Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptomic and Metabolomic Differentiation of Breast Cancers: Progression and Therapy Response via Magnetic Resonance Spectroscopy and Quantitative Expression Profiling in the Choline pathway

Tumor choline metabolites have potential for use as diagnostic indicators of breast cancer progression and can be non-invasively monitored in vivo by magnetic resonance spectroscopy. Both a 31P MR visible phosphomonoester peak (~3.9 ppm) and a total choline metabolite peak (~3.2 ppm) detected by 1H MRS are elevated in human breast tumors. As determined by tumor extract studies, the principle diagnostic component of these peaks is phosphocholine (PCho), the biosynthetic precursor to the membrane phospholipid, phosphatidylcholine (PtdCho). In extracts of human breast cells, PCho and the PtdCho breakdown product, glycerophosphocholine, are incrementally increased in cell lines of increasing metastatic potential. The ability to resolve and quantify PCho in vivo would improve the precision of this putative diagnostic tool. Additionally, determining the biochemical mechanisms underlying these metabolic perturbations will improve the understanding of metastatic cancer and could suggest potential molecular targets for drug development. Reported herein is the in vivo resolution and quantification of PCho in a breast cancer xenograft model in SCID mice via image-guided 31P MR spectroscopy, localized to small voxels. Also reported is the quantification of cytosolic and lipid metabolites in breast cancer cells of increasing cancer progression, and the identification of metabolites that differ amongst cell lines by degree of cancer aggressiveness. These metabolic differences are correlated with differences in expression of genes of the choline metabolic pathway. Gene expression changes following taxane therapy are also correlated with previously reported changes in choline metabolites following the same therapy in the same tumor model. Biochemical models explaining the metabolic changes are discussed. Corresponding author: Robert J. Gillies, The University of Arizona, Medical Research Building, P.O. Box 245215, Tucson, AZ 85724, USA, Tel: 520-626-5050, Fax: 520-626-5051. gillies@email.arizona. edu (R.J. Gillies). NIH Public Access Author Manuscript NMR Biomed. Author manuscript; available in PMC 2014 August 12. Published in final edited form as: NMR Biomed. 2009 January ; 22(1): 114–127. doi:10.1002/nbm.1318. N IH -P A A uhor M anscript N IH -P A A uhor M anscript N IH -P A A uhor M anscript
Postnatal Remodeling of GnRH-I Neurons: Towards Understanding the Mechanism of the Onset of Puberty

The concept that an increase in pulsatile release of gonadotropin-releasing hormone I (GnRH-I) triggers the onset of puberty has been firmly established: Knobil and collaborators (1) have shown that pulsatile infusion of GnRH-I results in precocious menarche followed by first ovulation in sexually immature female rhesus monkeys; GnRH-I analogs have been successfully used for treatments of precocious and delayed puberty in humans (2); We have shown that pulsatile GnRH-I release increases at the onset of puberty in female rhesus monkeys (3). However, the mechanism triggering the pubertal increase in GnRH-I release is still unclear (4). distribution
Perceived Income Inadequacy as a Predictor of Psychological Distress in Alzheimer’s Caregivers

The authors examined perceived income inadequacy as a predictor of self-reported depressive symptomatology and anxiety in the original sites of the Resources for Enhancing Alzheimer’s Caregiver Health I project. Perceived income inadequacy, self-reported household income, and control factors (e.g., subjective health) were entered into hierarchical regression analyses predicting psychological distress. Findings suggest that perceived income inadequacy and not household income significantly predicted more self-reported depressive symptomatology and greater self-reported anxiety. This supports previous findings that objective income measures alone are not adequate indicators of socioeconomic status in older adults.
WHAT PSYCHOLOGY HAS TO OFFER EDUCATION-NOW1'2

