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In the field of categorical probability, one uses concepts and techniques from category theory, such as monads and monoidal categories, to study the structures of probability and statistics. In this paper, we connect some ideas from categorical algebra, namely weakly cartesian functors and natural transformations, to the idea of conditioning in probability theory, using Markov categories and probability monads. First of all, we show that under some conditions, the monad associated to a Markov category with conditionals has a weakly cartesian functor and weakly cartesian multiplication (a condition known as Beck-Chevalley, or BC). In particular, we show that this is the case for the Giry monad on standard Borel spaces. We then connect this theory to existing results on statistical experiments. We show that for deterministic statistical experiments, the so-called standard measure construction (which can be seen as a generalization of the "hyper-normalizations" introduced by Jacobs) satisfies a universal property, allowing an equivalent definition which does not rely on the existence of conditionals., Comment: 33 pages, part of this research is appearing in Bohinen's MSc thesis (University of Oxford)

We present a large language models (LLMs) based multi-agent system to automate the refactoring of Haskell codebases. The multi-agent system consists of specialized agents performing tasks such as context analysis, refactoring, validation, and testing. Refactoring improvements are using metrics such as cyclomatic complexity, run-time, and memory allocation. Experimental evaluations conducted on Haskell codebases demonstrate improvements in code quality. Cyclomatic complexity was reduced by 13.64% and 47.06% in the respective codebases. Memory allocation improved by 4.17% and 41.73%, while runtime efficiency increased by up to 50%. These metrics highlight the systems ability to optimize Haskells functional paradigms while maintaining correctness and scalability. Results show reductions in complexity and performance enhancements across codebases. The integration of LLMs based multi-agent system enables precise task execution and inter-agent collaboration, addressing the challenges of refactoring in functional programming. This approach aims to address the challenges of refactoring functional programming languages through distributed and modular systems.

The long-term goal of this project is the non-perturbative renormalization of the energy-momentum tensor in the 2d $O(3)$ non-linear sigma model using different methods which have been developed for QCD applications. As a first step, we have identified all operators that mix with the energy-momentum tensor once a lattice discretization is employed, that is all which are compatible with power counting and with the symmetries of the theory. Since these operators are constrained by non-linear Ward identities arising from the non-linear realization of the $O(3)$ symmetry, this is not entirely straightforward on the technical level. We have also outlined the basics of ongoing numerical simulations with shifted boundary conditions and an optimized constraint action to minimize lattice artifacts., Comment: 7 pages, Contribution to the 41st International Symposium on Lattice Field Theory (LATTICE2024), 28 July - 3 August 2024, Liverpool, UK

Stroke is globally a major cause of mortality and morbidity, and hence accurate and rapid diagnosis of stroke is valuable. Retinal fundus imaging reveals the known markers of elevated stroke risk in the eyes, which are retinal venular widening, arteriolar narrowing, and increased tortuosity. In contrast to other imaging techniques used for stroke diagnosis, the acquisition of fundus images is easy, non-invasive, fast, and inexpensive. Therefore, in this study, we propose a multi-view stroke network (MVS-Net) to detect stroke and transient ischemic attack (TIA) using retinal fundus images. Contrary to existing studies, our study proposes for the first time a solution to discriminate stroke and TIA with deep multi-view learning by proposing an end-to-end deep network, consisting of multi-view inputs of fundus images captured from both right and left eyes. Accordingly, the proposed MVS-Net defines representative features from fundus images of both eyes and determines the relation within their macula-centered and optic nerve head-centered views. Experiments performed on a dataset collected from stroke and TIA patients, in addition to healthy controls, show that the proposed framework achieves an AUC score of 0.84 for stroke and TIA detection.

