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This paper explores the concept of topological transitivity in nonautonomous dynamical systems, which are defined as sequences of continuous maps from a compact metric space to itself. It investigates various conditions (including intersection of any pair of open sets and existence of a dense orbit) that could be taken as definitions of the topological transitivity of a nonautonomous system, and addresses their relation both in the case of a general compact metric space and in the case where, in addition, the space has no isolated point. This provides the necessary basis for further investigation of transitivity of nonautonomous dynamical systems., Comment: 7 pages, Two main Theorems, Twelwe considered properties, Three Examples

The commercial value of economically significant fruits, including date palm fruit (dates), is influenced by various factors, such as biochemical composition and morphological features like size, shape, and visual appearance, which are key determinants of their quality and market value. Dates are typically consumed at the dry stage (Tamar), during which they exhibit a wide range of physical characteristics, such as color, length, weight, and skin appearance. Understanding the genetic basis of these traits is crucial for improving crop quality and breeding new cultivars. In this study, we integrated a genome dataset from highly diverse date cultivars with phenotypes of dry fruit such as length, width, area, and weight, identifying multiple significant genetic loci (SNPs) associated with these traits. We also identified candidate genes located near the associated SNPs that are involved in biological processes such as cell differentiation, proliferation, growth, and the regulation of signalling pathways for growth regulators like auxin and abscisic acid, as observed in other plants. Gene expression analysis reveals that many of these genes are highly expressed in the early stage of fruit development when the fruit attains its maximum size and weight. These findings will enhance our understanding of genetic determinants of fruit size particularly at the commercially important Tamar stage.

We present the final 6'' resolution data release of the ELAIS-N1 field from the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey Deep Fields project (LoTSS Deep). The 144MHz images are the most sensitive achieved to date at this frequency and were created from 290 TB of data obtained from 505 hrs on-source observations taken over 7.5 years. The data were processed following the strategies developed for previous LoTSS and LoTSS Deep data releases. The resulting images span 24.53 square degrees and, using a refined source detection approach, we identified 154,952 radio sources formed from 182,184 Gaussian components within this area. The maps reach a noise level of 10.7 $\mu$Jy/beam at 6'' resolution where approximately half of the noise is due to source confusion. In about 7.4% of the image our limited dynamic range around bright sources results in a further > 5% increase in the noise. The images have a flux density scale accuracy of about 9% and the standard deviation of offsets between our source positions and those from Pan-STARRS is 0.2'' in RA and Dec for high significance detections. We searched individual epoch images for variable sources, identifying 39 objects with considerable variation. We also searched for circularly polarised sources achieving three detections of previously known emitters (two stars and one pulsar) whilst constraining the typical polarisation fraction plus leakage to be less than 0.045%., Comment: Accepted for publication in A&A. 16 figures, 1 table and 20 pages. The catalogues and images associated with this data release are publicly available via https://lofar-surveys.org/

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

Maximal supergravities in ten and eleven dimensions admit consistent truncations on particular spheres to maximal supergravities in lower dimensions. Concurrently, the truncation to singlets under any subgroup of the sphere isometry group leads to consistent truncations with less or no supersymmetry. We review the relation between these truncations in the framework of exceptional field theory. As an application, we derive three new G$_2$-invariant solutions of $D=11$ supergravity. Their geometry is of the form AdS$_4\times \Sigma_7$ where $\Sigma_7$ is a deformed seven-sphere, preserving SO(7) isometries., Comment: 35 pages, 7 figures

Inner speech recognition has gained enormous interest in recent years due to its applications in rehabilitation, developing assistive technology, and cognitive assessment. However, since language and speech productions are a complex process, for which identifying speech components has remained a challenging task. Different approaches were taken previously to reach this goal, but new approaches remain to be explored. Also, a subject-oriented analysis is necessary to understand the underlying brain dynamics during inner speech production, which can bring novel methods to neurological research. A publicly available dataset, Thinking Out Loud Dataset, has been used to develop a Machine Learning (ML)-based technique to classify inner speech using 128-channel surface EEG signals. The dataset is collected on a Spanish cohort of ten subjects while uttering four words (Arriba, Abajo, Derecha, and Izquierda) by each participant. Statistical methods were employed to detect and remove motion artifacts from the Electroencephalography (EEG) signals. A large number (191 per channel) of time-, frequency- and time-frequency-domain features were extracted. Eight feature selection algorithms are explored, and the best feature selection technique is selected for subsequent evaluations. The performance of six ML algorithms is evaluated, and an ensemble model is proposed. Deep Learning (DL) models are also explored, and the results are compared with the classical ML approach. The proposed ensemble model, by stacking the five best logistic regression models, generated an overall accuracy of 81.13% and an F1 score of 81.12% in the classification of four inner speech words using surface EEG signals. The proposed framework with the proposed ensemble of classical ML models shows promise in the classification of inner speech using surface EEG signals., Comment: 13 Figures, 3 Tables

