Miracle-dz/UniFAR-FTUs huggingface dataset:
Correction of specimen absorption in X-ray diffuse scattering experiments with area-detector systems

Methods for correcting specimen absorption in X-ray diffraction experiments, dedicated to two-dimensional area-detector systems and broad diffuse scattering phenomena, are implemented and tested. The respective transmission factors, in relation to the crystal or specimen shape, are applied to the intensity of every pixel of the two-dimensional detector. The pixel-wise approach allows for a fully quantitative treatment of continuous diffraction information, such as disorder diffuse scattering or scattering from amorphous materials, which is collected with a two-dimensional detector system. This is in contrast to routine data reduction, where the transmission factors are applied to the integrated Bragg intensities only. Furthermore, it is possible to assign different linear attenuation coefficients to the paths of the incident and scattered beams, as is desirable, for instance, in the case of X-ray holography with an internal resonant scatterer. Broad diffuse scattering data collected with synchrotron radiation and numerical calculations are used to study in detail the influence of experimental parameters on the accuracy of the pixel-wise absorption correction.
Faithful Representations of Free Products

In 1940 Nisnevic̆ published the following theorem [3]. Let (Gα)α∈Λ be a family of groups indexed by some set Λ and (Fα)α∈Λ a family of fields of the same characteristic p⩾0. If for each α the group Gα has a faithful representation of degree n over Fα then the free product*α∈Λ Gα has a faithful representation of degree n+1 over some field of characteristic p. In [6] Wehrfritz extended this idea. If (Gα)α∈Λ ⩽GL(n, F) is a family of subgroups for which there exists Z⩽GL(n, F) such that for all α the intersection Gα∩F.1n=Z, then the free product of the groups *ZGα with Z amalgamated via the identity map is isomorphic to a linear group of degree n over some purely transcendental extension of F.
General finite-dimensional risk-sensitive problems and small noise limits

For a risk-sensitive, partially observed stochastic control problem, the modified Zakai equation includes an extra term related to the exponential running cost. The finite-dimensional solutions of this modified Zakai equation are obtained. These are analogs of the Kalman and Benes filters. The small noise limits of the finite-dimensional risk-sensitive problems are then obtained. These lead to differential games with deterministic disturbances.
AutoGDA: Automated Graph Data Augmentation for Node Classification

Graph data augmentation has been used to improve generalizability of graph machine learning. However, by only applying ﬁxed augmentation operations on entire graphs, existing methods overlook the unique characteristics of communities which naturally exist in the graphs. For example, different communities can have various degree distributions and homophily ratios. Ignoring such discrepancy with uniﬁed augmentation strategies on the entire graph could lead to sub-optimal performance for graph data augmentation methods. In this paper, we study a novel problem of automated graph data augmentation for node classiﬁcation from the localized perspective of communities. We formulate it as a bilevel optimization problem: ﬁnding a set of augmentation strategies for each community, which maximizes the performance of graph neural networks on node classiﬁcation. As the bilevel optimization is hard to solve directly and the search space for community-customized augmentations strategy is huge, we propose a reinforcement learning framework AutoGDA that learns the local-optimal augmentation strategy for each community sequentially. Our proposed approach outperforms established and popular baselines on public node classiﬁcation benchmarks as well as real industry e-commerce networks by up to +12.5% accuracy.
Surface Analysis near and within the Tipton, Kansas, Tornado on 29 May 2008

Abstract Data collected by a mesonet within the near-tornado environment and in the Tipton tornado on 29 May 2008 provided a rare opportunity to analyze rear-flank downdraft (RFD) outflow properties closely bounding a tornado and to characterize parcel thermodynamics being ingested into a tornado from the rear-flank downdraft. Parcels moving into the tornado on its right flank had very small negative buoyancy and considerable potential buoyancy. Measurements within and very near the tornado showed similar buoyancy characteristics to the storm inflow. Analyzed surface divergence and videographic evidence indicated that the RFD outflow just to the right and wrapping in front of the tornado was supported by parcels moving out of a narrow downdraft bordering the right flank of the tornado. Surface flow field analysis showed that parcels moved out of the downdraft-associated divergence region and into the right side of, as well as in front of, the tornado. An internal RFD surge boundary was positioned roughly .
Anova for repeated ordinal data with small sample size? a comparison of anova, manova, wls and gee methods by simulation