The use of Large Language Models (LLMs) for autonomous code generation is gaining attention in emerging technologies. As LLM capabilities expand, they offer new possibilities such as code refactoring, security enhancements, and legacy application upgrades. Many outdated web applications pose security and reliability challenges, yet companies continue using them due to the complexity and cost of upgrades. To address this, we propose an LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions. The system distributes tasks across multiple phases, updating all relevant files. To evaluate its effectiveness, we employed Zero-Shot Learning (ZSL) and One-Shot Learning (OSL) prompts, applying identical instructions in both cases. The evaluation involved updating view files and measuring the number and types of errors in the output. For complex tasks, we counted the successfully met requirements. The experiments compared the proposed system with standalone LLM execution, repeated multiple times to account for stochastic behavior. Results indicate that our system maintains context across tasks and agents, improving solution quality over the base model in some cases. This study provides a foundation for future model implementations in legacy code updates. Additionally, findings highlight LLMs' ability to update small outdated files with high precision, even with basic prompts. The source code is publicly available on GitHub: https://github.com/alasalm1/Multi-agent-pipeline., Comment: 13 pages, 2 figures

We report the observations of J=1-0 of HCN, HCO+, H13CO+, and H13CN, HC3N (J=11-10) emission towards 135 massive star-forming clumps, as part of the ATOMS (ALMA Three-millimeter Observations of Massive Star-forming regions) Survey. We present the integrated intensity probability distribution function for these molecular tracers, modeled as a combination of a log-normal distribution and a power-law tail. The molecular line luminosities for the power-law tail segment, Lmol(p), have been calculated. We have investigated the correlation between the bolometric luminosity, Lbol, and the power-law part of the molecular line luminosity, Lmol(p). Our findings suggest that the scaling relationships between Lbol and Lmol(p) for HCN and HCO+ are sublinear, indicating that these molecules might not be the most effective tracers for the dense gas. In contrast, H13CN and HC3N exhibit a nearly linear relationship between Lbol and Lmol(p), indicating that they can well trace gravitationally bound dense gas. The ratios of Lbol-to-Lmol(p), serving as indicators of star formation efficiency within massive star-forming clumps, exhibit a weak anti-correlation with the power-law index in the I-PDF. In addition, the star formation efficiency is also weakly anti-correlated with the exponent U of the corresponding equivalent density distribution. Our results implie that clumps with substantial gas accumulation may still display low star formation efficiencies.

The Casimir force follows from quantum fluctuations of the electromagnetic field and yields a nonlinear attractive force between closely spaced conductive objects. Its magnitude depends on the conductivity of the objects up to optical frequencies. Measuring the Casimir force in superconductors should allow to isolate frequency-specific contributions to the Casimir effect, as frequencies below the superconducting gap energy are expected to contribute differently than those above it. There is significant interest in this contribution as it is suspected to contribute to an unexplained discrepancy between predictions and measurements of the Casimir force, which questions the basic principles on which estimates of the magnitude are based. Here, we observe the Casimir force between superconducting objects for the first time, through the nonlinear dynamics it imparts to a superconducting drum resonator in a microwave optomechanical system. There is excellent agreement between the experiment and the Casimir force magnitude computed for this device across three orders of magnitude of displacement. Furthermore, the Casimir nonlinearity is intense enough that, with a modified design, this device type should operate in the single-phonon nonlinear regime. Accessing this regime has been a long-standing goal that would greatly facilitate quantum operations of mechanical resonators., Comment: 6 pages main text (3 figures), 5 pages methods (5 figures), 12 pages supplementary information (16 figures)

Context: OMC-3 in the Orion A Cloud is a nearby, high-mass star-forming region, and therefore ideal to study massive filaments in detail. Aims: We analyze how the inclusion of higher-resolution data changes the estimates of the filament properties and test the robustness of filament fitting routines. Methods. ArT\'eMiS and Herschel data are combined to create high-resolution images. Column densities and temperatures are estimated with modified blackbody fitting. The nearby OMC-3 cloud is compared to the more distant G202 and G17 clouds. We compare the OMC-3 cloud as it appears at Herschel and ArT\'eMiS resolution. Results. Column densities of dense clumps in OMC-3 are higher in combined ArT\'eMiS and Herschel data (FWHM 8.5"), when compared to Herschel-only data (FWHM 20"). Estimated filament widths are smaller in the combined maps, and also show signs of further fragmentation when observed with the ArT\'eMiS resolution. In the analysis of Herschel data the estimated filament widths are correlated with the distance of the field. Conclusions. Median filament FWHM in OMC-3 at higher resolution is 0.05 pc, but 0.1 pc with the Herschel resolution, 0.3 pc in G202 and 1.0 pc in G17, also at the Herschel resolution. It is unclear what causes the steep relation between distance and filament FWHM, but likely reasons include the effect of the limited telescope resolution combined with existing hierarchical structure, and convolution of large-scale background structures within the ISM. Estimates of the asymptotic power-law index of the filament profile function p is high. When fit with the Plummer function, the individual parameters of the profile function are degenerate, while the FWHM is better constrained. OMC-3 shows negative kurtosis, and all but OMC-3 at the Herschel resolution some asymmetry., Comment: Accepted into Astronomy & Astrophysics