Some day, the question, "What does psychology have to offer education-now? " will be answered by psychologists with some measure of agreement. But at present the answer to this question depends almost entirely on the particular orientation of the individual to whom the question is put, for psychologists differ enormously in their conception of the subject matter and objectives of their discipline. Some say its subject matter is the domain of the mind, others, that it is the observable interaction of an individual's behavior with environmental events. When the question of the objectives of psychology arises, some claim them to be the understanding and explanation of psychological phenomena, others, the prediction and control of behavior. Psychologists also differ markedly in their choice of research methodology, with one segment stressing statistical designs that compare achievement of groups, and others emphasizing changes in the behavior of an individual organism. In addition, psychologists vary greatly in their approach to theory construction, with some favoring the hypothetico-deductive method, and others, the empirico-inductive procedure. In spite of these great individual variations, there would probably be three basic approaches to the question of what psychology lhas to offer education: that which would be taken by the great majority, that of the large minority, and that of the small minority. The great majority would probably say that psy-
Risk Strataification of Brugada Syndrome Revisited

Brugada syndrome is a widely recognized syndrome associated with a high risk of sudden death, particularly in males as they approach their third and fourth decades of life. It is characterized by an ST-segment elevation in the right precordial electrocardiogram leads, which is often concealed but can be unmasked with potent sodium channel blockers or by recordings obtained with the right precordial leads raised two intercostal spaces [1,2]. A consensus report published in 2002 delineated diagnostic criteria for the syndrome [3,4]. A second consensus conference report published in 2005 focused on risk stratification schemes and approaches to therapy [5,6].
DETERMINING THE POSITION AND STATE OF POST-IMPACT MOTION OF A MOTOR VEHICLE STRUCK ON ITS SIDE AT A ROAD INTERSECTION, BASED ON EXPERIMENTAL TESTS

The modelling, simulation, and reconstruction of road accidents are difficult processes, where experimental test results must be used at different stages of the work. Such data are needed e.g. to create or improve models of vehicle collision dynamics and to validate the results of computer simulations of vehicle motion during and after the collision. Within the work, experimental tests were carried out and the test results were utilized for determining specific values of the parameters that characterize the state of motion of the impacted vehicle during the collision and after the vehicle separation from each other. The test results make it possible to determine the initial values to be used in calculations of further free motion of the vehicle after a side impact. In particular, they enable defining the influence of the location of the point of impact against the vehicle side on the position and angle of rotation of the vehicle in relation to the global reference frame and on the value, direction, and sense of the vector of linear velocity of the centre of vehicle mass and the vector of angular velocity of the vehicle body around the vertical axis. These data constitute information sufficient to determine the energy of translation and rotation of the vehicle struck on its side at a road intersection and to carry out a simulation of the vehicle motion immediately following the collision
Rainfall-integrated traffic speed prediction using deep learning method

Traffic information prediction is one of the most essential studies for traffic research, operation and management. The successful prediction of traffic speed is increasingly significant for the benefits of both road users and traffic authorities. However, accurate prediction is challenging, due to the stochastic feature of traffic flow and shallow model structure. Furthermore, environmental factors, such as rainfall influence, should also be incorporated to improve accuracy. Inspired by deep learning, this paper investigates the performance of deep belief network (DBN) and long short-term memory (LSTM) to conduct short-term traffic speed prediction with the consideration of rainfall impact as a non-traffic input. The deep learning models have the ability to learn complex features of traffic flow pattern under various rainfall conditions. To validate the performance of rainfall-integrated DBN and LSTM, the traffic detector data from an arterial in Beijing are utilised for model training and testing. The experiment results indicate that with the combination input of speed and additional rainfall data, deep learning models have better prediction accuracy over other existing models, and also yields improvements over the models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time-series characteristics of traffic speed data.
An Iterated Block Particle Filter for Inference on Coupled Dynamic Systems With Shared and Unit-Specific Parameters

