Miracle-dz/UniFAR-FTUs huggingface dataset:
Resting State fMRI for Motor Cortex Mapping in Children with Epilepsy

Task-based fMRI is commonly used to localize motor functions prior to epilepsy surgery. Not all children are able to follow task instructions in the scanner, limiting fMRI motor mapping. We present a data-driven method to automatically delineate the motor cortex using task-free, resting state fMRI (rsfMRI) data of children with epilepsy and controls. We preprocessed anatomical and rsfMRI images (64 patients, 12 controls) using fMRIPrep. We used FSL MELODIC tool for whole brain ICA estimation in multiple orders, to generate several sets of independent components. We defined a motor component selection process based on Discriminability Index-based Component Identification (DICI) score, resulting from comparing binarized component to the Human Motor Area Template (HMAT) in subject's native brain space. Components with the largest DICI score and their peak in the template were combined to form the whole-brain ICA Motor Map (wIMM). We validated wIMM by comparing individual results with finger tapping motor task activation, and evaluated its reproducibility in controls with two runs of rsfMRI. The hit rate between wIMM and motor task activation ranged 60 to 77% across all participants. Sensitivity of wIMM for capturing the task activation peak was 87.5% among 32 patients and 100% in 11 controls with available task results. The mean Dice similarity in results from repeated runs was 0.70 in controls. Our results shows the sensitivity and reproducibility of an automated motor mapping based on ICA analysis of rsfMRI in children with epilepsy. The ICA maps may provide different, but useful, information than task fMRI. Future studies will expand our method to mapping other brain functions, and may lead to a surgical planning tool for patients who cannot perform task fMRI and help predict their postsurgical function.
SARS-CoV-2 Omicron Symptomatic Infections in Previously Infected or Vaccinated South African Healthcare Workers

We investigated Omicron infections among healthcare workers (HCW) presenting with symptoms of SARS-CoV-2 infection and evaluated the protective effect of vaccination or prior infection. Between 24th November and 31st December 2021, HCW in Johannesburg, South Africa, were tested for SARS-CoV-2 infection by Nucleic Acid Amplification Test (NAAT). Blood samples collected either at the symptomatic visit or within 3-months prior, were tested for spike protein immunoglobulin G (IgG). Overall, 433 symptomatic HCW were included in the analysis, with 190 (43.9%) having an Omicron infection; 69 (16.7%) were unvaccinated and 270 (62.4%) received a single dose of Ad26.COV. 2 vaccine. There was no difference in the odds of identifying Omicron between unvaccinated and Ad26.COV. 2 vaccinated HCW (adjusted odds ratio [aOR] 0.81, 95% confidence interval [CI]: 0.46, 1.43). One-hundred and fifty-four (35.3%) HCW had at least one SARS-CoV-2 NAAT-confirmed prior infection; these had lower odds of Omicron infection compared with those without past infection (aOR 0.55, 95%CI: 0.36, 0.84). Anti-spike IgG concentration of 1549 binding antibody unit/mL was suggestive of significant reduction in the risk of symptomatic Omicron infection. We found high reinfection and vaccine breakthrough infection rates with the Omicron variant among HCW. Prior infection and high anti-spike IgG concentration were protective against Omicron infection.
Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

A bias in health research to favor understanding of diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature that used machine learning or NLP techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (1) "women" or "men" or "sex," (2) "big data" or "artificial intelligence" or "NLP", and (3) "disparities" or "differences. " From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in the included studies is disproportionately less than women. Even though AI and NLP techniques are widely applied in health research, few studies use them to take advatage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process to- wards correction is slow. We reflected on what would be the best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.
A PRACTICAL MODEL FOR EARLY IDENTIFICATION OF PROSPECTIVE HIGH NEED HIGH COST PATIENTS

Background: High Need-High Cost (HNHC) patients are those who experience poor health outcomes and high health care costs. Early identification may improve outcomes and lower costs. Aim: Development of a model using retrospective data to identify patients at risk for becoming HNHC patients, in order to efficaciously plan interventions. Methods: Data from a large Israeli Health Maintenance Organization (HMO) that includes 488,615 clients above the age of 21 were examined. Multivariate linear regression models were developed using 2012 to 2016 health expenditure as a dependent variable. Results: The number of yearly purchases of medications for chronic disorders, yearly outpatient visits, yearly emergency department and hospital admissions and the last measured HgA1c level were highly predictive of increased expenditure over a five-year period. Each of these indicators has a different coefficient of influence. Conclusions: We developed a predictive model, based on easily obtained data from electronic medical records that enabled us to identify a population at risk for becoming HNHC in the next five years, a time window allows for intervention. Further research is needed to evaluate whether this is an early enough stage to implement pro-active intervention in the primary care setting.
Ethics education in the quantum information science classroom: Exploring attitudes, barriers, and opportunities