This study investigated the students' perceptions of using Generative Artificial Intelligence (GenAI) in upper-secondary mathematics education. Data was collected from Finnish high school students to represent how key constructs of the Technology Acceptance Model (Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, and Intention to Use) influence the adoption of AI tools. First, a structural equation model for a comparative study with a prior study was constructed and analyzed. Then, an extended model with the additional construct of Compatibility, which represents the alignment of AI tools with students' educational experiences and needs, was proposed and analyzed. The results demonstrated a strong influence of perceived usefulness on the intention to use GenAI, emphasizing the statistically significant role of perceived enjoyment in determining perceived usefulness and ease of use. The inclusion of compatibility improved the model's explanatory power, particularly in predicting perceived usefulness. This study contributes to a deeper understanding of how AI tools can be integrated into mathematics education and highlights key differences between the Finnish educational context and previous studies based on structural equation modeling., Comment: To be published in the Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC'25), March 31--April 4, 2025, Catania, Italy

Modern software systems produce vast amounts of logs, serving as an essential resource for anomaly detection. Artificial Intelligence for IT Operations (AIOps) tools have been developed to automate the process of log-based anomaly detection for software systems. Three practical challenges are widely recognized in this field: high data labeling costs, evolving logs in dynamic systems, and adaptability across different systems. In this paper, we propose CroSysLog, an AIOps tool for log-event level anomaly detection, specifically designed in response to these challenges. Following prior approaches, CroSysLog uses a neural representation approach to gain a nuanced understanding of logs and generate representations for individual log events accordingly. CroSysLog can be trained on source systems with sufficient labeled logs from open datasets to achieve robustness, and then efficiently adapt to target systems with a few labeled log events for effective anomaly detection. We evaluate CroSysLog using open datasets of four large-scale distributed supercomputing systems: BGL, Thunderbird, Liberty, and Spirit. We used random log splits, maintaining the chronological order of consecutive log events, from these systems to train and evaluate CroSysLog. These splits were widely distributed across a one/two-year span of each system's log collection duration, thereby capturing the evolving nature of the logs in each system. Our results show that, after training CroSysLog on Liberty and BGL as source systems, CroSysLog can efficiently adapt to target systems Thunderbird and Spirit using a few labeled log events from each target system, effectively performing anomaly detection for these target systems. The results demonstrate that CroSysLog is a practical, scalable, and adaptable tool for log-event level anomaly detection in operational and maintenance contexts of software systems.

Direct sum theorems state that the cost of solving $k$ instances of a problem is at least $\Omega(k)$ times the cost of solving a single instance. We prove the first such results in the randomised parity decision tree model. We show that a direct sum theorem holds whenever (1) the lower bound for parity decision trees is proved using the discrepancy method; or (2) the lower bound is proved relative to a product distribution., Comment: 37 pages

Large language models (LLMs) integrated into multistep agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliability, making uncertainty estimation crucial. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multistep decision-making process and the dynamic interactions between agents and their environments. To address these limitations, we propose SAUP (Situation Awareness Uncertainty Propagation), a novel framework that propagates uncertainty through each step of an LLM-based agent's reasoning process. SAUP incorporates situational awareness by assigning situational weights to each step's uncertainty during the propagation. Our method, compatible with various one-step uncertainty estimation techniques, provides a comprehensive and accurate uncertainty measure. Extensive experiments on benchmark datasets demonstrate that SAUP significantly outperforms existing state-of-the-art methods, achieving up to 20% improvement in AUROC.

