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Recent advances in deep learning structured state space models, especially the Mamba architecture, have demonstrated remarkable performance improvements while maintaining linear complexity. In this study, we introduce functional spatiotemporal Mamba (FST-Mamba), a Mamba-based model designed for discovering neurological biomarkers using functional magnetic resonance imaging (fMRI). We focus on dynamic functional network connectivity (dFNC) derived from fMRI and propose a hierarchical spatiotemporal Mamba-based network that processes spatial and temporal information separately using Mamba-based encoders. Leveraging the topological uniqueness of the FNC matrix, we introduce a component-wise varied-scale aggregation (CVA) mechanism to aggregate connectivity across individual components within brain networks, enabling the model to capture both inter-component and inter-network information. To better handle the FNC data, we develop a new component-specific scanning order. Additionally, we propose symmetric rotary position encoding (SymRope) to encode the relative positions of each functional connection while considering the symmetric nature of the FNC matrix. Experimental results demonstrate significant improvements in the proposed FST-Mamba model on various brain-based classification and regression tasks. Our work reveals the substantial potential of attention-free sequence modeling in brain discovery.

We present galaxy MACS0416-Y1 at z$_{\rm{spec}} = 8.312$ as observed by the CAnadian NIRISS Unbiased Cluster Survey (CANUCS). MACS0416-Y1 has been shown to have extreme dust properties, thus, we study the physical properties and star formation histories of its resolved components. Overall, we find that MACS0416-Y1 is undergoing a star formation burst in three resolved clumps. The central clump is less massive compared to the other clumps and possibly formed in the merging process of the two larger clumps. Although the star formation history indicates an ongoing star formation burst, this gas-rich galaxy shows comparable star formation efficiency to cosmic noon galaxies. Using NIRSpec prism spectroscopy, we measure metallicity, $12 +\log\rm{(O/H)} = 7.76\pm0.03$ , ionisation parameter, $\log U = -2.48\pm0.03$, and electron temperature $\rm{T}_e = 18000\pm 4000 K $. The emission line ratios of the galaxy indicate an evolved Interstellar medium (ISM) similar to $z\sim2$ star-forming galaxies. Further, we find possible presence of ionisation from an active galactic nuclei (AGN) using emission line diagnostics, however, we do not detect broad line component in H$\beta$ emission line. As this gas-rich galaxy is undergoing a major merger, we hypothesise that the high dust temperature in MACS0416-Y1 is caused by the star formation burst or a possible narrow-line AGN., Comment: Submitted to ApJL

Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, emphasizing the importance of early detection for proper treatment. Functional magnetic resonance imaging (fMRI), particularly dynamic functional network connectivity (dFNC), has emerged as an important biomarker for AD. Nevertheless, studies probing at-risk subjects in the pre-symptomatic stage using dFNC are limited. To identify at-risk subjects and understand alterations of dFNC in different stages, we leverage deep learning advancements and introduce a transformer-convolution framework for predicting at-risk subjects based on dFNC, incorporating spatial-temporal self-attention to capture brain network dependencies and temporal dynamics. Our model significantly outperforms other popular machine learning methods. By analyzing individuals with diagnosed AD and mild cognitive impairment (MCI), we studied the AD progression and observed a higher similarity between MCI and asymptomatic AD. The interpretable analysis highlights the cognitive-control network's diagnostic importance, with the model focusing on intra-visual domain dFNC when predicting asymptomatic AD subjects., Comment: Accepted by EMBC 2024

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and overlap with other psychiatric conditions challenge traditional diagnostic methods, necessitating advanced systems to improve precision. Existing research studies have mostly focused on imaging data, such as structural and functional MRI, for SZ diagnosis. There has been less focus on the integration of genomic features despite their potential in identifying heritable SZ traits. In this study, we introduce a Multi-modal Imaging Genomics Transformer (MIGTrans), that attentively integrates genomics with structural and functional imaging data to capture SZ-related neuroanatomical and connectome abnormalities. MIGTrans demonstrated improved SZ classification performance with an accuracy of 86.05% (+/- 0.02), offering clear interpretations and identifying significant genomic locations and brain morphological/connectivity patterns associated with SZ., Comment: Accepted for presentation at the AI for Imaging Genomic Learning (AIIG) Workshop, MICCAI 2024