Recent observations reveal a rapid dust build-up in high-redshift galaxies (z > 4), challenging current models of galaxy formation. While our understanding of dust production and destruction in the interstellar medium (ISM) is advancing, probing baryonic processes in the early Universe remains a complex task. We characterize the evolution of 98 z~5 star-forming galaxies observed as part of the ALPINE survey by constraining the physical processes underpinning the gas and dust production, consumption, and destruction in their ISM. We make use of chemical evolution models to simultaneously reproduce the observed dust and gas content. For each galaxy, we estimate initial gas mass, inflows and outflows, and efficiencies of dust growth and destruction. We test the models with the canonical Chabrier and top-heavy initial mass functions (IMFs), with the latter enabling rapid dust production on shorter timescales. Our models successfully reproduce gas and dust content in older galaxies (> 600 Myr) regardless of the IMF, with Type II SNe as the primary dust source and no dust growth in ISM with moderate inflow of primordial gas. In case of intermediate-age galaxies (300 - 600 Myr), we reproduce the gas and dust content through Type II SNe and dust growth in ISM, though we observe an over-prediction of dust mass in older galaxies, potentially indicating an unaccounted dust destruction mechanism and/or an overestimation of the observed dust masses. The number of young galaxies (< 300 Myr) reproduced, increases for models assuming top-heavy IMF but with maximal prescriptions of dust production. Galactic outflows are necessary to reproduce observed gas and dust masses. The Chabrier IMF models reproduce 65% of galaxies, while top-heavy IMF models improve this to 93%, easing tensions with observations. Upcoming JWST data will refine these models by resolving degeneracies in intrinsic galaxy properties., Comment: Accepted for publication in A&A

The pervasiveness of mobile apps in everyday life necessitates robust testing strategies to ensure quality and efficiency, especially through end-to-end usage-based tests for mobile apps' user interfaces (UIs). However, manually creating and maintaining such tests can be costly for developers. Since many apps share similar functionalities beneath diverse UIs, previous works have shown the possibility of transferring UI tests across different apps within the same domain, thereby eliminating the need for writing the tests manually. However, these methods have struggled to accommodate real-world variations, often facing limitations in scenarios where source and target apps are not very similar or fail to accurately transfer test oracles. This paper introduces an innovative technique, LLMigrate, which leverages Large Language Models (LLMs) to efficiently transfer usage-based UI tests across mobile apps. Our experimental evaluation shows LLMigrate can achieve a 97.5% success rate in automated test transfer, reducing the manual effort required to write tests from scratch by 91.1%. This represents an improvement of 9.1% in success rate and 38.2% in effort reduction compared to the best-performing prior technique, setting a new benchmark for automated test transfer.

This study introduces a novel approach for EUR/USD exchange rate forecasting that integrates deep learning, textual analysis, and particle swarm optimization (PSO). By incorporating online news and analysis texts as qualitative data, the proposed PSO-LSTM model demonstrates superior performance compared to traditional econometric and machine learning models. The research employs advanced text mining techniques, including sentiment analysis using the RoBERTa-Large model and topic modeling with LDA. Empirical findings underscore the significant advantage of incorporating textual data, with the PSO-LSTM model outperforming benchmark models such as SVM, SVR, ARIMA, and GARCH. Ablation experiments reveal the contribution of each textual data category to the overall forecasting performance. The study highlights the transformative potential of artificial intelligence in finance and paves the way for future research in real-time forecasting and the integration of alternative data sources.

We characterise the co-evolution of radio-loud AGN and their galaxies by mapping the dependence of radio-loud AGN activity on stellar mass and star-formation rate (SFR) across cosmic time (out to $z \sim 1.5$). Deep LOFAR radio observations are combined with large galaxy samples to study the incidence of radio-loud AGN across the galaxy population; the AGN are further split into low-excitation radio galaxies (LERGs) and high-excitation radio galaxies (HERGs). We find that LERG activity occurs over a wide range of SFRs, whereas HERGs are typically found in galaxies with ongoing star formation. The LERGs are then split based on their SFRs relative to the main sequence, across redshift. Within quiescent galaxies, LERG activity shows a steep stellar mass dependence with the same normalisation across the past $\sim$ 10 Gyr; this indicates that hot gas fuels LERGs in quiescent galaxies across cosmic time. In massive galaxies ($\log_{10}(M/\rm{M_{\odot}}) \gtrsim 11$), the incidence of LERGs is roughly constant across the galaxy population, suggesting that LERGs in massive galaxies may be fuelled by hot gas regardless of the star-formation activity. At lower masses, however, LERG activity is significantly more enhanced (by a factor of up to 10) in star-forming galaxies compared to quiescent galaxies; this suggests that an additional fuelling mechanism, likely associated with cold gas, may fuel the LERGs in galaxies with higher SFRs. We find that HERGs typically accrete above 1 per cent of the Eddington-scaled accretion rate, and the LERGs typically accrete below this level., Comment: Accepted for publication in MNRAS, 18 pages, 10 figures

In this paper, we present a deterministic algorithm to count the low-weight codewords of punctured and shortened pure and pre-transformed polar codes. The method first evaluates the weight properties of punctured/shortened polar cosets. Then, a method that discards the cosets that have no impact on the computation of the low-weight codewords is introduced. A key advantage of this method is its applicability, regardless of the frozen bit set, puncturing/shortening pattern, or pretransformation. Results confirm the method's efficiency while showing reduced computational complexity compared to stateof-the-art algorithms.

This paper presents a 610 MHz radio survey covering 1.94 square degrees around the North Ecliptic Pole (NEP), which includes parts of the AKARI (ADF-N) and Euclid, Deep Fields North. The median 5-sigma sensitivity is 28 microJy beam per beam, reaching as low as 19 microJy per beam, with a synthesised beam of 3.6 x 4.1 arcsec. The catalogue contains 1675 radio components, with 339 grouped into multi-component sources and 284 isolated components likely part of double radio sources. Imaging, cataloguing, and source identification are presented, along with preliminary scientific results. From a non-statistical sub-set of 169 objects with multi-wavelength AKARI and other detections, luminous infrared galaxies (LIRGs) represent 66 percent of the sample, ultra-luminous infrared galaxies (ULIRGs) 4 percent, and sources with L_IR < 1011 L_sun 30 percent. In total, 56 percent of sources show some AGN presence, though only seven are AGN-dominated. ULIRGs require three times higher AGN contribution to produce high-quality SED fits compared to lower luminosity galaxies, and AGN presence increases with AGN fraction. The PAH mass fraction is insignificant, although ULIRGs have about half the PAH strength of lower IR-luminosity galaxies. Higher luminosity galaxies show gas and stellar masses an order of magnitude larger, suggesting higher star formation rates. For LIRGs, AGN presence increases with redshift, indicating that part of the total luminosity could be contributed by AGN activity rather than star formation. Simple cross-matching revealed 13 ROSAT QSOs, 45 X-ray sources, and 61 sub-mm galaxies coincident with GMRT radio sources.

