The last decades have witnessed the resurgence of research on linguistic relativity, which provides empirical evidence of possible language effects on thought across various perceptual domains. This study investigated the linguistic relativity hypothesis in the abstract domain of time by looking at how L1 Chinese - L2 English bilinguals conceptualize time in two-dimensional space. English primarily relies on horizontal spatial items to talk about time (e.g., back to youth); in addition to horizontal spatial metaphors (e.g., ‘front year’), Chinese speakers also commonly use vertical metaphors to describe time (e.g., ‘up week’). If language has an effect on thought, then spatial-temporal metaphors should shape people’s temporal cognition. In this study, we examined whether spatial-temporal metaphors impact online processing of time and long-term habitual thinking about time. Experiment 1 showed that bilinguals could automatically access the timeline which corresponded to the immediate linguistic context. In Experiment 2, a majority of bilinguals demonstrated salient vertical bias for temporal reasoning, whereas a small number of participants relied on the horizontal axis to represent time. The dominant thinking patterns for time documented here (65% prefer a vertical representation of time; 35% horizontal) run counter to the fact that horizontal metaphors are twice as common in Chinese as vertical metaphors. Further, it was found that bilinguals who used English more frequently were more likely to have a less vertical bias, which suggested a role of L2 experience in conceptual representations. Taken together, the evidence in this study showed that spatial-temporal metaphors have both short-term and long-term effects on mental representations of time, but also that space-time mappings do not depend solely on linguistic factors.

This work deals with the and preemptive road users behaviour safety analysis problem. Safety analysis is based on a processing of road users trajectories obtained from processed aerial videos captured by drons. A system for traffic conflicts detection from spatial-temporal data is presented in this work. The standard approach for pro-active traffic conflict indicators evaluation was extended by simulating traffic objects movement in the scene using Ackerman steering geometry in order to get more accurate results.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Protein malfunction can cause human diseases, which makes the protein a target in the process of drug discovery. In-depth knowledge of how protein functions can widely contribute to the understanding of the mechanism of these diseases. Protein functions are determined by protein structures and their dynamic properties. Protein dynamics refers to the constant physical movement of atoms in a protein, which may result in the transition between different conformational states of the protein. These conformational transitions are critically important for the proteins to function. Understanding protein dynamics can help to understand and interfere with the conformational states and transitions, and thus with the function of the protein. If we can understand the mechanism of conformational transition of protein, we can design molecules to regulate this process and regulate the protein functions for new drug discovery. Protein Dynamics can be simulated by Molecular Dynamics (MD) Simulations. The MD simulation data generated are spatial-temporal and therefore very high dimensional. To analyze the data, distinguishing various atomic interactions within a protein by interpreting their 3D coordinate values plays a significant role. Since the data is humongous, the essential step is to find ways to interpret the data by generating more efficient algorithms to reduce the dimensionality and developing user-friendly visualization tools to find patterns and trends, which are not usually attainable by traditional methods of data process. The typical allosteric long-range nature of the interactions that lead to large conformational transition, pin-pointing the underlying forces and pathways responsible for the global conformational transition at atomic level is very challenging. To address the problems, Various analytical techniques are performed on the simulation data to better understand the mechanism of protein dynamics at atomic level by developing a new program called Probing Long-distance interactions by Tapping into Paired-Distances (PLITIP), which contains a set of new tools based on analysis of paired distances to remove the interference of the translation and rotation of the protein itself and therefore can capture the absolute changes within the protein. Firstly, we developed a tool called Decomposition of Paired Distances (DPD). This tool generates a distance matrix of all paired residues from our simulation data. This paired distance matrix therefore is not subjected to the interference of the translation or rotation of the protein and can capture the absolute changes within the protein. This matrix is then decomposed by DPD using Principal Component Analysis (PCA) to reduce dimensionality and to capture the largest structural variation. To showcase how DPD works, two protein systems, HIV-1 protease and 14-3-3 σ, that both have tremendous structural changes and conformational transitions as displayed by their MD simulation trajectories. The largest structural variation and conformational transition were captured by the first principal component in both cases. In addition, structural clustering and ranking of representative frames by their PC1 values revealed the long-distance nature of the conformational transition and locked the key candidate regions that might be responsible for the large conformational transitions. Secondly, to facilitate further analysis of identification of the long-distance path, a tool called Pearson Coefficient Spiral (PCP) that generates and visualizes Pearson Coefficient to measure the linear correlation between any two sets of residue pairs is developed. PCP allows users to fix one residue pair and examine the correlation of its change with other residue pairs. Thirdly, a set of visualization tools that generate paired atomic distances for the shortlisted candidate residue and captured significant interactions among them were developed. The first tool is the Residue Interaction Network Graph for Paired Atomic Distances (NG-PAD), which not only generates paired atomic distances for the shortlisted candidate residues, but also display significant interactions by a Network Graph for convenient visualization. Second, the Chord Diagram for Interaction Mapping (CD-IP) was developed to map the interactions to protein secondary structural elements and to further narrow down important interactions. Third, a Distance Plotting for Direct Comparison (DP-DC), which plots any two paired distances at user’s choice, either at residue or atomic level, to facilitate identification of similar or opposite pattern change of distances along the simulation time. All the above tools of PLITIP enabled us to identify critical residues contributing to the large conformational transitions in both HIV-1 protease and 14-3-3σ proteins. Beside the above major project, a side project of developing tools to study protein pseudo-symmetry is also reported. It has been proposed that symmetry provides protein stability, opportunities for allosteric regulation, and even functionality. This tool helps us to answer the questions of why there is a deviation from perfect symmetry in protein and how to quantify it.

