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Machine-learning (ML) models in flow cytometry have the potential to reduce error rates, increase reproducibility, and boost the efficiency of clinical labs. While numerous ML models for flow cytometry data have been proposed, few studies have described the clinical deployment of such models. Realizing the potential gains of ML models in clinical labs requires not only an accurate model, but infrastructure for automated inference, error detection, analytics and monitoring, and structured data extraction. Here, we describe an ML model for detection of Acute Myeloid Leukemia (AML), along with the infrastructure supporting clinical implementation. Our infrastructure leverages the resilience and scalability of the cloud for model inference, a Kubernetes-based workflow system that provides model reproducibility and resource management, and a system for extracting structured diagnoses from full-text reports. We also describe our model monitoring and visualization platform, an essential element for ensuring continued model accuracy. Finally, we present a post-deployment analysis of impacts on turn-around time and compare production accuracy to the original validation statistics.

Let $\mathrm{ex}(n,H,\mathcal{F})$ be the maximum number of copies of $H$ in an $n$-vertex graph which contains no copy of a graph from $\mathcal{F}$. Thinking of $H$ and $\mathcal{F}$ as fixed, we study the asymptotics of $\mathrm{ex}(n,H,\mathcal{F})$ in $n$. We say that a rational number $r$ is \emph{realizable for $H$} if there exists a finite family $\mathcal{F}$ such that $\mathrm{ex}(n,H,\mathcal{F}) = \Theta(n^r)$. Using randomized algebraic constructions, Bukh and Conlon showed that every rational between $1$ and $2$ is realizable for $K_2$. We generalize their result to show that every rational between $1$ and $t$ is realizable for $K_t$, for all $t \geq 2$. We also determine the realizable rationals for stars and note the connection to a related Sidorenko-type supersaturation problem., Comment: 28 pages, 8 figures

Path planners that can interpret free-form natural language instructions hold promise to automate a wide range of robotics applications. These planners simplify user interactions and enable intuitive control over complex semi-autonomous systems. While existing symbolic approaches offer guarantees on the correctness and efficiency, they struggle to parse free-form natural language inputs. Conversely, neural approaches based on pre-trained Large Language Models (LLMs) can manage natural language inputs but lack performance guarantees. In this paper, we propose a neuro-symbolic framework for path planning from natural language inputs called NSP. The framework leverages the neural reasoning abilities of LLMs to i) craft symbolic representations of the environment and ii) a symbolic path planning algorithm. Next, a solution to the path planning problem is obtained by executing the algorithm on the environment representation. The framework uses a feedback loop from the symbolic execution environment to the neural generation process to self-correct syntax errors and satisfy execution time constraints. We evaluate our neuro-symbolic approach using a benchmark suite with 1500 path-planning problems. The experimental evaluation shows that our neuro-symbolic approach produces 90.1% valid paths that are on average 19-77% shorter than state-of-the-art neural approaches., Comment: 10 pages, Preprint of paper accepted at 23rd International Conference on Machine Learning and Applications (ICMLA) 2024

There exists an invisible barrier between healthcare professionals' perception of a patient's clinical experience and the reality. This barrier may be induced by the environment that hinders patients from sharing their experiences openly with healthcare professionals. As patients are observed to discuss and exchange knowledge more candidly on social media, valuable insights can be leveraged from these platforms. However, the abundance of non-patient posts on social media necessitates filtering out such irrelevant content to distinguish the genuine voices of patients, a task we refer to as patient voice classification. In this study, we analyse the importance of linguistic characteristics in accurately classifying patient voices. Our findings underscore the essential role of linguistic and statistical text similarity analysis in identifying common patterns among patient groups. These results allude to even starker differences in the way patients express themselves at a disease level and across various therapeutic domains. Additionally, we fine-tuned a pre-trained Language Model on the combined datasets with similar linguistic patterns, resulting in a highly accurate automatic patient voice classification. Being the pioneering study on the topic, our focus on extracting authentic patient experiences from social media stands as a crucial step towards advancing healthcare standards and fostering a patient-centric approach., Comment: 14 pages, 4 figures, 5 tables, funded by Talking Medicines Limited

Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicability to time series data. Furthermore, existing approaches struggle to handle multi-step ahead prediction tasks, where uncertainty estimates across multiple future time points are crucial. We propose JANET (Joint Adaptive predictioN-region Estimation for Time-series), a novel framework for constructing conformal prediction regions that are valid for both univariate and multivariate time series. JANET generalises the inductive conformal framework and efficiently produces joint prediction regions with controlled K-familywise error rates, enabling flexible adaptation to specific application needs. Our empirical evaluation demonstrates JANET's superior performance in multi-step prediction tasks across diverse time series datasets, highlighting its potential for reliable and interpretable uncertainty quantification in sequential data., Comment: Alternate Title: Conformalised Joint Prediction Region for Time Series

The Tully-Fisher Relation (TFR) is a well-known empirical relationship between the luminosity of a spiral galaxy and its circular velocity, allowing us to estimate redshift independent distances. Here we use high signal-to-noise HI 21-cm integrated spectra from the second pilot data release (PDR2, 180 deg2) of the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY). In order to prepare for the full WALLABY survey, we have investigated the TFR in phase 2 of the pilot survey with a further three fields. The data were obtained with wide-field Phased Array Feeds on the Australian Square Kilometre Array Pathfinder (ASKAP) and have an angular resolution of 30 arcsec and a velocity resolution of ~4 km/s. Galaxy luminosities have been measured from the Wide-field Infrared Survey Explorer (WISE), and optical galaxy inclinations from the Dark Energy Camera Legacy Survey. We present TFRs for wavelengths from 0.8-3.4{\mu}m. We examine sources of galaxy inclination data and investigate magnitudes from the DECam Local Volume Exploration Survey (DELVE) and DENIS catalogues and the 4HS target catalogue based on the VISTA Hemisphere Survey (VHS). We consider the baryonic TFR. These are all of interest for TFR using the full WALLABY survey of 200,000 galaxies. We demonstrate that WALLABY TFR distances can take their place among state of the art studies of the local velocity field., Comment: to appear in MNRAS. One figure removed

Machine Learning algorithms are notorious for providing point predictions but not prediction intervals. There are many applications where one requires confidence in predictions and prediction intervals. Stringing together, these intervals give rise to joint prediction regions with the desired significance level. It is an easy task to compute Joint Prediction regions (JPR) when the data is IID. However, the task becomes overly difficult when JPR is needed for time series because of the dependence between the observations. This project aims to implement Wolf and Wunderli's method for constructing JPRs and compare it with other methods (e.g. NP heuristic, Joint Marginals). The method under study is based on bootstrapping and is applied to different datasets (Min Temp, Sunspots), using different predictors (e.g. ARIMA and LSTM). One challenge of applying the method under study is to derive prediction standard errors for models, it cannot be obtained analytically. A novel method to estimate prediction standard error for different predictors is also devised. Finally, the method is applied to a synthetic dataset to find empirical averages and empirical widths and the results from the Wolf and Wunderli paper are consolidated. The experimental results show a narrowing of width with strong predictors like neural nets, widening of width with increasing forecast horizon H and decreasing significance level alpha, controlling the width with parameter k in K-FWE, and loss of information using Joint Marginals., Comment: This work is a Master Thesis

Hypergraphs have been a recent focus of study in mathematical data science as a tool to understand complex networks with high-order connections. One question of particular relevance is how to leverage information carried in hypergraph attributions when doing walk-based techniques. In this work, we focus on a new generalization of a walk in a network that recovers previous approaches and allows for a description of permissible walks in hypergraphs. Permissible walk graphs are constructed by intersecting the attributed $s$-line graph of a hypergraph with a relation respecting graph. The attribution of the hypergraph's line graph commonly carries over information from categorical and temporal attributions of the original hypergraph. To demonstrate this approach on a temporally attributed example, we apply our framework to a Reddit data set composed of hyperedges as threads and authors as nodes where post times are tracked.

