Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishment of H3K9me3 is influenced by genomic context and is temporally uncoupled from signal growth during early zebrafish embryogenesis

The structural organization of eukaryotic genomes is contingent upon the fractionation of DNA into transcriptionally active euchromatin and repressive heterochromatin. However, we have a limited understanding of how these distinct states are first established during animal embryogenesis. Histone 3 lysine 9 trimethylation (H3K9me3) is critical to heterochromatin formation and bulk establishment of this mark is thought to help drive large-scale remodeling of an initially naive chromatin state during animal embryogenesis. However, a detailed understanding of this process is lacking. Here, we leverage CUT&RUN to define the emerging H3K9me3 landscape of the zebrafish embryo with high sensitivity and temporal resolution. Despite the prevalence of DNA transposons in the zebrafish genome, we found that LTR transposons are preferentially targeted for embryonic H3K9me3 deposition, with different families exhibiting distinct establishment timelines. High signal-to-noise ratios afforded by CUT&RUN revealed new, emerging sites of low-amplitude H3K9me3 that initiated before the major wave of embryonic genome activation (EGA). Early sites of establishment predominated at specific subsets of transposons and were enriched for sequences with maternal piRNAs or pericentromeric localization. Notably, the number of H3K9me3 enriched sites increased linearly across blastula development, while quantitative comparison revealed a >10-fold genome-wide increase in H3K9me3 signal at established sites over just 30 minutes at the onset of EGA. Continued maturation of the H3K9me3 landscape was observed beyond the initial wave of bulk establishment. Our findings uncover distinct features of pre- and post-EGA H3K9me3 targeting and reveal temporal decoupling of H3K9me3 establishment and signal reinforcement during zebrafish heterochromatin formation.
Rapid Identification of Genomic Alterations in Tumors affecting lymphocyte Infiltration (RIGATonI)

Recently identified tumor genomic alterations are associated with altered tumor immune microenvironments and therapeutic outcomes. These studies beg the question: are there other genomic variations altering the immune microenvironment in tumors that can provide insight into mechanisms of immune evasion? Current approaches to estimate immunity in bulk RNAseq from tumors include gene set enrichment and cellular deconvolution. In contrast, our new software package RIGATonI utilizes a gene agnostic approach to classify immune cell infiltration in tumors. Using pathologist reviewed histology slides and paired bulk RNA sequencing expression data, we trained a machine learning algorithm to detect inflamed and non-inflamed immune microenvironments. RIGATonI enables identifcation of candidate genomic alterations associated with immune infilration in subsets of cancer through evaluation of various features including gene expression, genomic alterations, pathologist-classified tumors, clinical outcomes, and protein validation. In addition to immune infiltrate classification, RIGATonI leverages a novel algorithm using known protein-protein interactions for prediction of gain-of-function and loss-of-function for genomic alterations to improve filtering of genomic alteration candidats. Applying these two novel methods, we analyzed all genomic alterations present in The Cancer Genome Altas and visualized promising results with R shiny for public use. Thus, we present our R tool and online database for Rapid Identification of Genomic Alterations in Tumors affecting lymphocyte Infiltration (RIGATonI).
Coupling of saccade plans to endogenous attention during urgent choices

The neural mechanisms that willfully direct attention to specific locations in space are closely related to those for generating targeting eye movements (saccades). However, the degree to which the voluntary deployment of attention to a location is necessarily accompanied by a corresponding saccade plan remains unclear. One problem is that attention and saccades are both automatically driven by salient sensory events; another is that the underlying processes unfold within tens of milliseconds only. Here, we use an urgent task design to resolve the evolution of a visuomotor choice on a moment-by-moment basis while independently controlling the endogenous (goal-driven) and exogenous (salience-driven) contributions to performance. Human participants saw a peripheral cue and, depending on its color, either looked at it (prosaccade) or looked at a diametrically opposite, uninformative non-cue (antisaccade). By varying the luminance of the stimuli, the exogenous contributions could be cleanly dissociated from the endogenous process guiding the choice over time. According to the measured timecourses, generating a correct antisaccade requires about 30 ms more processing time than generating a correct prosaccade based on the same perceptual signal. The results indicate that saccade plans are biased toward the location where attention is endogenously deployed, but the coupling is weak and can be willfully overridden very rapidly.
A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN

