Miracle-dz/UniFAR-FTUs huggingface dataset:
Local connectome phenotypes predict social, health, and cognitive factors

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N=841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions. Author Summary The local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel and reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This inter-subject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how shared variability across individuals is reflected in the local connectome.
Skeletal Dysplasias Associated with Mild Myopathy—A Clinical and Molecular Review

Musculoskeletal system is a complex assembly of tissues which acts as scaffold for the body and enables locomotion. It is often overlooked that different components of this system may biomechanically interact and affect each other. Skeletal dysplasias are diseases predominantly affecting the development of the osseous skeleton. However, in some cases skeletal dysplasia patients are referred to neuromuscular clinics prior to the correct skeletal diagnosis. The muscular complications seen in these cases are usually mild and may stem directly from the muscle defect and/or from the altered interactions between the individual components of the musculoskeletal system. A correct early diagnosis may enable better management of the patients and a better quality of life. This paper attempts to summarise the different components of the musculoskeletal system which are affected in skeletal dysplasias and lists several interesting examples of such diseases in order to enable better understanding of the complexity of human musculoskeletal system.
Nonexistence of invariant nodal line and improved $L^2$ restriction bounds for Neumann data on negatively curved surface

The problem of obtaining the lower bounds on the restriction of Laplacian eigenfunctions to hypersurfaces inside a compact Riemannian manifold $(M,g)$ is challenging and has been attempted by many authors \cite{BR, GRS, Jun, ET}. This paper aims to show that if $(M,g)$ is assumed to be a negatively curved surface then one can get the corresponding restricted lower bounds, as well as quantitative improvement of restricted bounds for Neumann data.
The Network Structure of Exploration and Exploitation

Whether as team members brainstorming or cultures experimenting with new technologies, problem solvers communicate and share ideas. This paper examines how the structure of communication networks among actors can affect system-level performance. We present an agent-based computer simulation model of information sharing in which the less successful emulate the more successful. Results suggest that when agents are dealing with a complex problem, the more efficient the network at disseminating information, the better the short-run but the lower the long-run performance of the system. The dynamic underlying this result is that an inefficient network maintains diversity in the system and is thus better for exploration than an efficient network, supporting a more thorough search for solutions in the long run. For intermediate time frames, there is an inverted-U relationship between connectedness and performance, in which both poorly and well-connected systems perform badly, and moderately connected systems perform best. This curvilinear relationship between connectivity and group performance can be seen in several diverse instances of organizational and social behavior.
Kitaev Interactions Through an Extended Superexchange Pathway in the jeff = 1/2 Ru3+ Honeycomb Magnet, RuP3SiO11

Magnetic materials are composed of the simple building blocks of magnetic moments on a crystal lattice that interact via short-range magnetic exchange interactions. Yet from these simple building blocks emerges a remarkable diversity of magnetic states. Some of these, such as ferromagnetism, are familiar in our everyday lives, while others reveal the deep quantum mechanical origins of magnetism. A prime example of the latter are quantum spin liquid (QSL) states in which -- unlike in a ferromagnet where magnetic moments are driven by their exchange interactions to adopt long-range order -- magnetic moments remain disordered at low temperatures but are simultaneously correlated over long length scales through quantum entanglement. A particularly promising theoretical model of a QSL is the Kitaev model, composed of unusual bond-dependent exchange interactions between magnetic moments on a honeycomb lattice. However, the Kitaev QSL is extremely challenging to realise experimentally as it is unstable to competing exchange interactions and crystal lattice perturbations that inevitably arise in real materials. This makes it essential to understand the relationship between the structure and interactions that may give rise to Kitaev interactions in new candidate materials. Here we show that the material requirements for the Kitaev QSL survive for an extended pseudo-edge-sharing superexchange pathway of Ru3+ 4d5 octahedra within the honeycomb layers of the inorganic framework solid, RuP3SiO11. Through materials synthesis and structural characterisation, resonant inelastic X-ray and neutron scattering experiments, we confirm the requisite jeff = 1/2 state of Ru3+ in RuP3SiO11 and resolve the hierarchy of exchange interactions that provide experimental access to an otherwise unexplored region of the extended Kitaev phase diagram.
Planning, Executing, Sensing, and Replanning for Information Gathering