We consider inference for a collection of partially observed, stochastic, interacting, nonlinear dynamic processes. Each process is identified with a label called its unit, and our primary motivation arises in biological metapopulation systems where a unit corresponds to a spatially distinct sub-population. Metapopulation systems are characterized by strong dependence through time within a single unit and relatively weak interactions between units, and these properties make block particle filters an effective tool for simulation-based likelihood evaluation. Iterated filtering algorithms can facilitate likelihood maximization for simulation-based filters. We introduce an iterated block particle filter applicable when parameters are unit-specific or shared between units. We demonstrate this algorithm by performing inference on a coupled epidemiological model describing spatiotemporal measles case report data for twenty towns.
Population synthesis of AX J1745.6−2901 X-ray nova-type binaries with rapidly decreasing orbital periods

The neutron star low-mass X-ray binary (LMXB) AX J1745.6−2901 was detected with an anomalously fast decrease of its orbital period. The decreasing rate of the orbital period exceeds the contribution of all processes extracting angular momentum from the binary star in the standard model. Using the scenario machine code, we conducted a population synthesis study of X-ray novae with neutron stars to investigate a possible formation and evolution of such binaries. Such close LMXBs should experience a preceding common envelope stage, in which the magnetic fields of the low-mass main-sequence donor stars can be dramatically amplified. Our calculations show that the magnetic stellar wind of the optical companion can efficiently extract angular momentum from the binary systems, and produce the observed orbital-period derivatives of AX J1745.6−2901 and black hole LMXBs. The estimated values of the required magnetic field induction are the following: Bd ≈ 400 G (AX J1745.6−2901), Bd ≈ 1500 G (KV UMa), Bd ≈ 400 G (A0620−00) and Bd ≈ 1800 G (Nova Muscae). We successfully reproduced the current observational abundance of such anomalous neutron star X-ray novae, and computed the appropriate value of the parameter of magnetic braking λMSW (0.8−0.6 for Roche lobe filling stars and 0.4−0.15 for binaries with partial Roche lobe filling).
Sparse Fusion Mixture-of-Experts are Domain Generalizable Learners

Domain generalization (DG) aims at learning generalizable models under distribution shifts to avoid redundantly overﬁtting massive training data. Previous works with complex loss design and gradient constraint have not yet led to empirical success on large-scale benchmarks. In this work, we reveal the mixture-of-experts (MoE) model’s generalizability on DG by leveraging to distributively handle multiple aspects of the predictive features across domains. To this end, we propose Sparse Fusion Mixture-of-Experts (SF-MoE) , which incorporates sparsity and fusion mechanisms into the MoE framework to keep the model both sparse and predictive. SF-MoE has two dedicated modules: 1) sparse block and 2) fusion block, which disentangle and aggregate the diverse learned signals of an object, respectively. Extensive experiments demonstrate that SF-MoE is a domain-generalizable learner on large-scale benchmarks. It outperforms state-of-the-art counterparts by more than 2% across 5 large-scale DG datasets ( e.g., DomainNet), with the same or even lower computational costs. We further reveal the internal mechanism of SF-MoE from distributed representation perspective ( e.g., visual attributes). We hope this framework could facilitate future research to push generalizable object recognition to the real world. Our code and models will be released at SF-MoE .
Predictable forward performance processes in complete markets

We establish existence of Predictable Forward Performance Processes (PFPPs) in complete markets, which has been previously shown only in the binomial setting. Our market model can be a discrete-time or a continuous-time model, and the investment horizon can be finite or infinite. We show that the main step in construction of PFPPs is solving a one-period problem involving an integral equation, which is the counterpart of the functional equation found in the binomial case. Although this integral equation has been partially studied in the existing literature, we provide a new solution method using the Fourier transform for tempered distributions. We also provide closed-form solutions for PFPPs with inverse marginal functions that are completely monotonic and establish uniqueness of PFPPs within this class. We apply our results to two special cases. The first one is the binomial market and is included to relate our work to the existing literature. The second example considers a generalized Black-Scholes model which, to the best of our knowledge, is a new result.
An Old, Metal-rich Accreted Stellar Component in the Milky Way Stellar Disk