Repeated ordinal outcomes are common in behavioral and medical sciences. Due to the familiarity, simplicity and robustness of ANOVA methodology, this approach has been frequently used for repeated ordinal data. Weighted least squares (WLS) and generalized estimating equations (GEE) are usually the procedures of choice for repeated ordinal data since, unlike ANOVA, they generally make no or few untenable assumptions. However, these methods are based on asymptotic results and their properties are not well understood for small samples. Moreover, few software packages have procedures for implementing these methods. This paper investigates the performance of ANOVA, MANOVA, WLS, and GEE for repeated ordinal data with small sample sizes. For a design with two groups and four time points, our simulation results indicated that ANOVA with the Huynh‐Feldt adjustment performed matrix, known as sphericity, or the H‐F condition, is a sufficient condition for the usual F tests to be valid. The sphericity condition is me.
Explainability Guided COVID-19 Detection in CT Scans

Radiological examination of chest CT is an effective method for screening COVID-19 cases. In this work, we overcome three challenges in the automation of this process: (i) the limited number of supervised positive cases, (ii) the lack of region-based supervision, and (iii) variability across acquisition sites. These challenges are met by incorporating a recent augmentation solution called SnapMix, a novel explainability-driven contrastive loss for patch embedding, and by performing test-time augmentation that masks out the most relevant patches in order to analyse the prediction stability. The three techniques are complementary and are all based on utilizing the heatmaps produced by the Class Activation Mapping (CAM) explainability method. State-of-the-art performance is obtained on three different datasets for COVID detection in CT scans.
On Interference Effects in Light-Ion Nuclear Reactions with Three Particles in the Final State

We consider the coincidence events of two identical particles in light-ion nuclear reactions which lead to three bodies in the final state. In the description of the energy dependence of the coincidence rate it is shown that it is generally necessary to consider interference effects caused by resonance and background amplitude phases and also by space-time shifts between the sources of particle emission.
Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection

The safe application of machine learning systems in healthcare relies on valid performance claims. Such claims are typically established in a clinical validation setting designed to be as close as possible to the intended use, but inadvertent domain or population shifts remain a fundamental problem. In particular, subgroups may be differently represented in the data distribution in the validation compared to the application setting. For example, algorithms trained on population cohort data spanning all age groups may be predominantly applied in elderly people. While these data are not “out-of distribution”, changes in the prevalence of different subgroups may have considerable impact on algorithm performance or will at least render original performance claims invalid. Both are serious problems for safely deploying machine learning systems. In this paper, we demonstrate the fundamental limitations of individual example out-of-distribution detection for such scenarios, and show that subgroup shifts can be detected on a population-level instead. We formulate population-level shift detection in the framework of statistical hypothesis testing and show that recent state-of-the-art statistical tests can be effectively applied to subgroup shift detection in a synthetic scenario as well as real histopathology images.
Electroacupuncture prevents astrocyte atrophy to alleviate depression

Astrocyte atrophy is the main histopathological hallmark of the major depressive disorder (MDD) in humans and in animal models of depression. Here we show that electroacupuncture prevents astrocyte atrophy in the prefrontal cortex and alleviates depressive-like behaviour in mice subjected to the chronic unpredictable mild stress (CUMS). Treatment of mice with CUMS induced depressive-like phenotypes as confirmed by sucrose preference test, tail suspension test, and forced swim test. These behavioural changes were paralleled with morphological atrophy of astrocytes in the prefrontal cortex, revealed by analysis of 3D reconstructions of confocal Z-stack images of mCherry expressing astrocytes. This morphological atrophy was accompanied with a decrease in expression of cytoskeletal linker Ezrin, associated with formation of astrocytic leaflets, which form astroglial synaptic cradle. Electroacupuncture at the acupoint ST36 as well as treatment with anti-depressant fluoxetine prevented depressive-like behaviours, astrocytic atrophy and down-regulation of astrocytic ezrin. In conclusion, our data further strengthen the notion of a primary role of astrocytic atrophy in depression and reveal astrocytes as cellular target for electroacupuncture in treatment of depressive disorders.
First Integral Method for Systems of (1+1)-Dimensional Dispersive Long Wave

