Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of magnetic resonance spectroscopy to differentiate high-grade gliomas from metastases

This study is to measure the diagnostic examination quality of magnetic resonance spectroscopy in differentiating high-grade gliomas from metastases. PubMed, Embase, and Chinese Biomedical databases were systematically searched for relevant studies published through 10 July 2016. Based on the data from eligible studies, heterogeneity and threshold effect tests were performed; pooled sensitivity, specificity, and areas under summary receiver-operating characteristic curve of magnetic resonance spectroscopy were calculated. Finally, seven studies with a total of 261 patients were included. Quantitative synthesis of studies showed that pooled sensitivity/specificity of Cho/NAA and Cho/Cr ratio in peritumoral region was 0.85 (95% confidence interval: 0.79–0.90)/0.93 (95% confidence interval: 0.80–0.99) and 0.86 (95% confidence interval: 0.76–0.92)/0.86 (95% confidence interval: 0.73–0.94). The area under the curve of the summary receiver-operating characteristic curve was 0.95 and 0.90. Pooled sensitivity, specificity, and area under the curve of magnetic resonance spectroscopy to identify high-grade gliomas from metastases were 0.85 (95% confidence interval: 0.79–0.90), 0.84 (95% confidence interval: 0.75–0.90), and 0.90, respectively. We concluded that magnetic resonance spectroscopy demonstrated moderate diagnostic performance in distinguishing high-grade gliomas from metastases. Furthermore, Cho/NAA ratio showed higher specificity and higher value of area under the curve than Cho/Cr ratio in peritumoral region. We suggest that Cho/NAA ratio of peritumoral region should be used to improve diagnostic accuracy of magnetic resonance spectroscopy for differentiating high-grade gliomas from metastases.
Theory of nonexponential NMR signal decay in liver with iron overload or superparamagnetic iron oxide particles

A quantitative theory is proposed for the nonexponential NMR proton signal decay observed in liver with iron overload or superparamagnetic iron oxide particles. This effect occurs for Carr‐Purcell‐Meiboom‐Gill (CPMG) sequences and is argued to be a direct consequence of the strong magnetic field inhomogeneities generated by the iron, rather than being due to tissue compartments. An approximate mathematical form is given for the signal decay, which is fit to experimental data for samples of rat liver with iron oxide particles, for samples of marmoset liver with hemosiderosis, and for in vivo human liver with hereditary hemochromatosis. The fitting parameters obtained are consistent with the pattern of iron deposition determined from histology. For the case of hereditary hemochromatosis, a good correlation is found between a parameter characterizing the nonexponential decay and the iron concentration. Implications for practical MR quantification of hepatic iron are discussed. Magn Reson Med 47:1131–1138, 2002. © 2002 Wiley‐Liss, Inc.
Learning Task-specific Representation for Novel Words in Sequence Labeling

Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance. This is commonly referred to as the out-of-vocabulary (OOV) problem. In this work, we address the OOV problem in sequence labeling using only training data of the task. To this end, we propose a novel method to predict representations for OOV words from their surface-forms (e.g., character sequence) and contexts. The method is specifically designed to avoid the error propagation problem suffered by existing approaches in the same paradigm. To evaluate its effectiveness, we performed extensive empirical studies on four part-of-speech tagging (POS) tasks and four named entity recognition (NER) tasks. Experimental results show that the proposed method can achieve better or competitive performance on the OOV problem compared with existing state-of-the-art methods.
Unitary designs from statistical mechanics in random quantum circuits.

Random quantum circuits are proficient information scramblers and efficient generators of randomness, rapidly approximating moments of the unitary group. We study the convergence of local random quantum circuits to unitary $k$-designs. Employing a statistical mechanical mapping, we give an exact expression of the distance to forming an approximate design as a lattice partition function. In the statistical mechanics model, the approach to randomness has a simple interpretation in terms of domain walls extending through the circuit. We analytically compute the second moment, showing that random circuits acting on $n$ qudits form approximate 2-designs in $O(n)$ depth, as is known. Furthermore, we argue that random circuits form approximate unitary $k$-designs in $O(nk)$ depth and are thus essentially optimal in both $n$ and $k$. We can show this in the limit of large local dimension, but more generally rely on a conjecture about the dominance of certain domain wall configurations.
Cell proteins bind specifically to West Nile virus minus-strand 3' stem-loop RNA

