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Cardiovascular diseases (CVD) and depression exhibit significant comorbidity, which is highly predictive of poor clinical outcomes. Yet, the underlying biological pathways remain challenging to decipher, presumably due to the non-linear associations across multiple mechanisms. In this study, we introduced a multipartite projection method based on mutual information correlations to construct multilayer disease networks as a novel approach to explore such intricate relationships. We applied this method to a cross-sectional dataset from a wave of the Young Finns Study, which includes data on CVD and depression, along with related risk factors and two omics of biomarkers: metabolites and lipids. Rather than directly correlating CVD-related phenotypes and depressive symptoms, we extended the notion of bipartite networks to create a multipartite network, linking these phenotypes and symptoms to intermediate biological variables. Projecting from these intermediate variables results in a weighted multilayer network, where each link between CVD and depression variables is marked by its layer (i.e., metabolome or lipidome). Applying this projection method, we identified potential mediating biomarkers that connect CVD with depression. These biomarkers may therefore play significant roles in the biological pathways underlying CVD-depression comorbidity. Additionally, the network projection highlighted sex and BMI as key risk factors, or confounders, in this comorbidity. Our method is scalable to incorporate any number of omics layers and various disease phenotypes, offering a comprehensive, system-level perspective on the biological pathways contributing to comorbidity.

Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated from independent latent components. If these components are found, they can be modelled univariately. Blind source separation aims to recover the latent components by estimating the unmixing transformation based on the observed data only. The current methods for spatio-temporal blind source separation are restricted to linear unmixing, and nonlinear variants have not been implemented. In this paper, we extend identifiable variational autoencoder to the nonlinear nonstationary spatio-temporal blind source separation setting and demonstrate its performance using comprehensive simulation studies. Additionally, we introduce two alternative methods for the latent dimension estimation, which is a crucial task in order to obtain the correct latent representation. Finally, we illustrate the proposed methods using a meteorological application, where we estimate the latent dimension and the latent components, interpret the components, and show how nonstationarity can be accounted and prediction accuracy can be improved by using the proposed nonlinear blind source separation method as a preprocessing method.

The current event cameras are bio-inspired sensors that respond to brightness changes in the scene asynchronously and independently for every pixel, and transmit these changes as ternary event streams. Event cameras have several benefits over conventional digital cameras, such as significantly higher temporal resolution and pixel bandwidth resulting in reduced motion blur, and very high dynamic range. However, they also introduce challenges such as the difficulty of applying existing computer vision algorithms to the output event streams, and the flood of uninformative events in the presence of oscillating light sources. Here we propose a new type of event, the gradient event, which benefits from the same properties as a conventional brightness event, but which is by design much less sensitive to oscillating light sources, and which enables considerably better grayscale frame reconstruction. We show that the gradient event -based video reconstruction outperforms existing state-of-the-art brightness event -based methods by a significant margin, when evaluated on publicly available event-to-video datasets. Our results show how gradient information can be used to significantly improve the acquisition of visual information by an event camera., Comment: 8 pages, 6 figures. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no 101016734

HMYSOs gain most of their mass in short bursts of accretion. Maser emission is an invaluable tool in discovering and probing accretion bursts. We observed the 22 GHz water maser response induced by the accretion burst in NGC6334I-MM1B and identified the underlying maser variability mechanisms. We report seven epochs of VLBI observations of 22 GHz water masers in NGC6334I with the VERA array, from 2014 to 2016, spanning the onset of the accretion burst in 2015.1. We also report 2019 ALMA observations of 321 GHz water masers and 22 GHz maser monitoring by HartRAO. We analyze variability patterns and use proper motions with the 22 GHz to 321 GHz line ratio to distinguish between masers in C-shocks and J-shocks. We also calculated the burst-to-quiescent variance ratio of the single-dish time series. The constant mean proper motion before and after the burst indicates that maser variability is due to excitation effects from variable radiation rather than jet ejecta. We find that the flux density variance ratio in the single-dish time series can identify maser efficiency variations in 22 GHz masers. The northern region, CM2-W2, is excited in C-shocks and showed long-term flaring with velocity-dependent excitation of new maser features. We propose that radiative heating of H2 due to high-energy radiation from the accretion burst be the mechanism for the flaring in CM2-W2. The southern regions are excited by J-shocks and have short-term flaring and dampening of water masers. We attributed the diverse variability patterns in the southern regions to the radiative transfer of the burst energy in the source. Our results indicate that the effects of source geometry, shock type, and incident radiation spectrum are fundamental factors affecting 22 GHz maser variability. Investigating water masers in irradiated shocks will improve their use as a diagnostic in time-variable radiation environments., Comment: 21 pages, 12 figures, Table 2 in electronic publication. Contact: jakobus.vorster@helsinki.fi