We study the possibility that the Milky Ways’ cool stellar disk includes mergers with ancient stars. Galaxies are understood to form in a hierarchical manner, where smaller (proto-)galaxies merge into larger ones. Stars in galaxies, like the Milky Way, contain in their motions and elemental abundance tracers of past events and can be used to disentangle merger remnants from stars that formed in the main galaxy. The merger history of the Milky Way is generally understood to be particularly easy to study in the stellar halo. The advent of the ESA astrometric satellite Gaia has enabled the detection of completely new structures in the halo such as the Gaia-Enceladus-Sausage. However, simulations also show that mergers may be important for the build-up of the cool stellar disks. Combining elemental abundances for ∼100 giant branch stars from APOGEE DR17 and astrometric data from Gaia we use elemental abundance ratios to find a hitherto unknown, old stellar component in the cool stellar disk in the Milky Way. We further identify a small sample of RR Lyrae variables with disk kinematics that also show the same chemical signature as the accreted red giant stars in the disk. These stars allow us to date the stars in the accreted component. We find that they are exclusively old.
Observational constraints on anisotropies for bouncing alternatives to inflation

We calculate how primordial anisotropies in the background space-time affect the evolution of cosmological perturbations for bouncing alternatives to inflation, like ekpyrosis and the matter bounce scenario. We find that the leading order effect of anisotropies in the contracting phase of the universe is to induce anisotropies in the cosmic microwave background with a very concrete form: a scale-invariant quadrupolar angular distribution. Sub-leading effects are the generation of higher-order moments in the angular distribution, as well as cross-correlations between scalar and tensor modes. We also find that observational constraints from the cosmic microwave background on the quadrupole moment provide strong bounds on allowed anisotropies for bouncing alternatives to inflation that are significantly more constraining than the bounds previously obtained using scaling arguments based on the conjectured Belinski-Khalatnikov-Lifshitz instability.
GRMHD Simulations of Neutron-star Mergers with Weak Interactions: r-process Nucleosynthesis and Electromagnetic Signatures of Dynamical Ejecta

Fast neutron-rich material ejected dynamically over ≲10 ms during the merger of a binary neutron star (BNS) can give rise to distinctive electromagnetic counterparts to the system’s gravitational-wave emission that serve as a “smoking gun” to distinguish between a BNS and an NS–black hole merger. We present novel ab initio modeling of the kilonova precursor and kilonova afterglow based on 3D general-relativistic magnetohydrodynamic simulations of BNS mergers with nuclear, tabulated, finite-temperature equations of state (EOSs), weak interactions, and approximate neutrino transport. We analyze dynamical mass ejection from 1.35–1.35 M ⊙ binaries, consistent with properties of the first observed BNS merger GW170817, using three nuclear EOSs that span the range of allowed compactness of 1.35 M ⊙-neutron stars. Nuclear reaction network calculations yield a robust second-to-third-peak r-process. We find few ×10−6 M ⊙ of fast (v > 0.6c) ejecta that give rise to broadband synchrotron emission on ∼years timescales, consistent with tentative evidence for excess X-ray/radio emission following GW170817. We find ≈2 × 10−5 M ⊙ of free neutrons that power a kilonova precursor on ≲ hours timescale. A boost in early UV/optical brightness by a factor of a few due to previously neglected relativistic effects, with enhancements up to ≲10 hr post-merger, is promising for future detection with UV/optical telescopes like Swift or ULTRASAT. We find that a recently predicted opacity boost due to highly ionized lanthanides at ≳70,000 K is unlikely to affect the early kilonova based on the obtained ejecta structures. Azimuthal inhomogeneities in dynamical ejecta composition for soft EOSs found here (“lanthanide/actinide pockets”) may have observable consequences for both early kilonova and late-time nebular emission.
Interpreting systems of continuity equations in spaces of probability measures through PDE duality

