Machine learning algorithms play a pivotal role in a wide range of Artificial Intelligence (AI) applications. Explaining the results and behavior of a machine learning model, however, remains a challenge. In this paper, we present a new approach to the explanation of machine learning models using a large language model (LLM). In this work, we seek natural language descriptions of the behavioral patterns of a machine learning model by a combination of prompting and model sampling. A subspace sampling technique is developed to generate ML model outputs using partial features in a user defined space. A projective visualization method is employed to guide the sampling process, including user-directed interactive sampling and feature-based sampling, so that an optimal amount of information can be provided to the LLM to ensure accurate and concise natural language explanations. Two public datasets, a student performance dataset and a weather dataset, were used to test our approach under various conditions.

Advances in imaging technology and fluorescent calcium indicators have increased the popularity of the application of miniature microscopes (miniscope) in freely-moving rodents when they are performing sophisticated behaviors. The recently established analysis tools, such as Minian, allow for the efficient extraction of neuronal calcium transients from raw videos. However, there is still a gap in front of neuroscientists to draw any conclusions based on the neuronal calcium signals detected during behavioral tests. The poor signal-to-noise ratio and unreliable neuron registration in miniscope images bring significant challenges to the data analysis tasks. This paper describes a visual analytics approach to the exploration of potential neuronal clustering based on the calcium signals and the behavioral tasks. This new interactive solution can be applied for the analysis and exploration of the correlations between calcium imaging data and addiction behavioral data using a combination of visualization, user interaction, visual clustering, and event-related feature selection techniques. The associated interactive software tool is designed to help neuroscientists develop neurobiological hypotheses underlying both physiological and pathological behaviors, which could be a milestone in the field of neuroscience to understanding behavioral coding in the brain at the neuronal level.

Seeking to understand students' differences, including their different levels of prior knowledge and potential barriers to learning, is an essential part of teaching. Adapting teaching in a responsive way, including by providing targeted support to students who are struggling, is likely to increase student success. In this study, we apply digital courseware in the teaching of the undergraduate discrete mathematics course. Digital courseware has improved the analytics available to faculty and increased personalized responses to students on designated topics. We developed a tool to collect data from digital courseware and allow instructors and TAs to visualize the performance of students from pre-class exercises, in-class quizzes, weekly assignments, collaborative projects, and exams. Based on the data, instructors and the TAs could divide students into groups and adjust classroom lectures and recitation sessions to better address each student groups' areas of deficiency.

Recent data mining research has focused on the analysis of social media text, content and networks to identify suicide ideation online. However, there has been limited research on the temporal dynamics of users and suicide ideation. In this work, we use time-to-event modeling to identify which subreddits have a higher association with users transitioning to posting on r/suicidewatch. For this purpose we use a Cox proportional hazards model that takes as input text and subreddit network features and outputs a probability distribution for the time until a Reddit user posts on r/suicidewatch. In our analysis we find a number of statistically significant features that predict earlier transitions to r/suicidewatch. While some patterns match existing intuition, for example r/depression is positively associated with posting sooner on r/suicidewatch, others were more surprising (for example, the average time between a high risk post on r/Wishlist and a post on r/suicidewatch is 10.2 days). We then discuss these results as well as directions for future research.

BACKGROUND: Brain image genetics provides enormous opportunities for examining the effects of genetic variations on the brain. Many studies have shown that the structure, function, and abnormality (e.g., those related to Alzheimer's disease) of the brain are heritable. However, which genetic variations contribute to these phenotypic changes is not completely clear. Advances in neuroimaging and genetics have led us to obtain detailed brain anatomy and genome-wide information. These data offer us new opportunities to identify genetic variations such as single nucleotide polymorphisms (SNPs) that affect brain structure. In this paper, we perform a genome-wide variant-based study, and aim to identify top SNPs or SNP sets which have genetic effects with the largest neuroanotomic coverage at both voxel and region-of-interest (ROI) levels. Based on the voxelwise genome-wide association study (GWAS) results, we used the exhaustive search to find the top SNPs or SNP sets that have the largest voxel-based or ROI-based neuroanatomic coverage. For SNP sets with >2 SNPs, we proposed an efficient genetic algorithm to identify top SNP sets that can cover all ROIs or a specific ROI. RESULTS: We identified an ensemble of top SNPs, SNP-pairs and SNP-sets, whose effects have the largest neuroanatomic coverage. Experimental results on real imaging genetics data show that the proposed genetic algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets. CONCLUSIONS: We proposed and applied an informatics strategy to identify top SNPs, SNP-pairs and SNP-sets that have genetic effects with the largest neuroanatomic coverage. The proposed genetic algorithm offers an efficient solution to accomplish the task, especially for identifying top SNP-sets.

