Local Ambiguities Research Articles

Prediction of successful reanalysis based on eye-blink rate and reading times in sentences with local ambiguity

AbstractThe present study focuses on individual differences in the ability to recover from an initial misinterpretation during the processing of garden path (GP) sentences with local syntactic ambiguity. The performance of reanalysis in GP sentences is a cognitive task that requires efficient use of executive functions and allocation of working memory resources. In this study, we explored the possible role of the neurotransmitter dopamine, which has long been implicated in cognitive control processes, in the successful performance of reanalysis. We examined whether participants’ ability to successfully reanalyze a sentence with local ambiguity can be predicted based on (1) their tonic dopamine levels, as reflected by their resting state spontaneous eye-blink rate, measured prior to the experiment; and (2) their reading time patterns in the critical region of the sentence. We ran a self-paced reading experiment in Hebrew, assessing reanalysis performance via a paraphrasing task. We observed a linear and polynomial effect of eye-blink rate on reanalysis performance, with medium rates, corresponding to medium dopamine levels, associated with best performance. We also observed an effect of reading times, with longer reading times in the critical region predicting better reanalysis performance.

Providing a simple and efficient algorithm for passive sound localization

The sound source localization (SSL) system is one of the main approaches for detecting targets. This kind of localization method is carried out in a passive way, without the emission of any signals, and cannot be detected easily. This paper investigates the two-dimensional SSL of stationary targets using only three receivers. Given that, flow-induced noise of the bluff-bodies in viscous fluids is one of the most important noise generation sources, firstly, this noise is simulated numerically. Due to the distance between the receivers in an array, the sound reaches the receiver with time delay. High accuracy SSL needs highly accurate time delays measurement. In the following, time delays are obtained by calculating the cross-correlation in a time domain of sound signals arriving at the receiver array, and next, the bearing angle of the sound source is estimated. Later, using a new, simple (easy installation), fast (real-time), and accurate pure geometric algorithm, the source candidate locations are obtained. Then, using an elimination algorithm, the ambiguity of the noise source location is removed, and source imaginary locations are removed. Finally, this process is validated by a physical algorithm. Numerical results verify the effectiveness of this geometric algorithm.

Smartphone User Tracking by Incorporating User Orientation Using a Double-Layer HMM

We consider the problem of localizing a smartphone user using received signal strength (RSS) measured by a set of known network nodes in a harsh indoor environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is non-trivial in the presence of multipath propagation, shadowing and radio interference. Auxiliary information, such as the indoor building map and user's orientation information, potentially can help to improve localization performance. As the indoor layout is usually known as a priori, a user's moving direction or orientation in a given indoor map may contain valuable information to assist for reducing location ambiguities at estimation, typically when the radio signal channel is corrupted with noise. In this paper, we propose a double-layer hidden Markov model (DHMM) within a Bayesian learning framework for combining user orientation information and processing RSS data in the localization process to deal with RSS fluctuations induced by human body shadowing and multipath interference. Simulation and experimental results show that incorporating user orientation can potentially provide promising indoor positioning results.

Grammatical Disambiguation: The Natural Language Linear Complexity Hypothesis

By combining concatenations of constant complexity with a strictly time-linear derivation order, the computational complexity degree of DBS (AIJ’01) is linear time (TCS’92). The only way to increase DBS complexity above linear would be a recursive ambiguity in the hear mode. In natural language, however, recursive ambiguity is prevented by grammatical disambiguation. An example of grammatically disambiguating a nonrecursive ambiguity is the ‘garden path’ sentence The horse raced by the barn fell (Bever 1970). The continuation horse+raced introduces a local ambiguity between horse raced (active) and horse which was raced (passive), leading to two parallel derivation strands up to and including barn. Depending on continuing after barn with an interpunctuation or a verb, one of the [-global] readings (FoCL 11.3) is grammatically eliminated. An example of grammatically disambiguating a recursive ambiguity is The man who loves the woman who loves Tom who Lucy loves, with the subordinating conjunction who. Depending on whether the continuation after who is a verb or a noun, one of the two [-global] readings is grammatically eliminated (momentary choice between who being subject or object).

