Provocative feature deletion: a syntactic model of agreement alternations in noun incorporation contexts

The relationship between noun incorporation (NI) and the agreement alternations that occur in such contexts (NI Transitivity Alternations) remains inadequately understood. Three interpretations of these alternations (Baker, Aranovich & Golluscio 2005; Mithun 1984; Rosen 1989) are shown to be undermined by foundational or mechanical issues. I propose a syntactic model, adopting Branigan's (2011) interpretation of NI as the result of “provocative” feature valuation, which triggers generation of a copy of the object that subsequently merges inside the verb. Provocation triggers a reflexive Refine operation that deletes duplicate features from chains, making them interpretable for Transfer. NI Transitivity Alternations result from variant deletion preferences exhibited during Refine. I argue that the NI contexts discussed (Generic NI, Partial NI and Double Object NI) result from different restrictions on phonetic and semantic identity in chain formation. This provides us with a consistent definition of NI Transitivity Alternations across contexts, as well as a new typology that distinguishes NI contexts, rather than incorporating languages.

Bayesian adaptive estimation of arbitrary points on a psychometric function

Bayesian adaptive methods have been extensively used in psychophysics to estimate the point at which performance on a task attains arbitrary percentage levels, although the statistical properties of these estimators have never been assessed. We used simulation techniques to determine the small-sample properties of Bayesian estimators of arbitrary performance points, specifically addressing the issues of bias and precision as a function of the target percentage level. The study covered three major types of psychophysical task (yes-no detection, 2AFC discrimination and 2AFC detection) and explored the entire range of target performance levels allowed for by each task. Other factors included in the study were the form and parameters of the actual psychometric function Psi, the form and parameters of the model function M assumed in the Bayesian method, and the location of Psi within the parameter space. Our results indicate that Bayesian adaptive methods render unbiased estimators of any arbitrary point on psi only when M=Psi, and otherwise they yield bias whose magnitude can be considerable as the target level moves away from the midpoint of the range of Psi. The standard error of the estimator also increases as the target level approaches extreme values whether or not M=Psi. Contrary to widespread belief, neither the performance level at which bias is null nor that at which standard error is minimal can be predicted by the sweat factor. A closed-form expression nevertheless gives a reasonable fit to data describing the dependence of standard error on number of trials and target level, which allows determination of the number of trials that must be administered to obtain estimates with prescribed precision.

The role of parametric assumptions in adaptive Bayesian estimation.

Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter values larger than their counterparts in the psychometric function, (c) stimulus placement at the prior mean, and (d) estimates defined as the posterior mean. Unbiasedness arises in only 10 trials, and 20 trials ensure constant standard errors. The standard error of the estimates equals 0.617 times the inverse of the square root of the number of trials. Other variants yielded bias and larger standard errors.

An X-ray study of the SNR G344.7-0.1 and the central object CXOU J170357.8-414302

Aims. We report results of an X-ray study of the supernova remnant (SNR) G344.7-0.1 and the point-like X-ray source located at the geometrical center of the SNR radio structure. Methods. The morphology and spectral properties of the remnant and the central X-ray point-like source were studied using data from the XMM-Newton and Chandra satellites. Archival radio data and infrared Spitzer observations at 8 and 24 mu m were used to compare and study its multi-band properties at different wavelengths. Results. The XMM-Newton and Chandra observations reveal that the overall X-ray emission of G344.7-0.1 is extended and correlates very well with regions of bright radio and infrared emission. The X-ray spectrum is dominated by prominent atomic emission lines. These characteristics suggest that the X-ray emission originated in a thin thermal plasma, whose radiation is represented well by a plane-parallel shock plasma model (PSHOCK). Our study favors the scenario in which G344.7-0.1 is a 6 x 10^3 year old SNR expanding in a medium with a high density gradient and is most likely encountering a molecular cloud on the western side. In addition, we report the discovery of a soft point-like X-ray source located at the geometrical center of the radio SNR structure. The object presents some characteristics of the so-called compact central objects (CCO). However, its neutral hydrogen absorption column (N_H) is inconsistent with that of the SNR. Coincident with the position of the source, we found infrared and optical objects with typical early-K star characteristics. The X-ray source may be a foreground star or the CCO associated with the SNR. If this latter possibility were confirmed, the point-like source would be the farthest CCO detected so far and the eighth member of the new population of isolated and weakly magnetized neutron stars.

Light self-focusing in the atmosphere:thin window model

Ultra-high power (exceeding the self-focusing threshold by more than three orders of magnitude) light beams from ground-based laser systems may find applications in space-debris cleaning. The propagation of such powerful laser beams through the atmosphere reveals many novel interesting features compared to traditional light self-focusing. It is demonstrated here that for the relevant laser parameters, when the thickness of the atmosphere is much shorter than the focusing length (that is, of the orbit scale), the beam transit through the atmosphere in lowest order produces phase distortion only. This means that by using adaptive optics it may be possible to eliminate the impact of self-focusing in the atmosphere on the laser beam. The area of applicability of the proposed "thin window" model is broader than the specific physical problem considered here. For instance, it might find applications in femtosecond laser material processing.