The First Integral method (FIM) is applied to solve a dispersive long wave system. In this method the division theorem, as a statement in commutative Algebra has an important role. To show the ability and the efficiency of this approach an example is provided. Application of FIM to the illustrative example leads to six exact solutions, which it is shown that these six solutions are independent to each other. So there are six different exact solutions. doi: 10.14456/WJST.2015. 45
On the martingale property of stochastic exponentials

We present a necessary and sufficient condition for a stochastic exponential to be a true martingale. It is proved that the criteria for the true martingale property are related to whether a related process explodes. An alternative and interesting interpretation of this result is that the stochastic exponential is a true martingale if and only if under a ‘candidate measure’ the integrand process is square integrable over time. Applications of our theorem to problems arising in mathematical finance are also given.
Robustness analysis of global exponential stability of non-linear systems with time delays and neutral terms

The global stability of non-linear dynamical systems has been investigated extensively in recent decades. It is well known that time delay and neutral term could derail the stability of non-linear systems. This study presents new results on the robustness of the global exponential stability of non-linear systems with respect to time delay and neutral term. Given globally exponentially stable non-linear systems, the problems to be addressed herein are how much time delay and neutral term contraction coefficient are allowed so that the non-linear systems can remain to be globally exponentially stable, in the presence of time delay and neutral term. Upper bounds of allowable time delay and neutral term contraction coefficient will be derived for non-linear systems to sustain their global exponential stability. A numerical example is provided to illustrate the results.
A global SAW ID tag with large data capacity

The Global SAW Tag uses a recently-invented digital modulation based on simultaneous time position and phase shifting. A unique feature of this tag is that it satisfies global RFID requirements using the international 2.44 GHz ISM band. Precision amplitude and phase weighting of reflectors and accurate control of parasitic effects is critical to implementing this device. This tag has significantly more data bits, lower insertion loss, and smaller die area (i.e. lower cost) than previous SAW tags. These improvements eliminate the shortcomings that have previously limited the market for SAW RFID (radio frequency identification). A 2.44 GHz fundamental mode tag on 128/spl deg/ LiNbO/sub 3/ with 64-bit data capacity plus 16-bit error detection coding is described.
Analysis and Forecasting of Sea Ice Conditions with Three-Dimensional Variational Data Assimilation and a Coupled Ice–Ocean Model

Abstract A three-dimensional variational data assimilation (3DVAR) system has been developed to provide analyses of the ice–ocean state and to initialize a coupled ice–ocean numerical model for forecasting sea ice conditions. This study focuses on the estimation of the background-error statistics, including the spatial and multivariate covariances, and their impact on the quality of the resulting sea ice analyses and forecasts. The covariances are assumed to be horizontally homogeneous and fixed in time. The horizontal correlations are assumed to have a Gaussian shape and are modeled by integrating a diffusion equation. A relatively simple implementation of the ensemble Kalman filter is used to produce ensembles of the ice–ocean model state that are representative of background error and from which the 3DVAR covariance parameters are estimated. Data assimilation experiments, using various configurations of 3DVAR and simpler assimilation approaches, are conducted over a 7-month period during the winter of .
Development of a human glioblastoma model using humanized DRAG mice for immunotherapy