An ecological approach to binocular vision.

An ecological approach to binocular vision was already demonstrated in Wheatstone's initial stereograms and was explicitly called for by J. J. Gibson, but detailed analysis and experimentation supporting this approach has been more recent. This paper discusses several aspects of this more recent research on environmentally occurring spatial layouts that can influence binocular vision. These include gradients of depth and regions that can be seen by only one eye. The resolution of local stereoscopic ambiguity by more global factors is also discussed.

Overcoming snapshot-deficient measurements with knowledge-aided approaches.

The use of knowledge-aided covariance is considered for processing underwater acoustic array data in snapshot-deficient scenarios. The knowledge-aided formalism is a technique that combines array data with a known covariance to produce an invertible estimate. For underwater acoustics, simulations of ambient noise provide the a priori covariance allowing degraded signals to be processed adaptively in situations where the sample covariance matrix is rank-deficient. The method is demonstrated for matched field processing using the 21 element array event S5 from the SWellEx-96 experiment. With five snapshots, the knowledge-aided approach significantly reduces localization ambiguity compared to the adaptive white noise gain constraint processor.

Resolution of matched field processing for a single hydrophone in a rigid waveguide

This paper studies resolution of matched field processing for locating, in range and depth, a broadband underwater acoustic source from data measured at a single hydrophone receiver. For the case of an ideal rigid shallow-water waveguide with a pressure-release top boundary and a rigid bottom boundary, we derive approximations for the main-lobe widths of the ambiguity surface. The two cases studied in this paper are (1) when coherent measurements of the pressure are available, with the transmitted source waveform precisely known, and (2) when only measurements of the received signal power spectral density (PSD) are available, such as occur when the transmitted signal is random and unknown. The analysis uses the normal-mode expansion for the pressure field to derive approximate expressions for the ambiguity surface main-lobe widths, as a function of the number of modes and frequency band, for both range and depth. Numerical results are presented corroborating the analytical analysis. Finally, we argue that this ambiguity analysis also gives insights into real ocean waveguide localization characteristics under appropriate conditions, and show numerical simulations of matched field localization ambiguity surfaces for some realistic shallow-water Pekeris environments.

Localizing Submerged Acoustic Sources Under Adverse Conditions

This paper reviews various approaches for localizing submerged acoustic sources under adverse conditions. It is essential to obtain data of the highest possible quality when there are adverse conditions, such as uncertainties in the environment, source motion, and low signal-to-noise ratio. Focalization is an approach in which the source location and environmental parameters are treated as unknowns. Due to a parameter hierarchy in which source location outranks environmental parameters, there may be many realizations of the environment that bring the source into focus; the ambiguity in the environment can be an advantage if the primary objective is to localize the source. Environmental uncertainty is often associated with environmental complexity, which can be an advantage by reducing the ambiguity of the source location. Obtaining a high-quality estimate of the covariance matrix may be difficult when there is source motion, but the complexity of the received field from a moving source is another factor that can reduce ambiguity. It may be possible to localize a source that is buried in noise when an estimate of the noise covariance matrix is available. During the development of approaches for localizing submerged sources, much of the focus has been on one-dimensional vertical arrays. An extension of the multi-valued Bartlett processor to the case of a rectangular array was designed to take advantage of the extra dimension of the array and appears to have the potential to be the most powerful combination of hardware and signal processing to date.

Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization

The unsupervised detection and localization of anomalies in natural images is an intriguing and challenging problem. Anomalies manifest themselves in very different ways and an ideal benchmark dataset for this task should contain representative examples for all of them. We find that existing datasets are biased towards local structural anomalies such as scratches, dents, or contaminations. In particular, they lack anomalies in the form of violations of logical constraints, e.g., permissible objects occurring in invalid locations. We contribute a new dataset based on industrial inspection scenarios that evenly covers both types of anomalies. We provide pixel-precise ground truth data for each anomalous region and define a generalized evaluation metric that addresses localization ambiguities that can arise for logical anomalies. Furthermore, we propose a novel algorithm that improves over the state of the art in the joint detection of structural and logical anomalies. It consists of a local and a global network branch. The first one inspects confined regions independent of their spatial locations in the input image and is primarily responsible for the detection of entirely new local structures. The second one learns a globally consistent representation of the training data through a bottleneck that enables the detection of violations of long-range dependencies, a key characteristic of many logical anomalies. We perform extensive evaluations on our new dataset to corroborate our claims.

Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas

Coherent processing of multiple-input multiple-output (MIMO) radar with widely separated antennas has high resolution capability, but it also brings ambiguity in target localization. In view of the ambiguity problem, different from other signal processing sub-directions such as array configuration optimization or continuity of phase in space/time, this paper analyzes it from the information level, that is, the tracking method is adopted. First, by using the state equation and measurement equation, the echo data of multiple coherent processing intervals (CPI) are collected to improve the target localization accuracy as much as possible. Second, the non-coherent joint probability data association filter (JPDAF) is used to achieve stable tracking of spatial cross targets without ambiguity measurements. Third, based on the tracking results of the non-coherent JPDAF, the ambiguity of coherent measurement is resolved, that is, the coherent JPDAF is realized. By means of non-coherent and coherent alternating JPDAF (NCCAF) algorithms, high accuracy localization of multiple targets is achieved. Finally, numerical simulations are carried out to validate the effectiveness of the proposed NCCAF algorithm.

Unimodular Sequence and Receiving Filter Design for Local Ambiguity Function Shaping

The ambiguity function (AF), which is used to evaluate the range and Doppler resolutions, plays an important role in radar systems. In this article, we consider the problems of jointly designing unimodular sequence and receiving filters, and also unimodular complementary sequences and corresponding receiving filters with desired AF shapes. The design problems are formulated as the minimization of the weighted integrated sidelobe level (WISL) and the minimization of the complementary integrated sidelobe level (CISL), respectively, under the constraint of the signal-to-noise ratio (SNR) loss. Algorithms based on the alternately iterative minimization method and the majorization-minimization method are developed to tackle the optimization problems. Numerical results are demonstrated to show the superior performance of the proposed algorithms in terms of the achieved objective and running time in comparison with the state-of-the-art algorithms. Meanwhile, the excellent capability of the designed sequences to detect multiple moving targets under the strong clutter is evaluated via simulations. Moreover, the designed sequences are also implemented on an indoor hardware system, and their performance is verified.

Building Outline Delineation From VHR Remote Sensing Images Using the Convolutional Recurrent Neural Network Embedded With Line Segment Information

Recently, several recurrent neural network (RNN)-based models have been proposed to delineate the outlines of buildings from very high resolution (VHR) remote sensing images. These models first use convolutional neural networks (CNNs) to recognize the boundary fragments by learning probability maps of both edges and corners and then feed them into RNN to find and link a set of sequent corners into external boundaries of buildings. However, caused by the category imbalance of edges and corners, the local ambiguity of edge detection is very serious, which significantly affects the accuracy of predicted outline corners. To tackle this challenge, this article introduces a convolutional RNN embedded with line segment information (LSI-RNN), a novel network that aims to directly detect line segment instead of edges. To achieve this, LSI-RNN utilizes an additional cotraining branch to generate an attraction field map (AFM) by neural discriminative dimensionality reduction (NDDR) layer. Consequently, the conventional classification problem of edges is converted to a regression problem of line segments, thus solving the aforementioned issues. Experimental results over three remote sensing datasets with different spatial resolutions show that the proposed method consistently outperforms other state-of-the-art methods.

A Time Difference of Arrival Based Target Motion Analysis for Localization of Underwater Vehicles

We present a non-convex, partially observable solution for underwater vehicle localization, referred to as the <i>time difference of arrival target motion analysis</i> (TD-TMA). TD-TMA is especially suitable for the practical case in which localization ambiguities arise due to a lack of receivers, the effective sound velocity is unknown, and cooperation between the nodes is not available. The method relies on periodic emissions from a target vehicle, which are detected by an observer to yield a series of time difference of arrival (TDoA) measurements. The measurements are arranged in the framework of target motion analysis to obtain a set of localization solutions for the target. Maneuvering of either the target or the observer removes ambiguities and yields a single solution. We formalize TD-TMA as a least squares optimization problem, and solve it in polynomial time. Numerical results show that TD-TMA provides accurate localization performance in terms of both the target&#x0027;s kinematics and positioning, which are close to the posterior Cram&#x00E9;r-Rao lower bound. Two sea experiments successfully demonstrate the effectiveness of the solution in real sea environments.