Radiative transfer (RT) modelling is a necessary tool in the interpretation of observations of the thermal emission of interstellar dust. It is also often part of multi-physics modelling. In this context, the efficiency of radiative transfer calculations is important, even for one-dimensional models. We investigate the use of the so-called immediate re-emission (IRE) method for fast calculation of one-dimensional spherical cloud models. We wish to determine whether weighting methods similar to those used in traditional Monte Carlo simulations can speed up the estimation of dust temperature. We present the program DIES, a parallel implementation of the IRE method, which makes it possible to do the calculations also on graphics processing units (GPUs). We tested the program with externally and internally heated cloud models, and examined the potential improvements from the use of different weighted sampling schemes. The execution times of the program compare favourably with previous programs, especially when run on GPUs. On the other hand, weighting schemes produce only limited improvements. In the case of an internal radiation source, the basic IRE method samples the re-emission well, while traditional Monte Carlo requires the use of spatial importance sampling. Some noise reduction could be achieved for externally heated models by weighting the initial photon directions. Only in optically very thin models does weighting - such as the proposed method of forced first interaction - result in noise reduction by a factor of several. The IRE method performs well for both internally and externally heated models, typically without the need for any additional weighting schemes. With run times of the order of one second for our test models, the DIES program is suitable even for larger parameter studies., Comment: Accepted for publication in A&A

Due to their length and complexity, long regulatory texts are challenging to summarize. To address this, a multi-step extractive-abstractive architecture is proposed to handle lengthy regulatory documents more effectively. In this paper, we show that the effectiveness of a two-step architecture for summarizing long regulatory texts varies significantly depending on the model used. Specifically, the two-step architecture improves the performance of decoder-only models. For abstractive encoder-decoder models with short context lengths, the effectiveness of an extractive step varies, whereas for long-context encoder-decoder models, the extractive step worsens their performance. This research also highlights the challenges of evaluating generated texts, as evidenced by the differing results from human and automated evaluations. Most notably, human evaluations favoured language models pretrained on legal text, while automated metrics rank general-purpose language models higher. The results underscore the importance of selecting the appropriate summarization strategy based on model architecture and context length., Comment: Under review

In the last decade, an impressive increase in software adaptions has led to a surge in log data production, making manual log analysis impractical and establishing the necessity for automated methods. Conversely, most automated analysis tools include a component designed to separate log templates from their parameters, commonly referred to as a "log parser". This paper aims to introduce a new fast and accurate log parser, named "Tipping". Tipping combines rule-based tokenizers, interdependency token graphs, strongly connected components, and various techniques to ensure rapid, scalable, and precise log parsing. Furthermore, Tipping is parallelized and capable of running on multiple processing cores with close to linear efficiency. We evaluated Tipping against other state-of-the-art log parsers in terms of accuracy, performance, and the downstream task of anomaly detection. Accordingly, we found that Tipping outperformed existing methods in accuracy and performance in our evaluations. More in-depth, Tipping can parse 11 million lines of logs in less than 20 seconds on a laptop machine. Furthermore, we re-implemented a parallelized version of the past IpLom algorithm to demonstrate the effect of parallel processing, and it became the second-fastest parser. As logs keep growing in volume and complexity, the software engineering community needs to ensure automated log analysis tools keep up with the demand, being capable of efficiently handling massive volumes of logs with high accuracy. Tipping's robustness, versatility, efficiency, and scalability make it a viable tool for the modern automated log analysis task.

Every known communication problem whose randomized communication cost is constant (independent of the input size) can be reduced to $k$-Hamming Distance, that is, solved with a constant number of deterministic queries to some $k$-Hamming Distance oracle. We exhibit the first examples of constant-cost problems which cannot be reduced to $k$-Hamming Distance. To prove this separation, we relate it to a natural coding-theoretic question. For $f : \{2, 4, 6\} \to \mathbb{N}$, we say an encoding function $E : \{0, 1\}^n \to \{0, 1\}^m$ is an $f$-code if it transforms Hamming distances according to $\mathrm{dist}(E(x), E(y)) = f(\mathrm{dist}(x, y))$ whenever $f$ is defined. We prove that, if there exist $f$-codes for infinitely many $n$, then $f$ must be affine: $f(4) = (f(2) + f(6))/2$.

The Thermal Management Integrated System (TheMIS) is a key element of the Australia-Italy Space Industry Responsive Intelligent Thermal (SpIRIT) mission, launched in a 510km Polar Sun-Synchronous orbit in December 2023. SpIRIT is a 6U CubeSat led by The University of Melbourne in cooperation with ASI, with support from ASA and with contributions from Australian space industry and international research organizations. The TheMIS subsystem actively cools and controls the temperature of sensitive instruments, increasing the potential range of payloads supported on small spacecraft systems. TheMIS core functionality is based on a commercial Stirling Cycle Cryocooler in-principle capable of reaching cold-tip temperatures below T=100K. The cooler is operated by customized control electronics and is connected to deployable radiators through pyrolytic graphite sheet thermal straps, all developed by the University of Melbourne. Until now, this level of thermal control has been relatively uncommon in nanosatellites. TheMIS aims to validate the design and performance by controlling the thermal environment of SpIRIT's HERMES payload, an X-ray instrument provided by ASI which has a noise background strongly sensitive to temperature. Beyond SpIRIT, TheMIS has the potential to support a broad range of applications, including holding infrared focal plane arrays at cryogenic temperatures, and increasing resilience of electronics to space weather. This paper provides an overview of TheMIS's design, implementation, and operational performance, detailing the commissioning phase and the early results obtained from its operations in orbit, with comparison to the thermal model developed during the mission environmental testing campaign. Finally, the paper discusses ongoing challenges for thermal management of payloads in small satellite systems and potential future strategies for continuous improvement, Comment: Smallsats 2024

