Finding an optimal decision tree that minimizes classification error is known to be NP-hard. While exact algorithms based on MILP, CP, SAT, or dynamic programming guarantee optimality, they often suffer from poor anytime behavior -- meaning they struggle to find high-quality decision trees quickly when the search is stopped before completion -- due to unbalanced search space exploration. To address this, several anytime extensions of exact methods have been proposed, such as LDS-DL8.5, Top-k-DL8.5, and Blossom, but they have not been systematically compared, making it difficult to assess their relative effectiveness. In this paper, we propose CA-DL8.5, a generic, complete, and anytime beam search algorithm that extends the DL8.5 framework and unifies some existing anytime strategies. In particular, CA-DL8.5 generalizes previous approaches LDS-DL8.5 and Top-k-DL8.5, by allowing the integration of various heuristics and relaxation mechanisms through a modular design. The algorithm reuses DL8.5's efficient branch-and-bound pruning and trie-based caching, combined with a restart-based beam search that gradually relaxes pruning criteria to improve solution quality over time. Our contributions are twofold: (1) We introduce this new generic framework for exact and anytime decision tree learning, enabling the incorporation of diverse heuristics and search strategies; (2) We conduct a rigorous empirical comparison of several instantiations of CA-DL8.5 -- based on Purity, Gain, Discrepancy, and Top-k heuristics -- using an anytime evaluation metric called the primal gap integral. Experimental results on standard classification benchmarks show that CA-DL8.5 using LDS (limited discrepancy) consistently provides the best anytime performance, outperforming both other CA-DL8.5 variants and the Blossom algorithm while maintaining completeness and optimality guarantees.

We combine the ideas behind trees and Gaussian graphical models to form a new nonparametric family of graphical models. Our approach is to attach nonparanormal "blossoms", with arbitrary graphs, to a collection of nonparametric trees. The tree edges are chosen to connect variables that most violate joint Gaussianity. The non-tree edges are partitioned into disjoint groups, and assigned to tree nodes using a nonparametric partial correlation statistic. A nonparanormal blossom is then "grown" for each group using established methods based on the graphical lasso. The result is a factorization with respect to the union of the tree branches and blossoms, defining a high-dimensional joint density that can be efficiently estimated and evaluated on test points. Theoretical properties and experiments with simulated and real data demonstrate the effectiveness of blossom trees.

Abstract-- This work presents a method of incorporating lowcost capacitive tactile sensors on a soft socially assistive robot platform. By embedding conductive thread into the robot's crocheted exterior, we formed a set of low-cost, flexible capacitive tactile sensors that do not disrupt the robot's soft, zoomorphic embodiment. We evaluated the sensors' performance through a user study (N=20) and found that the sensors reliably detected user touch events and localized touch inputs to one of three regions on the robot's exterior. Touch plays an important role in human-human and human-animal social interactions, but it is not often utilized in social interactions between humans and robots. Most Flexibility and Cohesiveness Sensors should be easy to reconfigure commercially available platforms for robotics research do for different interaction contexts without disrupting not incorporate tactile sensing, and those that do cannot Blossom's soft, handcrafted embodiment. In this work, we propose and A. Physical Device Design evaluate a method of incorporating low-cost capacitive tactile We embedded conductive thread into the crocheted exterior sensors onto the Blossom open-source soft robot platform [1] to enable Blossom to detect touch from the user.

We combine the ideas behind trees and Gaussian graphical models to form a new nonparametric family of graphical models. Our approach is to attach nonparanormal "blossoms", with arbitrary graphs, to a collection of nonparametric trees. The tree edges are chosen to connect variables that most violate joint Gaussianity. The non-tree edges are partitioned into disjoint groups, and assigned to tree nodes using a nonparametric partial correlation statistic. A nonparanormal blossom is then "grown" for each group using established methods based on the graphical lasso. The result is a factorization with respect to the union of the tree branches and blossoms, defining a high-dimensional joint density that can be efficiently estimated and evaluated on test points. Theoretical properties and experiments with simulated and real data demonstrate the effectiveness of blossom trees.

Cognitive behavioral therapy (CBT) is a widely used therapeutic method for guiding individuals toward restructuring their thinking patterns as a means of addressing anxiety, depression, and other challenges. We developed a large language model (LLM)-powered prompt-engineered socially assistive robot (SAR) that guides participants through interactive CBT at-home exercises. We evaluated the performance of the SAR through a 15-day study with 38 university students randomly assigned to interact daily with the robot or a chatbot (using the same LLM), or complete traditional CBT worksheets throughout the duration of the study. We measured weekly therapeutic outcomes, changes in pre-/post-session anxiety measures, and adherence to completing CBT exercises. We found that self-reported measures of general psychological distress significantly decreased over the study period in the robot and worksheet conditions but not the chatbot condition. Furthermore, the SAR enabled significant single-session improvements for more sessions than the other two conditions combined. Our findings suggest that SAR-guided LLM-powered CBT may be as effective as traditional worksheet methods in supporting therapeutic progress from the beginning to the end of the study and superior in decreasing user anxiety immediately after completing the CBT exercise.