Low mass ratio contact binary systems are more likely to have unstable orbits and potentially merge. In addition, such systems exhibit characteristics such as starspots and high energy emissions (UV) suggestive of chromospheric and magnetic activity. Light curve modelling of ten contact binary systems is reported. All were found to be of extreme low mass ratio ranging from 0.122 to 0.24 and three were found to be potentially unstable and possible merger candidates. Filling of the infrared Calcium absorption lines is a marker of increased chromospheric activity. We use the available LAMOST spectra along with matched standard spectra (broadened for rotation) to measure the excess filling of the central core depression flux of the two main infrared Calcium absorption lines at 8542 and 8662 angstroms. We find that all reported contact binaries have excess filling of the core flux in the infrared Calcium lines. Three of the systems reported were also observed by the GALEX mission and we find that all three have features of excess ultraviolet emissions further adding evidence for increased chromospheric activity in low mass ratio contact binaries. Analysis of both orbital stability and absorption line filling is dependent on the determination of geometric and absolute parameters from light curve modelling. Not an insignificant number of contact binary light curves exhibit the O'Connell effect, usually attributed to starspots. We discuss the inclusion of starspots in light curve solutions and how they influence the geometric and absolute parameters, Comment: 17 Pages 2 Figures 5 Tables Accepted - Research in Astronomy and Astrophysics (RAA)

Amyloid-$\beta$ (A$\beta$) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease (AD). In particular, the accumulation of A$\beta$ plaques, as evinced by the A/T/N (amyloid/tau/neurodegeneration) framework, marks the initial stage. Thus, the identification of individuals with A$\beta$ positivity could enable early diagnosis and potentially lead to more effective interventions. Deep learning methods relying mainly on amyloid PET images have been employed to this end. However, PET imaging has some disadvantages, including the need of radiotracers and expensive acquisitions. Hence, in this work, we propose a novel multimodal approach that integrates information from structural, functional, and diffusion MRI data to discriminate A$\beta$ status in the AD continuum. Our method achieved an accuracy of $0.762\pm0.04$. Furthermore, a \textit{post-hoc} explainability analysis (guided backpropagation) was performed to retrieve the brain regions that most influenced the model predictions. This analysis identified some key regions that were common across modalities, some of which were well-established AD-discriminative biomarkers and related to A$\beta$ deposition, such as the hippocampus, thalamus, precuneus, and cingulate gyrus. Hence, our study demonstrates the potential viability of MRI-based characterization of A$\beta$ status, paving the way for further research in this domain., Comment: 15 pages, 3 figures, submitted to a journal

Alzheimer's disease (AD) is the most prevalent form of dementia with a progressive decline in cognitive abilities. The AD continuum encompasses a prodormal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD or remain stable. In this study, we leveraged structural and functional MRI to investigate the disease-induced grey matter and functional network connectivity changes. Moreover, considering AD's strong genetic component, we introduce SNPs as a third channel. Given such diverse inputs, missing one or more modalities is a typical concern of multimodal methods. We hence propose a novel deep learning-based classification framework where generative module employing Cycle GANs was adopted to impute missing data within the latent space. Additionally, we adopted an Explainable AI method, Integrated Gradients, to extract input features relevance, enhancing our understanding of the learned representations. Two critical tasks were addressed: AD detection and MCI conversion prediction. Experimental results showed that our model was able to reach the SOA in the classification of CN/AD reaching an average test accuracy of $0.926\pm0.02$. For the MCI task, we achieved an average prediction accuracy of $0.711\pm0.01$ using the pre-trained model for CN/AD. The interpretability analysis revealed significant grey matter modulations in cortical and subcortical brain areas well known for their association with AD. Moreover, impairments in sensory-motor and visual resting state network connectivity along the disease continuum, as well as mutations in SNPs defining biological processes linked to amyloid-beta and cholesterol formation clearance and regulation, were identified as contributors to the achieved performance. Overall, our integrative deep learning approach shows promise for AD detection and MCI prediction, while shading light on important biological insights., Comment: 27 pages, 7 figures, submitted to a journal

Schizophrenia is a debilitating, chronic mental disorder that significantly impacts an individual's cognitive abilities, behavior, and social interactions. It is characterized by subtle morphological changes in the brain, particularly in the gray matter. These changes are often imperceptible through manual observation, demanding an automated approach to diagnosis. This study introduces a deep learning methodology for the classification of individuals with Schizophrenia. We achieve this by implementing a diversified attention mechanism known as Spatial Sequence Attention (SSA) which is designed to extract and emphasize significant feature representations from structural MRI (sMRI). Initially, we employ the transfer learning paradigm by leveraging pre-trained DenseNet to extract initial feature maps from the final convolutional block which contains morphological alterations associated with Schizophrenia. These features are further processed by the proposed SSA to capture and emphasize intricate spatial interactions and relationships across volumes within the brain. Our experimental studies conducted on a clinical dataset have revealed that the proposed attention mechanism outperforms the existing Squeeze & Excitation Network for Schizophrenia classification., Comment: This paper has been accepted for the 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)

