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Tenant-landlord relationships exhibit a power asymmetry where landlords' power to evict the tenants at a low-cost results in their dominating status in such relationships. Tenant concerns are thus often unspoken, unresolved, or ignored and this could lead to blatant conflicts as suppressed tenant concerns accumulate. Modern machine learning methods and Large Language Models (LLM) have demonstrated immense abilities to perform language tasks. In this study, we incorporate Latent Dirichlet Allocation (LDA) with GPT-4 to classify Reddit post data scraped from the subreddit r/Tenant, aiming to unveil trends in tenant concerns while exploring the adoption of LLMs and machine learning methods in social science research. We find that tenant concerns in topics like fee dispute and utility issues are consistently dominant in all four states analyzed while each state has other common tenant concerns special to itself. Moreover, we discover temporal trends in tenant concerns that provide important implications regarding the impact of the pandemic and the Eviction Moratorium.

Shrinking hardware structures and decreasing operating voltages lead to an increasing number of transient hardware faults,which thus become a core problem to consider for safety-critical systems. Here, systematic fault injection (FI), where one program-under-test is systematically stressed with faults, provides an in-depth resilience analysis in the presence of faults. However, FI campaigns require many independent injection experiments and, combined, long run times, especially if we aim for a high coverage of the fault space. One cost factor is the forwarding phase, which is the time required to bring the system-under test into the fault-free state at injection time. One common technique to speed up the forwarding are checkpoints of the fault-free system state at fixed points in time. In this paper, we show that the placement of checkpoints has a significant influence on the required forwarding cycles, especially if we place faults non-uniformly on the time axis. For this, we discuss the checkpoint-selection problem in general, formalize it as a maximum-weight reward path problem in graphs, propose an ILP formulation and a dynamic programming algorithm that find the optimal solution, and provide a heuristic checkpoint-selection method based on a genetic algorithm. Applied to the MiBench benchmark suite, our approach consistently reduces the forward-phase cycles by at least 88 percent and up to 99.934 percent when placing 16 checkpoints., Comment: Preprint for accepted version at ICCAD'23

Dismantling nuclear power plants entails the production of a large amount of contaminated (or potentially contaminated) material whose disposal is of crucial importance. Most of the end products have to be stored in special repositories but it can happen that some of them are slightly contaminated or not contaminated at all, making it possible to free release them. One possible approach to free release measurements uses Large Clearance Monitors, chambers surrounded by plastic scintillation detectors that allow a decision about the clearance of waste packages up to 1000 kg. Due to the composite nature of the detectors in a Large Clearance Monitor, it is easy to imagine that one can apply 3D imaging algorithms to localize radioactive sources inside a waste package. In this work we will show how a special algorithm that maximizes the conditional informational entropy allows decisions about the clearance of portions of the sample., Comment: 15 pages

We propose a standardized methodology for developing and evaluating use cases for quantum computers and quantum inspired methods. This methodology consists of a standardized set of questions which should be asked to determine how and indeed if, near term quantum computing can play a role in a given application. Developing such a set of questions is important because it allows different use cases to be evaluated in a fair and objective way, rather than considering each case on an ad hoc basis which could lead to an evaluation which focuses on positives of a use case, while ignoring weaknesses. To demonstrate our methodology we apply it to a concrete use case, ambulance dispatch, and find that there are some ways in which near term quantum computing could be deployed sensibly, but also demonstrate some cases ways in which its use would not be advised. The purpose of this paper is to initiate a dialogue within the community of quantum computing scientists and potential end users on what questions should be asked when developing real world use cases., Comment: 9 pages, 3 figures