The production of silicon monoxide (SiO) can be considered as a fingerprint of shock interaction. In this work, we use high-sensitivity observations of the SiO (2-1) and H$^{13}$CO$^{+}$ (1-0) emission to investigate the broad and narrow SiO emission toward 146 massive star-forming regions in the ATOMS survey. We detected SiO emission in 136 regions and distinguished broad and narrow components across the extension of 118 sources (including 58 UC $H_{II}$ regions) with an average angular resolution of 2.5$^{\prime}$$^{\prime}$. The derived SiO luminosity ($L_{SiO}$) across the whole sample shows that the majority of $L_{SiO}$ (above 66$\%$) can be attributed to broad SiO, indicating its association with strong outflows. The comparison of the ALMA SiO images with the filamentary skeletons identified from H$^{13}$CO$^{+}$ and in the infrared data (at 4.5, 8, and 24 $mu$m), further confirms that most SiO emission originates from outflows. However, note that for nine sources in our sample, the observed SiO emission may be generated by expanding UC $H_{II}$ regions. There is a moderate positive correlation between the bolometric luminosity ($L_{bol}$) and $L_{SiO}$ for both components (narrow and broad). The UC $H_{II}$ sources show a weaker positive correlation between $L_{bol}$ and $L_{SiO}$ and higher $L_{SiO}$ compared to the sources without UC $H_{II}$ regions. These results imply that the SiO emission from UC $H_{II}$ sources might be affected by UV-photochemistry induced by UC $H_{II}$ regions., Comment: 23 pages, 14 figures

The use of deep learning in stylistic effect generation has seen increasing use over recent years. In this work, we use simple convolutional neural networks to model Cinestill800T film given a digital input. We test the effect of different loss functions, the addition of an input noise channel and the use of random scales of patches during training. We find that a combination of MSE/VGG loss gives the best colour production and that some grain can be produced, but it is not of a high quality, and no halation is produced. We contribute our dataset of aligned paired images taken with a film and digital camera for further work.

We exhibit a monotone function computable by a monotone circuit of quasipolynomial size such that any monotone circuit of polynomial depth requires exponential size. This is the first size-depth tradeoff result for monotone circuits in the so-called supercritical regime. Our proof is based on an analogous result in proof complexity: We introduce a new family of unsatisfiable 3-CNF formulas (called bracket formulas) that admit resolution refutations of quasipolynomial size while any refutation of polynomial depth requires exponential size.

We present a novel digital humanities method for representing our Twitch chatters as user embeddings created by a large language model (LLM). We cluster these embeddings automatically using affinity propagation and further narrow this clustering down through manual analysis. We analyze the chat of one stream by each Twitch streamer: SmallAnt, DougDoug and PointCrow. Our findings suggest that each streamer has their own type of chatters, however two categories emerge for all of the streamers: supportive viewers and emoji and reaction senders. Repetitive message spammers is a shared chatter category for two of the streamers., Comment: NLP4DH 2024

Present-day software development faces three major challenges: complexity, time consumption, and high costs. Developing large software systems often requires battalions of teams and considerable time for meetings, which end without any action, resulting in unproductive cycles, delayed progress, and increased cost. What if, instead of large meetings with no immediate results, the software product is completed by the end of the meeting? In response, we present a vision for a system called TimeLess, designed to reshape the software development process by enabling immediate action during meetings. The goal is to shift meetings from planning discussions to productive, action-oriented sessions. This approach minimizes the time and effort required for development, allowing teams to focus on critical decision-making while AI agents execute development tasks based on the meeting discussions. We will employ multiple AI agents that work collaboratively to capture human discussions and execute development tasks in real time. This represents a step toward next-generation software development environments, where human expertise drives strategy and AI accelerates task execution., Comment: 5 pages, 4 figure, and 1 table

Particle in cell simulations are widely used in most fields of physics to investigate known and new phenomena which cannot be directly observed or measured yet. However, the computational and time resources needed for PICs make them impractical when high resolution and long time/distance simulations are required. In this work, we present a new PIC simulation code that takes advantage of the use of a relativistic reference frame and consequent time dilation and length contraction. These properties make a simulation capable of long (meter length) and high resolution simulations without the need of supercomputers. This new code is a step forward with regards to the previous tries enabling complex multiple body situations without additional filtering and smoothing of fields and currents. The usefulness of the relativistic frame PIC code is displayed by simulating electron beam bunching obtained in long undulator propagation and also the potential as a beam "buncher" of 10s of cm long low density plasmas.

We exhibit a total search problem whose communication complexity in the quantum SMP (simultaneous message passing) model is exponentially smaller than in the classical two-way randomized model. Moreover, the quantum protocol is computationally efficient and its solutions are classically verifiable, that is, the problem lies in the communication analogue of the class TFNP. Our problem is a bipartite version of a query complexity problem recently introduced by Yamakawa and Zhandry (JACM 2024). We prove the classical lower bound using the structure-vs-randomness paradigm for analyzing communication protocols.