The extraplanar diffuse ionized gas (eDIG) represents ionized gases traced by optical/UV lines beyond the stellar extent of galaxies. We herein introduce a novel multi-slit narrow-band spectroscopy method to conduct spatially resolved spectroscopy of the eDIG around a sample of nearby edge-on disk galaxies (eDIG-CHANGES). In this paper, we introduce the project design and major scientific goals, as well as a pilot study of NGC 3556 (M108). The eDIG is detected to a vertical extent of a few kpc above the disk, comparable to the X-ray and radio images. We do not see significant vertical variation of the [N II]/H$\alpha$ line ratio. A rough examination of the pressure balance between different circum-galactic medium (CGM) phases indicates the magnetic field is in a rough pressure balance with the X-ray emitting hot gas, and may play an important role in the global motion of both the eDIG and the hot gas in the lower halo. At the location of an HST/COS observed UV bright background AGN $\sim29\rm~kpc$ from the center of NGC 3556, the magnetic pressure is much lower than that of the hot gas and the ionized gas traced by UV absorption lines, although the extrapolation of the pressure profiles may cause some biases in this comparison. By comparing the position-velocity diagrams of the optical and CO lines, we also find the dynamics of the two gas phases are consistent with each other, with no evidence of a global inflow/outflow and a maximum rotation velocity of $\sim150\rm~km~s^{-1}$., Comment: 19 pages, 9 figures, 1 table, accepted for publication in ApJ

The MHONGOOSE (MeerKAT HI Observations of Nearby Galactic Objects: Observing Southern Emitters) survey maps the distribution and kinematics of the neutral atomic hydrogen (HI) gas in and around 30 nearby star-forming spiral and dwarf galaxies to extremely low HI column densities. The HI column density sensitivity (3 sigma over 16 km/s) ranges from ~ 5 x 10^{17} cm^{-2} at 90'' resolution to ~4 x 10^{19} cm^{-2} at the highest resolution of 7''. The HI mass sensitivity (3 sigma over 50 km/s) is ~5.5 X 10^5 M_sun at a distance of 10 Mpc (the median distance of the sample galaxies). The velocity resolution of the data is 1.4 km/s. One of the main science goals of the survey is the detection of cold, accreting gas in the outskirts of the sample galaxies. The sample was selected to cover a range in HI masses, from 10^7 M_sun to almost 10^{11} M_sun, to optimally sample possible accretion scenarios and environments. The distance to the sample galaxies ranges from 3 to 23 Mpc. In this paper, we present the sample selection, survey design, and observation and reduction procedures. We compare the integrated HI fluxes based on the MeerKAT data with those derived from single-dish measurement and find good agreement, indicating that our MeerKAT observations are recovering all flux. We present HI moment maps of the entire sample based on the first ten percent of the survey data, and find that a comparison of the zeroth- and second-moment values shows a clear separation between the physical properties of the HI in areas with star formation and areas without, related to the formation of a cold neutral medium. Finally, we give an overview of the HI-detected companion and satellite galaxies in the 30 fields, five of which have not previously been catalogued. We find a clear relation between the number of companion galaxies and the mass of the main target galaxy., Comment: Accepted for publication in Astronomy & Astrophysics

GitGuardian monitored secrets exposure in public GitHub repositories and reported developers leaked over 12 million secrets (database and other credentials) in 2023, indicating a 113% surge from 2021. Despite the availability of secret detection tools, developers ignore the tools' reported warnings because of false positives (25%-99%). However, each secret protects assets of different values accessible through asset identifiers (a DNS name and a public or private IP address). The asset information for a secret can aid developers in filtering false positives and prioritizing secret removal from the source code. However, existing secret detection tools do not provide the asset information, thus presenting difficulty to developers in filtering secrets only by looking at the secret value or finding the assets manually for each reported secret. The goal of our study is to aid software practitioners in prioritizing secrets removal by providing the assets information protected by the secrets through our novel static analysis tool. We present AssetHarvester, a static analysis tool to detect secret-asset pairs in a repository. Since the location of the asset can be distant from where the secret is defined, we investigated secret-asset co-location patterns and found four patterns. To identify the secret-asset pairs of the four patterns, we utilized three approaches (pattern matching, data flow analysis, and fast-approximation heuristics). We curated a benchmark of 1,791 secret-asset pairs of four database types extracted from 188 public GitHub repositories to evaluate the performance of AssetHarvester. AssetHarvester demonstrates precision of (97%), recall (90%), and F1-score (94%) in detecting secret-asset pairs. Our findings indicate that data flow analysis employed in AssetHarvester detects secret-asset pairs with 0% false positives and aids in improving the recall of secret detection tools.