Quantum information science (QIS) is an emerging interdisciplinary field at the intersection of physics, computer science, electrical engineering, and mathematics leveraging the laws of quantum mechanics to circumvent classical limitations on information processing. With QIS coursework proliferating across US institutions, including at the undergraduate level, we argue that it is imperative that ethics and social responsibility be incorporated into QIS education from the beginning. We discuss ethical issues of particular relevance to QIS education that educators may wish to incorporate into their curricula. We then report on findings from focus interviews with six faculty who have taught introductory QIS courses, focusing on barriers to and opportunities for incorporation of ethics and social responsibility (ESR) into the QIS classroom. Few faculty had explicitly considered discussion of ethical issues in the classroom prior to the interview, yet instructor attitudes shifted markedly in support of incorporating ESR in the classroom as a result of the interview process itself. Taking into account faculty's perception of obstacles to discussing issues of ESR in coursework, we propose next steps toward making ESR education in the QIS classroom a reality.
Solutions of the Einstein Equations for a Black Hole Surrounded by a Galactic Halo

Various profiles of matter distribution in galactic halos (such as the Navarro–Frenk–White, Burkert, Hernquist, Moore, Taylor–Silk models, and others) are considered here as the source term for the Einstein equations. We solve these equations and find exact solutions that represent the metric of a central black hole immersed in a galactic halo. Even though in the general case the solution is numerical, very accurate general analytical metrics, which include all the particular models, are found in the astrophysically relevant regime, when the mass of the galaxy is much smaller than the characteristic scale in the halo.
Two-photon spontaneous emission of an atom in a cosmic string background

It is well known that the vicinities of an atomic system may substantially affect its radiative properties. In this work, we consider the influence of a cosmic string background in the spontaneous emission of an excited atom. We start by computing the one-photon spontaneous emission rate of a quantum emitter, which is a narrow band process, and then we analyze the more complex case of the two-photon spontaneous emission, which is a broad band and much richer phenomenon. In the former case, we analyze not only the behavior of the decay rate with the distance from the atom to the string, but also with the deficit angle associated with the cosmic string metric. In the latter case, we show that the spectral distribution of the emitted photons is substantially affected by the cosmic string background.
Urban Region Profiling via A Multi-Graph Representation Learning Framework

Urban region profiling can benefit urban analytics. Although existing studies have made great efforts to learn urban region representation from multi-source urban data, there are still three limitations: (1) Most related methods focused merely on global-level inter-region relations while overlooking local-level geographical contextual signals and intra-region information; (2) Most previous works failed to develop an effective yet integrated fusion module which can deeply fuse multi-graph correlations; (3) State-of-the-art methods do not perform well in regions with high variance socioeconomic attributes. To address these challenges, we propose a multi-graph representative learning framework, called Region2Vec, for urban region profiling. Specifically, except that human mobility is encoded for inter-region relations, geographic neighborhood is introduced for capturing geographical contextual information while POI side information is adopted for representing intra-region information by knowledge graph. Then, graphs are used to capture accessibility, vicinity, and functionality correlations among regions. To consider the discriminative properties of multiple graphs, an encoder-decoder multi-graph fusion module is further proposed to jointly learn comprehensive representations. Experiments on real-world datasets show that Region2Vec can be employed in three applications and outperforms all state-of-the-art baselines. Particularly, Region2Vec has better performance than previous studies in regions with high variance socioeconomic attributes.
Direct Telemetry Access