This paper presents a methodology for training a transformer-based model to classify lexical and morphosyntactic features of Skolt Sami, an endangered Uralic language characterized by complex morphology. The goal of our approach is to create an effective system for understanding and analyzing Skolt Sami, given the limited data availability and linguistic intricacies inherent to the language. Our end-to-end pipeline includes data extraction, augmentation, and training a transformer-based model capable of predicting inflection classes. The motivation behind this work is to support language preservation and revitalization efforts for minority languages like Skolt Sami. Accurate classification not only helps improve the state of Finite-State Transducers (FSTs) by providing greater lexical coverage but also contributes to systematic linguistic documentation for researchers working with newly discovered words from literature and native speakers. Our model achieves an average weighted F1 score of 1.00 for POS classification and 0.81 for inflection class classification. The trained model and code will be released publicly to facilitate future research in endangered NLP., Comment: IWCLUL 2024

We showcase that ChatGPT can be used to disambiguate lemmas in two endangered languages ChatGPT is not proficient in, namely Erzya and Skolt Sami. We augment our prompt by providing dictionary translations of the candidate lemmas to a majority language - Finnish in our case. This dictionary augmented generation approach results in 50\% accuracy for Skolt Sami and 41\% accuracy for Erzya. On a closer inspection, many of the error types were of the kind even an untrained human annotator would make., Comment: IWCLUL 2024

We present 850 {\mu}m thermal dust polarization observations with a resolution of 14.4"(~ 0.13 pc) towards an infrared dark cloud G16.96+0.27 using JCMT/POL-2. The average magnetic field orientation, which roughly agrees with the larger-scale magnetic field orientation traced by the Planck 353 GHz data, is approximately perpendicular to the filament structure. The estimated plane-of-sky magnetic field strength is ~ 96 {\mu}G and ~ 60 {\mu}G using two variants of the Davis-Chandrasekhar-Fermi methods. We calculate the virial and magnetic critical parameters to evaluate the relative importance of gravity, the magnetic field, and turbulence. The magnetic field and turbulence are both weaker than gravity, but magnetic fields and turbulence together are equal to gravity, suggesting that G16.96+0.27 is in a quasi-equilibrium state. The cloud-magnetic-field alignment is found to have a trend moving away from perpendicularity in the dense regions, which may serve as a tracer of potential fragmentation in such quiescent filaments., Comment: Accepted by ApJ. 13 pages, 5 figures

In this paper, we present a detailed analysis of the IRS 17 filament within the intermediate-mass protocluster IRAS 08448-4343 (of $\sim\,10^3\,\rm L_{\odot}$), using ALMA data from the ATOMS 3-mm and QUARKS 1.3-mm surveys. The IRS 17 filament, which spans $\sim$54000 au ($0.26\,\rm pc$) in length and $\sim$4000 au ($0.02\,\rm pc$) in width, exhibits a complex, multi-component velocity field, and harbours hierarchical substructures. These substructures include three bundles of seven velocity-coherent fibers, and 29 dense ($n\sim 10^8\,\rm cm^{-3}$) condensations. The fibers have a median length of $\sim 4500\,\rm au$ and a median width of $\sim 1400\,\rm au$. Among these fibers, four are identified as ``fertile", each hosting at least three dense condensations, which are regarded as the ``seeds" of star formation. While the detected cores are randomly spaced within the IRS\,17 filament based on the 3-mm dust continuum image, periodic spacing ($\sim1600\,\rm au$) of condensations is observed in the fertile fibers according to the 1.3-mm dust map, consistent with the predictions of linear isothermal cylinder fragmentation models. These findings underscore the crucial role of fibers in star formation and suggest a hierarchical fragmentation process that extends from the filament to the fibers, and ultimately, to the smallest-scale condensations., Comment: 19 pages, 10 figures, 4 tables, accepted by ApJ

In this paper, we explore how performers' embodied interactions with a Neural Audio Synthesis model allow the exploration of the latent space of such a model, mediated through movements sensed by e-textiles. We provide background and context for the performance, highlighting the potential of embodied practices to contribute to developing explainable AI systems. By integrating various artistic domains with explainable AI principles, our interdisciplinary exploration contributes to the discourse on art, embodiment, and AI, offering insights into intuitive approaches found through bodily expression., Comment: In Proceedings of Explainable AI for the Arts Workshop 2024 (XAIxArts 2024) arXiv:2406.14485