It is becoming increasingly difficult to improve the performance of a a single process (thread) on a computer due to physical limitations. Modern systems use multi-core processors in which multiple processes (threads) may run concurrently. A lock-free data structure can allow these processes to communicate with each other without requiring mutual exclusion, and may increase the amount of work they may perform in parallel rather than sequentially, thus improving the performance of the system as a whole. This paper contains an implementation of Ko's Lock-Free Binary Trie, which stores a dynamic set of keys from an ordered universe. It supports insert, remove, search and predecessor operations. One novel component of this implementation is a lock-free linked list which allows multiple processes to attempt to insert the same node, but which prevents a node from being reinserted once it has been removed from the list. The final section of this paper contains an experimental comparison of this implementation against other data structures which implement the same abstract data type (ADT) as the lock-free trie. Analysis of these experiments reveal that the implementation of Ko's Trie performs better than existing theoretical implementations of this ADT when the universe of keys is large, when removes are rare and when the number of processes performing operations concurrently is low., Comment: 6 Chapters, 71 Pages

We investigate the geometric response of chiral superfluids when coupled to a dynamic background geometry. We find that geometry fluctuations, represented by the flexural mode, interact with the superfluid phase fluctuations (the Goldstone mode). Starting from a minimally coupled theory, we derive the equilibrium conditions for a static background defined by supercurrent, curvature, and tension, and then obtain linearized equations for the propagation of the Goldstone and flexural modes. The equations reveal distinctive chirality-dependent effects in the propagation of the flexural mode. Specifically, a background supercurrent induces a chiral drag effect, localizing flexural waves at the superfluid boundary, while background curvature introduces anisotropic corrections to the superfluid phase and group velocities, as well as a tension in the flexural mode dispersion. Furthermore, curvature couples flexural and phase modes into dressed excitations, with tilted Dirac cones along the principal curvature directions. These effects provide dynamical signatures of the formation of a chiral condensate, and can be tuned by manipulating the background geometry., Comment: 12 pages, 9 figures

The second law of thermodynamics stipulates that entropy production is non-negative in macroscopic systems, reaching zero at thermodynamic equilibrium. As a corollary, this implies that the state trajectory of macroscopic systems is inherently time-irreversible under out-of-equilibrium conditions. However, over the past half-century, various studies have demonstrated that this principle does not hold for the composition sample path of some isothermal reactive systems. Theoretical frameworks leading to this unexpected observation primarily focus on zero-dimensional (perfectly homogeneous) systems, which exclude the effect of local fluctuations. This oversimplification might account for the paradoxical theoretical prediction. In the absence of pertinent experimental data, this paper aims to investigate this phenomenon through microscopic simulations.

Robots hold great promise for performing repetitive or hazardous tasks, but achieving human-like dexterity, especially in contact-rich and dynamic environments, remains challenging. Rigid robots, which rely on position or velocity control, often struggle with maintaining stable contact and applying consistent force in force-intensive tasks. Learning from Demonstration has emerged as a solution, but tasks requiring intricate maneuvers, such as powder grinding, present unique difficulties. This paper introduces Diffusion Policies For Compliant Manipulation (DIPCOM), a novel diffusion-based framework designed for compliant control tasks. By leveraging generative diffusion models, we develop a policy that predicts Cartesian end-effector poses and adjusts arm stiffness to maintain the necessary force. Our approach enhances force control through multimodal distribution modeling, improves the integration of diffusion policies in compliance control, and extends our previous work by demonstrating its effectiveness in real-world tasks. We present a detailed comparison between our framework and existing methods, highlighting the advantages and best practices for deploying diffusion-based compliance control.

We analyze all possible symmetry reductions of Lagrangians that yield fully equivalent field equations for any 4-dimensional metric theory of gravity. Specifically, we present a complete list of infinitesimal group actions obeying the principle of symmetric criticality, identify the corresponding invariant metrics (and $l$-chains), discuss relations among them, and analyze simplifications allowed by the residual diffeomorphism group and Noether identities before and after variation of the reduced Lagrangian. We employ rigorous treatment of the symmetry reduction by Fels and Torre and use the Hicks classification of infinitesimal group actions by means of Lie algebras pairs of isometries and their isotropy subalgebras. The classification is recast in the coordinates in which the well-known symmetries and geometries are easily recognizable and relatable to each other. The paper is accompanied by a code for the symmetry reduction of Lagrangians that we developed in \texttt{xAct} package of \textsc{Mathematica}., Comment: 32 pages, 2 figures, 10 tables, an example in Sec. VI.G and various typos corrected, comments welcome!

A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$ interaction cross section, resulting in a factor of two reduction in uncertainties from this source and $\sim$10\% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times\mathrm{10^{34}~years}$ for $p\rightarrow e^+\eta$ and $7.3\times\mathrm{10^{33}~years}$ for $p\rightarrow \mu^+\eta$ at the 90$\%$ C.L. were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.