Current specialized planners for query processing are designed to work in local, reliable, and predictable environments. However, a number of problems arise in gathering information from large networks of distributed information. In this environment, the same information may reside in multiple places, actions can be executed in parallel to exploit distributed resources, new goals come into the system during execution, actions may fail due to problems with remote databases or networks, and sensing may need to be interleaved with planning in order to formulate efficient queries. We have developed a planner called Sage that, addresses the issues that arise in this environment. This system integrates previous work on planning, execution, replanning, and sensing and extends this work to support simultaneous and interleaved planning and execution. Sage has been applied to the problem of information gathering to provide a flexible and efficient system for integrating heterogeneous and distributed data.
SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing

In this paper, we present a Scale-adaptive method for Anti-aliasing Gaussian Splatting (SA-GS). While the state-of-the-art method Mip-Splatting needs modifying the training procedure of Gaussian splatting, our method functions at test-time and is training-free. Specifically, SA-GS can be applied to any pretrained Gaussian splatting field as a plugin to significantly improve the field's anti-alising performance. The core technique is to apply 2D scale-adaptive filters to each Gaussian during test time. As pointed out by Mip-Splatting, observing Gaussians at different frequencies leads to mismatches between the Gaussian scales during training and testing. Mip-Splatting resolves this issue using 3D smoothing and 2D Mip filters, which are unfortunately not aware of testing frequency. In this work, we show that a 2D scale-adaptive filter that is informed of testing frequency can effectively match the Gaussian scale, thus making the Gaussian primitive distribution remain consistent across different testing frequencies. When scale inconsistency is eliminated, sampling rates smaller than the scene frequency result in conventional jaggedness, and we propose to integrate the projected 2D Gaussian within each pixel during testing. This integration is actually a limiting case of super-sampling, which significantly improves anti-aliasing performance over vanilla Gaussian Splatting. Through extensive experiments using various settings and both bounded and unbounded scenes, we show SA-GS performs comparably with or better than Mip-Splatting. Note that super-sampling and integration are only effective when our scale-adaptive filtering is activated. Our codes, data and models are available at https://github.com/zsy1987/SA-GS.
Neural Post-Einsteinian Framework for Efficient Theory-Agnostic Tests of General Relativity with Gravitational Waves

The parametrized post-Einsteinian (ppE) framework and its variants are widely used to probe gravity through gravitational-wave tests that apply to a large class of theories beyond general relativity. However, the ppE framework is not truly theory-agnostic as it only captures certain types of deviations from general relativity: those that admit a post-Newtonian series representation in the inspiral of coalescencing compact objects. Moreover, each type of deviation in the ppE framework has to be tested separately, making the whole process computationally inefficient and expensive, possibly obscuring the theoretical interpretation of potential deviations that could be detected in the future. We here present the neural post-Einsteinian (npE) framework, an extension of the ppE formalism that overcomes the above weaknesses using deep-learning neural networks. The core of the npE framework is a variantional autoencoder that maps the discrete ppE theories into a continuous latent space in a well-organized manner. This design enables the npE framework to test many theories simultaneously and to select the theory that best describes the observation in a single parameter estimation run. The smooth extension of the ppE parametrization also allows for more general types of deviations to be searched for with the npE model. We showcase the application of the new npE framework to future tests of general relativity with the fifth observing run of the LIGO-Virgo-KAGRA collaboration. In particular, the npE framework is demonstrated to efficiently explore modifications to general relativity beyond what can be mapped by the ppE framework, including modifications coming from higher-order curvature corrections to the Einstein-Hilbert action at high post-Newtonian order, and dark-photon interactions in possibly hidden sectors of matter that do not admit a post-Newtonian representation.
Alteration of Auditory Cortex Activity with a Visual Stimulus through Conditioning: A 2-Deoxyglucose Analysis

