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Mobile applications (apps) are often developed by only a small number of developers with limited resources, especially in the early years of the app's development. In this setting, many requirements acquisition activities, such as interviews, are challenging or lower priority than development and release activities. Moreover, in this early period, requirements are frequently changing as mobile apps evolve to compete in the marketplace. As app development companies move to standardize their development processes, however, they will shift to documenting and analyzing requirements. One low-cost source of requirements post-deployment are user-authored scenarios describing how they interact with an app. We propose a method for extracting use case components from user-authored scenarios using large language models (LLMs). The method consists of a series of prompts that were developed to improve precision and recall on a ground truth dataset of 50 scenarios independently labeled with UC components. Our results reveal that LLMs require additional domain knowledge to extract UC components, and that refining prompts to include this knowledge improves the quality of the extracted UC components.

The widespread use of mobile applications has driven the growth of the industry, with companies relying heavily on user data for services like targeted advertising and personalized offerings. In this context, privacy regulations such as the General Data Protection Regulation (GDPR) play a crucial role. One of the GDPR requirements is the maintenance of a Record of Processing Activities (RoPA) by companies. RoPA encompasses various details, including the description of data processing activities, their purposes, types of data involved, and other relevant external entities. Small app-developing companies face challenges in meeting such compliance requirements due to resource limitations and tight timelines. To aid these developers and prevent fines, we propose a method to generate segments of RoPA from user-authored usage scenarios using large language models (LLMs). Our method employs few-shot learning with GPT-3.5 Turbo to summarize usage scenarios and generate RoPA segments. We evaluate different factors that can affect few-shot learning performance consistency for our summarization task, including the number of examples in few-shot learning prompts, repetition, and order permutation of examples in the prompts. Our findings highlight the significant influence of the number of examples in prompts on summarization F1 scores, while demonstrating negligible variability in F1 scores across multiple prompt repetitions. Our prompts achieve successful summarization of processing activities with an average 70% ROUGE-L F1 score. Finally, we discuss avenues for improving results through manual evaluation of the generated summaries., Comment: Accepted in the the 11th International Workshop on Evolving Security & Privacy Requirements Engineering (ESPRE)

Recognizing the promise of natural language interfaces to databases, prior studies have emphasized the development of text-to-SQL systems. While substantial progress has been made in this field, existing research has concentrated on generating SQL statements from text queries. The broader challenge, however, lies in inferring new information about the returned data. Our research makes two major contributions to address this gap. First, we introduce a novel Internet-of-Things (IoT) text-to-SQL dataset comprising 10,985 text-SQL pairs and 239,398 rows of network traffic activity. The dataset contains additional query types limited in prior text-to-SQL datasets, notably temporal-related queries. Our dataset is sourced from a smart building's IoT ecosystem exploring sensor read and network traffic data. Second, our dataset allows two-stage processing, where the returned data (network traffic) from a generated SQL can be categorized as malicious or not. Our results show that joint training to query and infer information about the data can improve overall text-to-SQL performance, nearly matching substantially larger models. We also show that current large language models (e.g., GPT3.5) struggle to infer new information about returned data, thus our dataset provides a novel test bed for integrating complex domain-specific reasoning into LLMs.

The giant electro- and elasto-caloric effects in spin chain materials are predicted. The theory is based on the exact quantum mechanical solution of the problem. It is shown that the giant jumps in the entropy and the temperature caused by the caloric effect are weakly affected by the initial temperature. The effect can be used for the cooling of new quantum devices (like systems of qubits in quantum computers). On the other hand, since large changes are predicted in the narrow neighborhood of the critical point, the predicted effect can be used in ultra-sensitive electric and stress sensors for modern microelectronics., Comment: Typo is corrected

We consider a quantum system of large size $N$ and its subsystem of size $L$ assuming that $N$ is much larger than $L$, which can also be sufficiently large, i.e., $1 \ll L \lesssim N $. A widely accepted mathematical version of this heuristic inequality is the asymptotic regime of successive limits: first the macroscopic limit $N \to \infty$, then an asymptotic analysis of the entanglement entropy as $L \to \infty$. In this paper, we consider another version of the above heuristic inequality: the regime of asymptotically proportional $L$ and $N$, i.e., the simultaneous limits $L \to \infty,\; N \to \infty, L/N \to \lambda >0$. Specifically, we consider the system of free fermions which is in its ground state and such that its one-body Hamiltonian is a large random matrix, that is often used to model the long-range hopping. By using random matrix theory, we show that in this case, the entanglement entropy obeys the volume law known for systems with short-ranged hopping but described either by a mixed state or a pure strongly excited state of the Hamiltonian. We also give a streamlined proof of Page's formula for the entanglement entropy of the black hole radiation for a wide class of typical ground states, thereby proving the universality of the formula.

This study investigates the escape of Mercury's sodium-group ions (Na+-group, including ions with m/q from 21 to 30 amu/e) and their dependence on true anomaly angle (TAA), i.e., Mercury's orbital phase around the Sun, using measurements from MESSENGER. The measurements are categorized into solar wind, magnetosheath, and magnetosphere, and further divided into four TAA intervals. Na+-group ions form escape plumes in the solar wind and magnetosheath, with higher fluxes along the solar wind's motional electric field. The total escape rates vary from 0.2 to 1 times 10^{25} atoms/s with the magnetosheath being the main escaping region. These rates exhibit a TAA dependence, peaking near the perihelion and similar during Mercury's remaining orbit. Despite Mercury's tenuous exosphere, Na+-group ions escape rate is comparable to other inner planets. This can be attributed to several processes, including that Na+-group ions may include several ion species, efficient photoionization frequency for elements within Na+-group, etc., Comment: 21 pages, 4 figures