Neural networks, whice have had a profound effect on how researchers study complex phenomena, do so through a complex, nonlinear mathematical structure which can be difficult for human researchers to interpret. This obstacle can be especially salient when researchers want to better understand the emergence of particular model behaviors such as bias, overfitting, overparametrization, and more. In Neuroimaging, the understanding of how such phenomena emerge is fundamental to preventing and informing users of the potential risks involved in practice. In this work, we present a novel introspection framework for Deep Learning on Neuroimaging data, which exploits the natural structure of gradient computations via the singular value decomposition of gradient components during reverse-mode auto-differentiation. Unlike post-hoc introspection techniques, which require fully-trained models for evaluation, our method allows for the study of training dynamics on the fly, and even more interestingly, allow for the decomposition of gradients based on which samples belong to particular groups of interest. We demonstrate how the gradient spectra for several common deep learning models differ between schizophrenia and control participants from the COBRE study, and illustrate how these trajectories may reveal specific training dynamics helpful for further analysis.

Today's programmers can choose from an exceptional range of programming languages, each with its own traits, purpose, and complexity. A key aspect of a language's complexity is how hard it is to compile programs in the language. While most programmers have an intuition about compilation hardness for different programming languages, no metric exists to quantify it. We introduce the compilation quotient (CQ), a metric to quantify the compilation hardness of compiled programming languages. The key idea is to measure the compilation success rates of programs sampled from context-free grammars. To this end, we fairly sample over 12 million programs in total. CQ ranges between 0 and 100, where 0 indicates that no programs compile, and 100 means that all programs compile. Our findings on 12 popular compiled programming languages show high variation in CQ. C has a CQ of 48.11, C++ has 0.60, Java has 0.27 and Haskell has 0.13. Strikingly, Rust's CQ is nearly 0, and for C, even a large fraction of very sizable programs compile. We believe CQ can help understand the differences of compiled programming languages better and help language designers.

Building and maintaining High-Definition (HD) maps represents a large barrier to autonomous vehicle deployment. This, along with advances in modern online map detection models, has sparked renewed interest in the online mapping problem. However, effectively predicting online maps at a high enough quality to enable safe, driverless deployments remains a significant challenge. Recent work on these models proposes training robust online mapping systems using low quality map priors with synthetic perturbations in an attempt to simulate out-of-date HD map priors. In this paper, we investigate how models trained on these synthetically perturbed map priors generalize to performance on deployment-scale, real world map changes. We present a large-scale experimental study to determine which synthetic perturbations are most useful in generalizing to real world HD map changes, evaluated using multiple years of real-world autonomous driving data. We show there is still a substantial sim2real gap between synthetic prior perturbations and observed real-world changes, which limits the utility of current prior-informed HD map prediction models., Comment: Accepted to CVPR 2024, Workshop on Autonomous Driving

We probed the pole structure of the $P_\psi^{N}(4312)^{+}$ using a trained deep neural network. The training dataset was generated using uniformized independent S-matrix poles to ensure that the obtained interpretation is as model-independent as possible. To prevent possible ambiguity in the interpretation of the pole structure, we included the contribution from the off-diagonal element of the S-matrix. Five out of the six neural networks we trained favor $P_\psi^{N}(4312)^{+}$ as possibly having a three-pole structure, with one pole on each of the unphysical sheets - a first in its report. The two poles can be associated to a pole-shadow pair which is a characteristic of a true resonance. On the other hand, the last pole is most likely associated with the coupled-channel effect. The combined effect of these poles produced a peak below the $\Sigma^{+}_C\bar{D}^0$ which mimic the line shape of a hadronic molecule., Comment: 11 pages, 4 figures, 7 tables

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. While deep learning has gained substantial popularity for modeling complex relational data, its application to uncovering the spatiotemporal dynamics of the brain is still limited. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification. Our model, DSAM, leverages temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations, a temporal attention unit to identify important time points, a self-attention unit to construct the goal-specific connectivity matrix, and a novel variant of graph neural network to capture the spatial dynamics for downstream classification. To validate our approach, we conducted experiments on the Human Connectome Project dataset with 1075 samples to build and interpret the model for the classification of sex group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples for independent testing. Compared our proposed framework with other state-of-art models, results suggested this novel approach goes beyond the assumption of a fixed connectivity matrix and provides evidence of goal-specific brain connectivity patterns, which opens up the potential to gain deeper insights into how the human brain adapts its functional connectivity specific to the task at hand., Comment: 18 Pages, 4 figures

To investigate the short-term variability and determine the size of the optical continuum emitting size of intermediate-mass black holes (IMBHs), we carried out high-cadence, multi-band photometric monitoring of a Seyfert 1 galaxy J0249-0815 across two nights, together with a one-night single-band preliminary test. The presence of the broad Ha component in our target was confirmed by recent Palomar/P200 spectroscopic observations, 23 years after Sloan Digital Sky Survey, ruling out the supernovae origin of the broad Ha line. The photometric experiment was primarily conducted utilizing four-channel imagers MuSCAT 3 & 4 mounted on 2-meter telescopes within the Las Cumbres Observatory Global Telescope Network. Despite the expectation of variability, we observed no significant variation (<1.4%) on timescales of 6-10 hours. This non-detection is likely due to substantial host galaxy light diluting the subtle AGN variability. Dual-band preliminary tests and tailored simulations may enhance the possibility of detecting variability and lag in future IMBH reverberation campaigns., Comment: 14 pages, 6 figures, submitted to ApJ, comments welcome

