Is a Free Appropriate Public Education Really Free? How the Denial of Expert Witness Fees Will Adversely Impact Children with Autism

This Comment addresses how the Arlington decision, in disallowing the recovery of expert witness fees, will adversely impact parents of children with ASD who seek to invoke due process against the school district. Part III examines the controversy between parents and school districts in providing the most appropriate behavioral interventions and therapies to the child with ASD and the reasons behind the increase in IDEA-related litigation, particularly with respect to students diagnosed with ASD. Part III also explores the meaning of a FAPE, the procedural safeguards afforded to parents of children eligible under the Act, and the congressional intent behind the IDEA\u27s costs provision with respect to whether Congress intended expert witness fees to be recoverable as costs. Part IV includes a discussion of the majority and dissenting opinions in Arlington and an analysis of the Supreme Court\u27s use of specific statutory approaches to interpret the costs provision. Part IV also details why the Supreme Court made an incorrect ruling based on the legislative history of the Act and prior case law and describes how this decision, coupled with the Supreme Court\u27s ruling in Schaffer v. Weast, which placed the burden of proof on the party seeking relief in an IDEA suit, will discourage parents from challenging the school district. Part V explains how these decisions will impact children with ASD and emphasizes what must now take place from the parent\u27s perspective, post-Arlington, to secure an appropriately tailored education for students with ASD. This Comment concludes that to uphold the IDEA\u27s purpose of providing the student a free appropriate public education, Congress must revise the IDEA\u27s costs provision to clearly express its intent to include expert witness fees as a recoverable cost by the prevailing party

Sensory-based Services in Adult Mental Health

Purpose: The purpose of this Capstone Project was to evaluate the sensory room program used by occupational therapy to determine whether use of the sensory room and the elements within the room reduced perceived levels of distress and acting out and/or aggressive behaviors of patients with mental illness. Methods: This Capstone Project was an outcome evaluation of a routine clinical program using retrospective analysis of existing patient records to ascertain physical aggression episodes, sensory modulation ability, and self-ratings and staff ratings of patient distress levels pre- and post-sensory intervention. Results: Through analysis of quantitative data, the results of the project demonstrated a statistically significant difference in Subjective Units of Distress Scale ratings, reflecting an overall decrease in patient distress levels from time of entry to time of exit of the sensory room. The majority of patients did not exhibit acting out behaviors within 24 hours post sensory intervention. Though there were no significant correlations identified via SPSS data analysis, the patient ACL scores generally indicated less personal insight. Conclusion: Data analysis confirms that the use of a sensory room and sensory-based treatment approaches had positive effects among patients of varied ages, diagnoses, and ACL scores. Outcomes of this study align well with person-centered and recovery-oriented mental health care that supports the patient’s preferences, responsibility and accountability, and oversight of their own recovery

Fortenberry Achieves $11M in Funding for USDA Agriculture Research Facility at Nebraska

Congressman Jeff Fortenberry, ranking member of the House Appropriations Subcommittee on Agriculture, was successful in achieving $11.2 million in federal funding for the planning and design of a USDA Agricultural Research Service facility

NSF grant helps preserve parasite collections

The National Science Foundation has awarded a $500,000 grant that will allow four major parasite collections to be digitized. The collections are part of UNL\u27s Harold W. Manter Laboratory of Parasitology. Scott Gardner (pictured) is the curator and director of the Manter Laboratory. A$500,000 grant from the National Science Foundation will allow the Harold W. Manter Laboratory of Parasitology to digitally preserve four major collections of parasite specimens donated to the University of Nebraska-Lincoln during the past five years

NSF grant helps preserve parasite collections

The National Science Foundation has awarded a $500,000 grant that will allow four major parasite collections to be digitized. The collections are part of UNL\u27s Harold W. Manter Laboratory of Parasitology. Scott Gardner (pictured) is the curator and director of the Manter Laboratory. A$500,000 grant from the National Science Foundation will allow the Harold W. Manter Laboratory of Parasitology to digitally preserve four major collections of parasite specimens donated to the University of Nebraska-Lincoln during the past five years

USDA meatpacking inspectors receive hand sanitizer made at Nebraska U

Food safety inspectors employed by the USDA’s Food Safety and Inspection Service are receiving a supply of hand sanitizer thanks to an innovative partnership between Nebraska’s ethanol industry and the University of Nebraska–Lincoln. The hand sanitizer will be used by the men and women responsible for inspecting more than 6,500 meat-processing facilities across the country, ranging from “mom-and-pop” facilities that handle only a few animals at a time to the giant beef, poultry and pork plants that employ thousands of people. “Food Safety and Inspection Service inspectors provide critical support to our food supply chain and also to the livestock industry across our nation,” Chancellor Ronnie Green said.“Thanks to our UNL ingenuity and the generosity of our Nebraska ethanol industry, we are pleased to get hand sanitizer into their possession so they can stay safe at their jobs.” Hunter Flodman, an engineering professor of practice at Nebraska who has helped spearhead the project, said more than 6,800 gallons of hand sanitizer has been shipped to the USDA,with the possibility of significantly more gallons being supplied in coming weeks. Flodman serves as technical adviser to the Nebraska Ethanol Board. Terry Howell, executivedirector of the Food Processing Center at Nebraska Innovation Campus, also leads the project on behalf of the university. “This project represents the true grit of Nebraskans and the innovative ways the agriculture community joins together to take care of one another,” said Nebraska Department of Agriculture Director Steve Wellman. “We appreciate the dedication and donations that theethanol industry, hard hit by this virus themselves, has made to see this project through, as well as the perseverance of the Food Processing Center staff to create a product that will help slow the spread of COVID-19.

Sampling-Based Methods for Factored Task and Motion Planning

This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing

PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning

Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, collision, visibility, and motion constraints involving robot configurations, object poses, and robot trajectories. These constraints typically require specialized procedures to sample satisfying values. We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes. We provide domain-independent algorithms that reduce PDDLStream problems to a sequence of finite PDDL problems. We also introduce an algorithm that dynamically balances exploring new candidate plans and exploiting existing ones. This enables the algorithm to greedily search the space of parameter bindings to more quickly solve tightly-constrained problems as well as locally optimize to produce low-cost solutions. We evaluate our algorithms on three simulated robotic planning domains as well as several real-world robotic tasks.Comment: International Conference on Automated Planning and Scheduling (ICAPS) 202

Active model learning and diverse action sampling for task and motion planning

The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world. In order to plan to combine primitive actions, we must have models of the preconditions and effects of those actions: under what circumstances will executing this primitive achieve some particular effect in the world? We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the conditions of operator effectiveness from small numbers of expensive training examples collected by experimentation on a robot. We develop adaptive sampling methods for generating diverse elements of continuous sets (such as robot configurations and object poses) during planning for solving a new task, so that planning is as efficient as possible. We demonstrate these methods in an integrated system, combining newly learned models with an efficient continuous-space robot task and motion planner to learn to solve long horizon problems more efficiently than was previously possible.Comment: Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain. https://www.youtube.com/playlist?list=PLoWhBFPMfSzDbc8CYelsbHZa1d3uz-W_