Using new continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey and archival VLA, 4.86 GHz data, we present direct observational evidence of hierarchical triggering relating three epochs of massive star formation in a ring-like H II region, G24.47+0.49. We find from radio flux analysis that it is excited by a massive star(s) of spectral type O8.5V-O8V from the first epoch of star formation. The swept-up ionized ring structure shows evidence of secondary collapse, and within this ring a burst of massive star formation is observed in different evolutionary phases, which constitutes the second epoch. ATOMS spectral line (e.g., HCO$^+$(1-0)) observations reveal an outer concentric molecular gas ring expanding at a velocity of $\sim$ 9 $\rm km\,s^{-1}$, constituting the direct and unambiguous detection of an expanding molecular ring. It harbors twelve dense molecular cores with surface mass density greater than 0.05 $\rm g\,cm^{-2}$, a threshold typical of massive star formation. Half of them are found to be subvirial, and thus in gravitational collapse, making them third epoch of potential massive star-forming sites., Comment: 18 pages, 7 figures, accepted for publication in The Astrophysical Journal Letters

Large language models are redefining software engineering by implementing AI-powered techniques throughout the whole software development process, including requirement gathering, software architecture, code generation, testing, and deployment. However, it is still difficult to develop a cohesive platform that consistently produces the best outcomes across all stages. The objective of this study is to develop a unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables. These deliverables include user stories, prioritization, and UML sequence diagrams, along with the modular approach to APIs, unit tests, and end-to-end tests. Additionally, the platform will organize tasks, perform security and compliance, and suggest design patterns and improvements for non-functional requirements. We allow users to control and manage each phase according to their preferences. In addition, the platform provides security and compliance checks following European standards and proposes design optimizations. We use multiple models, such as GPT-3.5, GPT-4, and Llama3 to enable to generation of modular code as per user choice. The research also highlights the limitations and future research discussions to overall improve the software development life cycle. The source code for our uniform platform is hosted on GitHub, enabling additional experimentation and supporting both research and practical uses. \end

We report a study of a cavity optomechanical system driven by narrow-band electromagnetic fields, which are applied either in the form of uncorrelated noise, or as a more structured spectrum. The bandwidth of the driving spectra is smaller than the mechanical resonant frequency, and thus we can describe the resulting physics using concepts familiar from regular cavity optomechanics in the resolved-sideband limit. With a blue-detuned noise driving, the noise-induced interaction leads to anti-damping of the mechanical oscillator, and a self-oscillation threshold at an average noise power that is comparable to that of a coherent driving tone. This process can be seen as noise-induced dynamical amplification of mechanical motion. However, when the noise bandwidth is reduced down to the order of the mechanical damping, we discover a large shift of the power threshold of self-oscillation. This is due to the oscillator adiabatically following the instantaneous noise profile. In addition to blue-detuned noise driving, we investigate narrow-band driving consisting of two coherent drive tones nearby in frequency. Also in these cases, we observe deviations from a naive optomechanical description relying only on the tones' frequencies and powers.

Using the SMUDGes and SDSS catalogs, and our own reprocessing of the Legacy Surveys imaging, we investigate the properties of nuclear star clusters (NSCs) in galaxies having central surface brightnesses as low as 27 mag arcsec$^{-2}$. We identify 273 (123 with known redshift) and 32 NSC-bearing galaxies in the two samples, respectively, where we require candidate NSCs to have a separation of less than 0.10$r_e$ from the galaxy center. We find that galaxies with low central surface brightness ($\mu_{0,g} > 24$ mag arcsec$^{-2}$) are more likely to contain an NSC if 1) they have a higher stellar mass, 2) a higher stellar to total mass ratio, 3) a brighter central surface brightness, 4) a larger axis ratio, or 5) lie in a denser environment. Because of the correlations among these various quantities, it is likely that only one or two are true physical drivers. We also find scaling relations for the NSC mass with stellar mass ($M_{NSC}/$\Msol$ = 10^{6.02\pm0.03}(M_{*,gal}/10^{8} $\Msol$)^{0.77\pm0.04}$) and halo mass ($M_{NSC}/$\Msol$ = 10^{6.11\pm0.05}(M_{h,gal}/10^{10} $\Msol$)^{0.92\pm0.05}$), although it is the scaling with halo mass that is consistent with a direct proportionality. In galaxies with an NSC, $M_{NSC} \approx 10^{-4}M_{h,gal}$. This proportionality echoes the finding of a direct proportionality between the mass (or number) of globular clusters (GCs) in galaxies and the galaxy's total mass. These findings favor a related origin for GCs and NSCs., Comment: 21 pages, 16 figures, accepted for publication in AJ