Unmanned aerial vehicles (UAVs) have become increasingly popular in various fields, including precision agriculture, search and rescue, and remote sensing. However, exploring unknown environments remains a significant challenge. This study aims to address this challenge by utilizing on-policy Reinforcement Learning (RL) with Proximal Policy Optimization (PPO) to explore the {two dimensional} area of interest with multiple UAVs. The UAVs will avoid collision with obstacles and each other and do the exploration in a distributed manner. The proposed solution includes actor-critic networks using deep convolutional neural networks {(CNN)} and long short-term memory (LSTM) for identifying the UAVs and areas that have already been covered. Compared to other RL techniques, such as policy gradient (PG) and asynchronous advantage actor-critic (A3C), the simulation results demonstrate the superiority of the proposed PPO approach. Also, the results show that combining LSTM with CNN in critic can improve exploration. Since the proposed exploration has to work in unknown environments, the results showed that the proposed setup can complete the coverage when we have new maps that differ from the trained maps. Finally, we showed how tuning hyper parameters may affect the overall performance.

Solving combinatorial optimization problems involve satisfying a set of hard constraints while optimizing some objectives. In this context, exact or approximate methods can be used. While exact methods guarantee the optimal solution, they often come with an exponential running time as opposed to approximate methods that trade the solutions quality for a better running time. In this context, we tackle the Nurse Scheduling Problem (NSP). The NSP consist in assigning nurses to daily shifts within a planning horizon such that workload constraints are satisfied while hospitals costs and nurses preferences are optimized. To solve the NSP, we propose implicit and explicit approaches. In the implicit solving approach, we rely on Machine Learning methods using historical data to learn and generate new solutions through the constraints and objectives that may be embedded in the learned patterns. To quantify the quality of using our implicit approach in capturing the embedded constraints and objectives, we rely on the Frobenius Norm, a quality measure used to compute the average error between the generated solutions and historical data. To compensate for the uncertainty related to the implicit approach given that the constraints and objectives may not be concretely visible in the produced solutions, we propose an alternative explicit approach where we first model the NSP using the Constraint Satisfaction Problem (CSP) framework. Then we develop Stochastic Local Search methods and a new Branch and Bound algorithm enhanced with constraint propagation techniques and variables/values ordering heuristics. Since our implicit approach may not guarantee the feasibility or optimality of the generated solution, we propose a data-driven approach to passively learn the NSP as a constraint network. The learned constraint network, formulated as a CSP, will then be solved using the methods we listed earlier.

One of the major challenges arising from single-cell transcriptomics experiments is the question of how to annotate the associated single-cell transcriptomic profiles. Because of the large size and the high dimensionality of the data, automated methods for annotation are needed. We focus here on datasets obtained in the context of developmental biology, where the differentiation process leads to a hierarchical structure. We consider a frequent setting where both labeled and unlabeled data are available at training time, but the sets of the labels of labeled data on one side and of the unlabeled data on the other side, are disjoint. It is an instance of the Novel Class Discovery problem. The goal is to achieve two objectives, clustering the data and mapping the clusters with labels. We propose extensions of k-Means and GMM clustering methods for solving the problem and report comparative results on artificial and experimental transcriptomic datasets. Our approaches take advantage of the hierarchical nature of the data., Comment: 11 pages, 1 figures, 2 tables

We utilize a combination of radio continuum observations and optical integral field spectroscopic (IFS) data to explore the impact of radio AGN on the evolution of their host galaxies at both global and sub-galactic scales. We construct a comprehensive radio-IFS sample comprising 5548 galaxies with redshift z<0.15 by cross-matching the LoTSS with the MaNGA survey. We revisit the tight linear radio continuum - star formation relation and quantify its intrinsic scatter, then use the relation to classify 616 radio-excess AGN with excessive radio luminosities over that expected from their star formation rate. Massive radio AGN host galaxies are predominantly quiescent systems, but the quenching level shows no correlation with the jet luminosity. The mass assembly histories derived from the stellar population synthesis model fitting agree with the cosmological simulations incorporating radio-mode AGN feedback models. We observe that radio AGN hosts grow faster than a control sample of galaxies matched in stellar mass, and the quenching age ($\sim$5 Gyr) is at larger lookback times than the typical radio jet age (<1 Gyr). By stacking the spectra in different radial bins and comparing results for radio AGN hosts and their controls, we find emission line excess features in the nuclear region of radio AGN hosts. This excess is more prominent in low-luminosity, low-mass, and compact radio AGN. The [NII]/H$\alpha$ ratios of the excessive emission line indicate that radio AGN or related jets are ionizing the surrounding interstellar medium in the vicinity of the nucleus. Our results support the scenario that the observed present-day radio AGN activity may help their host galaxies maintain quiescence through gas ionization and heating, but it is not responsible for the past quenching of their hosts., Comment: Accepted by A&A, 17 pages, 16 figures

Tree ensembles, including boosting methods, are highly effective and widely used for tabular data. However, large ensembles lack interpretability and require longer inference times. We introduce a method to prune a tree ensemble into a reduced version that is "functionally identical" to the original model. In other words, our method guarantees that the prediction function stays unchanged for any possible input. As a consequence, this pruning algorithm is lossless for any aggregated metric. We formalize the problem of functionally identical pruning on ensembles, introduce an exact optimization model, and provide a fast yet highly effective method to prune large ensembles. Our algorithm iteratively prunes considering a finite set of points, which is incrementally augmented using an adversarial model. In multiple computational experiments, we show that our approach is a "free lunch", significantly reducing the ensemble size without altering the model's behavior. Thus, we can preserve state-of-the-art performance at a fraction of the original model's size.

A novel asymptotic representation of the analytic solutions to a family of singularly perturbed $q-$difference-differential equations in the complex domain is obtained. Such asymptotic relation shows two different levels associated to the vanishing rate of the domains of the coefficients in the formal asymptotic expansion. On the way, a novel version of a multilevel sequential Ramis-Sibuya type theorem is achieved.