Facial landmark placement is a key step in many biomedical and biometrics applications. This paper presents a computational method that efficiently performs automatic 3D facial landmark placement based on training images containing manually placed anthropological facial landmarks. After 3D face registration by an iterative closest point (ICP) technique, a visual analytics approach is taken to generate local geometric patterns for individual landmark points. These individualized local geometric patterns are derived interactively by a user's initial visual pattern detection. They are used to guide the refinement process for landmark points projected from a template face to achieve accurate landmark placement. Compared to traditional methods, this technique is simple, robust, and does not require a large number of training samples (e.g. in machine learning based methods) or complex 3D image analysis procedures. This technique and the associated software tool are being used in a 3D biometrics project that aims to identify links between human facial phenotypes and their genetic association.

Machine learning algorithms and traditional data mining process usually require a large volume of data to train the algorithm-specific models, with little or no user feedback during the model building process. Such a "big data" based automatic learning strategy is sometimes unrealistic for applications where data collection or processing is very expensive or difficult, such as in clinical trials. Furthermore, expert knowledge can be very valuable in the model building process in some fields such as biomedical sciences. In this paper, we propose a new visual analytics approach to interactive machine learning and visual data mining. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning and mining process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building. In particular, this approach can significantly reduce the amount of data required for training an accurate model, and therefore can be highly impactful for applications where large amount of data is hard to obtain. The proposed approach is tested on two application problems: the handwriting recognition (classification) problem and the human cognitive score prediction (regression) problem. Both experiments show that visualization supported interactive machine learning and data mining can achieve the same accuracy as an automatic process can with much smaller training data sets.

In spatiotemporal data visualization, integrating the time dimension with the spatial dimensions is a challenging problem. In this paper, we propose a new time representation method by mapping time onto a time curve in a color space. Since no spatial dimension is needed for the time axis, this approach is more effective in integrating time space and spatial dimensions. Several designs of the time curves in a 3D color space will be discussed. We apply this approach to the visualization application for a large taxi GPS dataset. The visualization is applied directly over an interactive map to depict the time patterns of a large set of driving paths on city roads. Spatial optimization techniques are also implemented to process large volumes of GPS data. This approach provides a new alternative for many spatiotemporal data visualization application.

Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns.

Alzheimer’s disease (AD) is the most common form of de-mentia that causes progressive impairment of memory and other cognitive functions. Multivariate regression models have been studied in AD for revealing relationships between neuroimaging measures and cognitive scores to understand how structural changes in brain can influence cognitive sta-tus. Existing regression methods, however, do not explic-itly model dependence relation among multiple scores de-rived from a single cognitive test. It has been found that such dependence can deteriorate the performance of these methods. To overcome this limitation, we propose an effi-cient sparse Bayesian multi-task learning algorithm, which adaptively learns and exploits the dependence to achieve improved prediction performance. The proposed algorithm is applied to a real world neuroimaging study in AD to pre-dict cognitive performance using MRI scans. The effective-ness of the proposed algorithm is demonstrated by its supe-rior prediction performance over multiple state-of-the-art competing methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are con-sistent with prior knowledge. 1.

SUMMARY Osteoporosis is characterized by reduced bone mass and debilitating fractures and is likely to reach epidemic proportions. Because of the vigorous research taking place in fields related to osteoporosis, bone biologists are overwhelmed by the amount of literature being generated on a regular basis. This problem can be alleviated by inferring and extracting novel relationships among biological entities appearing in the biological literature. With the development of large online publicly available databases of biological literature, such an approach becomes even more appealing. The novel relationships between biological terms thus discovered constitute new hypotheses that can be verified using experiments. This paper presents a novel method called multilevel text mining for the extraction of potentially meaningful biological relationships. Multilevel mining uses transitive maximum flow graph analysis coupled with set combination operations of union and intersection. Set operators are applied along and across the paths of a transitive flow graph to combine the data. In the first level of the multilevel mining process, protein domain names are used. Novel relationships between domains are extracted by the transitive text mining analysis. In the second level, these newly discovered relationships are used to extract relevant protein names. Set operators are used in various combinations to obtain different sets of results. Copyright © 2011 John Wiley & Sons, Ltd.

To date, comparing and visualizing genome sequences remain challenging due to the large genome size. Existing approaches take advantage of the stable property of oligonucleotides and exhibit the main characteristics of the whole genome, yet they commonly fail to show progression patterns of the genome adjustably. This paper presents a novel visual encoding technique, which not only supports the binning process (phylogenetic analysis), but also allows the sequential analysis of the genome. The key idea is to regard the combination of each k -nucleotide and its reverse complement as a visual word, and to represent a long genome sequence with a list of local statistical feature vectors derived from the local frequency of the visual words. Experimental results on a variety of examples demonstrate that the presented approach has the ability to quickly and intuitively visualize DNA sequences, and to help the user identify regions of differences among multiple datasets.