Context. While Class II stars have already accreted most of their mass, the continued inflow of fresh material via Bondi-Hoyle accretion acts as an additional mass reservoir for their circumstellar disks. This may explain the observed accretion rates of pre-main- sequence (PMS) stars, as well as observational inconsistencies in the mass and angular momentum balance of their disks. Aims. Using a new simulation that reproduces the stellar IMF, we want to quantify the role of Bondi-Hoyle accretion in the formation of Class II disks, as well address the prospect of its observational detection with the James Webb Space Telescope (JWST). Methods. We study the mass and angular momentum of the accreting gas using passively-advected tracer particles in the simulation, and we carry out radiative transfer calculations of near-infrared scattering to generate synthetic JWST observations of Bondi-Hoyle trails of PMS stars. Results. Gas accreting on Class II PMS stars approximately 1 Myr after their formation has enough mass and angular momentum to strongly affect the evolution of the preexisting disks. The accreted angular momentum is large enough to also explain the observed size of Class II disks. The orientation of the angular momentum vector can differ significantly from that of the previously accreted gas, which may result in a significant disk warping or misalignment. We also predict that JWST observations of Class II stars will be able to detect Bondi-Hoyle trails with a 90% success rate with only 2 min exposure time, if stars with accretion rates \dot{M} > 5e-10 Msol/yr and luminosity of L > 0.5 Lsol are selected., Comment: 12 pages, 16 figures, submitted to A&A

Systematic review (SR) is a popular research method in software engineering (SE). However, conducting an SR takes an average of 67 weeks. Thus, automating any step of the SR process could reduce the effort associated with SRs. Our objective is to investigate if Large Language Models (LLMs) can accelerate title-abstract screening by simplifying abstracts for human screeners, and automating title-abstract screening. We performed an experiment where humans screened titles and abstracts for 20 papers with both original and simplified abstracts from a prior SR. The experiment with human screeners was reproduced with GPT-3.5 and GPT-4 LLMs to perform the same screening tasks. We also studied if different prompting techniques (Zero-shot (ZS), One-shot (OS), Few-shot (FS), and Few-shot with Chain-of-Thought (FS-CoT)) improve the screening performance of LLMs. Lastly, we studied if redesigning the prompt used in the LLM reproduction of screening leads to improved performance. Text simplification did not increase the screeners' screening performance, but reduced the time used in screening. Screeners' scientific literacy skills and researcher status predict screening performance. Some LLM and prompt combinations perform as well as human screeners in the screening tasks. Our results indicate that the GPT-4 LLM is better than its predecessor, GPT-3.5. Additionally, Few-shot and One-shot prompting outperforms Zero-shot prompting. Using LLMs for text simplification in the screening process does not significantly improve human performance. Using LLMs to automate title-abstract screening seems promising, but current LLMs are not significantly more accurate than human screeners. To recommend the use of LLMs in the screening process of SRs, more research is needed. We recommend future SR studies publish replication packages with screening data to enable more conclusive experimenting with LLM screening., Comment: Accepted to the International Conference on Evaluation and Assessment in Software Engineering (EASE), 2024 edition

The increasing cloudification and softwarization of networks foster the interplay among multiple independently managed deployments. An appealing reason for such an interplay lies in distributed Machine Learning (ML), which allows the creation of robust ML models by leveraging collective intelligence and computational power. In this paper, we study the application of the two cornerstones of distributed learning, namely Federated Learning (FL) and Knowledge Distillation (KD), on the Wi-Fi Access Point (AP) load prediction use case. The analysis conducted in this paper is done on a dataset that contains real measurements from a large Wi-Fi campus network, which we use to train the ML model under study based on different strategies. Performance evaluation includes relevant aspects for the suitability of distributed learning operation in real use cases, including the predictive performance, the associated communication overheads, or the energy consumption. In particular, we prove that distributed learning can improve the predictive accuracy centralized ML solutions by up to 93% while reducing the communication overheads and the energy cost by 80%.

Light is characterized by its electric field, yet quantum optics has revealed the importance of monitoring photon-photon correlations at all orders. We here present a comparative study of two experimental setups, composed of cold and warm Rubidium atoms, respectively, which allow us to probe and compare photon correlations up to the fourth order. The former operates in the quantum regime where spontaneous emission dominates, whereas the latter exhibits a temperature-limited coherence time. While both setups present almost-chaotic light statistics, we discuss how the access to different orders of photon correlations allows one to better characterize the mechanisms responsible for deviations from those statistics.