High-entropy alloys (HEAs) exhibit exceptional catalytic performance due to their complex surface structures. However, the vast number of active binding sites in HEAs, as opposed to conventional alloys, presents a significant computational challenge in catalytic applications. To tackle this challenge, robust methods must be developed to efficiently explore the configurational space of HEA catalysts. Here, we introduce a novel approach that combines alchemical perturbation density functional theory (APDFT) with a graph-based correction scheme to explore the binding energy landscape HEAs. Our results demonstrate that APDFT can accurately predict binding energies for isoelectronic permutations in HEAs at minimal computational cost, significantly accelerating configurational space sampling. However, APDFT errors increase substantially when permutations occur near binding sites. To address this issue, we developed a graph-based Gaussian process regression model to correct discrepancies between APDFT and conventional density functional theory values. Our approach enables the prediction of binding energies for hundreds of thousands of configurations with a mean average error of 30 meV, requiring a handful of ab initio simulations.

We study the mathematical properties of time-dependent flows of incompressible fluids that respond as an Euler fluid until the modulus of the symmetric part of the velocity gradient exceeds a certain, a-priori given but arbitrarily large, critical value. Once the velocity gradient exceeds this threshold, a dissipation mechanism is activated. Assuming that the fluid, after such an activation, dissipates the energy in a specific manner, we prove that the corresponding initial-boundary-value problem is globally-in-time well-posed in the sense of Hadamard. In particular, we show that for an arbitrary, sufficiently regular, initial velocity there is a unique weak solution to the spatially periodic problem.

The single photoelectron performance characteristics of a Large Area Picosecond PhotoDetector (LAPPD) were studied using a picosecond optical laser source. We verify that the LAPPD is capable of achieving transit time spreads of less than 70 ps and spatial resolutions on the order of millimetres when illuminated with single photons. Isolation of the single photoelectron peak is easily possible in the pulse height distribution and the mean single photoelectron gain is measured to be greater than $10^{6}$ with gains above $10^{7}$ possible in certain voltage configurations. We judge the performance of the LAPPD sufficient for application in large-scale water or scintillator-based neutrino experiments, with additional work required to develop the digital signal processing algorithms necessary for processing photon hits in high photon occupancy environments., Comment: 13 pages, 11 figures

Denoising diffusion probabilistic models enable high-fidelity image synthesis and editing. In biomedicine, these models facilitate counterfactual image editing, producing pairs of images where one is edited to simulate hypothetical conditions. For example, they can model the progression of specific diseases, such as stroke lesions. However, current image editing techniques often fail to generate realistic biomedical counterfactuals, either by inadequately modeling indirect pathological effects like brain atrophy or by excessively altering the scan, which disrupts correspondence to the original images. Here, we propose MedEdit, a conditional diffusion model for medical image editing. MedEdit induces pathology in specific areas while balancing the modeling of disease effects and preserving the integrity of the original scan. We evaluated MedEdit on the Atlas v2.0 stroke dataset using Frechet Inception Distance and Dice scores, outperforming state-of-the-art diffusion-based methods such as Palette (by 45%) and SDEdit (by 61%). Additionally, clinical evaluations by a board-certified neuroradiologist confirmed that MedEdit generated realistic stroke scans indistinguishable from real ones. We believe this work will enable counterfactual image editing research to further advance the development of realistic and clinically useful imaging tools., Comment: Accepted at MICCAI24 Simulation and Synthesis in Medical Imaging (SASHIMI) workshop

Extracting the speech of participants in a conversation amidst interfering speakers and noise presents a challenging problem. In this paper, we introduce the novel task of target conversation extraction, where the goal is to extract the audio of a target conversation based on the speaker embedding of one of its participants. To accomplish this, we propose leveraging temporal patterns inherent in human conversations, particularly turn-taking dynamics, which uniquely characterize speakers engaged in conversation and distinguish them from interfering speakers and noise. Using neural networks, we show the feasibility of our approach on English and Mandarin conversation datasets. In the presence of interfering speakers, our results show an 8.19 dB improvement in signal-to-noise ratio for 2-speaker conversations and a 7.92 dB improvement for 2-4-speaker conversations. Code, dataset available at https://github.com/chentuochao/Target-Conversation-Extraction., Comment: Accepted by Interspeech 2024

Non-Gaussianity-based Independent Vector Extraction leads to the famous one-unit FastICA/FastIVA algorithm when the likelihood function is optimized using an approximate Newton-Raphson algorithm under the orthogonality constraint. In this paper, we replace the constraint with the analytic form of the minimum variance distortionless beamformer (MVDR), by which a semi-blind variant of FastICA/FastIVA is obtained. The side information here is provided by a weighted covariance matrix replacing the noise covariance matrix, the estimation of which is a frequent goal of neural beamformers. The algorithm thus provides an intuitive connection between model-based blind extraction and learning-based extraction. The algorithm is tested in simulations and speaker ID-guided speaker extraction, showing fast convergence and promising performance., Comment: accepted for IWAENC 2024

Models for low-latency, streaming applications could benefit from the knowledge capacity of larger models, but edge devices cannot run these models due to resource constraints. A possible solution is to transfer hints during inference from a large model running remotely to a small model running on-device. However, this incurs a communication delay that breaks real-time requirements and does not guarantee that both models will operate on the same data at the same time. We propose knowledge boosting, a novel technique that allows a large model to operate on time-delayed input during inference, while still boosting small model performance. Using a streaming neural network that processes 8 ms chunks, we evaluate different speech separation and enhancement tasks with communication delays of up to six chunks or 48 ms. Our results show larger gains where the performance gap between the small and large models is wide, demonstrating a promising method for large-small model collaboration for low-latency applications. Code, dataset, and audio samples available at https://knowledgeboosting.cs.washington.edu/., Comment: Accepted by Interspeech 2024