Forecasting of highway traffic is a common practice for real traffic information system, and is of vital importance to traffic management and control on highways. As a typical time-series forecasting task, we want to propose a deep learning model to map the historical sensory traffic values (e.g., speed, flow) to future traffic forecasts. Prevailing traffic forecasting methods focus on the graph representation of the urban road. However, compared to the dense connectivity of urban road networks, highway traffic flows normally run on road segments of serial topology. This indicates that the highway traffic flows do not have the same type of spatial interaction, therefore motivating us to resort to a new forecasting paradigm. While traffic patterns can be intuitively represented by spatial-temporal (ST) images, this study transforms the traffic forecasting task into the conditional image generation task. Our approach explores the inherent properties of ST-images from the perspectives of physical meaning and traffic dynamics. An innovative deep learning based architecture is designed to process the ST-image, and a diffusion model is trained to obtain traffic forecasts by generating future ST-image based on the historical STimages. We demonstrate the effectiveness of the architecture in processing ST-image through ablation studies and the effectiveness of the model through comparison with popular baseline models, i.e., LSTM and T-GCN. / Prognos av vägtrafik är en vanlig praxis för riktiga trafikinformationssystem och är av vital betydelse för trafikhantering och kontroll på motorvägar. Som en typisk tidsserieförutsägelseuppgift vill vi föreslå en djupinlärningsmodell för att kartlägga historiska sensoriska trafikvärden (t.ex. hastighet, flöde) till framtida trafikprognoser. Rådande trafikprognosmetoder fokuserar på grafrepresentationen av stadsvägar. Jämfört med den täta anslutningen av stadsvägnät, löper motorvägstrafik normalt på vägsegment med seriell topologi. Detta indikerar att motorvägstrafikflöden inte har samma typ av rumslig interaktion, vilket motiverar oss att använda en ny prognosparadigm. Medan trafikmönster intuitivt kan representeras av spatial-temporala (ST) bilder, omvandlar denna studie trafikprognosuppgiften till en uppgift för betingad bildgenerering. Vår metod utforskar de inneboende egenskaperna hos ST-bilder från perspektiven fysisk betydelse och trafikdynamik. En innovativ djupinlärningsbaserad arkitektur är utformad för att behandla STbilden, och en diffusionsmodell tränas för att erhålla trafikprognoser genom att generera framtida ST-bilder baserat på historiska ST-bilder. Vi demonstrerar effektiviteten hos arkitekturen genom avbränningsstudier och modellens effektivitet genom jämförelse med populära baslinjemodeller, dvs. LSTM och T-GCN.

No description available.

This dissertation is concerned with effects dispersal and forest structure on forest insect population dynamics, and with identifying generating processes by comparing observed patterns to model predictions. In chapter 2, we investigated effects of changing forest landscape patterns on integro-difference models of host-parasitoid population dynamics. We demonstrated that removing habitat can increase herbivore density when herbivores don't disperse far, and parasitoids disperse further, due to differences in dispersal success between trophic levels. This is a novel potential explanation for why forest fragmentation increases the duration of forest tent caterpillar outbreaks. To better understand spatial model behaviour, we proposed a new local variation of the dispersal success approximation. The approximation successfully predicts effects of habitat loss and fragmentation on realistically complex landscapes, except when outbreak cycle amplitude is very large. Local dispersal success is useful in part because parameters can be estimated from widely available habitat data. In chapter 3, we investigated how well a discretized integro-difference model of mountain pine beetle population dynamics predicted the occurrence of new infestations in British Columbia. We found that a model with a large dispersal kernel, and high emigration from new, low severity infestations yielded the best predictions. However, we do not believe this to be convincing evidence that many beetles disperse from new, low severity infestations. Rather, we argued that differences in habitat quality, detection errors, and Moran effects can all confound dispersal patterns, making it difficult to infer dispersal parameters from observed infestation patterns. Nonetheless, predicting infestation risk is useful, and large kernels improve predictions. In chapter 4, we used generalized linear mixed models to characterize spatial and temporal variation in the propensity of jack pine trees to produce pollen cones, and account for confounding effects on the relationship between pollen cone production and previous defoliation by jack pine budworm. We found effects of stand age, and synchronous variation in pollen cone production among years. Accounting for background patterns in pollen cone production clarified that pollen cone production declines in with previous defoliation, as expected.