Background: Humor is a fundamental part of human communication, with prior work linking positive humor in the workplace to positive outcomes, such as improved performance and job satisfaction. Aims: This study aims to investigate programming-related humor in a large social media community. Methodology: We collected 139,718 submissions from Reddit subreddit r/ProgrammerHumor. Both textual and image-based (memes) submissions were considered. The image data was processed with OCR to extract text from images for NLP analysis. Multiple regression models were built to investigate what makes submissions humorous. Additionally, a random sample of 800 submissions was labeled by human annotators regarding their relation to theories of humor, suitability for the workplace, the need for programming knowledge to understand the submission, and whether images in image-based submissions added context to the submission. Results: Our results indicate that predicting the humor of software developers is difficult. Our best regression model was able to explain only 10% of the variance. However, statistically significant differences were observed between topics, submission times, and associated humor theories. Our analysis reveals that the highest submission scores are achieved by imagebased submissions that are created during the winter months in the northern hemisphere, between 2-3pm UTC on weekends, which are distinctly related to superiority and incongruity theories of humor, and are about the topic of "Learning". Conclusions: Predicting humor with natural language processing methods is challenging. We discuss the benefits and inherent difficulties in assessing perceived humor of submissions, as well as possible avenues for future work. Additionally, our replication package should help future studies and can act as a joke repository for the software industry and education., Comment: 11 pages. Accepted to International Symposium on Empirical Software Engineering and Measurement (ESEM) 2024 research track

State-of the-art infrared photodetectors operating in the mid- and long-wavelength infrared (MWIR and LWIR) are largely dominated by cryogenically cooled quantum sensors when the target is the highest sensitivity and detection speeds. Nano-thermoelectrics provide a route towards competitive uncooled infrared bolometer technology that can obtain high speed and sensitivity, low-power operation, and cost-effectiveness. We demonstrate nano-thermoelectric LWIR bolometers with fast and high-sensitivity response to LWIR around 10 $\mu$m. These devices are based on ultra-thin silicon membranes that utilize the dimensional scaling of silicon nanomembranes in thermoelectric elements and are combined with metallic nanomembranes with subwavelength absorber structures. The fast device performance stems from a low heat capacity design where the thermoelectric beams act both as mechanical supports and transducer elements. Furthermore, by scaling down the thickness of the thermoelectric beams the thermal conductivity is reduced owing to enhanced phonon boundary scattering, resulting in increased sensitivity. The nano-thermoelectric LWIR bolometers are based on 40-nm-thick n- and p-type silicon membranes with LWIR (voltage) responsivities up to 1636 V/W and 1350 V/W and time constants in the range of 300-600 $\mu$s, resulting in specific detectivities up to $1.56\times10^8$ cmHz$^{1/2}$/W. We also investigate the use of a heavily doped N++ substrate to increase optical cavity back reflection, resulting in an increased Si substrate reflectance from 30% to 70%-75% for wavelengths between 8-10 $\mu$m, resulting in an increase in device responsivity by approximately 20%., Comment: 18 pages, 5 figures, 2 tables, supplementary information included (4 pages, 4 figures)

Large power consumption of silicon CMOS electronics is a challenge in very-large-scale integrated circuits and a major roadblock to fault-tolerant quantum computation. Matching the power dissipation of Si-MOSFETs to the thermal budget at deep cryogenic temperatures, below 1 K, requires switching performance beyond levels facilitated by currently available CMOS technologies. We have manufactured fully depleted silicon-on-insulator MOSFETs tailored for overcoming the power dissipation barrier towards sub-1 K applications. With these cryo-optimized transistors we achieve a major milestone of reaching subthreshold swing of 0.3 mV/dec at 420 mK, thereby enabling very-large-scale integration of cryo-CMOS electronics for ultra-low temperature applications.