Objectives Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. Methods We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. Results In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. Conclusions This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.
Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods

Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by ‘decoding’ neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks. Teaser A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.
Final report on project SP1210: Lowland peatland systems in England and Wales – evaluating greenhouse gas fluxes and carbon balances

Lowland peatlands represent one of the most carbon-rich ecosystems in the UK. As a result of widespread habitat modification and drainage to support agriculture and peat extraction, they have been converted from natural carbon sinks into major carbon sources, and are now amongst the largest sources of greenhouse gas (GHG) emissions from the UK land-use sector. Despite this, they have previously received relatively little policy attention, and measures to reduce GHG emissions either through re-wetting and restoration or improved management of agricultural land remain at a relatively early stage. In part, this has stemmed from a lack of reliable measurements on the carbon and GHG balance of UK lowland peatlands. This project aimed to address this evidence gap via an unprecedented programme of consistent, multi year field measurements at a total of 15 lowland peatland sites in England and Wales, ranging from conservation managed ‘near-natural’ ecosystems to intensively managed agricultural and extraction sites. The use of standardised measurement and data analysis protocols allowed the magnitude of GHG emissions and removals by peatlands to be quantified across this heterogeneous data set, and for controlling factors to be identified. The network of seven flux towers established during the project is believed to be unique on peatlands globally, and has provided new insights into the processes the control GHG fluxes in lowland peatlands. The work undertaken is intended to support the future development and implementation of agricultural management and restoration measures aimed at reducing the contribution of these important ecosystems to UK GHG emissions.
Analysis of the Radiation Hazard Observed by RAD on the Surface of Mars During the September 2017 Solar Particle Event

We report dosimetric quantities measured by the Mars Science Laboratory Radiation Assessment Detector (RAD) on the surface of Mars during the 10–12 September 2017 solar particle event. Despite 23 g/cm2 of CO2 shielding provided by the atmosphere above RAD, dose rates rose above background galactic cosmic ray levels by factors of 2 to 3 over the course of several hours and leveled off at sustained peak rates for about 12 hr before declining over the following 36 hr. As the solar particle event flux was gradually declining, a shock front reached Mars and caused a sudden drop of about 15% in instantaneous dose rates. No solar particles followed the shock arrival, and the magnetic shielding of galactic cosmic rays by the shock reduced their intensity to levels below those seen before the start of the event. This event is the largest seen to date by RAD on Mars.
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology

Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations performed by clinicians, which are scarce and costly to generate. The emergence of self-supervised learning (SSL) methods remove this barrier, allowing for large-scale analyses on non-annotated data. However, recent SSL approaches apply increasingly expansive model architectures and larger datasets, causing the rapid escalation of data volumes, hardware prerequisites, and overall expenses, limiting access to these resources to few institutions. Therefore, we investigated the complexity of contrastive SSL in computational pathology in relation to classification performance with the utilization of consumer-grade hardware. Specifically, we analyzed the effects of adaptations in data volume, architecture, and algorithms on downstream classification tasks, emphasizing their impact on computational resources. We trained breast cancer foundation models on a large public patient cohort and validated them on various downstream classification tasks in a weakly supervised manner on two external public patient cohorts. Our experiments demonstrate that we can improve downstream classification performance whilst reducing SSL training duration by 90%. In summary, we propose a set of adaptations which enable the utilization of SSL in computational pathology in non-resource abundant environments.
Derivation of a Biot-Plate-System for a thin poroelastic layer

