Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201

How bacterial cells and colonies move on solid substrates

Many bacteria rely on active cell appendages, such as type IV pili, to move over substrates and interact with neighboring cells. Here, we study the motion of individual cells and bacterial colonies, mediated by the collective interactions of multiple pili. It was shown experimentally that the substrate motility of Neisseria gonorrhoeae cells can be described as a persistent random walk with a persistence length that exceeds the mean pili length. Moreover, the persistence length increases for a higher number of pili per cell. With the help of a simple, tractable stochastic model, we test whether a tug-of-war without directional memory can explain the persistent motion of single Neisseria gonorrhoeae cells. While the persistent motion of single cells indeed emerges naturally in the model, a tug-of-war alone is not capable of explaining the motility of microcolonies, which becomes weaker with increasing colony size. We suggest sliding friction between the microcolonies and the substrate as the missing ingredient. While such friction almost does not affect the general mechanism of single cell motility, it has a strong effect on colony motility. We validate the theoretical predictions by using a three-dimensional computational model that includes explicit details of the pili dynamics, force generation and geometry of cells.Comment: 25 pages, 17 figure

Modeling Virus Transport and Removal during Storage and Recovery in Heterogeneous Aquifers

A quantitative understanding of virus removal during aquifer storage and recovery (ASR) in physically and geochemically heterogeneous aquifers is needed to accurately assess human health risks from viral infections. A two-dimensional axisymmetric numerical model incorporating processes of virus attachment, detachment, and inactivation in aqueous and solid phases was developed to systematically evaluate the virus removal performance of ASR schemes. Physical heterogeneity was considered as either layered or randomly distributed hydraulic conductivities (with selected variance and horizontal correlation length). Geochemical heterogeneity in the aquifer was accounted for using Colloid Filtration Theory to predict the spatial distribution of attachment rate coefficient. Simulation results demonstrate that the combined effects of aquifer physical heterogeneity and spatial variability of attachment rate resulted in higher virus concentrations in the recovered water at the ASR well (i.e. reduced virus removal). While the sticking efficiency of viruses to aquifer sediments was found to significantly influence virus concentration in the recovered water, the solid phase inactivation under realistic field conditions combined with the duration of storage phase had a predominant influence on the overall virus removal. The relative importance of physical heterogeneity increased under physicochemical conditions that reduced virus removal (e.g. lower value of sticking efficiency or solid phase inactivation rate). This study provides valuable insight on site selection of ASR projects and an approach to optimize ASR operational parameters (e.g. storage time) for virus removal and to minimize costs associated with post-recovery treatment

HIJING 1.0: A Monte Carlo Program for Parton and Particle Production in High Energy Hadronic and Nuclear Collisions

Based on QCD-inspired models for multiple jets production, we developed a Monte Carlo program to study jet and the associated particle production in high energy $pp$, $pA$ and $AA$ collisions. The physics behind the program which includes multiple minijet production, soft excitation, nuclear shadowing of parton distribution functions and jet interaction in dense matter is briefly discussed. A detailed description of the program and instructions on how to use it are given.Comment: 38 pages in LaTex, published in Comp. Phys. Comm. 83, 307 (1994)

Dendritic Actin Filament Nucleation Causes Traveling Waves and Patches

The polymerization of actin via branching at a cell membrane containing nucleation-promoting factors is simulated using a stochastic-growth methodology. The polymerized-actin distribution displays three types of behavior: a) traveling waves, b) moving patches, and c) random fluctuations. Increasing actin concentration causes a transition from patches to waves. The waves and patches move by a treadmilling mechanism which does not require myosin II. The effects of downregulation of key proteins on actin wave behavior are evaluated.Comment: 10 pages, 4 figure

PADS: Practical Attestation for Highly Dynamic Swarm Topologies

Remote attestation protocols are widely used to detect device configuration (e.g., software and/or data) compromise in Internet of Things (IoT) scenarios. Unfortunately, the performances of such protocols are unsatisfactory when dealing with thousands of smart devices. Recently, researchers are focusing on addressing this limitation. The approach is to run attestation in a collective way, with the goal of reducing computation and communication. Despite these advances, current solutions for attestation are still unsatisfactory because of their complex management and strict assumptions concerning the topology (e.g., being time invariant or maintaining a fixed topology). In this paper, we propose PADS, a secure, efficient, and practical protocol for attesting potentially large networks of smart devices with unstructured or dynamic topologies. PADS builds upon the recent concept of non-interactive attestation, by reducing the collective attestation problem into a minimum consensus one. We compare PADS with a state-of-the art collective attestation protocol and validate it by using realistic simulations that show practicality and efficiency. The results confirm the suitability of PADS for low-end devices, and highly unstructured networks.Comment: Submitted to ESORICS 201

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common method for training recurrent neural networks, back-propagation through time (BPTT), requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. This becomes computationally expensive or even infeasible when used with long sequences. Importantly, biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states (consider days, months, or years.) However, humans are often reminded of past memories or mental states which are associated with the current mental state. We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state. Based on this principle, we study a novel algorithm which only back-propagates through a few of these temporal skip connections, realized by a learned attention mechanism that associates current states with relevant past states. We demonstrate in experiments that our method matches or outperforms regular BPTT and truncated BPTT in tasks involving particularly long-term dependencies, but without requiring the biologically implausible backward replay through the whole history of states. Additionally, we demonstrate that the proposed method transfers to longer sequences significantly better than LSTMs trained with BPTT and LSTMs trained with full self-attention.Comment: To appear as a Spotlight presentation at NIPS 201