Fine-grained network telemetry is becoming a modern datacenter standard and is the basis of essential applications such as congestion control, load balancing, and advanced troubleshooting. As network size increases and telemetry gets more fine-grained, there is a tremendous growth in the amount of data needed to be reported from switches to collectors to enable network-wide view. As a consequence, it is progressively hard to scale data collection systems. We introduce Direct Telemetry Access (DTA), a solution optimized for aggregating and moving hundreds of millions of reports per second from switches into queryable data structures in collectors' memory. DTA is lightweight and it is able to greatly reduce overheads at collectors. DTA is built on top of RDMA, and we propose novel and expressive reporting primitives to allow easy integration with existing state-of-the-art telemetry mechanisms such as INT or Marple. We show that DTA significantly improves telemetry collection rates. For example, when used with INT, it can collect and aggregate over 400M reports per second with a single server, improving over the Atomic MultiLog by up to 16x.
Image-to-Image MLP-mixer for Image Reconstruction

Neural networks are highly effective tools for image reconstruction problems such as denoising and compressive sensing. To date, neural networks for image reconstruction are almost exclusively convolutional. The most popular architecture is the U-Net, a convolutional network with a multi-resolution architecture. In this work, we show that a simple network based on the multi-layer perceptron (MLP)-mixer enables state-of-the art image reconstruction performance without convolutions and without a multi-resolution architecture, provided that the training set and the size of the network are moderately large. Similar to the original MLP-mixer, the image-to-image MLP-mixer is based exclusively on MLPs operating on linearly-transformed image patches. Contrary to the original MLP-mixer, we incorporate structure by retaining the relative positions of the image patches. This imposes an inductive bias towards natural images which enables the image-to-image MLP-mixer to learn to denoise images based on fewer examples than the original MLP-mixer. Moreover, the image-to-image MLP-mixer requires fewer parameters to achieve the same denoising performance than the U-Net and its parameters scale linearly in the image resolution instead of quadratically as for the original MLP-mixer. If trained on a moderate amount of examples for denoising, the image-to-image MLP-mixer outperforms the U-Net by a slight margin. It also outperforms the vision transformer tailored for image reconstruction and classical un-trained methods such as BM3D, making it a very effective tool for image reconstruction problems.
Answering Count Queries for Genomic Data With Perfect Privacy

In this paper, we consider the problem of answering count queries for genomic data subject to perfect privacy constraints. Count queries are often used in applications that collect aggregate (population-wide) information from biomedical Databases (DBs) for analysis, such as Genome-wide association studies. Our goal is to design mechanisms for answering count queries of the following form: How many users in the database have a specific set of genotypes at certain locations in their genome? At the same time, we aim to achieve perfect privacy (zero information leakage) of the sensitive genotypes at a pre-specified set of secret locations. The sensitive genotypes could indicate rare diseases and/or other health traits one may want to keep private. We present both local and central count-query mechanisms for the above problem that achieves perfect information-theoretic privacy for sensitive genotypes while minimizing the expected absolute error (or per-user error probability, depending on the setting) of the query answer. We also derived a lower bound of the per-user probability of error for an arbitrary query-answering mechanism that satisfies perfect privacy. We show that our mechanisms achieve error close to the lower bound, and match the lower bound for some special cases. We numerically show that the performance of each mechanism depends on the data prior distribution, the intersection between the queried and sensitive genotypes, and the strength of the correlation in the genomic data sequence.
Modified ResNet Model for MSI and MSS Classification of Gastrointestinal Cancer

In this work, a modified ResNet model is proposed for the classification of Microsatellite instability(MSI) and Microsatellite stability(MSS) of gastrointestinal cancer. The performance of this model is analyzed and compared with existing models. The proposed model surpassed the existing models with an accuracy of 0.8981 and F1 score of
Robust Dynamic State Estimator of Integrated Energy Systems Based on Natural Gas Partial Differential Equations

The reliability and precision of dynamic database are vital for the optimal operating and global control of integrated energy systems. One of the effective ways to obtain the accurate states is state estimations.A novel robust dynamic state estimation methodology for integrated natural gas and electric power systems is proposed based on the Kalman filter. To take full advantage of measurement redundancies and predictions for enhancing the estimating accuracy, the dynamic state estimation model coupling gas and power systems by gas turbine units are established. The exponential smoothing technique and gas physical model are integrated into the Kalman filter. Additionally, the time-varying scalar matrix is proposed to conquer bad data in the Kalman filter algorithm. The proposed method is applied to integrated gas and power systems formed by GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating results show that the method can obtain the accurate dynamic states under three different measurement error conditions, and the filtering performance is better than separate estimation methods. Additionally, the proposed method is robust when the measurements experience bad data.
Relating Cognition to both Brain Structure and Function: A Systematic Review of Methods