We report the discovery of the 10 kilo-parsec (kpc) scale radio lobes in the Sombrero galaxy (NGC 4594), using data from the Continuum Halos in Nearby Galaxies - an Expanded Very Large Array (VLA) Survey (CHANG-ES) project. We further examine the balance between the magnetic pressure inside the lobes and the thermal pressure of the ambient hot gas. At the radii $r$ of ~(1-10) kpc, the magnetic pressure inside the lobes and the thermal pressure of the ambient hot gas are generally in balance. This implies that the jets could expand into the surroundings at least to r ~ 10 kpc. The feedback from the active galactic nucleus (AGN) jet responsible for the large-scale lobes may help to explain the unusually high X-ray luminosity of this massive quiescent isolated disk galaxy, although more theoretical work is needed to further examine this possibility., Comment: 8 pages, 3 figures, 2 tables, accepted for publication in ApJ

Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension, typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations. Our largest models outperform state-of-the-art models, and we will make our experimental data, code, and model weights publicly available.

The existing reservoirs of neutral atomic hydrogen gas (H$\,$I) in galaxies are insufficient to have maintained the observed levels of star formation without some kind of replenishment. {This refuelling of the H$\,$I reservoirs} is likely to occur at column densities an order of magnitude lower than previous observational limits (N$_{\rm{H\,I}\, limit} \sim 10^{19}\,$cm$^{-2}$ at 30$''$ resolution over a linewidth of $20\,$km/s). In this paper, we present recent deep H$\,$I observations of NGC 5068, a nearby isolated star-forming galaxy observed by MeerKAT as part of the MHONGOOSE survey. With these new data, we are able to detect low column density H$\,$I around NGC 5068 with a $3\sigma$ detection limit of N$_{\rm{H\,I}} = 6.4 \times 10^{17}\,$cm$^{-2}$ at 90$''$ resolution over a $20\,$km/s linewidth. The high sensitivity and resolution of the MeerKAT data reveal a complex morphology of the H$\,$I in this galaxy -- a regularly rotating inner disk coincident with the main star-forming disk of the galaxy, a warped outer disk of low column density gas (N$_{\rm{H\,I}} < 9 \times 10^{19}\,$cm$^{-2}$), in addition to clumps of gas on the north west side of the galaxy. We employ a simple two disk model that describe the inner and outer disks, and are able to identify anomalous gas that deviates from the rotation of the main galaxy. The morphology and the kinematics of the anomalous gas suggest a possible extra-galactic origin. We explore a number of possible origin scenarios that may explain the anomalous gas, and conclude that fresh accretion is the most likely scenario., Comment: 17 pages, 13 figures, 5 tables. Accepted for publication in A&A

Feature location attempts to assist developers in discovering functionality in source code. Many textual feature location techniques utilize information retrieval and rely on comments and identifiers of source code to describe software entities. An interesting alternative would be to employ the changeset descriptions of the code altered in that changeset as a data source to describe such software entities. To investigate this we implement a technique utilizing changeset descriptions and conduct an empirical study to observe this technique's overall performance. Moreover, we study how the granularity (i.e. file or method level of software entities) and changeset range inclusion (i.e. most recent or all historical changesets) affect such an approach. The results of a preliminary study with Rhino and Mylyn.Tasks systems suggest that the approach could lead to a potentially efficient feature location technique. They also suggest that it is advantageous in terms of the effort to configure the technique at method level granularity and that older changesets from older systems may reduce the effectiveness of the technique.

In the Constructor-Blocker game, two players, Constructor and Blocker, alternatively claim unclaimed edges of the complete graph $K_n$. For given graphs $F$ and $H$, Constructor can only claim edges that leave her graph $F$-free, while Blocker has no restrictions. Constructor's goal is to build as many copies of $H$ as she can, while Blocker attempts to stop this. The game ends once there are no more edges that Constructor can claim. The score $g(n,H,F)$ of the game is the number of copies of $H$ in Constructor's graph at the end of the game, when both players play optimally and Constructor plays first. In this paper, we extend results of Patk\'os, Stojakovi\'c and Vizer on $g(n, H, F)$ to many pairs of $H$ and $F$: We determine $g(n, H, F)$ when $H=K_r$ and $\chi(F)>r$, also when both $H$ and $F$ are odd cycles, using Szemer\'edi's Regularity Lemma. We also obtain bounds of $g(n, H, F)$ when $H=K_3$ and $F=K_{2,2}$., Comment: 16 pages