We introduce a notion of duality solution for a single or a system of transport equations in spaces of probability measures reminiscent of the viscosity solution notion for nonlinear parabolic equations. Our notion of solution by duality is, under suitable assumptions, equivalent to gradient flow solutions in case the single/system of equations has this structure. In contrast, we can deal with a quite general system of nonlinear nonlocal, diffusive or not, system of PDEs without any variational structure.
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning

Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure. Specifically, the loss landscape of MAML is much more complex with possibly more saddle points and local minimizers than its empirical risk minimization counterpart. To address this challenge, we leverage the recently invented sharpness-aware minimization and develop a sharpness-aware MAML approach that we term Sharp-MAML. We empirically demonstrate that Sharp-MAML and its computation-efficient variant can outperform the plain-vanilla MAML baseline (e.g., $+3\%$ accuracy on Mini-Imagenet). We complement the empirical study with the convergence rate analysis and the generalization bound of Sharp-MAML. To the best of our knowledge, this is the first empirical and theoretical study on sharpness-aware minimization in the context of bilevel learning. The code is available at https://github.com/mominabbass/Sharp-MAML.
Meta-Learning Transferable Parameterized Skills

We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efﬁcient learning in long-horizon tasks. We ﬁrst propose novel learning objectives—trajectory-centric diversity and smoothness—that allow an agent to meta-learn reusable parameterized skills. Our agent can use these learned skills to construct a temporally-extended parameterized-action Markov decision process, for which we propose a hierarchical actor-critic algorithm that aims to efﬁciently learn a high-level control policy with the learned skills. We empirically demonstrate that the proposed algorithms enable an agent to solve a complicated long-horizon obstacle-course environment 1 .
Quantized anomalous Hall resistivity achieved in molecular beam epitaxy-grown MnBi2Te4 thin films

ABSTRACT The intrinsic magnetic topological insulator MnBi2Te4 provides a feasible pathway to the high-temperature quantum anomalous Hall (QAH) effect as well as various novel topological quantum phases. Although quantized transport properties have been observed in exfoliated MnBi2Te4 thin flakes, it remains a big challenge to achieve molecular beam epitaxy (MBE)-grown MnBi2Te4 thin films even close to the quantized regime. In this work, we report the realization of quantized anomalous Hall resistivity in MBE-grown MnBi2Te4 thin films with the chemical potential tuned by both controlled in situ oxygen exposure and top gating. We find that elongated post-annealing obviously elevates the temperature to achieve quantization of the Hall resistivity, but also increases the residual longitudinal resistivity, indicating a picture of high-quality QAH puddles weakly coupled by tunnel barriers. These results help to clarify the puzzles in previous experimental studies on MnBi2Te4 and to find a way out of the big difficulty in obtaining MnBi2Te4 samples showing quantized transport properties.
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization

In this paper we present an extension of rotation-based iterative Gaussianization (RBIG). Although RBIG has been successfully applied to many tasks, it is limited to medium dimensionality data (on the order of a thousand dimensions). In images its application has been restricted to small image patches or isolated pixels, because the rotation operation in RBIG is based on principal or independent component analysis and these transformations are difficult to learn and scale. Here we present Convolutional RBIG: an extension that alleviates this issue by imposing that the rotation in RBIG is a convolution. We propose to learn convolutional rotations (i.e. orthonormal convolutions) by optimising for the reconstruction loss between the input and an approximate inverse of the transformation using the transposed convolution operation. Additionally, we suggest different regularizers in learning orthonormal convolutions. For example, imposing sparsity in the activations leads to a transformation that extends convolutional independent component analysis to multilayer architectures. We also highlight how statistical properties of the data, such as multivariate mutual information, can be obtained. We illustrate the behavior of the transform with a simple example of texture synthesis, and analyze its properties by visualizing the stimuli that maximize the response in certain feature and layer.