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Abstract Maxwell lattices possess distinct topological states that feature mechanically polarized edge behaviors and asymmetric dynamic responses protected by the topology of their phonon bands. Until now, demonstrations of non‐trivial topological behaviors from Maxwell lattices have been limited to fixed configurations or have achieved reconfigurability using mechanical linkages. Here, a monolithic transformable topological mechanical metamaterial is introduced in the form of a generalized kagome lattice made from a shape memory polymer (SMP). It is capable of reversibly exploring topologically distinct phases of the non‐trivial phase space via a kinematic strategy that converts sparse mechanical inputs at free edge pairs into a biaxial, global transformation that switches its topological state. All configurations are stable in the absence of confinement or a continuous mechanical input. Its topologically‐protected, polarized mechanical edge stiffness is robust against broken hinges or conformational defects. More importantly, it shows that the phase transition of SMPs that modulate chain mobility, can effectively shield a dynamic metamaterial's topological response from its own kinematic stress history, referred to as “stress caching”. This work provides a blueprint for monolithic transformable mechanical metamaterials with topological mechanical behavior that is robust against defects and disorder while circumventing their vulnerability to stored elastic energy, which will find applications in switchable acoustic diodes and tunable vibration dampers or isolators. 

Existing near-data processing (NDP)-powered architectures have demonstrated their strength for some data-intensive applications. Data center servers, however, have to serve not only data-intensive but also compute-intensive applications. An in-depth understanding of the impact of NDP on various data center applications is still needed. For example, can a compute-intensive application also benefit from NDP? In addition, current NDP techniques focus on maximizing the data processing rate by always utilizing all computing resources at all times. Is this “always running in full gear” strategy consistently beneficial for an application? To answer these questions, we first propose two reconfigurable NDP-powered servers called RANS (ReconfigurableARM-basedNDPServer) and RFNS (ReconfigurableFPGA-basedNDPServer). Next, we implement a single-engine prototype for each of them based on a conventional data center and then evaluate their effectiveness. Experimental results measured from the two prototypes are then extrapolated to estimate the properties of the two full-size reconfigurable NDP servers. Finally, several new findings are presented. For example, we find that while RANS can only benefit data-intensive applications, RFNS can offer benefits for both data-intensive and compute-intensive applications. Moreover, we find that for certain applications the reconfigurability of RANS/RFNS can deliver noticeable energy efficiency without any performance degradation. 

Due to the importance of Android app quality assurance, many Android UI testing tools have been developed by researchers over the years. However, recent studies show that these tools typically achieve low code coverage on popular industrial apps. In fact, given a reasonable amount of run time, most state-of-the-art tools cannot even outperform a simple tool, Monkey, on popular industrial apps with large codebases and sophisticated functionalities. Our motivating study finds that these tools perform two types of operations, UI Hierarchy Capturing (capturing information about the contents on the screen) and UI Event Execution (executing UI events, such as clicks), often inefficiently using UIAutomator, a component of the Android framework. In total, these two types of operations use on average 70% of the given test time. Based on this finding, to improve the effectiveness of Android testing tools, we propose TOLLER, a tool consisting of infrastructure enhancements to the Android operating system. TOLLER injects itself into the same virtual machine as the app under test, giving TOLLER direct access to the app’s runtime memory. TOLLER is thus able to directly (1) access UI data structures, and thus capture contents on the screen without the overhead of invoking the Android framework services or remote procedure calls (RPCs), and (2) invoke UI event handlers without needing to execute the UI events. Compared with the often-used UIAutomator, TOLLER reduces average time usage of UI Hierarchy Capturing and UI Event Execution operations by up to 97% and 95%, respectively. We integrate TOLLER with existing state-of-the-art/practice Android UI testing tools and achieve the range of 11.8% to 70.1% relative code coverage improvement on average. We also find that TOLLER-enhanced tools are able to trigger 1.4x to 3.6x distinct crashes compared with their original versions without TOLLER enhancement. These improvements are so substantial that they also change the relative competitiveness of the tools under empirical comparison. Our findings highlight the practicality of TOLLER as well as raising the community awareness of infrastructure support’s significance beyond the community’s existing heavy focus on algorithms.