We study incompressible fluid flow through a thin poroelastic layer and rigorously derive a macroscopic model when the thickness of the layer tends to zero. Within the layer we assume a periodic structure and both, the periodicity and the thickness of the layer, are of order $\varepsilon$ which is small compared to the length of the layer. The fluid flow is described by quasistatic Stokes-equations and for the elastic solid we consider linear elasticity equations, and both are coupled via continuity of the velocities and the normal stresses. The aim is to pass to the limit $\varepsilon \to 0$ in the weak microscopic formulation by using multi-scale techniques adapted to the simultaneous homogenization and dimension reduction in continuum mechanics. The macroscopic limit model is given by a coupled Biot-Plate-system consisting of a generalized Darcy-law coupled to a Kirchhoff-Love-type plate equation including the Darcy pressure.
Real Time Charged Track Reconstruction for CLAS12

This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing.
Poisson equation with measure data, reconstruction formula and Doob classes of processes

We consider the Dirichlet problem for equation involving a general operator associated with a symmetric transient regular Dirichlet form and bounded Borel measure on the right-hand side of the equation. We introduce a new function space (depending on the form) which allows us to distinguish between solutions with diffuse measure and with general Borel measure. This new space can by characterized analytically in terms of the Poisson kernel associated with the underlying operator or probabilistically by using the notion of Doob class (D) of processes naturally associated with the operator. We also prove a reconstruction formula describing, in terms of the carr\'e du champ operator and jump measure associated with the underlying form, the behaviour of the solution on the set where it is very large.
CoTBal: Comprehensive Task Balancing for Multi-Task Visual Instruction Tuning

Visual instruction tuning is a key training stage of large multimodal models (LMMs). Nevertheless, the common practice of indiscriminately mixing instruction-following data from various tasks may result in suboptimal overall performance due to different instruction formats and knowledge domains across tasks. To mitigate this issue, we propose a novel Comprehensive Task Balancing (CoTBal) algorithm for multi-task visual instruction tuning of LMMs. To our knowledge, this is the first work that explores multi-task optimization in visual instruction tuning. Specifically, we consider two key dimensions for task balancing: (1) Inter-Task Contribution, the phenomenon where learning one task potentially enhances the performance in other tasks, attributable to the overlapping knowledge domains, and (2) Intra-Task Difficulty, which refers to the learning difficulty within a single task. By quantifying these two dimensions with performance-based metrics, task balancing is thus enabled by assigning more weights to tasks that offer substantial contributions to others, receive minimal contributions from others, and also have great intra-task difficulties. Experiments show that our CoTBal leads to superior overall performance in multi-task visual instruction tuning.
A General Calibrated Regret Metric for Detecting and Mitigating Human-Robot Interaction Failures

Robot decision-making increasingly relies on expressive data-driven human prediction models when operating around people. While these models are known to suffer from prediction errors in out-of-distribution interactions, not all prediction errors equally impact downstream robot performance. We identify that the mathematical notion of regret precisely characterizes the degree to which incorrect predictions of future interaction outcomes degraded closed-loop robot performance. However, canonical regret measures are poorly calibrated across diverse deployment interactions. We extend the canonical notion of regret by deriving a calibrated regret metric that generalizes from absolute reward space to probability space. With this transformation, our metric removes the need for explicit reward functions to calculate the robot's regret, enables fairer comparison of interaction anomalies across disparate deployment contexts, and facilitates targetted dataset construction of"system- level"prediction failures. We experimentally quantify the value of this high-regret interaction data for aiding the robot in improving its downstream decision-making. In a suite of closed- loop autonomous driving simulations, we find that fine-tuning ego-conditioned behavior predictors exclusively on high-regret human-robot interaction data can improve the robot's overall re-deployment performance with significantly (77%) less data.
An Explainable AI Framework for Artificial Intelligence of Medical Things

