Learning Non-Adjacent Dependencies in Continuous Presentation of an Artificial Language

Many grammatical dependencies in natural language involve elements that are not adjacent, such as between the subject and verb in the child always runs. To date, most experiments showing evidence of learning non- adjacent dependencies have used artificial languages in which the to-be-learned dependencies are presented in isolation by presenting the minimal sequences that contain the dependent elements. However, dependencies in natural language are not typically isolated in this way. In this study we exposed learners to non-adjacent dependencies in long sequences of words. We accelerated the speed of presentation and learners showed evidence for learning of non-adjacent dependencies. The previous pause-based positional mechanisms for learning of non-adjacent dependency are challenged

Grammatical Bracketing Determines Learning of Non-adjacent Dependencies

Grammatical dependencies often involve elements that are not adjacent. However, most experiments in which non-adjacent dependencies are learned bracketed the dependent material with pauses, which is not how dependencies appear in natural language. Here we report successful learning of embedded NAD without pause bracketing. Instead, we induce learners to compute structure in an artificial language by entraining them through processing English sentences. We also found that learning becomes difficult when grammatical entrainment causes learners to compute boundaries that are misaligned with NAD structures. In sum, we demonstrated that grammatical entrainment can induce boundaries that can carry over to reveal structures in novel language materials, and this effect can be used to induce learning of non-adjacent dependencies

Photovoltaic Power System and Power Distribution Demonstration for the Desert RATS Program

A stand alone, mobile photovoltaic power system along with a cable deployment system was designed and constructed to take part in the Desert Research And Technology Studies (RATS) lunar surface human interaction evaluation program at Cinder Lake, Arizona. The power system consisted of a photovoltaic array/battery system. It is capable of providing 1 kW of electrical power. The system outputs were 48 V DC, 110 V AC, and 220 V AC. A cable reel with 200 m of power cable was used to provide power from the trailer to a remote location. The cable reel was installed on a small trailer. The reel was powered to provide low to no tension deployment of the cable. The cable was connected to the 220 V AC output of the power system trailer. The power was then converted back to 110 V AC on the cable deployment trailer for use at the remote site. The Scout lunar rover demonstration vehicle was used to tow the cable trailer and deploy the power cable. This deployment was performed under a number of operational scenarios, manned operation, remote operation and tele-robotically. Once deployed, the cable was used to provide power, from the power system trailer, to run various operational tasks at the remote location

Simulation of a Lunar Surface Base Power Distribution Network for the Constellation Lunar Surface Systems

The Lunar Surface Power Distribution Network Study team worked to define, breadboard, build and test an electrical power distribution system consistent with NASA's goal of providing electrical power to sustain life and power equipment used to explore the lunar surface. A testbed was set up to simulate the connection of different power sources and loads together to form a mini-grid and gain an understanding of how the power systems would interact. Within the power distribution scheme, each power source contributes to the grid in an independent manner without communication among the power sources and without a master-slave scenario. The grid consisted of four separate power sources and the accompanying power conditioning equipment. Overall system design and testing was performed. The tests were performed to observe the output and interaction of the different power sources as some sources are added and others are removed from the grid connection. The loads on the system were also varied from no load to maximum load to observe the power source interactions

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Dynamic Action Facilitates Learning of Non-Adjacent Dependencies in Visual Sequences

Many events that humans and other organisms experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co-occurrences between temporarily adjacent stimuli, others involve tracking the co-occurrences between temporarily distant stimuli (i.e., non-adjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here we report on four experiments that examined NAD learning from various types of visual stimuli. The results suggest that continuous movements aid the acquisition of NADs. We also found that human motion leads to more robust NAD learning compared to object motions, perhaps because of a richer representation. This richer representation could result in better memory and recall, and provide a stronger signal for NAD learning

Characterizing the Difference between Learning about Adjacent and Non-adjacent Dependencies

Many studies of human sequential pattern learning demonstrate that learners detect adjacent and non-adjacent dependencies in many kinds of sequences. However, it is often assumed that the computational mechanisms behind extracting these dependencies are the same. We replicate the seminal finding that adults are capable of learning dependencies between non-adjacent words (Gómez, 2002). When we eliminate the positional information about the statistical structures by embedding the structure in phrases, learners can no longer learn the dependencies. Our methods allow us to study the learning mechanisms that are more representative of the patterns in natural languages, and show that when directly compared, adjacent and non-adjacent dependencies are not equally learnable. We suggest that learning non-adjacent dependencies in language involves a different computational mechanism from learning adjacent dependencie

Actively Detecting Patterns in an Artificial Language to Learn Non-AdjacentDependencies

Many grammatical dependencies in natural language involve elements that are not adjacent, such as between thesubject and verb in ”the dog always barks”. We recently showed that non-adjacent dependencies are easily learnable withoutpauses in the signal when speech is presented rapidly. In this study, we used an online measure to look at the relationshipbetween online parsing and the learning performance from the offline assessment of non-adjacent dependency learning. Wefound that participants who showed current parsing of the language online also learned the dependencies better. However, thispattern disappeared when they are explicitly told where the boundaries are before parsing. Theories of non-adjacent dependencylearning are discussed

Rochester Connectionist Simulator

Specifying, constructing and simulating structured connectionist networks requires significant programming effort. System tools can greatly reduce the effort required, and by providing a conceptual structure within which to work, make large and complex network simulations possible. The Rochester Connectionist Simulator is a system tool designed to aid specification, construction and simulation of connectionist networks. This report describes this tool in detail: the facilities provided and how to use them, as well as details of the implementation. Through this we hope not only to make designing and verifying connectionist networks easier, but also to encourage the development and refinement of connectionist research tools themselves