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Fruit flies or Drosophila larvae exhibit a diverse range of locomotion gaits enabled by their soft, segmented bodies and intricate muscle arrangements. Their bodies, composed of multiple segments, are synchronously activated to propel forward through a combination of muscle elongation and contraction. Soft robotic systems, inspired by such biological marvels, face significant challenges in replicating these complex behaviors due to the intricate interplay between muscle activation, soft body dynamics, and frictional forces. To address these challenges, we propose a reduced-order model that captures the essential features of larval crawling. By modeling segments as a combination of prismatic and revolute joints, we can simulate the nonlinear motion resulting from muscle activation and body deformation. Our model demonstrates the potential of this approach to accurately describe larval movement, as validated by comparisons with actual larval trajectories. This research offers valuable insights into the design and control of soft robots and provides a framework for biologists to investigate the complex mechanisms of neuromuscular coordination in larvae. 

Soft pneumatic actuators (SPAs) offer a promising alternative for biomedical applications requiring high sensitivity and precise manipulation due to their inherent compliance. 3D- printed multi-modal zig-zag SPAs exhibit potential in this area by achieving repeatable and precise shape changes due to their chambered design. However, achieving accurate position control remains a challenge. This work proposes a hybrid control strategy for multi-modal zig-zag SPAs that combines feed-forward and proportional-integral-derivative (PID) control to enhance positioning accuracy. A Pseudo Rigid Body (PRB) based inverse dynamic model is employed for the feed-forward component. The effectiveness of the controller is evaluated through extensive simulations and experiments. Results demonstrate that the hybrid control strategy achieves up to 29.5% and 31.6% improvement in accuracy compared to the PID and feed-forward controllers, respectively, within the operational bandwidth. 

The multimodal Zig-zag Soft Pneumatic Actuator (SPA) provides an effective design approach for achieving de- sired extensions and bending geometries under specific pressure conditions. The rigid body approximated model introduced in this study brings valuable insights into SPA dynamics by enabling faster simulations when compared to methods such as Finite Element Analysis (FEA). The model outlined in this paper forecasts static behavior by estimating the linear expansion of linear SPA and the bending angle of bending SPA. These two modes of motion can be combined to expand the degree of freedom. Depending on the configuration of the Strain Limiting Layer (SLL), the bending angle can be adjusted by controlling the actuator stiffness, a parameter that can be precisely characterized using the proposed actuator model. To address the hysteresis phenomena in linear expansion SPA, the Bouc-Wen hysteresis model is employed to model the actuator hysteresis responses at higher actuation rates. The validity of the proposed model is experimentally confirmed through the use of 3D-printed SPA prototypes that are designed for both extension and bending actuation. 

Fuzz testing has been gaining ground recently with substantial efforts devoted to the area. Typically, fuzzers take a set of seed inputs and leverage random mutations to continually improve the inputs with respect to a cost, e.g. program code coverage, to discover vulnerabilities or bugs. Following this methodology, fuzzers are very good at generating unstructured inputs that achieve high coverage. However fuzzers are less effective when the inputs are structured, say they conform to an input grammar. Due to the nature of random mutations, the overwhelming abundance of inputs generated by this common fuzzing practice often adversely hinders the effectiveness and efficiency of fuzzers on grammar-aware applications. The problem of testing becomes even harder, when the goal is not only to achieve increased code coverage, but also to nd complex vulnerabilities related to other cost measures, say high resource consumption in an application. We propose Saffron an adaptive grammar-based fuzzing approach to effectively and efficiently generate inputs that expose expensive executions in programs. Saffron takes as input a user-provided grammar, which describes the input space of the program under analysis, and uses it to generate test inputs. Saffron assumes that the grammar description is approximate since precisely describing the input program space is often difficult as a program may accept unintended inputs due to e.g., errors in parsing. Yet these inputs may reveal worst-case complexity vulnerabilities. The novelty of Saffron is then twofold: (1) Given the user-provided grammar, Saffron attempts to discover whether the program accepts unexpected inputs outside of the provided grammar, and if so, it repairs the grammar via grammar mutations. The repaired grammar serves as a specification of the actual inputs accepted by the application. (2) Based on the refined grammar, it generates concrete test inputs. It starts by treating every production rule in the grammar with equal probability of being used for generating concrete inputs. It then adaptively refines the probabilities along the way by increasing the probabilities for rules that have been used to generate inputs that improve a cost, e.g., code coverage or arbitrary user-defined cost. Evaluation results show that Saffron significantly outperforms state-of-the-art baselines. 

Abstract The superconducting transmon qubit is a leading platform for quantum computing and quantum science. Building large, useful quantum systems based on transmon qubits will require significant improvements in qubit relaxation and coherence times, which are orders of magnitude shorter than limits imposed by bulk properties of the constituent materials. This indicates that relaxation likely originates from uncontrolled surfaces, interfaces, and contaminants. Previous efforts to improve qubit lifetimes have focused primarily on designs that minimize contributions from surfaces. However, significant improvements in the lifetime of two-dimensional transmon qubits have remained elusive for several years. Here, we fabricate two-dimensional transmon qubits that have both lifetimes and coherence times with dynamical decoupling exceeding 0.3 milliseconds by replacing niobium with tantalum in the device. We have observed increased lifetimes for seventeen devices, indicating that these material improvements are robust, paving the way for higher gate fidelities in multi-qubit processors.