In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are communicated each round between the clients and the server. We validate SSFL's effectiveness using standard non-IID benchmarks, noting marked improvements in the sparsity--accuracy trade-offs. Finally, we deploy our method in a real-world federated learning framework and report improvement in communication time.

A compact dielectric resonator antenna has been fabricated on a microstrip transmission line for the purpose of C-band wireless communication using a ceramic material made out of a sintered mixture of BTO and NiO. The antenna parameters are optimized using Ansys HFSS software and verified experimentally. Ni replaces both Ba at A site and Ti at B site. Such a solid solution has a limit depending on the amount of NiO provided during sintering. A complete study of the structural changes and the dielectric constant enables the correlation with the resonating property. All the samples retain the ferroelectric tetragonal P4mm phase with a nominal decrease in the c/a ratio. NiO incorporation in BTO decreases the sintering temperature and shows two types of morphology associated with BTO-like and NiO-like phases. It induces prominent reduction in the permittivity and loss tangent (<0.01) in the range 100Hz to 1MHz. These properties make these samples suitable for DRA application in the C-Band range [4-8 GHz]. Experimental and theoretical assessment using HFSS software yields a C-band signal at ~7.27 GHz.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds., Comment: 12 pages

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training datasets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. The denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large datasets and labeling efforts. The classification step utilizes the Smooth Overlap of Atomic Positions (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100\% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems., Comment: 20 pages, 3 figures

Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focused on case-control data, the application of generative models to disorder-specific datasets and their ability to preserve diagnostic patterns remain relatively unexplored. Hence, in this study, we investigated the use of a generative adversarial network (GAN) in the context of Alzheimer's disease (AD) to generate functional network connectivity (FNC) and T1-weighted structural magnetic resonance imaging data from each other. We employed a cycle-GAN to synthesize data in an unpaired data transition and enhanced the transition by integrating weak supervision in cases where paired data were available. Our findings revealed that our model could offer remarkable capability, achieving a structural similarity index measure (SSIM) of $0.89 \pm 0.003$ for T1s and a correlation of $0.71 \pm 0.004$ for FNCs. Moreover, our qualitative analysis revealed similar patterns between generated and actual data when comparing AD to cognitively normal (CN) individuals. In particular, we observed significantly increased functional connectivity in cerebellar-sensory motor and cerebellar-visual networks and reduced connectivity in cerebellar-subcortical, auditory-sensory motor, sensory motor-visual, and cerebellar-cognitive control networks. Additionally, the T1 images generated by our model showed a similar pattern of atrophy in the hippocampal and other temporal regions of Alzheimer's patients.

In this paper we present the design and development of the Transhuman Ansambl, a novel interactive singing-voice interface which senses its environment and responds to vocal input with vocalisations using human voice. Designed for live performance with a human performer and as a standalone sound installation, the ansambl consists of sixteen bespoke virtual singers arranged in a circle. When performing live, the virtual singers listen to the human performer and respond to their singing by reading pitch, intonation and volume cues. In a standalone sound installation mode, singers use ultrasonic distance sensors to sense audience presence. Developed as part of the 1st author's practice-based PhD and artistic practice as a live performer, this work employs the singing-voice to explore voice interactions in HCI beyond language, and innovative ways of live performing. How is technology supporting the effect of intimacy produced through voice? Does the act of surrounding the audience with responsive virtual singers challenge the traditional roles of performer-listener? To answer these questions, we draw upon the 1st author's experience with the system, and the interdisciplinary field of voice studies that consider the voice as the sound medium independent of language, capable of enacting a reciprocal connection between bodies.