College students with ADHD respond positively to simple socially assistive robots (SARs) that monitor attention and provide nonverbal feedback, but studies have been done only in brief in-lab sessions. We present an initial design and evaluation of an in-dorm SAR study companion for college students with ADHD. This work represents the introductory stages of an ongoing user-centered, participatory design process. In a three-week within-subjects user study, university students (N=11) with self-reported symptoms of adult ADHD had a SAR study companion in their dorm room for two weeks and a computer-based system for one week. Toward Figure 1: Blossom and the study system, including the tripodmounted developing SARs for long-term, in-dorm use, we focus on 1) evaluating webcam and touch screen interface the usability and desire for SAR study companions by college students with ADHD, and 2) collecting participant feedback about the SAR design and functionality. Participants responded positively to the robot; after one week of regular use, 91% (10 of 11) chose to continue using the robot voluntarily in the second week.

As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.

The US apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruit thinning, and harvesting. Blossom thinning is one of the crucial crop load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical, and mechanical thinning are available for large-scale blossom thinning such approaches often yield unpredictable thinning results and may cause damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor intensive and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for blossom thinning in apple orchards using a computer vision system with artificial intelligence, a six degrees of freedom robotic manipulator, and an electrically actuated miniature end-effector for robotic blossom thinning. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system ability to remove varying proportion of flowers from apple flower clusters. During boundary thinning the end effector was actuated around the cluster boundary while center thinning involved end-effector actuation only at the cluster centroid for a fixed duration of 2 seconds. The boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 seconds per cluster, whereas center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 seconds per cluster. When commercially adopted, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.

Abstract: Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science. Although many different methods exist, their performance is often insufficient for providing quick insight into many contemporary datasets, and the unsupervised mode of use prevents the users from utilizing the methods for dataset exploration and finetuning the details for improved visualization quality. BlosSOM builds on a GPUaccelerated implementation of the EmbedSOM algorithm, complemented by several landmarkbased algorithms for interfacing the unsupervised model learning algorithms with the user supervision. We show the application of BlosSOM on realistic datasets, where it helps to produce high-quality visualizations that incorporate user-specified layout and focus on certain features. We believe the semi-supervised dimensionality reduction will improve the data visualization possibilities for science areas such as single-cell cytometry, and provide a fast and efficient base methodology for new directions in dataset exploration and annotation. Dimensionality reduction algorithms emerged as indispensable utilities that enable various forms of intuitive data visualization, providing insight that in turn simplifies rigorous data analysis. Various algorithms have been proposed for graphs and high-dimensional point-cloud data, and many different types of datasets that can be represented with a graph structure or embedded into vector spaces. Performance of the non-linear dimensionality reduction algorithms becomes a concern if the analysis pipeline is required to scale or when the results are required in a limited amount of time such as in clinical settings. The most popular methods, typically based on neighborhood embedding computed by stochastic descent, force-based layouting or neural autoencoders, reach applicability limits when the dataset size is too large. To tackle the limitations, we have previously developed EmbedSOM [15], a dimensionality reduction and visualization algorithm based on self-organizing maps (SOMs) [13]. EmbedSOM provided an order-of-magnitude speedup on datasets typical for the single-cell cytometry data visualization while retaining competitive quality of the results. The concept has proven useful for interactive and high-performance workflows in cytometry [16, 14], and easily applies to many other types of datasets.

Recorded with the help of a software model programmed with 10 hours of improvised recordings using wood, metal and drum skins. Emptyset, the experimental duo comprised of James Ginzburg and Paul Purgas, have developed a machine learning system for Blossoms, their second album for Thrill Jockey. Programmed using a collection of existing material from the duo, as well as 10 hours of improvised recordings using wood, metal and drum skins, the system was designed to recognize coherent patterns within a large sonic data set. "Blossoms is a work built on hybrids and mutations", explains the label, "combining complexly synthesized audio with reverbs derived from impulses taken in architectural sites Emptyset have worked in previously." "The assembled compositions are emblematic of Emptyset's dedication to forward-looking sound and examine patterns of emergence and augmentation, fragmentation and resilience, and the convolution of biotic and abiotic agency."