Convolutional Neural Network (CNN) models often lack the ability to incorporate human input, which can be addressed by Augmented Reality (AR) headsets. However, current AR headsets face limitations in processing power, which has prevented researchers from performing real-time, complex image recognition tasks using CNNs in AR headsets. This paper presents a method to deploy CNN models on AR headsets by training them on computers and transferring the optimized weight matrices to the headset. The approach transforms the image data and CNN layers into a one-dimensional format suitable for the AR platform. We demonstrate this method by training the LeNet-5 CNN model on the MNIST dataset using PyTorch and deploying it on a HoloLens AR headset. The results show that the model maintains an accuracy of approximately 98%, similar to its performance on a computer. This integration of CNN and AR enables real-time image processing on AR headsets, allowing for the incorporation of human input into AI models., Comment: 10 pages 8 figures 4300 words

Failures of fairness or robustness in machine learning predictive settings can be due to undesired dependencies between covariates, outcomes and auxiliary factors of variation. A common strategy to mitigate these failures is data balancing, which attempts to remove those undesired dependencies. In this work, we define conditions on the training distribution for data balancing to lead to fair or robust models. Our results display that, in many cases, the balanced distribution does not correspond to selectively removing the undesired dependencies in a causal graph of the task, leading to multiple failure modes and even interference with other mitigation techniques such as regularization. Overall, our results highlight the importance of taking the causal graph into account before performing data balancing.

The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The best-performing AF can vary significantly across optimization problems, often requiring ad-hoc and problem-specific choices. This work tackles the challenge of designing novel AFs that perform well across a variety of experimental settings. Based on FunSearch, a recent work using Large Language Models (LLMs) for discovery in mathematical sciences, we propose FunBO, an LLM-based method that can be used to learn new AFs written in computer code by leveraging access to a limited number of evaluations for a set of objective functions. We provide the analytic expression of all discovered AFs and evaluate them on various global optimization benchmarks and hyperparameter optimization tasks. We show how FunBO identifies AFs that generalize well in and out of the training distribution of functions, thus outperforming established general-purpose AFs and achieving competitive performance against AFs that are customized to specific function types and are learned via transfer-learning algorithms.

Recent astronomical observations revealed an increasing molecular complexity in the interstellar medium through the detection of a series of large cyclic carbon species. To correctly interpret these detections, a complex analysis is necessary that takes into account the non-local thermodynamic equilibrium (non-LTE) conditions of the emitting media (e.g. when energy level populations deviate from a Boltzman distribution). This requires proper state-to-state collisional data for the excitation and de-excitation processes of the molecular levels. Cyclopentadiene (c-C5H6), which was recently detected in multiple cold interstellar clouds, is extensively studied in many aspects due to its large importance for chemistry in general. At the same time, there are no collisional data available for this species, which are necessary for a more precise interpretation of the corresponding detections. In this work, we first provide an accurate 3D rigid-rotor interaction potential for the [c-C5H6 + He] complex from high-level of ab initio theories, which has been used to study their inelastic collision by the exact close coupling quantum scattering method. To the best of our knowledge, this is the first study where this method is systematically applied to treat the dynamics of molecular collisions involving more than ten atoms. We also analyse the collisional propensity rules and the differences in contrast to calculations, where the approximate coupled states scattering methods is used., Comment: Physical Chemistry Chemical Physics, 2024

Researchers are exploring Augmented Reality (AR) interfaces for online robot programming to streamline automation and user interaction in variable manufacturing environments. This study introduces an AR interface for online programming and data visualization that integrates the human in the randomized robot path planning, reducing the inherent randomness of the methods with human intervention. The interface uses holographic items which correspond to physical elements to interact with a redundant manipulator. Utilizing Rapidly Random Tree Star (RRT*) and Spherical Linear Interpolation (SLERP) algorithms, the interface achieves end-effector s progression through collision-free path with smooth rotation. Next, Sequential Quadratic Programming (SQP) achieve robot s configurations for this progression. The platform executes the RRT* algorithm in a loop, with each iteration independently exploring the shortest path through random sampling, leading to variations in the optimized paths produced. These paths are then demonstrated to AR users, who select the most appropriate path based on the environmental context and their intuition. The accuracy and effectiveness of the interface are validated through its implementation and testing with a seven Degree-OF-Freedom (DOF) manipulator, indicating its potential to advance current practices in robot programming. The validation of this paper include two implementations demonstrating the value of human-in-the-loop and context awareness in robotics., Comment: 10 pages, 13 figures

A control based power tower function at order 2 is proposed in this paper. This leads to a new sliding mode control, which allows employing backstepping technique that combines both guaranteed and finite time convergence. The proposed control is applied to a double integrator subject to perturbation $d$. Both guaranteed and finite convergence are ensured by the controller when $d$ is considered constant and bounded, without knowing its upper bound. For the case, when $d$ is variable and bounded with its upper bound known, only a finite time convergence is obtained. Simulation results are given to show the well founded of the proposed novel control.

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 12 pages, 4 figures

Life cycle assessment (LCA) plays a critical role in assessing the environmental impacts of a product, technology, or service throughout its entire life cycle. Nonetheless, many existing LCA tools and methods lack adequate metadata management, which can hinder their further development and wide adoption. In the example of LCA for clean energy technologies, metadata helps monitor data and the environment that holds the integrity of the energy assets and sustainability of the materials sources across their entire value chains. Ontologizing metadata, i.e. a common vocabulary and language to connect multiple data sources, as well as implementing AI-aware data management, can have long-lasting, positive, and accelerating effects along with collecting and utilizing quality data from different sources and across the entire data lifecycle. The integration of ontologies in life cycle assessments has garnered significant attention in recent years. We synthesized the existing literature on ontologies for LCAs, providing insights into this interdisciplinary field's evolution, current state, and future directions. We also proposed the framework for a suitable data model and the workflow thereof to warrant the alignment with existing ontologies, practical frameworks, and industry standards.