Brain-computer interface systems and the recording of brain activity has garnered significant attention across a diverse spectrum of applications. EEG signals have emerged as a modality for recording neural electrical activity. Among the methodologies designed for feature extraction from EEG data, the method of RCSP has proven to be an approach, particularly in the context of MI tasks. RCSP exhibits efficacy in the discrimination and classification of EEG signals. In optimizing the performance of this method, our research extends to a comparative analysis with conventional CSP techniques, as well as optimized methodologies designed for similar applications. Notably, we employ the meta-heuristic multi-objective Strength Pareto Evolutionary Algorithm II (SPEA-II) as a pivotal component of our research paradigm. This is a state-of-the-art approach in the selection of an subset of channels from a multichannel EEG signal with MI tasks. Our main objective is to formulate an optimum channel selection strategy aimed at identifying the most pertinent subset of channels from the multi-dimensional electroencephalogram (EEG) signals. One of the primary objectives inherent to channel selection in the EEG signal analysis pertains to the reduction of the channel count, an approach that enhances user comfort when utilizing gel-based EEG electrodes. Additionally, within this research, we took benefit of ensemble learning models as a component of our decision-making. This technique serves to mitigate the challenges associated with overfitting, especially when confronted with an extensive array of potentially redundant EEG channels and data noise. Our findings not only affirm the performance of RCSP in MI-based BCI systems, but also underscore the significance of channel selection strategies and ensemble learning techniques in optimizing the performance of EEG signal classification.

The correlations of several fundamental properties of human brain connections are investigated in a consensus connectome, constructed from 1064 braingraphs, each on 1015 vertices, corresponding to 1015 anatomical brain areas. The properties examined include the edge length, the fiber number, or edge width, meaning the number of discovered axon bundles forming the edge and the occurrence number of the edge, meaning the number of individual braingraphs where the edge exists. By using our previously published robust braingraphs at \url{https://braingraph.org}, we have prepared a single consensus graph from the data and compared the statistical similarity of the edge occurrence numbers, edge lengths, and fiber counts of the edges. We have found a strong positive Spearman correlation between the edge occurrence numbers and the fiber count numbers, showing that statistically, the most frequent cerebral connections have the largest widths, i.e., the fiber number. We have found a negative Spearman correlation between the fiber lengths and fiber counts, showing that, typically, the shortest edges are the widest or strongest by their fiber counts. We have also found a negative Spearman correlation between the occurrence numbers and the edge lengths: it shows that typically, the long edges are infrequent, and the frequent edges are short.

O-RAN introduces intelligent and flexible network control in all parts of the network. The use of controllers with open interfaces allow us to gather real time network measurements and make intelligent/informed decision. The work in this paper focuses on developing a use-case for open and reconfigurable networks to investigate the possibility to predict handover events and understand the value of such predictions for all stakeholders that rely on the communication network to conduct their business. We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events. The models were trained on real network data collected from a commercial O-RAN setup deployed in our OpenIreland testbed. Our results show that the proposed approach can be optimized for either recall or precision, depending on the defined application level objective. We also link the performance of the Machine Learning (ML) algorithm to the network operation cost. Our results show that ML-based matching between the required and available resources can reduce operational cost by more than 80%, compared to long term resource purchases.

Modeling networks can serve as a means of summarizing high-dimensional complex systems. Adapting an approach devised for dense, weighted networks, we propose a new method for generating and estimating unweighted networks. This approach can describe a broader class of potential networks than existing models, including those where nodes in different subnetworks connect to one another via various attachment mechanisms, inducing flexible and varied community structures. While unweighted edges provide less resolution than continuous weights, restricting to the binary case permits the use of likelihood-based estimation techniques, which can improve estimation of nodal features. The extra flexibility may contribute a different understanding of network generating structures, particularly for networks with heterogeneous densities in different regions., Comment: 29 pages, 11 figures

Large particle systems are often described by high-dimensional (linear) kinetic equations that are simulated using Monte Carlo methods for which the asymptotic convergence rate is independent of the dimensionality. Even though the asymptotic convergence rate is known, predicting the actual value of the statistical error remains a challenging problem. In this paper, we show how the statistical error of an analog particle tracing Monte Carlo method can be calculated (expensive) and predicted a priori (cheap) when estimating quantities of interest (QoI) on a histogram. We consider two types of QoI estimators: point estimators for which each particle provides one independent contribution to the QoI estimates, and analog estimators for which each particle provides multiple correlated contributions to the QoI estimates. The developed statistical error predictors can be applied to other QoI estimators and nonanalog simulation routines as well. The error analysis is based on interpreting the number of particle visits to a histogram bin as the result of a (correlated) binomial experiment. The resulting expressions can be used to optimize (non)analog particle tracing Monte Carlo methods and hybrid simulation methods involving a Monte Carlo component, as well as to select an optimal particle tracing Monte Carlo method from several available options. Additionally, the cheap statistical error predictors can be used to determine a priori the number of particles N that is needed to reach a desired accuracy. We illustrate the theory using a linear kinetic equation describing neutral particles in the plasma edge of a fusion device and show numerical results. The code used to perform the numerical experiments is openly available.