In two experiments, the 2-deoxyglucose metabolic mapping technique was used to examine the hypothesis that a stimulus of one modality (a light) will begin to activate the sensory cortex of a stimulus of another modality (a tone) with which it has been repeatedly paired. Adult gerbils received repeated presentations of either a light or the light paired with a tone known to affect 2DG labeling patterns in the auditory cortex. Intermittent footshock was included on a pseudo-random basis to maintain arousal in the subjects. One day after training, each gerbil was injected with 2DG and either received repeated presentations of the light only or was simply exposed to the training context. Analysis of the auditory cortex revealed no differences in overall metabolic activity of the auditory cortex between the groups. However, in both experiments, the light that was previously paired with the tone changed the relative activity of the cortical subfields compared to the light not previously paired with the tone. Specifically, the results indicate greater activity in the anterior auditory field (AAF-Experiments 1 and 2) and the posterior fields (DPVP-Experiment 2) relative to the primary field AI in response to the light that was previously paired with the tone during training. Gerbils either only placed in the context during the 2DG session or that received unpaired presentations of the light and tone during training did not show this shift in relative labeling between the subfields. Because no differences in overall activity of the auditory cortex were found, we conclude that the shift in relative labeling between the subfields reflects, on average, both an increase in activity of fields AAF and DPVP and a concomitant decrease in AI activity in response to the light stimulus. The results have implications for our understanding both of brain learning mechanisms in general and the potential functions of auditory cortex subfields in particular.
Risk-Aware Robotics: Tail Risk Measures in Planning, Control, and Verification

The need for a systematic approach to risk assessment has increased in recent years due to the ubiquity of autonomous systems that alter our day-to-day experiences and their need for safety, e.g., for self-driving vehicles, mobile service robots, and bipedal robots. These systems are expected to function safely in unpredictable environments and interact seamlessly with humans, whose behavior is notably challenging to forecast. We present a survey of risk-aware methodologies for autonomous systems. We adopt a contemporary risk-aware approach to mitigate rare and detrimental outcomes by advocating the use of tail risk measures, a concept borrowed from financial literature. This survey will introduce these measures and explain their relevance in the context of robotic systems for planning, control, and verification applications.
Transparent and Clinically Interpretable AI for Lung Cancer Detection in Chest X-Rays

The rapidly advancing field of Explainable Artificial Intelligence (XAI) aims to tackle the issue of trust regarding the use of complex black-box deep learning models in real-world applications. Existing post-hoc XAI techniques have recently been shown to have poor performance on medical data, producing unreliable explanations which are infeasible for clinical use. To address this, we propose an ante-hoc approach based on concept bottleneck models which introduces for the first time clinical concepts into the classification pipeline, allowing the user valuable insight into the decision-making process. On a large public dataset of chest X-rays and associated medical reports, we focus on the binary classification task of lung cancer detection. Our approach yields improved classification performance in lung cancer detection when compared to baseline deep learning models (F1>0.9), while also generating clinically relevant and more reliable explanations than existing techniques. We evaluate our approach against post-hoc image XAI techniques LIME and SHAP, as well as CXR-LLaVA, a recent textual XAI tool which operates in the context of question answering on chest X-rays.
Laser Interactions with Gas Jets: EMP Emission and Nozzle Damage

We present a theoretical model of electromagnetic pulse emission and nozzle damage from high-power laser interactions with gas jets. As hot electrons are accelerated beyond the laser channel, they produce a positively-charged plasma that expands into the ambient gas until it reaches the gas jet nozzle. A strong plasma potential leads to electrical breakdown that ionises the gas even faster than the expanding plasma ions. When contact is made between the plasma and the conducting nozzle surface, a discharge current propagates to ground and an electromagnetic pulse is radiated via an antenna emission mechanism. Damage to gas jet nozzles is caused by heating from plasma ions rather than Ohmic heating via a discharge current. Our model agrees with experimental data from experiments on proton acceleration in near-critical plasmas at the Vulcan and VEGA-3 lasers.
Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients

Kakade's natural policy gradient method has been studied extensively in the last years showing linear convergence with and without regularization. We study another natural gradient method which is based on the Fisher information matrix of the state-action distributions and has received little attention from the theoretical side. Here, the state-action distributions follow the Fisher-Rao gradient flow inside the state-action polytope with respect to a linear potential. Therefore, we study Fisher-Rao gradient flows of linear programs more generally and show linear convergence with a rate that depends on the geometry of the linear program. Equivalently, this yields an estimate on the error induced by entropic regularization of the linear program which improves existing results. We extend these results and show sublinear convergence for perturbed Fisher-Rao gradient flows and natural gradient flows up to an approximation error. In particular, these general results cover the case of state-action natural policy gradients.
Cytogenetics of Laboratory Colonies of Simulium vittatum Cytospecies IS-7 (Diptera: Simuliidae)