Glioblastoma (GBM) is the most common and lethal primary brain tumor with high mortality rates and a short median survival rate of about 15 months despite intensive multimodal treatment of maximal surgical resection, radiotherapy, and chemotherapy. Although immunotherapies have been successful in the treatment of various cancers, disappointing results from clinical trials for GBM immunotherapy represent our incomplete understanding. The development of alternative humanized mouse models with fully functional human immune cells will potentially accelerate the progress of GBM immunotherapy. In this study, we developed a humanized DRAG (NOD.Rag1KO.IL2RγcKO) mouse model, in which the human hematopoietic stem cells (HSCs) were well-engrafted and subsequently differentiated into a full lineage of immune cells. Using this humanized DRAG mouse model, GBM patient-derived tumorsphere lines were successfully engrafted to form xenografted tumors, which can recapitulate the pathological features and the immune cell composition of human GBM. Importantly, the administration of anti-human PD-1 antibodies in these DRAG mice bearing a GBM patient-derived tumorsphere line resulted in decreasing the major tumor-infiltrating immunosuppressive cell populations, including CD4+PD-1+ and CD8+PD-1+ T cells, CD11b+CD14+HLA-DR+ macrophages, CD11b+CD14+HLA-DR−CD15− and CD11b+CD14− CD15+ myeloid-derived suppressor cells, indicating the humanized DRAG mouse model as a useful model to test the efficacy of immune checkpoint inhibitors in GBM immunotherapy. Together, these results suggest that humanized DRAG mouse models are a reliable preclinical platform for brain cancer immunotherapy and beyond.
Semi-conservative transmission of DNA N6-adenine methylation in a unicellular eukaryote

While DNA N6-adenine methylation (6mA) is best known in prokaryotes, its presence in eukaryotes has generated great interest recently. Biochemical and genetic evidence supports that AMT1, a MT-A70 family methyltransferase (MTase), is crucial for 6mA deposition in unicellular eukaryotes. Nonetheless, 6mA transmission mechanism remains to be elucidated. Taking advantage of Single Molecule Real-Time Circular Consensus Sequencing (SMRT CCS), here we provide definitive evidence for semi-conservative transmission of 6mA, showcased in the unicellular eukaryote Tetrahymena thermophila. In wildtype (WT) cells, 6mA occurs at the self-complementary ApT dinucleotide, mostly in full methylation (full-6mApT); hemi-methylation (hemi-6mApT) is transiently present on the parental strand of newly replicated DNA. In ΔAMT1 cells, 6mA predominantly occurs as hemi-6mApT. Hemi-to-full conversion in WT cells is fast, robust, and likely processive, while de novo 6mA deposition in ΔAMT1 cells is slow and sporadic. In Tetrahymena, regularly spaced 6mA clusters coincide with linker DNA of the canonical nucleosome arrays in the gene body. Importantly, in vitro methylation of human chromatin by reconstituted AMT1 complex recapitulates preferential targeting of hemi-6mApT sites in linker DNA, supporting AMT1’s intrinsic and autonomous role in maintenance methylation. We conclude that 6mA is transmitted by a semi-conservative mechanism: full-6mApT is split by DNA replication into hemi-6mApT, which is restored to full-6mApT by AMT1-dependent maintenance methylation. Our study dissects AMT1-dependent maintenance methylation and AMT1-independent de novo methylation, reveals a molecular pathway for 6mA transmission with striking similarity to 5-methyl cytosine (5mC) transmission at the CpG dinucleotide, and establishes 6mA as a bona fide eukaryotic epigenetic mark.
Disentangled deep generative models reveal coding principles of the human face processing network