Kinetic equations describe physical processes in a high-dimensional phase space and are often simulated using Markov process-based Monte Carlo routines. The quantities of interest are typically defined on the lower-dimensional position space and estimated on a grid (histogram). In several applications, such as the construction of diffusion Monte Carlo-like techniques and variance prediction for particle tracing Monte Carlo methods, the cell escape probabilities, i.e., the probabilities with which particles escape a grid cell during one step of the Markov process, are of interest. In this paper, we derive formulas to calculate the cell escape probabilities for common mesh elements in one, two, and three dimensions. Deterministic calculation of cell escape probabilities in higher dimensions becomes expensive and prone to quadrature errors due to the involved high-dimensional integrals. We therefore also introduce a stochastic Monte Carlo algorithm to calculate the escape probabilities, which is more robust at the cost of a statistical error. The code used to perform the numerical experiments and accompanying GeoGebra tutorials are openly available at https://gitlab.kuleuven.be/numa/public/escape-probabilities-markov-processes.

Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder. However, combining complementary information from these two modalities is challenging due to their heterogeneity. Many existing methods fall short of capturing the interaction between these modalities, frequently defaulting to a simple combination of latent features. In this paper, we propose a novel Cross-Attentive Multi-modal Fusion framework (CAMF), which aims to capture both intra-modal and inter-modal relationships between fMRI and sMRI, enhancing multi-modal data representation. Specifically, our CAMF framework employs self-attention modules to identify interactions within each modality while cross-attention modules identify interactions between modalities. Subsequently, our approach optimizes the integration of latent features from both modalities. This approach significantly improves classification accuracy, as demonstrated by our evaluations on two extensive multi-modal brain imaging datasets, where CAMF consistently outperforms existing methods. Furthermore, the gradient-guided Score-CAM is applied to interpret critical functional networks and brain regions involved in schizophrenia. The bio-markers identified by CAMF align with established research, potentially offering new insights into the diagnosis and pathological endophenotypes of schizophrenia.

Enhancements in the invariant mass distribution or scattering cross-section are usually associated with resonances. However, the nature of exotic signals found near hadron-hadron thresholds remain a puzzle today. In fact, a purely kinematic triangle mechanism is also capable of producing similar structures, but do not correspond to any unstable quantum state. In this paper, we report for the first time, that a deep neural network can be trained to distinguish triangle singularity from pole-based enhancements. We also identify the type of dynamic pole structure that can be misidentified as triangle enhancement. We apply our method to confirm that the $P_\psi^N(4312)^+$ state is not due to a single triangle singularity, and is more favored towards a pole-based interpretation based solely on pure line-shape analysis. Lastly, we argue how our method can be used as a model-selection framework to help in classifying other exotic hadron candidates., Comment: 14 pages, 12 figures; added references

The BL Lac object 3C 371 has been observed by the Transiting Exoplanet Survey Satellite (\textit{TESS}) for approximately a year, between July 2019 and July 2020, with an unmatched 2-minute observing cadence. In parallel, the Whole Earth Blazar Telescope (WEBT) Collaboration organized an extensive observing campaign, providing three years of continuous optical monitoring between 2018 and 2020. These datasets allow for a thorough investigation of the variability of the source. The goal of this study is evaluating the optical variability of 3C 371. Taking advantage of the remarkable cadence of \textit{TESS} data, we aim to characterize the intra-day variability (IDV) displayed by the source and identify its shortest variability timescale. With this estimate, constraints on the size of the emitting region and black hole mass can be calculated. Moreover, WEBT data is used to investigate long-term variability (LTV), including understanding spectral behaviour of the source and the polarization variability. Based on the derived characteristics, information on the origin of the variability on different timescales is extracted. We evaluated the variability applying the variability amplitude tool that quantifies how variable the emission is. Moreover, we employed common tools like ANOVA (ANalysis Of VAariance) tests, wavelet and power spectral density (PSD) analyses to characterize the shortest variability timescales present in the emission and the underlying noise affecting the data. Short- and long-term colour behaviours have been evaluated to understand the spectral behaviour of the source. The polarized emission was analyzed, studying its variability and possible rotation patterns of the electric vector position angle (EVPA). Flux distributions of IDV and LTV were also studied with the aim of linking the flux variations to turbulent and/or accretion disc related processes., Comment: Accepted in A&A, 20 pages, 19 figures

PG 1553+113 is one of the few blazars with a convincing quasi-periodic emission in the gamma-ray band. The source is also a very high-energy (VHE; >100 GeV) gamma-ray emitter. To better understand its properties and identify the underlying physical processes driving its variability, the MAGIC Collaboration initiated a multiyear, multiwavelength monitoring campaign in 2015 involving the OVRO 40-m and Medicina radio telescopes, REM, KVA, and the MAGIC telescopes, Swift and Fermi satellites, and the WEBT network. The analysis presented in this paper uses data until 2017 and focuses on the characterization of the variability. The gamma-ray data show a (hint of a) periodic signal compatible with literature, but the X-ray and VHE gamma-ray data do not show statistical evidence for a periodic signal. In other bands, the data are compatible with the gamma-ray period, but with a relatively high p-value. The complex connection between the low and high-energy emission and the non-monochromatic modulation and changes in flux suggests that a simple one-zone model is unable to explain all the variability. Instead, a model including a periodic component along with multiple emission zones is required., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society. 19 pages, 9 figures. Corresponding authors: Elisa Prandini, Antonio Stamerra, Talvikki Hovatta