The development of modern information technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, and data from scientific domains (e.g., meteorology). To learn from these high dimensional and complex data, traditional machine learning techniques often suffer from the curse of dimensionality and unaffordable computational cost. However, learning from large-scale high-dimensional data promises big payoffs in text mining, gene analysis, and numerous other consequential tasks. Recently developed sparse learning techniques provide us a suite of tools for understanding and exploring high dimensional data from many areas in science and engineering. By exploring sparsity, we can always learn a parsimonious and compact model which is more interpretable and computationally tractable at application time. When it is known that the underlying model is indeed sparse, sparse learning methods can provide us a more consistent model and much improved prediction performance. However, the existing methods are still insufficient for modeling complex or dynamic structures of the data, such as those evidenced in pathways of genomic data, gene regulatory network, and synonyms in text data. This thesis develops structured sparse learning methods along with scalable optimization algorithms to explore and predict high dimensional data with complex structures. In particular, we address three aspects of structured sparse learning: 1. Efficient and scalable optimization methods with fast convergence guarantees for a wide spectrum of high-dimensional learning tasks, including single or multi-task structured regression, canonical correlation analysis as well as online sparse learning. 2. Learning dynamic structures of different types of undirected graphical models, e.g., conditional Gaussian or conditional forest graphical models. 3. Demonstrating the usefulness of the proposed methods in various applications, e.g., computational genomics and spatial-temporal climatological data. In addition, we also design specialized sparse learning methods for text mining applications, including ranking and latent semantic analysis. In the last part of the thesis, we also present the future direction of the high-dimensional structured sparse learning from both computational and statistical aspects.

This dissertation is concerned with effects dispersal and forest structure on forest insect population dynamics, and with identifying generating processes by comparing observed patterns to model predictions. In chapter 2, we investigated effects of changing forest landscape patterns on integro-difference models of host-parasitoid population dynamics. We demonstrated that removing habitat can increase herbivore density when herbivores don't disperse far, and parasitoids disperse further, due to differences in dispersal success between trophic levels. This is a novel potential explanation for why forest fragmentation increases the duration of forest tent caterpillar outbreaks. To better understand spatial model behaviour, we proposed a new local variation of the dispersal success approximation. The approximation successfully predicts effects of habitat loss and fragmentation on realistically complex landscapes, except when outbreak cycle amplitude is very large. Local dispersal success is useful in part because parameters can be estimated from widely available habitat data. In chapter 3, we investigated how well a discretized integro-difference model of mountain pine beetle population dynamics predicted the occurrence of new infestations in British Columbia. We found that a model with a large dispersal kernel, and high emigration from new, low severity infestations yielded the best predictions. However, we do not believe this to be convincing evidence that many beetles disperse from new, low severity infestations. Rather, we argued that differences in habitat quality, detection errors, and Moran effects can all confound dispersal patterns, making it difficult to infer dispersal parameters from observed infestation patterns. Nonetheless, predicting infestation risk is useful, and large kernels improve predictions. In chapter 4, we used generalized linear mixed models to characterize spatial and temporal variation in the propensity of jack pine trees to produce pollen cones, and account for confounding effects on the relationship between pollen cone production and previous defoliation by jack pine budworm. We found effects of stand age, and synchronous variation in pollen cone production among years. Accounting for background patterns in pollen cone production clarified that pollen cone production declines in with previous defoliation, as expected.

Image processing algorithms have developed rapidly in recent years. Imaging functions are becoming more common in electronic devices, demanding better image quality, and more robust image capture in challenging conditions. Increasingly more complicated algorithms are being developed in order to achieve better signal to noise characteristics, more accurate colours, and wider dynamic range, in order to approach the human visual system performance levels.