Leveraging the high resolution, high sensitivity, and wide frequency coverage of the Atacama Large Millimeter/submillimeter Array (ALMA), the QUARKS survey, standing for "Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures", is observing 139 massive star-forming clumps at ALMA Band 6 ($\lambda\sim$ 1.3 mm). This paper introduces the Atacama Compact Array (ACA) 7-m data. Combining multi-wavelength data, we provide the first edition of QUARKS atlas, offering insights into the multiscale and multiphase interstellar medium in high-mass star formation. The ACA 1.3 mm catalog includes 207 continuum sources that are called ACA sources. Their gas kinetic temperatures are estimated using three formaldehyde (H$_2$CO) transitions with a non-LTE radiation transfer model, and the mass and density are derived from a dust emission model. The ACA sources are massive (16-84 percentile values of 6-160 $M_{\odot}$), gravity-dominated ($M\propto R^{1.1}$) fragments within massive clumps, with supersonic turbulence ($\mathcal{M}>1$) and embedded star-forming protoclusters. We find a linear correlation between the masses of the fragments and the massive clumps, with a ratio of 6% between the two. When considering the fragments as representative of dense gas, the ratio indicates a dense gas fraction (DGF) of 6%, although with a wide scatter ranging from 1% to 10%. If we consider the QUARKS massive clumps to be what is observed at various scales, then the size-independent DGF indicates a self-similar fragmentation or collapsing mode in protocluster formation. With the ACA data over four orders of magnitude of luminosity-to-mass ratio ($L/M$), we find that the DGF increases significantly with $L/M$, which indicates clump evolutionary stage. We observed a limited fragmentation at the subclump scale, which can be explained by dynamic global collapse process., Comment: 24 pages, 7 figures. Accepted for publication in Research in Astronomy and Astrophysics. QUARKS atlas link: https://drive.google.com/file/d/1KTqXxCDduYepvLd9kIvZVSSytK48OmfL/view?usp=sharing

Microservice-based systems (MSS) may experience failures in various fault categories due to their complex and dynamic nature. To effectively handle failures, AIOps tools utilize trace-based anomaly detection and root cause analysis. In this paper, we propose a novel framework for few-shot abnormal trace classification for MSS. Our framework comprises two main components: (1) Multi-Head Attention Autoencoder for constructing system-specific trace representations, which enables (2) Transformer Encoder-based Model-Agnostic Meta-Learning to perform effective and efficient few-shot learning for abnormal trace classification. The proposed framework is evaluated on two representative MSS, Trainticket and OnlineBoutique, with open datasets. The results show that our framework can adapt the learned knowledge to classify new, unseen abnormal traces of novel fault categories both within the same system it was initially trained on and even in the different MSS. Within the same MSS, our framework achieves an average accuracy of 93.26\% and 85.2\% across 50 meta-testing tasks for Trainticket and OnlineBoutique, respectively, when provided with 10 instances for each task. In a cross-system context, our framework gets an average accuracy of 92.19\% and 84.77\% for the same meta-testing tasks of the respective system, also with 10 instances provided for each task. Our work demonstrates the applicability of achieving few-shot abnormal trace classification for MSS and shows how it can enable cross-system adaptability. This opens an avenue for building more generalized AIOps tools that require less system-specific data labeling for anomaly detection and root cause analysis., Comment: 12 pages

Swarm robotics promises adaptability to unknown situations and robustness against failures. However, it still struggles with global tasks that require understanding the broader context in which the robots operate, such as identifying the shape of the arena in which the robots are embedded. Biological swarms, such as shoals of fish, flocks of birds, and colonies of insects, routinely solve global geometrical problems through the diffusion of local cues. This paradigm can be explicitly described by mathematical models that could be directly computed and exploited by a robotic swarm. Diffusion over a domain is mathematically encapsulated by the Laplacian, a linear operator that measures the local curvature of a function. Crucially the geometry of a domain can generally be reconstructed from the eigenspectrum of its Laplacian. Here we introduce spectral swarm robotics where robots diffuse information to their neighbors to emulate the Laplacian operator - enabling them to "hear" the spectrum of their arena. We reveal a universal scaling that links the optimal number of robots (a global parameter) with their optimal radius of interaction (a local parameter). We validate experimentally spectral swarm robotics under challenging conditions with the one-shot classification of arena shapes using a sparse swarm of Kilobots. Spectral methods can assist with challenging tasks where robots need to build an emergent consensus on their environment, such as adaptation to unknown terrains, division of labor, or quorum sensing. Spectral methods may extend beyond robotics to analyze and coordinate swarms of agents of various natures, such as traffic or crowds, and to better understand the long-range dynamics of natural systems emerging from short-range interactions.

Spinel oxides are recognized as promising Fenton-like catalysts for the degradation of antibiotics. However, the catalytic performance is restrained by the poor electron transfer rate (ETR). Herein, hollow NiCo2O4@C nanocages are rationally designed and prepared to accelerate ETR in peroxymonosulfate (PMS) activation for tetracycline (TC) degradation.