The paper describes a system that uses large language model (LLM) technology to support the automatic learning of new entries in an intelligent agent's semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sentences. The learning method involves a sequence of LLM requests and includes an automatic quality control step. To date, this learning method has been applied to learning multiword expressions whose meanings are equivalent to those of transitive verbs in the agent's lexicon. The experiment demonstrates the benefits of a hybrid learning architecture that integrates knowledge-based methods and resources with both traditional data analytics and LLMs., Comment: 7 pages, 7 figures, AAAI Fall Symposium Series 2023 on Integrating Cognitive Architecture and Generative Models

It is essential that healthcare professionals and members of the healthcare community can access and easily understand patient experiences in the real world, so that care standards can be improved and driven towards personalised drug treatment. Social media platforms and message boards are deemed suitable sources of patient experience information, as patients have been observed to discuss and exchange knowledge, look for and provide support online. This paper tests the hypothesis that not all online patient experience information can be treated and collected in the same way, as a result of the inherent differences in the way individuals talk about their journeys, in different therapeutic domains and or data sources. We used linguistic analysis to understand and identify similarities between datasets, across patient language, between data sources (Reddit, SocialGist) and therapeutic domains (cardiovascular, oncology, immunology, neurology). We detected common vocabulary used by patients in the same therapeutic domain across data sources, except for immunology patients, who use unique vocabulary between the two data sources, and compared to all other datasets. We combined linguistically similar datasets to train classifiers (CNN, transformer) to accurately identify patient experience posts from social media, a task we refer to as patient voice classification. The cardiovascular and neurology transformer classifiers perform the best in their respective comparisons for the Reddit data source, achieving F1-scores of 0.865 and 1.0 respectively. The overall best performing classifier is the transformer classifier trained on all data collected for this experiment, achieving F1-scores ranging between 0.863 and 0.995 across all therapeutic domain and data source specific test datasets., Comment: 14 pages, 1 figure, 7 tables

In April to May 2023, the superBIT telescope was lifted to the Earth's stratosphere by a helium-filled super-pressure balloon, to acquire astronomical imaging from above (99.5% of) the Earth's atmosphere. It was launched from New Zealand then, for 40 days, circumnavigated the globe five times at a latitude 40 to 50 degrees South. Attached to the telescope were four 'DRS' (Data Recovery System) capsules containing 5 TB solid state data storage, plus a GNSS receiver, Iridium transmitter, and parachute. Data from the telescope were copied to these, and two were dropped over Argentina. They drifted 61 km horizontally while they descended 32 km, but we predicted their descent vectors within 2.4 km: in this location, the discrepancy appears irreducible below 2 km because of high speed, gusty winds and local topography. The capsules then reported their own locations to within a few metres. We recovered the capsules and successfully retrieved all of superBIT's data - despite the telescope itself being later destroyed on landing., Comment: 12 pages

Normalising flows are generative models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and data generation from a single model. %However, the requirement for bijectivity imposes the use of specialised architectures. In the context of image modelling, the predominant choice has been the Glow-based architecture, whereas alternative architectures remain largely unexplored in the research community. In this work, we propose a novel architecture called MixerFlow, based on the MLP-Mixer architecture, further unifying the generative and discriminative modelling architectures. MixerFlow offers an efficient mechanism for weight sharing for flow-based models. Our results demonstrate comparative or superior density estimation on image datasets and good scaling as the image resolution increases, making MixerFlow a simple yet powerful alternative to the Glow-based architectures. We also show that MixerFlow provides more informative embeddings than Glow-based architectures and can integrate many structured transformations such as splines or Kolmogorov-Arnold Networks., Comment: Alternative title: MixerFlow for Image Modelling; Accepted at ECML-PKDD 2024

The Office of English Language Acquisition (OELA) has synthesized key data on English learners (ELs) into two-page sheets on a variety of subjects. This fact sheet focuses on: (1) Most Common Languages Spoken by English Learners (Excluding Spanish and English): Fall 2020; (2) Number of Reported Arabic-Speaking English Learners: 2008 through 2020; (3) States Where Arabic Was a Top Five Language With the Largest Increase and Decrease in the Number of Arabic-Speaking English Learners: School Years 2016-17 to 2020-21; (4) Percentage of English Learners Who Speak Arabic as a Home Language: School Year 2020-21; and (5) Home Languages and English Language Fluency of United States Residents Age 5-17 with Arab/Arabic Ancestry: American Community Survey 2021 Five-Year Estimate.