Testing of Drought Exceedance Probability Index (DEPI) for Turkey using PERSIANN data for 2000-2021 period

Drought is a climatic event that threatens the environment and human life with an ambiguity of location and time. Recently, droughts can be analyzed for different periods with the help of different mathematical methods and developing technology. This study aims to perform a drought analysis in 126 designated study points of Turkey. The analyzed data includes monthly total precipitation values between March 2000 and February 2021, obtained from PERSIANN system (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). Monthly precipitation totals of these designated points were used as input parameters in the Drought Exceedance Probability Index (DEPI) which is a new drought analysis method. The analysis was conducted separately for the whole of Turkey from January to December. Moreover, the findings were compared with the Standardized Precipitation Index (SPI), a globally accepted and commonly used drought index, to measure the drought detection performance of DEPI. SPI was calculated for periods of 6, 12 and 24 months. Pearson correlation coefficients between drought values of SPI-6, SPI-12 and SPI-24 and DEPI results were calculated. The second part of the study includes possible trend of drought determined by the Mann-Kendall trend analysis method. Both DEPI and SPI results and trend analysis results were mapped and visualized with the help of ArcGIS package program. The highest correlation is between DEPI and SPI-12 with 0.75, while the lowest correlation is between DEPI and SPI-24 with a value of 0.62. SPI monthly drought maps indicated the wettest months were January and February, while the driest months were March and July. Besides the DEPI monthly drought maps, the wettest months were October and November, while the driest months were May and June. The Mann-Kendall trend maps showed a significant increase in drought for summer.

Ambiguity Avoidance by Means of Function Words in English? Providing Additional Corpus-based Counterevidence

AbstractThe present paper considers three types of constructions where optional function words have been claimed to be used primarily for the purpose of avoiding a global or local attachment ambiguity. a) In the absence of the complementiser inthat-clauses, certain subject NPs might be (temporarily) misconstrued as direct objects of the superordinate verb. b) In the absence of the complementiserthat, certain adverbials might be (wrongly) assigned to the subordinate or the superordinate clause. c) In the absence of a relativiser, certain combinations of the antecedent NP and the relative clause subject might be (temporarily) misconstrued as forming a single NP. The paper uses two corpus-based testing procedures to refute these claims. (i) Analysing otherwise comparable ambiguity-free and ambiguity-prone structures in a)–c) we find that they involve similar rates of function word use. (ii) Moreover, it is shown that a variety of other ambiguity-free constructions, containing the same or other optional grammatical markers, display similar distributional profiles.

Sonic Environments as Systems of Places: A Critical Reading of Husserl’s Thing and Space

Abstract This article offers a thorough and critical reading of Husserl’s Thing and Space. This reading is principally motivated by the effort to methodologically design a phenomenological–topological approach to the research of lived sonic environments. In this book, Husserl lays foundations of phenomenological topology by understanding perceptions as places and defining, consequently, the space as a system of places. The critical reading starts with pointing out the ambiguity of location in Thing and Space, which consists mainly in the insufficient implementation of the distinction between the location and the localization. Further investigations then reveal the roots of this ambiguity in both the preference of visual perception and the omission of subjective aspects of kinesthesia. The article critically examines Husserl’s notion of the appended localization that expresses the marginalization of (among others) acoustic experience. In conclusion, the article utilizes the critical findings to formulate the project of a place-based (phenomenological–topological), medium-centered, and multi-sensory approach to sonic environments.