Malaria is the leading cause of death globally, especially in sub-Saharan African countries claiming over 400,000 deaths globally each year, underscoring the critical need for continued efforts to combat this preventable and treatable disease. The objective of this study is to provide statistical guidance on the optimal preventive and control measures against malaria. Data have been collected from reliable sources, such as World Health Organization, UNICEF, Our World in Data, and STATcompiler. Data were categorized according to the factors and sub-factors related to deaths caused by malaria. These factors and sub-factors were determined based on root cause analysis and data sources. Using JMP 16 Pro software, both linear and multiple linear regression were conducted to analyze the data. The analyses aimed to establish a linear relationship between the dependent variable (malaria deaths in the overall population) and independent variables, such as life expectancy, malaria prevalence in children, net usage, indoor residual spraying usage, literate population, and population with inadequate sanitation in each selected sample country. The statistical analysis revealed that using insecticide treated nets (ITNs) by children and individuals significantly decreased the death count, as 1,000 individuals sleeping under ITNs could reduce the death count by eight. Based on the statistical analysis, this study suggests more rigorous research on the usage of ITNs.

In the last decade, the United States has lost more than 500,000 people from an overdose involving prescription and illicit opioids making it a national public health emergency (USDHHS, 2017). Medical practitioners require robust and timely tools that can effectively identify at-risk patients. Community-based social media platforms such as Reddit allow self-disclosure for users to discuss otherwise sensitive drug-related behaviors. We present a moderate size corpus of 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use: Medical Use, Misuse, Addiction, Recovery, Relapse, Not Using. For every post, we annotate span-level extractive explanations and crucially study their role both in annotation quality and model development. We evaluate several state-of-the-art models in a supervised, few-shot, or zero-shot setting. Experimental results and error analysis show that identifying the phases of opioid use disorder is highly contextual and challenging. However, we find that using explanations during modeling leads to a significant boost in classification accuracy demonstrating their beneficial role in a high-stakes domain such as studying the opioid use disorder continuum., Comment: NAACL 2024 Findings (Camera-Ready Version). Codes and Data are available at https://github.com/yangalan123/OpioidID

We discuss the synergies between heliospheric and dust science, the open science questions, the technological endeavors and programmatic aspects that are important to maintain or develop in the decade to come. In particular, we illustrate how we can use interstellar dust in the solar system as a tracer for the (dynamic) heliosphere properties, and emphasize the fairly unexplored, but potentially important science question of the role of cosmic dust in heliospheric and astrospheric physics. We show that an Interstellar Probe mission with a dedicated dust suite would bring unprecedented advances to interstellar dust research, and can also contribute-through measuring dust - to heliospheric science. This can, in particular, be done well if we work in synergy with other missions inside the solar system, thereby using multiple vantage points in space to measure the dust as it `rolls' into the heliosphere. Such synergies between missions inside the solar system and far out are crucial for disentangling the spatially and temporally varying dust flow. Finally, we highlight the relevant instrumentation and its suitability for contributing to finding answers to the research questions., Comment: 18 pages, 7 Figures, 5 Tables. Originally submitted as white paper for the National Academies Decadal Survey for Solar and Space Physics 2024-2033

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial neurons can be made that mimic many unique features of biological neurons. In this work, we train an artificial neural network of AFM neurons to perform pattern recognition. A simple machine learning algorithm called spike pattern association neuron (SPAN), which relies on the temporal position of neuron spikes, is used during training. In under a microsecond of physical time, the AFM neural network is trained to recognize symbols composed from a grid by producing a spike within a specified time window. We further achieve multi-symbol recognition with the addition of an output layer to suppress undesirable spikes. Through the utilization of AFM neurons and the SPAN algorithm, we create a neural network capable of high-accuracy recognition with overall power consumption on the order of picojoules.

We consider a general collection of function classes on the interval $[0,1]$ defined in terms of certain averages and show that monotone rearrangement does not increase the class constant in each case. The formulation includes BMO and $A_2$ with a special choice of the norm and, respectively, characteristic., Comment: 8 pages

We present Spitzer, WISE, and Herschel observations of the young supernova remnant (SNR) N132D in the LMC, including 3-40 microns Spitzer IRS mapping, 12 microns WISE and 70, 100, 160, 250, 350, and 500 microns Herschel images. The high-velocity lines of [Ne II] at 12.8 microns, [Ne III] at 15.5 microns, and [O IV] 26 microns reveal infrared ejecta concentrated in a central ring and coincide the optical and X-ray ejecta. Herschel images reveal far-IR emission coinciding with the central ejecta, which suggests that the IR emission is freshly formed, cold dust in the SN-ejecta. The infrared spectra are remarkably similar to those of another young SNR of 1E0102 with Ne and O lines. Shock modeling of the Ne ejecta emission suggests a gas temperature of 300 - 600 K and densities in the range 1000-20,000 cm^{-3} in the post-shock photoionized region. The IR continuum from the ejecta shows an 18 microns-peak dust feature. We performed spectral fitting to the IRS dust continuum and Herschel photometry. The dust mass associated with the central ejecta is 1.25+-0.65 Msun, while the 18 microns dust feature requires forsterite grains. The dust mass of the central ejecta region in N132D is higher than those of other young SNRs, which is likely associated with its higher progenitor mass. We discuss the dust productivity in the ejecta of N132D and infer its plausible implications for the dust in the early Universe., Comment: ApJ accepted, 18 pages, 19 figures

We consider a quantitative version of the space VMO on an interval, equipped with a quadratic Campanato-type norm, and prove that monotone rearrangement does not increase the norm in this space., Comment: 13 pages, 5 figures