Introduction: Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Many studies have combined structural and functional neuroimaging techniques to uncover the complex relationship between them. In this study, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Procedure: Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data and structural and functional neuroimaging data. Results: Five percent of screened studies met all inclusion criteria. Next, 50% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 31% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterization of the relationship between brain structure, function, and cognition; comparative, predictive, fusion, and complementary. Discussion: We discuss the insights provided in each approach about the relationship between brain structure and function and how it impacts cognitive performance. In addition, we discuss how authors can select approaches to suit their research questions. Impact statement The relationship between structural and functional brain networks and their relationship to cognition is a matter of current investigations. This work surveys how researchers have studied the relationship between brain structure and function and its impact on cognitive function in healthy adult populations. We review four emergent approaches of quantitative analysis of this multivariate problem; comparative, predictive, fusion, and complementary. We explain the characteristics of each approach, discuss the insights provided in each approach, and how authors can combine approaches to suit their research questions.
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport

Variational inference often minimizes the"reverse"Kullbeck-Leibler (KL) KL(q||p) from the approximate distribution q to the posterior p. Recent work studies the"forward"KL KL(p||q), which unlike reverse KL does not lead to variational approximations that underestimate uncertainty. This paper introduces Transport Score Climbing (TSC), a method that optimizes KL(p||q) by using Hamiltonian Monte Carlo (HMC) and a novel adaptive transport map. The transport map improves the trajectory of HMC by acting as a change of variable between the latent variable space and a warped space. TSC uses HMC samples to dynamically train the transport map while optimizing KL(p||q). TSC leverages synergies, where better transport maps lead to better HMC sampling, which then leads to better transport maps. We demonstrate TSC on synthetic and real data. We find that TSC achieves competitive performance when training variational autoencoders on large-scale data.
Some recent progress on the stationary measure for the open KPZ equation

This note, dedicated in Harold Widom's memory, is an expanded version of a lecture I gave in fall 2021 at the MSRI program"Universality and Integrability in Random Matrices and Interacting Particle Systems". I will focus on the behavior of the stationary measure for the open KPZ equation, a paradigmatic model for interface growth in contact with boundaries. Much of this will review elements of my joint work with A. Knizel as well as with H. Shen, as well as subsequent works of W. Bryc, A. Kuznetsov, Y. Wang, and J. Wesolowski and of G. Barraquand and P. Le Doussal. The basis for this advance is fundamental work of B. Derrida, M. Evans, V. Hakim and V. Pasquier from 1993, of T. Sasamoto, M. Uchiyama and M. Wadati from 2003, and of W. Bryc and J. Wesolowski from 2010 and 2017. I will try to explain how all of this fits together, without laboring details for the sake of exposition. Though this work does not directly follow from Harold Widom's own work, it (and a great deal of my research) is very much inspired by his and Craig Tracy's work on ASEP.
Uncertainty in fMRI Functional Networks of Autism Brain Imaging Data

In this paper we review the preprocessing pipeline through which fMRI data is transformed into a network. We discuss three parameters that mostly affect our understanding of the existence of functional correlations between the brain regions. In the end, we conclude that the existence of functional correlations between pairs of the brain's regions can be modeled with probabilistic edges, not to lose the uncertainty that is inherent in the network generation process.
The different structure of economic ecosystems at the scales of companies and countries

A key element to understand complex systems is the relationship between the spatial scale of investigation and the structure of the interrelation among its elements. When it comes to economic systems, it is now well-known that the country-product bipartite network exhibits a nested structure, which is the foundation of different algorithms that have been used to scientifically investigate countries’ development and forecast national economic growth. Changing the subject from countries to companies, a significantly different scenario emerges. Through the analysis of a unique dataset of Italian firms’ exports and a worldwide dataset comprising countries’ exports, here we find that, while a globally nested structure is observed at the country level, a local, in-block nested structure emerges at the level of firms. This in-block nestedness is statistically significant with respect to suitable null models and the algorithmic partitions of products into blocks correspond well with the UN-COMTRADE product classification. These findings lay a solid foundation for developing a scientific approach based on the physics of complex systems to the analysis of companies, which has been lacking until now.