Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typically rely on iterative, subjective testing, which lacks the efficiency and scalability required for modern demands. This paper presents three contributions to address these challenges. Firstly, we define a new problem domain for scientific agents in flavor science, conceptualized as the generation of hypotheses for flavor profile sourcing and understanding. By leveraging their capacity to identify relevant evidence and reason within large context spaces, language model-backed agents can perform the labor-intensive tasks of flavor sourcing and understanding with enhanced efficiency and precision. To facilitate research in this area, we introduce the FoodPuzzle dataset, a challenging benchmark consisting of 978 food items and 1,766 flavor molecule profiles. We propose a novel Scientific Agent approach, integrating in-context learning and retrieval augmented techniques to generate grounded hypotheses in the domain of food science. Experimental results indicate that our model significantly surpasses traditional methods in flavor profile prediction tasks, demonstrating its potential to transform flavor development practices.

Big data for security informatics requires an analysis of both actors and measurement instruments. By analogy with social and political network analyses, primary questions involve who is doing what and in concert with whom. We seek to examine these questions using cluster analysis, non-linear dimensionality reduction, and machine learning techniques. We apply them to available political science and international relations databases, as these are natural proxies for security informatics databases. In particular we develop an embedding/clustering algorithm that reveals those political themes driving UN voting patterns as well as IGO (Inter-Governmental Organization) memberships. Our algorithm could readily be applied to international conflict, urban crime, and military engagement databases.

Sociopolitical databases provide a rich source of high-dimensional data with hidden spatial-temporal structure; for example countries voting for/against certain UN resolutions is a manifestation of the underlying political alignment among nations. We introduce the notion of diffusion distance as a natural measure in such datasets. and applied diffusion maps to databases of intergovernmental organizations' memberships and UN roll calls. Examination of the embeddings from these data across time reveals interesting historical narratives, suggesting the results serve as a proxy for analysis of security and terrorism datasets.

Many databases provide tabular data relating objects to entities; for example, which countries belong to certain organizations. We seek to infer implicit organizational variables over such objects (countries) as a function of these properties (organizational memberships), and vice versa. If kernels existed over objects, then machine learning and non-linear dimensionality reduction techniques could be used. But this requires a similarity or distance defined over objects, which does not exist a priori. We are exploring an approach to kernel identification based on bi-clustering in which an average over randomized biclusters approximates a kernel. We claim that such kernels provide a viable alternative to other, more common kernel approaches. Experiments with a database of memberships in conventional intergovernmental organizations supports this claim.

The addition of extensive analog blocks to traditionally pure digital ASIC designs is driving the need to verify these analog functions. There are several possible solutions for verifying these analog blocks. These include design for test (DFT), mixed signal automated test equipment (ATE), and the use of custom measurement circuitry on a purely digital tester. This paper focuses entirely on the use of custom measurement circuitry on an otherwise purely digital tester.

In this paper we propose a new approach for multi-objective control using a cascade of filters that progressively removes candidate commands which do not satisfy task constraints. The approach is motivated by other control methods that prevent destructive control interactions through null space projections. We apply this approach to a practical leader/follower task in which a mobile robot must address the conflicting objectives of moving to a goal position while avoiding obstacles and keeping a region of the workspace within the field of view of a fixed camera mounted on the platform. We experimentally verify our approach using the Segway Robotic Mobility Platform (RMP), a dynamically stable, differential drive mobile robot.

Multiple, independent robot platforms promise significant advantage with respect to robustness and flexibility. However, coordination between otherwise independent robots requires the exchange of information; either implicitly (as in gestural communication), or explicitly (as in message passing in a communication network.) In either case, control processes resident on all coordinated peers must participate in the collective behavior. This paper evaluates the potential to scale such a coupled control framework to many participating individuals, where scalability is evaluated in terms of the schedulability of coupled, distributed control processes. We examine how schedulability affects the scalability of a robot system, and discuss an algorithm used for off-line schedulability analysis of a distributed task model. We present a distributed coordinated search task and analyze the schedulability of the designed task structure. We are able to analyze communication delays in the system that put upper bounds on the size of the robot teams. We show that hierarchical methods can be used to overcome the scalability problem. We propose that schedulability analysis should be an integrated part of a multi-robot team design process.

Presents techniques for exploiting redundancy in teams of mobile robots. In particular, we address tasks involving the kinematic coordination of several communicating robots. Teams area modeled as highly redundant spatial mechanisms for which multi-objective, concurrent controllers are constructed using a generalization of null-space control. The goal is to develop a methodology in which the robustness and error suppression in a control theoretic substrate can be used to preserve critical properties in teams of reactive robots. The resulting "safe" control options can then be explored while guaranteeing global compliance with system specifications. The proposed architecture depends on a set of concurrent, low-dimensional control processes that interact in a well-defined manner. Cascaded null space projections and coordination templates are used to manage control interactions across platforms that actively maintain constraints for pairs of robots. Pairwise policies can then be combined to represent coordinated, multi-robot tasks. To illustrate the approach, we demonstrate a distributed control that maintains critical connectivity in line-of-sight communication networks.

The author describes two test features in the VAX 6000 model 400 processor which help test and diagnose the module in a manufacturing environment. The continuity transistor structure allows an in-circuit tester to detect and diagnose open faults between the chips and the board using analog instrumentation. The observe boundary scan feature is designed to help detect and diagnose faults during full-speed system test. The overhead caused by both of these test features is negligible in terms of additional silicon area, device pins, and signal delay. The test features combined with a well-controlled assembly process and a very robust design have allowed the VAX 6000 model 400 processor to be manufactured economically.>