Hub-filament systems are considered as natural sites for high-mass star formation. Kinematic analysis of the surroundings of hub-filaments is essential to better understand high-mass star formation within such systems. In this work, we present a detailed study of the massive Galactic protocluster IRAS 15394$-$5358, using continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey. The 3~mm dust continuum map reveals the fragmentation of the massive ($\rm M=843~M_{\odot}$) clump into six cores. The core C-1A is the largest (radius = 0.04~pc), the most massive ($\rm M=157~M_{\odot}$), and lies within the dense central region, along with two smaller cores ($\rm M=7~and~3~M_{\odot}$). The fragmentation process is consistent with the thermal Jeans fragmentation mechanism and virial analysis shows that all the cores have small virial parameter values ($\rm \alpha_{vir}<<2$), suggesting that the cores are gravitationally bound. The mass vs. radius relation indicates that three cores can potentially form at least a single massive star. The integrated intensity map of $\rm H^{13}CO^{+}$ shows that the massive clump is associated with a hub-filament system, where the central hub is linked with four filaments. A sharp velocity gradient is observed towards the hub, suggesting a global collapse where the filaments are actively feeding the hub. We discuss the role of global collapse and the possible driving mechanisms for the massive star formation activity in the protocluster., Comment: 23 pages, 19 figures, accepted for publication in MNRAS

We demonstrate the interference of photons scattered from independent ensembles of warm atoms in atomic vapor. It employs finite temporal coherence of light scattered elastically in the forward and backward directions from Doppler-broadened atomic ensembles, such that photons with chaotic photon statistics from two opposite atomic velocity groups contribute to the same detection mode. While the random phase fluctuations of the scattered light caused by a large thermal motion prevent direct observability of the interference in the detected photon rate, the stable frequency difference between photons collected from scattering off counter-propagating laser beams provides strong periodic modulation of the photon coincidence rate with the period given by the detuning of the excitation laser from the atomic resonance. Presented interferometry promises direct applications in Doppler-free atomic and molecular spectroscopy., Comment: 8 pages, 4 figures

For any integer $k$, M.Kaneko defined $k$-th poly-Bernoulli numbers as a kind of generalization of classical Bernoulli numbers using $k$-th polylogarithm. In case when $k$ is positive, $k$-th poly-Bernoulli numbers is a sequence of rational numbers as same as classical Bernoulli numbers. On the other hand, in case when $k$ is negative, it is a sequence of positive integers, and many combinatoric and number theoretic properties has been investigated. In the present paper, the negative case is treated, and their congruence and $p$-adic properties are discussed. Beside of them, application of the results to obtain a congruence property for the number of lonesum matrices is also mentioned.

HOPS 87 is a Class 0 protostellar core known to harbor an extremely young bipolar outflow and a hot corino. We report the discovery of localized, chemically rich regions near the bases of the two-lobe bipolar molecular outflow in HOPS 87 containing molecules such as H$_2$CO, $^{13}$CS, H$_2$S, OCS, and CH$_3$OH, the simplest complex organic molecule (COM). The locations and kinematics suggest that these localized features are due to jet-driven shocks rather than being part of the hot corino region encasing the protostar. The COM compositions of the molecular gas in these jet-localized regions are relatively simpler than those in the hot corino zone. We speculate that this simplicity is due to either the liberation of ice with a less complex chemical history or the effects of shock chemistry. Our study highlights the dynamic interplay between the protostellar bipolar outflow, disk, inner core environment, and the surrounding medium, contributing to our understanding of molecular complexity in solar-like young stellar objects., Comment: 16 pages, 6+2 figures, accepted by ApJ

Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understanding of their performance characteristics when executing the intended AI workload. To facilitate this, we present an automated generation approach for fast performance models to accurately estimate the latency of a DNN mapped onto systematically modeled and concisely described accelerator architectures. Using our accelerator architecture description method, we modeled representative DNN accelerators such as Gemmini, UltraTrail, Plasticine-derived, and a parameterizable systolic array. Together with DNN mappings for those modeled architectures, we perform a combined DNN/hardware dependency graph analysis, which enables us, in the best case, to evaluate only 154 loop kernel iterations to estimate the performance for 4.19 billion instructions achieving a significant speedup. We outperform regression and analytical models in terms of mean absolute percentage error (MAPE) compared to simulation results, while being several magnitudes faster than an RTL simulation., Comment: Accepted version for: ACM Transactions on Embedded Computing Systems

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

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.

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: Published in: Proceedings of the 6th Workshop on Natural Legal Language Processing (NLLP 2024)

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.