only advantage of using the HMD was that movements were 22% faster in the moderate-to-severe stroke sub-group compared to the SPS.</p> <p>Conclusions</p> <p>Despite the similarity in majority of the movement kinematics, differences in movement speed and larger errors were observed for movements using the HMD. Use of the SPS may be a more comfortable and effective option to view VEs for upper limb rehabilitation post-stroke. This has implications for the use of VR applications to enhance upper limb recovery.</p