The most distant galaxies detected by JWST are assembling in a Universe that is less than 5\% of its present age. At these times, the progenitors of galaxies like the Milky Way are expected to be about 10,000 times less massive than they are now, with masses quite comparable to that of massive globular clusters seen in the local Universe. Composed today primarily of old stars and correlating with the properties of their parent dark matter halos, the first globular clusters are thought to have formed during the earliest stages of galaxy assembly. In this article we explore the connection between star clusters and galaxy assembly by showing JWST observations of a strongly lensed galaxy at zspec = 8.304, exhibiting a network of massive star clusters (the 'Firefly Sparkle') cocooned in a diffuse arc. The Firefly Sparkle exhibits the hallmarks expected of a future Milky Way-type galaxy captured during its earliest and most gas-rich stage of formation. The mass distribution of the galaxy seems to be concentrated in ten distinct clusters, with individual cluster masses that straddle the boundary between low-mass galaxies and high-mass globular clusters. The cluster ages suggest that they are gravitationally bound with star formation histories showing a recent starburst possibly triggered by the interaction with a companion galaxy at the same redshift at a projected distance of $\sim$2 kpc away from the Firefly Sparkle. The central star cluster shows nebular-dominated spectra consistent with high temperatures and a top-heavy initial mass function, the product of formation in a very metal poor environment. Combined with abundance matching that suggests that this is likely to be a progenitor of galaxies like our own, the Firefly Sparkle provides an unprecedented case study of a Milky Way-like galaxy in the earliest stages of its assembly in only a 600 million year old Universe., Comment: submitted, comments are welcome! 36 pages, 9 figures

In the clinical treatment of mood disorders, the complex behavioral symptoms presented by patients and variability of patient response to particular medication classes can create difficulties in providing fast and reliable treatment when standard diagnostic and prescription methods are used. Increasingly, the incorporation of physiological information such as neuroimaging scans and derivatives into the clinical process promises to alleviate some of the uncertainty surrounding this process. Particularly, if neural features can help to identify patients who may not respond to standard courses of anti-depressants or mood stabilizers, clinicians may elect to avoid lengthy and side-effect-laden treatments and seek out a different, more effective course that might otherwise not have been under consideration. Previously, approaches for the derivation of relevant neuroimaging features work at only one scale in the data, potentially limiting the depth of information available for clinical decision support. In this work, we show that the utilization of multi spatial scale neuroimaging features - particularly resting state functional networks and functional network connectivity measures - provide a rich and robust basis for the identification of relevant medication class and non-responders in the treatment of mood disorders. We demonstrate that the generated features, along with a novel approach for fast and automated feature selection, can support high accuracy rates in the identification of medication class and non-responders as well as the identification of novel, multi-scale biomarkers.

Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks, including the phenomenon of "Neural Collapse", where linear classifiers within DNNs converge to specific geometrical structures during late-stage training. However, the role of geometric constraints in learning extends beyond this terminal phase. For instance, gradients in fully-connected layers naturally develop a low-rank structure due to the accumulation of rank-one outer products over a training batch. Despite the attention given to methods that exploit this structure for memory saving or regularization, the emergence of low-rank learning as an inherent aspect of certain DNN architectures has been under-explored. In this paper, we conduct a comprehensive study of gradient rank in DNNs, examining how architectural choices and structure of the data effect gradient rank bounds. Our theoretical analysis provides these bounds for training fully-connected, recurrent, and convolutional neural networks. We also demonstrate, both theoretically and empirically, how design choices like activation function linearity, bottleneck layer introduction, convolutional stride, and sequence truncation influence these bounds. Our findings not only contribute to the understanding of learning dynamics in DNNs, but also provide practical guidance for deep learning engineers to make informed design decisions.

In this paper, we explore the capability of both the Adjacency Spectral Embedding (ASE) and the Graph Encoder Embedding (GEE) for capturing an embedded pseudo-clique structure in the random dot product graph setting. In both theory and experiments, we demonstrate that this pairing of model and methods can yield worse results than the best existing spectral clique detection methods, demonstrating at once the methods' potential inability to capture even modestly sized pseudo-cliques and the methods' robustness to the model contamination giving rise to the pseudo-clique structure. To further enrich our analysis, we also consider the Variational Graph Auto-Encoder (VGAE) model in our simulation and real data experiments.