Osteoporosis, which is characterized by reduced bone mass and debilitating fractures, may reach epidemic proportions with the aging of the US population. The intensity of research in this field of study is reflected by the facts that The American Society of Bone and Mineral Research has a membership of nearly 4,000 physicians, clinical investigators, and basic research scientists from over fifty countries and that NIH is expected to spend over 200 million dollars on osteoporosis research alone in 2010. Bone biologists may be overwhelmed by the amount of literature constantly being generated, thus the identification and extraction of existing and novel relationships among biological entities or terms appearing in the biological literature is an ongoing problem. The problem has become more pressing with the development of large online publicly available databases of biological literature. Extraction and visualization of relationships between biological entities appearing in these databases offers the opportunity of keeping researchers up-to-date in their research domain. This may be achieved through helping them visualize possible biological pathways and by generating likely new hypotheses concerning novel interactions through methods such as transitive closure network flow. All generated predictions can be verified against already existing data, and possible new relationships can be verified against experiment. This paper presents a method for the extraction and visualization of potentially meaningful relationships.

This paper describes an automatic 3D surface scanning technique using a 3D scanner. It allows the acquisition of a complete surface model of a 3D artifact without any manual registration and human interference. A two-pass approach is applied using a rotary table. In the first pass, a sequence of 2D images of the artifact are collected using a small rotation step. An image similarity measure is then taken to compare adjacent images to assess the differences between consecutive images to establish the optimal scanning angles. In the second pass, 3D scans are taken using the scanning angles derived in the first pass. An iterative closest point (ICP) algorithm is employed to provide precise data alignment of neighboring scans. A new space encoding algorithm is developed to filter the over sampling areas for smoother polygonization. This technique is being implemented in a digital library project for the reconstructions of 3D computer models for artifacts from a collection in several museums.

Computer vision and pattern recognition systems play an important role in our lives by means of automated face detection, face and gesture recognition, and estimation of gender and age. This paper addresses the problem of gender classification using frontal facial images. We have developed gender classifiers with performance superior to existing gender classifiers. We experiment on 500 images (250 females and 250 males) randomly withdrawn from the FERET facial database. Independent component analysis (ICA) is used to represent each image as a feature vector in a low dimensional subspace. Different classifiers are studied in this lower dimensional space. Our experimental results show the superior performance of our approach to the existing gender classifiers. We get a 96% accuracy using support vector machine (SVM) in ICA space.

In recent years, systems consisting of multiple modular neural networks have attracted substantial interest in the neural networks community because of various advantages they offer over a single large monolithic network. In this paper, we propose two basic feature decomposition models (namely, parallel model and tandem model) in which each of the neural network modules processes a disjoint subset of the input features. A novel feature decomposition algorithm is introduced to partition the input space into disjoint subsets solely based on the available training data. Under certain assumptions, the approximation error due to decomposition can be proved to be bounded by any desired small value over a compact set. Finally, the performance of feature decomposition networks is compared with that of a monolithic network in real world bench mark pattern recognition and modeling problems.

Transfer function design is an integrated component in volume visualization and data exploration. The common trial-and-error approach for transfer function searching is a very difficult and time consuming process. A goal oriented and parameterized transfer function model is therefore crucial in guiding the transfer function searching process for better and more meaningful visualization results. The paper presents an image based transfer function model that integrates 3D image processing tools into the volume visualization pipeline to facilitate the search for an image based transfer function in volume data visualization and exploration. The model defines a transfer function as a sequence of 3D image processing procedures, and allows the users to adjust a set of qualitative and descriptive parameters to achieve their subjective visualization goals. 3D image enhancement and boundary detection tools, and their integration methods with volume visualization algorithms are described. The application of this approach for 3D microscopy data exploration and analysis is also discussed.

Collaborative visualization is an active area of research in computer science. Many different techniques and implementations have been proposed. However, most of today's systems require many high-end graphics workstations. By implementing a new technique that separates the 2D and 3D components of the visualization process, we have developed a system called CEV that operates using only one high-end workstation along with many inexpensive machines that need only the capability of running a web browser. Using Java RMI, CEV provides a heterogeneous environment for many users that is easy to use. This paper describes the operation, methodology, and implementation of CEV. © 1998 John Wiley & Sons, Ltd.

Rendering deformable volume data currently needs separate processes for deformation and rendering, and is expensive in terms of both computational and memory costs. Recognizing the importance of unifying these processes, we present a new approach to the direct rendering of deformable volumes without explicitly constructing the intermediate deformed volumes. The volume deformation is done by a radial basis function that is piecewise linearly approximated by an adaptive subdivision of the octree encoded target volume. The octree blocks in the target volume are then projected, reverse morphed and texture mapped, using the SGI 3D texture mapping hardware, in a back-to-front order. A template-based Z-plane/block intersection method is used to expedite the block projection computation.

A general and unified method is presented for generating a wide range of 3D objects by smoothing the vertices and edges of a given polyhedron with arbitrary topology using bicubic Bezier patches. The common solution to the compatibility equations of geometric continuity between two Bezier patches is obtained and employed as the foundation of this new method such that this new solid and surface model is reliable and compatible with the solid modeling and surface modeling system in the most common use. The new method has been embeded in an algorithm supported by our newly developed solid modeling system MESSAGE. The performance and implementation of this new algorithm show that it is efficient, flexible and easy to manipulate.