Investigation of PHY, MAC and APP layers for adaptive and cross-layer optimization in IEEE802.11 WLAN

Due to huge popularity of portable terminals based on Wireless LANs and increasing demand for multimedia services from these terminals, the earlier structures and protocols are insufficient to cover the requirements of emerging networks and communications. Most research in this field is tailored to find more efficient ways to optimize the quality of wireless LAN regarding the requirements of multimedia services. Our work is to investigate the effects of modulation modes at the physical layer, retry limits at the MAC layer and packet sizes at the application layer over the quality of media packet transmission. Interrelation among these parameters to extract a cross-layer idea will be discussed as well. We will show how these parameters from different layers jointly contribute to the performance of service delivery by the network. The results obtained could form a basis to suggest independent optimization in each layer (an adaptive approach) or optimization of a set of parameters from different layers (a cross-layer approach). Our simulation model is implemented in the NS-2 simulator. Throughput and delay (latency) of packet transmission are the quantities of our assessments. © 2010 IEEE.

Adaptive Brain-Computer Interface Systems For Communication in People with Severe Neuromuscular Disabilities

Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

An Interactive Model to Teach Motor Skills

There has been a tremendous increase in our knowledge of hum motor performance over the last few decades. Our theoretical understanding of how an individual learns to move is sophisticated and complex. It is difficult however to relate much of this information in practical terms to physical educators, coaches, and therapists concerned with the learning of motor skills (Shumway-Cook & Woolcott, 1995). Much of our knowledge stems from lab testing which often appears to bear little relation to real-life situations. This lack of ecological validity has slowed the flow of information from the theorists and researchers to the practitioners. This paper is concerned with taking some small aspects of motor learning theory, unifying them, and presenting them in a usable fashion. The intention is not to present a recipe for teaching motor skills, but to present a framework from which solutions can be found. If motor performance research has taught us anything, it is that every individual and situation presents unique challenges. By increasing our ability to conceptualize the learning situation we should be able to develop more flexible and adaptive responses to the challege of teaching motor skills. The model presented here allows a teacher, coach, or therapist to use readily available observations and known characteristics about a motor task and to conceptualize them in a manner which allows them to make appropriate teaching/learning decisions.

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

A biologically inspired appearance model for robust visual tracking

In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Towards a Virtual-Acoustic String Instrument

In acoustic instruments, the controller and the sound producing system often are one and the same object. If virtualacoustic instruments are to be designed to not only simulate the vibrational behaviour of a real-world counterpart but also to inherit much of its interface dynamics, it would make sense that the physical form of the controller is similar to that of the emulated instrument. The specific physical model configuration discussed here reconnects a (silent) string controller with a modal synthesis string resonator across the real and virtual domains by direct routing of excitation signals and model parameters. The excitation signals are estimated in their original force-like form via careful calibration of the sensor, making use of adaptive filtering techniques to design an appropriate inverse filter. In addition, the excitation position is estimated from sensors mounted under the legs of the bridges on either end of the prototype string controller. The proposed methodology is explained and exemplified with preliminary results obtained with a number of off-line experiments.

Core Reactor Calculation Using the Adaptive Remeshing with a Current Error Estimator

With the objective to improve the reactor physics calculation on a 2D and 3D nuclear reactor via the Diffusion Equation, an adaptive automatic finite element remeshing method, based on the elementary area (2D) or volume (3D) constraints, has been developed. The adaptive remeshing technique, guided by a posteriori error estimator, makes use of two external mesh generator programs: Triangle and TetGen. The use of these free external finite element mesh generators and an adaptive remeshing technique based on the current field continuity show that they are powerful tools to improve the neutron flux distribution calculation and by consequence the power solution of the reactor core even though they have a minor influence on the critical coefficient of the calculated reactor core examples. Two numerical examples are presented: the 2D IAEA reactor core numerical benchmark and the 3D model of the Argonauta research reactor, built in Brasil.

Data-Adaptive Modeling using Convex Regression

Thesis (Ph.D.)--University of Washington, 2016-08

Relocating the Frame: Object-Oriented (1.) and Locative Art (2.) Environments

The locative project is in a condition of emergence, an embryonic state in which everything is still up for grabs, a zone of consistency yet to emerge. As an emergent practice locative art, like locative media generally, it is simultaneously opening up new ways of engaging in the world and mapping its own domain. (Drew Hemment, 2004) Artists and scientists have always used whatever emerging technologies existed at their particular time throughout history to push the boundaries of their fields of practice. The use of new technologies or the notion of ‘new’ media is neither particularly new nor novel. Humans are adaptive, evolving and will continue to invent and explore technological innovation. This paper asks the following questions: what role does adaptive and/or intelligent art play in the future of public spaces, and how does this intervention alter the relationship between theory and practice? Does locative or installation-based art reach more people, and does ‘intelligent’ or ‘smart’ art have a larger role to play in the beginning of this century? The speakers will discuss their current collaborative prototype and within the presentation demonstrate how software art has the potential to activate public spaces, and therefore contribute to a change in spatial or locative awareness. It is argued that the role and perhaps even the representation of the audience/viewer is left altered through this intervention. 1. A form of electronic imagery created by a collection of mathematically defined lines and/or curves. 2. An experiential form of art which engages the viewer both from within a specific location and in response to their intentional or unintentional input.