Systematic Literature Reviews (SLRs) have become the foundation of evidence-based studies, enabling researchers to identify, classify, and combine existing studies based on specific research questions. Conducting an SLR is largely a manual process. Over the previous years, researchers have made significant progress in automating certain phases of the SLR process, aiming to reduce the effort and time needed to carry out high-quality SLRs. However, there is still a lack of AI agent-based models that automate the entire SLR process. To this end, we introduce a novel multi-AI agent model designed to fully automate the process of conducting an SLR. By utilizing the capabilities of Large Language Models (LLMs), our proposed model streamlines the review process, enhancing efficiency and accuracy. The model operates through a user-friendly interface where researchers input their topic, and in response, the model generates a search string used to retrieve relevant academic papers. Subsequently, an inclusive and exclusive filtering process is applied, focusing on titles relevant to the specific research area. The model then autonomously summarizes the abstracts of these papers, retaining only those directly related to the field of study. In the final phase, the model conducts a thorough analysis of the selected papers concerning predefined research questions. We also evaluated the proposed model by sharing it with ten competent software engineering researchers for testing and analysis. The researchers expressed strong satisfaction with the proposed model and provided feedback for further improvement. The code for this project can be found on the GitHub repository at https://github.com/GPT-Laboratory/SLR-automation., Comment: 12 Pages, 7 Figures

The fitting of spectral lines is a common step in the analysis of line observations and simulations. However, the observational noise, the presence of multiple velocity components, and potentially large data sets make it a non-trivial task. We present a new computer program Spectrum Iterative Fitter (SPIF) for the fitting of spectra with Gaussians or with hyperfine line profiles. The aim is to show the computational efficiency of the program and to use it to examine the general accuracy of approximating spectra with simple models. We describe the implementation of the program. To characterise its performance, we examined spectra with isolated Gaussian components or a hyperfine structure, also using synthetic observations from numerical simulations of interstellar clouds. We examined the search for the globally optimal fit and the accuracy to which single-velocity-component and multi-component fits recover true values for parameters such as line areas, velocity dispersion, and optical depth. The program is shown to be fast, with fits of single Gaussian components reaching on graphics processing units speeds approaching one million spectra per second. This also makes it feasible to use Monte Carlo simulations or Markov chain Monte Carlo calculations for the error estimation. However, in the case of hyperfine structure lines, degeneracies affect the parameter estimation and can complicate the derivation of the error estimates. The use of many random initial values makes the fits more robust, both for locating the global $\chi^2$ minimum and for the selection of the optimal number of velocity components., Comment: Accepted for publication in A&A

Feedback-based control of nano- and micromechanical resonators can enable the study of macroscopic quantum phenomena and also sensitive force measurements. Here, we demonstrate the feedback cooling of a low-loss and high-stress macroscopic SiN membrane resonator close to its quantum ground state. We use the microwave optomechanical platform, where the resonator is coupled to a microwave cavity. The experiment utilizes a Josephson travelling wave parametric amplifier, which is nearly quantum-limited in added noise, and is important to mitigate resonator heating due to system noise in the feedback loop. We reach a thermal phonon number as low as 1.6, which is limited primarily by microwave-induced heating. We also discuss the sideband asymmetry observed when a weak microwave tone for independent readout is applied in addition to other tones used for the cooling. The asymmetry can be qualitatively attributed to the quantum-mechanical imbalance between emission and absorption. However, we find that the observed asymmetry is only partially due to this quantum effect. In specific situations, the asymmetry is fully dominated by a cavity Kerr effect under multitone irradiation.

We present our work on predicting United Nations sustainable development goals (SDG) for university courses. We use an LLM named PaLM 2 to generate training data given a noisy human-authored course description input as input. We use this data to train several different smaller language models to predict SDGs for university courses. This work contributes to better university level adaptation of SDGs. The best performing model in our experiments was BART with an F1-score of 0.786., Comment: 3 figures, 2 tables

This study pioneers the use of synthetically generated data for training generative models in document-level text simplification of German texts. We demonstrate the effectiveness of our approach with real-world online texts. Addressing the challenge of data scarcity in language simplification, we crawled professionally simplified German texts and synthesized a corpus using GPT-4. We finetune Large Language Models with up to 13 billion parameters on this data and evaluate their performance. This paper employs various methodologies for evaluation and demonstrates the limitations of currently used rule-based metrics. Both automatic and manual evaluations reveal that our models can significantly simplify real-world online texts, indicating the potential of synthetic data in improving text simplification., Comment: Accepted at Fourth Workshop on Language Technology for Equality, Diversity, Inclusion - EACL 2024