The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In this work, we proposed a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to enrich datasets. We extended multivariate time series data by predicting the time courses of independent component networks (ICNs) in both one-step and recursive configurations. The effectiveness of these augmented datasets was then compared with the original data using various deep learning models designed for chronological age prediction tasks. The results suggest that our approach improves model performance, providing a robust solution to overcome the challenges presented by the limited size of neuroimaging datasets., Comment: 4 PAGES, 3 FIGURES, CONFERENCE

Mutated genes may lead to cancer development in numerous tissues. While more than 600 cancer-causing genes are known today, some of the most widespread mutations are connected to the RAS gene: RAS mutations are found in approximately 25% of all human tumors. Specifically, KRAS mutations are involved in the three most lethal cancers in U.S.: pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and lung adenocarcinoma. These cancers are among the most difficult to treat, and they are frequently excluded from chemotherapeutic attacks as hopeless cases. The mutated KRAS proteins have specific 3-dimensional conformations, which perturb functional interaction with the GAP protein on the GAP:RAS complex surface leading to a signaling cascade and uncontrolled cell growth. Here we describe a gluing docking method for finding small molecules that bind to both the GAP and the mutated KRAS molecules. These small molecules glue together the GAP and the mutated KRAS molecules and may serve as new cancer drugs for the most lethal, most difficult-to-treat carcinomas. As a proof of concept, we identify two new, drug-like small molecules with the new method: these compounds specifically inhibit the growth of PANC-1 cell line with KRAS mutation G12D in vitro and in vivo. Importantly, the two new compounds show significantly lower IC50 and higher specificity against the G12D KRAS mutant as compared to the recently described MRTX-1133 inhibitor against the G12D KRAS mutant.

We examine indivisibility for classes of graphs. We show that the class of hereditarily $\alpha$-sparse graphs is indivisible if and only if $\alpha > 2$. Additionally, we show that the following classes of graphs are indivisible: perfect graphs, cographs, and chordal graphs, and the following classes of graphs are not indivisible: threshold graphs, split graphs, and distance-hereditary graphs., Comment: 23 pages

We present a new sample of 158 galaxies at redshift $z>7.5$ selected from deep \jwst\ NIRCam imaging of five widely-separated sightlines in the CANUCS survey. Two-thirds of the pointings and 80\% of the galaxies are covered by 12 to 14 NIRCam filters, including seven to nine medium bands, providing accurate photometric redshifts and robustness against low redshift interlopers. A sample of 28 galaxies at $z>7.5$ with spectroscopic redshifts shows a low systematic offset and scatter in the difference between photometric and spectroscopic redshifts. We derive the galaxy UV luminosity function at redshifts 8 to 12, finding a slightly higher normalization than previously seen with \hst\ at redshifts 8 to 10. We observe a steeper decline in the galaxy space density from $z=8$ to $12$ than found by most \jwst\ Cycle 1 studies. In particular, we find only eight galaxies at $z>10$ and none at $z>12.5$, with no $z>10$ galaxies brighter than F277W AB=28 or $M_{\rm UV}=-20$ in our unmasked, delensed survey area of 53.4 square arcminutes. We attribute the lack of bright $z>10$ galaxies in CANUCS compared to GLASS and CEERS to intrinsic variance in the galaxy density along different sightlines. The evolution in the CANUCS luminosity function between $z=8$ and $12$ is comparable to that predicted by simulations that assume a standard star formation efficiency, without invoking any special adjustments., Comment: 30 pages, 12 figures, ApJ, in press

We present an extensive recent multi-band optical photometric observations of the blazar S5 0716+714 carried out over 53 nights with two telescopes in India, two in Bulgaria, one in Serbia, and one in Egypt during 2019 November -- 2022 December. We collected 1401, 689, 14726, and 165 photometric image frames in B, V, R, and I bands, respectively. We montiored the blazar quasi-simultaneously during 3 nights in B, V, R, and I bands; 4 nights in B, V, and R; 2 nights in V, R, and I; 5 nights in B and R; and 2 nights in V and R bands. We also took 37 nights of data only in R band. Single band data are used to study intraday flux variability and two or more bands quasi-simultaneous observations allow us to search for colour variation in the source. We employ the power-enhanced F-test and the nested ANOVA test to search for genuine flux and color variations in the light curves of the blazar on intraday timescales. Out of 12, 11, 53, and 5 nights observations, intraday variations with amplitudes between ~3% and ~20% are detected in 9, 8, 31 and 3 nights in B, V, R, and I bands, respectively, corresponding to duty cycles of 75%, 73%, 58% and 60%. These duty cycles are lower than those typically measured at earlier times. On these timescales color variations with both bluer-when-brighter and redder-when-brighter are seen, though nights with no measurable colour variation are also present. We briefly discuss possible explanations for this observed intraday variability., Comment: 19 pages, 5 figures, 4 tables, Accepted for Publication in MNRAS