Despite decades of research, much is still unknown about the computations carried out in the human face processing network. Recently deep networks have been proposed as a computational account of human visual processing, but while they provide a good match to neural data throughout visual cortex, they lack interpretability. We introduce a method for interpreting brain activity using a new class of deep generative models, disentangled representation learning models, which learn a low-dimensional latent space that “disentangles” different semantically meaningful dimensions of faces, such as rotation, lighting, or hairstyle, in an unsupervised manner by enforcing statistical independence between dimensions. We find that the majority of our model’s learned latent dimensions are interpretable by human raters. Further, these latent dimensions serve as a good encoding model for human fMRI data. We next investigated the representation of different latent dimensions across face-selective voxels. We find a gradient from low- to high-level face feature representations along posterior to anterior face-selective regions, corroborating prior models of human face recognition. Interestingly, though, we find no spatial segregation between identity-relevant and irrelevant face features. Finally, we provide new insight into the few “entangled” (uninterpretable) dimensions in our model by showing that they match responses across the ventral stream and carry significant information about facial identity. Disentangled face encoding models provide an exciting alternative to standard “black box” deep learning approaches for modeling and interpreting human brain data.
SleepQA: A Health Coaching Dataset on Sleep for Extractive Question Answering

Question Answering (QA) systems can support health coaches in facilitating clients’ lifestyle behavior changes (e.g., in adopting healthy sleep habits). In this paper, we design a domain-specific QA pipeline for sleep coaching. To this end, we release SleepQA, a dataset created from 7,005 passages comprising 4,250 training examples with single annotations and 750 examples with 5-way annotations 1 . We fine-tuned different domain-specific BERT models on our dataset and perform extensive automatic and human evaluation of the resulting end-to-end QA pipeline. Comparisons of our pipeline with baseline show improvements in domain-specific natural language processing on real-world questions. We hope that this dataset will lead to wider research interest in this important health domain.
Inverse problems for impulsive Sturm–Liouville operator with spectral parameter linearly contained in boundary conditions

In this work, the Sturm–Liouville problem, with discontinuities in the case when an eigenparameter appears not only in the differential equation but also in one of the boundary conditions, is studied. The spectrum and the resolvent operator of this problem are given. Moreover, the Weyl function for this problem under consideration has been defined and uniqueness theorems for the solution of the inverse problem according to this function and spectral data have been proved.
A Hilbert space of Dirichlet series and systems of dilated functions in $L^2(0,1)$

For a function $\varphi$ in $L^2(0,1)$, extended to the whole real line as an odd periodic function of period 2, we ask when the collection of dilates $\varphi(nx)$, $n=1,2,3,\ldots$, constitutes a Riesz basis or a complete sequence in $L^2(0,1)$. The problem translates into a question concerning multipliers and cyclic vectors in the Hilbert space $\cal H$ of Dirichlet series $f(s)=\sum_n a_nn^{-s}$, where the coefficients $a_n$ are square summable. It proves useful to model $\cal H$ as the $H^2$ space of the infinite-dimensional polydisk, or, which is the same, the $H^2$ space of the character space, where a character is a multiplicative homomorphism from the positive integers to the unit circle. For given $f$ in $\cal H$ and characters $\chi$, $f_\chi(s)=\sum_na_n\chi(n)n^{-s}$ is a vertical limit function of $f$. We study certain probabilistic properties of these vertical limit functions.
Coupled Biomechanical and Ionic Excitability in Developing Neural Cell Networks

Waves and oscillations play a key role in the flow and processing of information in the brain. Recent work has demonstrated that in addition to electrical activity, biomechanical signaling can also be excitable and thus capable of self-sustaining oscillations and waves. Here we measured the biomechanical dynamics of actin polymerization in neural precursor cells throughout their differentiation into populations of neurons and astrocytes. Fluorescence-based live-cell imaging allowed us to analyze the dynamics of actin in conjunction with the dynamics of calcium signals. Actin dynamics throughout differentiation showed a rhythmic character, localized mostly in processes, with changes in scale associated with differentiation. Furthermore, actin dynamics impact ionic dynamics, with an increase in the frequency of calcium bursts accompanied by a decrease in cell-cell correlations when actin dynamics is inhibited. This impact of cytoskeletal dynamics on cell-cell coupling and ionic neural cell signaling suggests that information flow in the brain may be able to harness both biomechanical and electrical/ionic excitability.