In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the prompt. However, trustworthy issues with LLM's response, such as hallucination, have also been actively discussed. Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning. In this work, we delve into the predictive uncertainty of LLMs associated with in-context learning, highlighting that such uncertainties may stem from both the provided demonstrations (aleatoric uncertainty) and ambiguities tied to the model's configurations (epistemic uncertainty). We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. Extensive experiments are conducted to demonstrate the effectiveness of the decomposition. The code and data are available at: https://github.com/lingchen0331/UQ_ICL., Comment: Accepted to the main conference of NAACL 2024

We expand on earlier research on the topic by discussing an infinitely repeated game model with a subgame perfect equilibrium strategy profile (SPE) as a solution concept that diminishes incentives to violate speed limits in a carrot and stick fashion. In attempts to construct an SPE strategy profile, the initial state is chosen such that the drivers are playing a mixed strategy whereas the police is not enforcing with certainty. We also postulate a short period version of the repeated game with generalized stage game payoffs. For this game, we construct a multistage strategy profile that is a Nash equilibrium but not an SPE. Some solution candidates are excluded by showing that they do not satisfy a one shot deviation property that is a necessary condition for an SPE profile in a repeated game of perfect information., Comment: 26 pages

Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) are reshaping the field of Software Engineering (SE). Existing LLM-based multi-agent systems have successfully resolved simple dialogue tasks. However, the potential of LLMs for more complex tasks, such as automated code generation for large and complex projects, have been explored in only a few existing works. This paper introduces CodePori, a novel model designed to automate code generation for extensive and complex software projects based on natural language prompts. We employ LLM-based multi-AI agents to handle creative and challenging tasks in autonomous software development. Each agent engages with a specific task, including system design, code development, code review, code verification, and test engineering. We show in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars. It identifies and mitigates potential security vulnerabilities and corrects errors while maintaining a solid code performance level. We also conducted an evaluation of CodePori against existing solutions using HumanEval and the Massively Multitask Benchmark for Python (MBPP) benchmark. The results indicate that CodePori improves upon the benchmarks in terms of code accuracy, efficiency, and overall performance. For example, CodePori improves the pass@1 metric on HumanEval to 87.5% and on MBPP to 86.5%, representing a clear improvement over the existing models. We also assessed CodePori's performance through practitioner evaluations, with 91% expressing satisfaction with the model's performance., Comment: 10 pages and 3 figures

Artificial Intelligence (AI) has witnessed remarkable growth, particularly through the proliferation of Deep Neural Networks (DNNs). These powerful models drive technological advancements across various domains. However, to harness their potential in real-world applications, specialized hardware accelerators are essential. This demand has sparked a market for parameterizable AI hardware accelerators offered by different vendors. Manufacturers of AI-integrated products face a critical challenge: selecting an accelerator that aligns with their product's performance requirements. The decision involves choosing the right hardware and configuring a suitable set of parameters. However, comparing different accelerator design alternatives remains a complex task. Often, engineers rely on data sheets, spreadsheet calculations, or slow black-box simulators, which only offer a coarse understanding of the performance characteristics. The Abstract Computer Architecture Description Language (ACADL) is a concise formalization of computer architecture block diagrams, which helps to communicate computer architecture on different abstraction levels and allows for inferring performance characteristics. In this paper, we demonstrate how to use the ACADL to model AI hardware accelerators, use their ACADL description to map DNNs onto them, and explain the timing simulation semantics to gather performance results., Comment: Accepted Version for: MBMV'24

High-latitude ($|b|>30^{\circ}$) molecular clouds have virial parameters that exceed 1, but whether these clouds can form stars has not been studied systematically. Using JCMT SCUBA-2 archival data, we surveyed 70 fields that target high-latitude Planck galactic cold clumps (HLPCs) to find dense cores with density of $10^{5}$-$10^{6}$ cm$^{-3}$ and size of $<0.1$ pc. The sample benefits from both the representativeness of the parent sample and covering densest clumps at the high column density end ($>1\times10^{21}$ cm$^{-2}$). At an average noise rms of 15 mJy/beam, we detected Galactic dense cores in only one field, G6.04+36.77 (L183), while also identifying 12 extragalactic objects and two young stellar objects. Compared to the low-latitude clumps, dense cores are scarce in HLPCs. With synthetic observations, the densities of cores are constrained to be $n_c\lesssim10^5$ cm$^{-3}$, should they exist in HLPCs. Low-latitude clumps, Taurus clumps, and HLPCs form a sequence where a higher virial parameter corresponds to a lower dense core detection rate. If HLPCs were affected by the Local Bubble, the scarcity should favor turbulence-inhibited rather than supernova-driven star formation. Studies of the formation mechanism of the L183 molecular cloud are warranted., Comment: 9 pages for the main text. 4 figures, 1 table. Published in Astrophysical Journal Letter