Abstract Laboratory colonies of the black fly Simulium vittatum Zetterstedt cytospecies IS-7 were analyzed cytogenetically and compared with the original New York population from which source material was collected 18 yr earlier. All sex chromosomes and major autosomal polymorphisms that were present in the source population were still represented in the laboratory colonies. However, the extent of sex linkage and the frequencies of four of the five major autosomal inversions changed significantly in at least one colony, perhaps because of bottlenecks experienced by the colony. A complete absence of males homozygous for the IS-7 inversion in both field and colony material is explained by postulating that the Y2 chromosome, representing the inverted condition for the IS-7 sequence, is absent from the population or acts as a rare reproductive lethal. This example possibly represents pseudo-partial sex linkage involving the X-linked sequences.
Clinical significance of Blastocystis hominis: experience from a medical center in northern Taiwan.

BACKGROUND AND PURPOSE Blastocystis hominis is an intestinal protozoan. The pathogenic role of this organism in human beings is still controversial and has varied among reports from different geographic areas. The purpose of this study was to determine the clinical significance of B. hominis in northern Taiwan. METHODS A total of 100 patients who had a positive B. hominis stool examination during the period April to December of 2001 were retrospectively identified from Taipei Veterans General Hospital. The demographic and clinical characteristics of these patients were reviewed from the medical records. RESULTS All of the patients were adults. Fifty nine patients had more than one underlying diseases, including malignancies. Twenty one patients presented with fever and 10 patients had gastrointestinal symptoms, including diarrhea and/or abdominal pain. However, all of the patients had other conditions that might have contributed to the clinical presentation, and they improved without specific treatment for B. hominis. Furthermore, there were no significant differences in clinical symptoms and white blood cell count between patients with malignancy or diabetes mellitus and those without. Six patients had hypereosinophilia that could not be attributed to other causes. Among 34 patients who had a further stool examination within one year, B. hominis was undetectable in 31 patients (91.2%), despite their having no specific antiprotozoal treatment. CONCLUSIONS The association of clinical symptoms and B. hominis could not be delineated from our study, even in immunocompromised patients. All of the patients improved without receiving any specific therapy. More studies from different areas are needed in order to delineate the clinical significance B. hominis.
Development of a ddPCR approach for the absolute quantification of soil microorganisms involved in atmospheric CO2 fixation

Carbon fixing microorganisms (CFMs) play a crucial role in soil carbon (C) cycling contributing to carbon uptake and sequestration through various metabolic pathways. Despite their significance, quantification of the absolute abundance of CFMs in soils remains elusive. This study employed a digital droplet PCR (ddPCR) approach to quantify the abundance of key and emerging CFM pathways in fen and bog across different depths (0-15 cm). Targeting total prokaryotes (16S rRNA gene), oxygenic phototrophs (23S rRNA gene), aerobic anoxygenic phototrophic bacteria (AAnPB, pufM gene), and chemoautotrophs (cbbL gene), we optimized ddPCR conditions to achieve absolute quantification of these genes. Overall, our results revealed that oxygenic phototrophs were the most abundant CFMs, constituting 12% of total prokaryotic abundance, followed by chemoautotrophs (10%) and AAnPBs (9%). Fen exhibited higher gene concentrations than bog. Depth variations were also observed, differing between fen and bog for all genes. Our findings highlight the abundance of oxygenic phototrophs and chemoautotrophs in peatlands, challenging previous estimations that relied solely on oxygenic phototrophs for microbial CO2 fixation assessments. Incorporating absolute gene quantification is crucial for a comprehensive understanding of microbial contributions to soil processes, shedding light on the intricate mechanisms of soil functioning in peatlands.
Sub-cone visual resolution by active, adaptive sampling in the human foveola

The foveated architecture of the human retina and the eye’s mobility enable prime spatial vision, yet the interplay between photoreceptor cell topography and the constant motion of the eye during fixation remains unexplored. With in vivo foveal cone-resolved imaging and simultaneous microscopic photo stimulation, we examined visual acuity in both eyes of 16 participants while precisely recording the stimulus path on the retina. We find that resolution thresholds were correlated with the individual retina’s sampling capacity, and exceeded what static sampling limits would predict by 18 %, on average. The amplitude and direction of fixational drift motion, previously thought to be primarily random, played a key role in achieving this sub-cone diameter resolution. The oculomotor system finely adjusts drift behavior towards retinal areas with higher cone densities within only a few hundred milliseconds to enhance retinal sampling.