In crowded settings, the human brain can focus on speech from a target speaker, given prior knowledge of how they sound. We introduce a novel intelligent hearable system that achieves this capability, enabling target speech hearing to ignore all interfering speech and noise, but the target speaker. A naive approach is to require a clean speech example to enroll the target speaker. This is however not well aligned with the hearable application domain since obtaining a clean example is challenging in real world scenarios, creating a unique user interface problem. We present the first enrollment interface where the wearer looks at the target speaker for a few seconds to capture a single, short, highly noisy, binaural example of the target speaker. This noisy example is used for enrollment and subsequent speech extraction in the presence of interfering speakers and noise. Our system achieves a signal quality improvement of 7.01 dB using less than 5 seconds of noisy enrollment audio and can process 8 ms of audio chunks in 6.24 ms on an embedded CPU. Our user studies demonstrate generalization to real-world static and mobile speakers in previously unseen indoor and outdoor multipath environments. Finally, our enrollment interface for noisy examples does not cause performance degradation compared to clean examples, while being convenient and user-friendly. Taking a step back, this paper takes an important step towards enhancing the human auditory perception with artificial intelligence. We provide code and data at: https://github.com/vb000/LookOnceToHear., Comment: Best paper honorable mention at CHI 2024

Spectral energy distribution (SED) fitting has been extensively used to determine the nature of the faint radio source population. Recent efforts have combined fits from multiple SED-fitting codes to account for the host galaxy and any active nucleus that may be present. We show that it is possible to produce similar-quality classifications using a single energy-balance SED fitting code, Prospector, to model up to 26 bands of UV$-$far-infrared aperture-matched photometry for $\sim$31,000 sources in the ELAIS-N1 field from the LOFAR Two-Metre Sky Survey (LoTSS) Deep fields first data release. One of a new generation of SED-fitting codes, Prospector accounts for potential contributions from radiative active galactic nuclei (AGN) when estimating galaxy properties, including star formation rates (SFRs) derived using non-parametric star formation histories. Combining this information with radio luminosities, we classify 92 per cent of the radio sources as a star-forming galaxy, high/low-excitation radio galaxy, or radio-quiet AGN and study the population demographics as a function of 150 MHz flux density, luminosity, SFR, stellar mass, redshift and apparent $r$-band magnitude. Finally, we use Prospector SED fits to investigate the SFR$-$150 MHz luminosity relation for a sample of $\sim$$133,000~3.6~\mu$m-selected $z<1$ sources, finding that the stellar mass dependence is significantly weaker than previously reported, and may disappear altogether at $\log_{10} (\mathrm{SFR}/M_\odot~\mathrm{yr}^{-1}) > 0.5$. This approach makes it significantly easier to classify radio sources from LoTSS and elsewhere, and may have important implications for future studies of star-forming galaxies at radio wavelengths., Comment: 20 pages, 13 figures, accepted for publication in MNRAS

Recent discoveries of copious amounts of dust in quiescent galaxies (QGs) at high redshifts ($z\gtrsim 1-2$) challenge the conventional view that these objects have poor interstellar medium (ISM) in proportion to their stellar mass. We use the SIMBA cosmological simulation to explore the evolution of dust and cold gas content in QGs in relation to the quenching processes affecting them. We track the changes in the ISM dust abundance across the evolutionary history of QGs identified at $0 \lesssim z \lesssim2$ in the field and cluster environments. The QGs quench via diverse pathways, both rapid and slow, and exhibit a wide range of times elapsed between the quenching event and cold gas removal (from $\sim650$ Myr to $\sim8$ Gyr). We find that quenching modes attributed to the feedback from active galactic nuclei (AGN) do not affect dust and cold gas within the same timescales. Remarkably, QGs may replenish their dust content in the quenched phase primarily due to internal processes and marginally by external factors such as minor mergers. The key mechanism for re-formation of dust is prolonged grain growth on gas-phase metals, it is effective within $\sim100$ Myr after the quenching event, and rapidly increases the dust-to-gas mass ratio in QGs above the standard values ($\delta_{\rm DGR}\gtrsim1/100$). As a result, despite heavily depleted cold gas reservoirs, roughly half of QGs maintain little evolution in their ISM dust with stellar age within the first 2 Gyr following the quenching. Overall, we predict that relatively dusty QGs ($M_{\rm dust}/M_{\star}\gtrsim10^{-3}-10^{-4}$) arise from both fast and slow quenchers, and are prevalent in systems of intermediate and low stellar masses ($9<\log(M_{\star}/M_{\odot})<10.5$). This prediction poses an immediate quest for observational synergy between e.g., James Webb Space Telescope (JWST) and the Atacama Large Millimeter Array (ALMA)., Comment: 21 pages, 10 figures, 1 table, submitted to A&A

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande, both located in the Kamioka mine in Japan, have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M$_{\odot}$ star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance., Comment: Resubmitted to ApJ. 22 pages, 16 figures, for more information about the combined pre-supernova alert system, see https://www.lowbg.org/presnalarm/

Detecting and handling misspecified objectives, such as reward functions, has been widely recognized as one of the central challenges within the domain of Artificial Intelligence (AI) safety research. However, even with the recognition of the importance of this problem, we are unaware of any works that attempt to provide a clear definition for what constitutes (a) misspecified objectives and (b) successfully resolving such misspecifications. In this work, we use the theory of mind, i.e., the human user's beliefs about the AI agent, as a basis to develop a formal explanatory framework called Expectation Alignment (EAL) to understand the objective misspecification and its causes. Our EAL framework not only acts as an explanatory framework for existing works but also provides us with concrete insights into the limitations of existing methods to handle reward misspecification and novel solution strategies. We use these insights to propose a new interactive algorithm that uses the specified reward to infer potential user expectations about the system behavior. We show how one can efficiently implement this algorithm by mapping the inference problem into linear programs. We evaluate our method on a set of standard Markov Decision Process (MDP) benchmarks.

Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10\,s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that is critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new DAQ modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit PMTs during a supernova burst and the second, the Veto module, prescales the high rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead time less than 1\,ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800~pc will trigger Veto module resulting in a prescaling of the observed neutrino data., Comment: 28 pages, 18 figures. Submitted to PTEP

We study poly-crystalline spherical drops of an aqueous suspension of highly charged colloidal spheres exposed to a colloid-free aqueous environment. Crystal contours were obtained from standard optical imaging. The crystal spheres first expand to nearly four times their initial volume before slowly shrinking due to dilution-induced melting. Exploiting coherent multiple-scattering by (110) Bragg reflecting crystals, time-dependent density profiles were recorded deep within the drop interior. These show a continuously flattening radial density gradient and a decreasing central density. Expansion curves and density profiles are qualitatively consistent with theoretical expectations based on dynamical density functional theory for the expansion of a spherical crystallite made of charged Brownian spheres. We anticipate that our study opens novel experimental access to densi-ty determination in turbid crystals., Comment: Thoroughly revised version. now 23 pages with 9 figures and 57 references; supplementary materials 15 pages with 11 figures and 22 references. Accepted for publication in Soft Matter in August 2024

Vertebral fracture grading classifies the severity of vertebral fractures, which is a challenging task in medical imaging and has recently attracted Deep Learning (DL) models. Only a few works attempted to make such models human-interpretable despite the need for transparency and trustworthiness in critical use cases like DL-assisted medical diagnosis. Moreover, such models either rely on post-hoc methods or additional annotations. In this work, we propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way. Specifically, we introduce a novel diversity-promoting loss to mitigate prototype repetitions in small datasets with intricate semantics. We have experimented with the VerSe'19 dataset and outperformed the existing prototype-based method. Further, our model provides superior interpretability against the post-hoc method. Importantly, expert radiologists validated the visual interpretability of our results, showing clinical applicability., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:015

Viscoelastic rate-type fluids are popular models of choice in many applications involving flows of fluid-like materials with complex micro-structure. A well-developed mathematical theory for the most of these classical fluid models is however missing. The main purpose of this study is to provide a complete proof of long-time and large-data existence of weak solutions to unsteady internal three-dimensional flows of Giesekus fluids subject to a no-slip boundary condition. As a new auxiliary tool, we provide the identification of certain biting limits in the parabolic setting, presented here within the framework of evolutionary Stokes problems. We also generalize the long-time and large-data existence result to higher dimensions, to viscoelastic models with multiple relaxation mechanisms and to viscoelastic models with different type of dissipation.

We consider a compact manifold $(M,\mathfrak{F})$ with a foliation $\mathfrak{F}$, and a smooth affine connection $\nabla$ on the tangent bundle of the foliation $T\mathfrak{F}$. We introduce and study a foliated completeness problem. Namely, under which conditions on $\nabla$ the leaves are complete? We consider different natural geometric settings: the first one is the case of a totally geodesic lightlike foliation of a compact Lorentzian manifold, and the second one is the case where the leaves have particular affine structures. In the first case, we characterize the completeness, and obtain in particular that if a compact Lorentzian manifold admits a null Killing field $V$ such that the distribution orthogonal to $V$ is integrable, then it defines a (totally geodesic) foliation with complete leaves. In the second case, we give a completeness result for a specific affine structure called "the unimodular affine lightlike geometry", and characterize the completeness for a natural relaxation of the geometry. On the other hand, we study the global completeness of a compact Lorentzian manifold in the presence of a null Killing field. We give two non-complete examples, starting from dimension $3$: one is a locally homogeneous manifold, and the other is a $3$D example where the Killing field dynamics is equicontinuous., Comment: 23 pages, 1 figure

Aims. In this work, we intend to examine how environment influences the merger fraction, from the low density field environment to higher density groups and clusters. We also aim to study how the properties of a group or cluster, as well as the position of a galaxy in the group or cluster, influences the merger fraction. Methods. We identified galaxy groups and clusters in the North Ecliptic Pole using a friends-of-friends algorithm and the local density. Once identified, we determined the central galaxies, group radii, velocity dispersions, and group masses of these groups and clusters. Merging systems were identified with a neural network as well as visually. With these, we examined how the merger fraction changes as the local density changes for all galaxies as well as how the merger fraction changes as the properties of the groups or clusters change. Results. We find that the merger fraction increases as local density increases and decreases as the velocity dispersion increases, as is often found in literature. A decrease in merger fraction as the group mass increases is also found. We also find groups with larger radii have higher merger fractions. The number of galaxies in a group does not influence the merger fraction. Conclusions. The decrease in merger fraction as group mass increases is a result of the link between group mass and velocity dispersion. Hence, this decrease of merger fraction with increasing mass is a result of the decrease of merger fraction with velocity dispersion. The increasing relation between group radii and merger fraction may be a result of larger groups having smaller velocity dispersion at a larger distance from the centre or larger groups hosting smaller, infalling groups with more mergers. However, we do not find evidence of smaller groups having higher merger fractions., Comment: 15 pages, 10 figures, 8 tables, 2 appendices, accepted for publication in Astronomy & Astrophysics