The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems. With the increasing complexity of Artificial Intelligence (AI) models, the need for Explainable Artificial Intelligence (XAI) techniques become paramount, particularly in the medical domain, where transparent and interpretable decision-making becomes crucial. Therefore, in this work, we leverage a custom XAI framework, incorporating techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-Cam), explicitly designed for the domain of AIoMT. The proposed framework enhances the effectiveness of strategic healthcare methods and aims to instill trust and promote understanding in AI-driven medical applications. Moreover, we utilize a majority voting technique that aggregates predictions from multiple convolutional neural networks (CNNs) and leverages their collective intelligence to make robust and accurate decisions in the healthcare system. Building upon this decision-making process, we apply the XAI framework to brain tumor detection as a use case demon strating accurate and transparent diagnosis. Evaluation results underscore the exceptional performance of the XAI framework, achieving high precision, recall, and F1 scores with a training accuracy of 99% and a validation accuracy of 98%. Combining advanced XAI techniques with ensemble-based deep-learning (DL) methodologies allows for precise and reliable brain tumor diagnoses as an application of AIoMT.
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks

Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling. The expressivity of GNNs with respect to their ability to discriminate non-isomorphic graphs critically depends on the functions employed for message aggregation and graph-level readout. By applying signal propagation theory, we propose a variance-preserving aggregation function (VPA) that maintains expressivity, but yields improved forward and backward dynamics. Experiments demonstrate that VPA leads to increased predictive performance for popular GNN architectures as well as improved learning dynamics. Our results could pave the way towards normalizer-free or self-normalizing GNNs.
Scalable On-Chip Optical Linear Processing Unit Using a Single Thin-Film Lithium Niobate Ring Modulator

Advancements in artificial intelligence (AI) and neuromorphic computing increasingly rely on the integration of photonics to achieve breakthroughs in processing capabilities. Our pioneering work introduces a photonic linear processing unit (LPU) that utilizes a cascading modulator structure incorporating micro-rings. This device, elegantly designed with only two thin-film lithium niobate (TFLN) modulators coupled with a micro-ring, stands as a paradigm of innovation that merges low-power consumption with formidable computational throughput, achieving 36.7 billion operations per second (GOPs). The crux of our design lies in the ring-modulator's flexible architecture, engineered as a compact and singular unit, which markedly streamlines system complexity while bolstering energy efficiency. It adeptly facilitates large-scale dot-product operations, supporting vector dimensions up to 5832, an impressive feat by current standards. Furthermore, this ring-modulator-based LPU exhibits proficiency in image classification, processing 28*28-pixel resolution imagery post hardware training. As data volume demands surge, our architecture offers a scalable solution through parallel matrix multiplications, which hinge solely on increased modulation rates. This development paves the way for a new class of photonic processors that promise to handle escalating data workloads with unprecedented flexibility and efficiency.
Critical quantum metrology robust against dissipation and non-adiabaticity

Critical systems near quantum phase transitions were predicted to be useful for improvement of metrological precision, thanks to their ultra-sensitive response to a tiny variation of the control Hamiltonian. Despite the promising perspective, realization of criticality-enhanced quantum metrology is an experimentally challenging task, mainly owing to the extremely long time needed to encode the signal to some physical quantity of a critical system. We here circumvent this problem by making use of the critical behaviors in the Jaynes-Cummings model, comprising a single qubit and a photonic resonator, to which the signal field is coupled. The information about the field amplitude is encoded in the qubit's excitation number in the dark state, which displays a divergent changing rate at the critical point. The most remarkable feature of this critical sensor is that the performance is insensitive to the leakage to bright eigenstates, caused by decoherence and non-adiabatic effects. We demonstrate such a metrological protocol in a superconducting circuit, where an Xmon qubit, interacting with a resonator, is used as a probe for estimating the amplitude of a microwave field coupled to the resonator. The measured quantum Fisher information exhibits a critical quantum enhancement, confirming the potential of this system for quantum metrology.