A dual-typed and omnidirectional infrared perceptual network for indoor human tracking

This paper proposes a dual-typed and omnidirectional infrared perceptual network for indoor human target location and tracking. Two types of infrared sensors, pyroelectric infrared sensors and thermopile array sensors, are used in the sensor network for side-view and top-view perception respectively. The sensor nodes are deployed to construct an omnidirectional sensing model for detecting human targets in an indoor scenario with irregular boundary, furniture and other obstacles. The improved credit location algorithm and the adaptive threshold algorithm are applied for the human location. The location ambiguity existing in the actual situations is analysed and the compensation methods are designed by using an equivalent measurement line and a state machine to reduce the location ambiguity. Finally, a fuzzy adaptive CS-jerk (current statistical-jerk) algorithm is applied for tracking the human target. The tracking results indicate that the dual-typed and omnidirectional system works well in the indoor environment.

Four-dimensional tracer flow reconstruction in fractured rock through borehole ground-penetrating radar (GPR) monitoring

Abstract. Two borehole ground-penetrating radar (GPR) surveys were conducted during saline tracer injection experiments in fully saturated crystalline rock at the Grimsel Test Site in Switzerland. The saline tracer is characterized by an increased electrical conductivity in comparison to formation water. It was injected under steady-state flow conditions into the rock mass that features sub-millimeter fracture apertures. The GPR surveys were designed as time-lapse reflection GPR from separate boreholes and a time-lapse transmission survey between the two boreholes. The local increase in conductivity, introduced by the injected tracer, was captured by GPR in terms of reflectivity increase for the reflection surveys, and attenuation increase for the transmission survey. Data processing and difference imaging was used to extract the tracer signal in the reflection surveys, despite the presence of multiple static reflectors that could shadow the tracer reflection. The transmission survey was analyzed by a difference attenuation inversion scheme, targeting conductivity changes in the tomography plane. By combining the time-lapse difference reflection images, it was possible to reconstruct and visualize the tracer propagation in 3D. This was achieved by calculating the potential radially symmetric tracer reflection locations in each survey and determining their intersections, to delineate the possible tracer locations. Localization ambiguity imposed by the lack of a third borehole for a full triangulation was reduced by including the attenuation tomography results in the analysis. The resulting tracer flow reconstruction was found to be in good agreement with data from conductivity sensors in multiple observation locations in the experiment volume and gave a realistic visualization of the hydrological processes during the tracer experiments. Our methodology was demonstrated to be applicable for monitoring tracer flow and transport and characterizing flow paths related to geothermal reservoirs in crystalline rocks, but it can be transferred in a straightforward manner to other applications, such as radioactive repository monitoring or civil engineering projects.

Unmixing Convolutional Features for Crisp Edge Detection.

This article presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge detectors is mainly caused by the mixing phenomenon of convolutional neural networks: Feature mixing in edge classification and side mixing during fusing side predictions. The CATS consists of two modules: A novel tracing loss that performs feature unmixing by tracing boundaries for better side edge learning, and a context-aware fusion block that tackles the side mixing by aggregating the complementary merits of learned side edges. Experiments demonstrate that the proposed CATS can be integrated into modern deep edge detectors to improve localization accuracy. With the vanilla VGG16 backbone, in terms of BSDS500 dataset, our CATS improves the F-measure (ODS) of the RCF and BDCN deep edge detectors by 12 and 6 percent, respectively when evaluating without using the morphological non-maximal suppression scheme for edge detection.

The Effect of Intergovernmental Policy Conflict on Immigrants’ Behavior: Insights from a Survey Experiment in California

AbstractAs Congress remains gridlocked on the issue of comprehensive immigration reform, immigration policy debates, particularly with respect to interior immigration enforcement, are increasingly taking place at state and local levels. Scholarship on immigration federalism has focused on federal and local governments, while states are passing laws that tighten or delimit cooperation with Immigration and Customs Enforcement (i.e., “sanctuary policies”). Simultaneously, cities are passing laws contradictory to state policy. We examine how these state and local enforcement ambiguities affect undocumented immigrants’ trust in the efficacy of sanctuary policies. Using California as a case, we embedded an experiment in a survey of undocumented immigrants and find trust in sanctuary policies decreases when cities seek to opt out of statewide sanctuary laws. Further, “opting out” has negative implications for the daily behavior of undocumented immigrants, like the chilling effects resulting from local law enforcement cooperation with federal immigration enforcement.