Electrostatically tunable Josephson field-effect transistors (JoFETs) are one of the most desired building blocks of quantum electronics. JoFET applications range from parametric amplifiers and superconducting qubits to a variety of integrated superconducting circuits. Here, we report on graphene JoFET devices fabricated with wafer-scale complementary metal-oxide-semiconductor (CMOS) compatible processing based on wet transfer of chemical vapour deposited graphene, atomic-layer-deposited Al$_{2}$O$_{3}$ gate oxide, and evaporated superconducting Ti/Al source, drain, and gate contacts. By optimizing the contact resistance down to $\sim$ 170 $\Omega \mu m$, we observe proximity-induced superconductivity in the JoFET channels with different gate lengths of 150 - 350 nm. The Josephson junction devices show reproducible critical current $I_{\text{C}}$ tunablity with the local top gate. Our JoFETs are in short diffusive limit with the $I_{\text{C}}$ reaching up to $\sim\,$3 $\mu A$ for a 50 $\mu m$ channel width. Overall, our demonstration of CMOS-compatible 2D-material-based JoFET fabrication process is an important step toward graphene-based integrated quantum circuits., Comment: revised manuscript; 30 pages, 4 figures, 1 table, 12 supplementary figures

We observe the magnetic field morphology towards a nearby star-forming filamentary cloud, G202.3+2.5, by the JCMT/POL-2 850 {\mu}m thermal dust polarization observation with an angular resolution of 14.4" (~0.053 pc). The average magnetic field orientation is found to be perpendicular to the filaments while showing different behaviors in the four subregions, suggesting various effects from filaments' collision in these subregions. With the kinematics obtained by N2H+ observation by IRAM, we estimate the plane-of-sky (POS) magnetic field strength by two methods, the classical Davis-Chandrasekhar-Fermi (DCF) method and the angular dispersion function (ADF) method, B_{pos,dcf} and B_{pos,adf} are ~90 {\mu}G and ~53 {\mu}G. We study the relative importance between the gravity (G), magnetic field (B) and turbulence (T) in the four subregions, find G > T > B, G >= T > B, G ~ T > B and T > G > B in the north tail, west trunk, south root and east wing, respectively. In addition, we investigate the projection effect on the DCF and ADF methods based on a similar simulation case and find the 3D magnetic field strength may be underestimated by a factor of ~3 if applying the widely-used statistical B_{pos}-to-B_{3D} factor when using DCF or ADF method, which may further underestimate/overestimate related parameters., Comment: Accepted by ApJ. 20 pages, 9 figures

Only a handful of massive starless core candidates have been discovered so far, but none of them have been fully confirmed. Within the MM1 clump in the filamentary infrared dark cloud G34.43+0.24 that was covered by the ALMA-ATOMS survey at Band 3 ($\sim2\arcsec$, 6000\,au) and the ALMA-QUARKS survey at Band 6 ($\sim 0.3\arcsec$, 900\,au), two prestellar core candidates MM1-C and E1 with masses of 71 and 20 \solarmass~and radii of 2100--4400\,au were discovered. The two cores show no obvious sign of star-formation activities. In particular, MM1-C is a very promising massive prestellar core candidate with a total gas mass of 71\,\solarmass. Within MM1-C, we detected two extremely dense substructures, C1 and C2, as characterized by their high densities of $\rm n_{H_2}\sim 10^{8-9} cm^{-3}$. Moreover, evidence of further fragmentation in C2 was also revealed. We have detected the primordial fragmentation in the earliest stage of massive star formation, and we speculate that MM1-C would be the birthplace of a massive multiple system. However, we cannot fully rule out the possibility that the massive prestellar core MM1-C will just form a cluster of low-mass stars if it undergoes further fragmentation., Comment: 12 pages, 6 figures

Cubic silicon-carbide crystals, known for their high thermal conductivity and in-plane stress, hold significant promise for the development of high-quality ($Q$) mechanical oscillators. Enabling coherent electrical manipulation of long-lived mechanical resonators would be instrumental in advancing the development of phononic memories, repeaters, and transducers for microwave quantum states. In this study, we demonstrate the compatibility of high-stress and crystalline (3C-phase) silicon-carbide membranes with superconducting microwave circuits. We establish a coherent electromechanical interface for long-lived phonons, allowing precise control over the electromechanical cooperativity. This interface enables tunable slow-light time with group delays extending up to an impressive duration of \emph{an hour}. We then investigate a phononic memory based on the high-$Q$ ($10^{8}$) silicon-carbide membrane, capable of storing and retrieving microwave coherent states \emph{on-demand}. The thermal and coherent components can be distinguished through state tomography in quadrature phase space, which shows an exponential increase and decay trend respectively as the storage time increases. The electromechanical interface and phononic memory made from crystalline silicon-carbide membrane possess enticing attributes, including low microwave-induced mechanical heating, phase coherence, an energy decay time of $T_{1}=19.9$~s, and it acquires less than one quantum noise within $\tau_{\textrm{coh}}=41.3$~ms storage period. These findings underscore the unique opportunities provided by cubic silicon-carbide membrane crystals for the storage and transfer of quantum information across distinct components of hybrid quantum systems.