The first 96 nucleotides of the 5'noncoding region (NCR) of West Nile virus (WNV) genomic RNA were previously reported to form thermodynamically predicted stem-loop (SL) structures that are conserved among flaviviruses. The complementary minus-strand 3' NCR RNA, which is thought to function as a promoter for the synthesis of plus-strand RNA, forms a corresponding predicted SL structure. RNase probing of the WNV 3' minus-strand stem-loop RNA [WNV (-)3' SL RNA] confirmed the existence of a terminal secondary structure. RNA-protein binding studies were performed with BHK S100 cytoplasmic extracts and in vitro-synthesized WNV (-)3' SL RNA as the probe. Three RNA-protein complexes (complexes 1,2, and 3) were detected by a gel mobility shift assay, and the specificity of the RNA-protein interactions was confirmed by gel mobility shift and UV-induced cross-linking competition assays. Four BHK cell proteins with molecular masses of 108, 60, 50, and 42 kDa were detected by UV-induced cross-linking to the WNV (-)3' SL RNA. A preliminary mapping study indicated that all four proteins bound to the first 75 nucleotides of the WNV 3' minus-strand RNA, the region that contains the terminal SL. A flavivirus resistance phenotype was previously shown to be inherited in mice as a single, autosomal dominant allele. The efficiencies of infection of resistant cells and susceptible cells are similar, but resistant cells (C3H/RV) produce less genomic RNA than congenic, susceptible cells (C3H/He). Three RNA-protein complexes and four UV-induced cross-linked cell proteins with mobilities identical to those detected in BHK cell extracts with the WNV (-)3' SL RNA were found in both C3H/RV and C3H/He cell extracts. However, the half-life of the C3H/RV complex 1 was three times longer than that of the C3H/He complex 1. It is possible that the increased binding activity of one of the resistant cell proteins for the flavivirus minus-strand RNA could result in a reduced synthesis of plus-strand RNA as observed with the flavivirus resistance phenotype.
Effect of titrated parenteral nutrition on body composition after allogeneic hematopoietic stem cell transplantation in children: a double-blind, randomized, multicenter trial.

BACKGROUND Children undergoing hematopoietic stem cell transplantation (HSCT) often require parenteral nutrition (PN) to optimize caloric intake. Standard approaches to nutritional supplementation provide 130-150% of estimated energy expenditure, but resting energy expenditure (REE) may be lower than expected after HSCT. Provision of PN exceeding energy needs may lead to overfeeding and associated complications. OBJECTIVE We conducted a blinded, randomized, controlled, multicenter trial in children undergoing HSCT to determine the effect on body composition of 2 different approaches of nutrition support: standard amounts of energy from PN (130-150% of REE) compared with PN titrated to match measured REE. DESIGN Twenty-six children undergoing HSCT were randomly assigned to standard or titrated PN. Energy intake was monitored until day 30 after HSCT. Body-composition and anthropometric measures were obtained through day 100. The primary outcome variable was percentage body fat (%BF) measured by dual energy X-ray absorptiometry. RESULTS The estimated change in %BF from baseline to day 30 was 1.2 ± 0.5% in the standard group and 0.1 ± 0.5% in the experimental group, but the overall time course of %BF did not differ significantly by treatment (P = 0.39 for time × treatment interaction). A profound loss of lean body mass (LBM) occurred in both groups during the intervention period and persisted through day 100. CONCLUSIONS Parenteral energy intake titrated to energy expenditure does not result in a lower accumulation of BF than does standard energy intake. Neither titrated nor standard PN regimens during HSCT preserve LBM. Alternative approaches to preserve LBM are needed. This trial is registered at clinicaltrials.gov as 00115258.
The Self-gravitating Gas Fraction and the Critical Density for Star Formation

We analytically calculate the star formation efficiency and dense self-gravitating gas fraction in the presence of magneto-gravo-turbulence using the model of Burkhart, which employs a piecewise lognormal and powerlaw density probability distribution function (PDF). We show that the PDF transition density from lognormal to powerlaw forms is a mathematically motivated critical density for star formation and can be physically related to the density where the Jeans length is comparable to the sonic length, i.e., the post-shock critical density for collapse. When the PDF transition density is taken as the critical density, the instantaneous star formation efficiency (ϵinst) and depletion time (τdepl) can be calculated from the dense self-gravitating gas fraction represented as the fraction of gas in the PDF powerlaw tail. We minimize the number of free parameters in the analytic expressions for ϵinst and τdepl by using the PDF transition density instead of a parameterized critical density for collapse, and thus provide a more direct pathway for comparison with observations. We test the analytic predictions for the transition density and self-gravitating gas fraction against AREPO moving mesh gravo-turbulent simulations and find good agreement. We predict that, when gravity dominates the density distribution in the star-forming gas, the star formation efficiency should be weakly anti-correlated with the sonic Mach number while the depletion time should increase with increasing sonic Mach number. The star formation efficiency and depletion time depend primarily on the dense self-gravitating gas fraction, which in turn depends on the interplay of gravity, turbulence, and stellar feedback. Our model prediction is in agreement with recent observations, such as the M51 PdBI Arcsecond Whirlpool Survey.
Learning Navigation Subroutines by Watching Videos

Hierarchies are an effective way to boost sample efficiency in reinforcement learning, and computational efficiency in classical planning. However, acquiring hierarchies via hand-design (as in classical planning) is suboptimal, while acquiring them via end-to-end reward based training (as in reinforcement learning) is unstable and still prohibitively expensive. In this paper, we pursue an alternate paradigm for acquiring such hierarchical abstractions (or visuo-motor subroutines), via use of passive first person observation data. We use an inverse model trained on small amounts of interaction data to pseudo-label the passive first person videos with agent actions. Visuo-motor subroutines are acquired from these pseudo-labeled videos by learning a latent intent-conditioned policy that predicts the inferred pseudo-actions from the corresponding image observations. We demonstrate our proposed approach in context of navigation, and show that we can successfully learn consistent and diverse visuo-motor subroutines from passive first-person videos. We demonstrate the utility of our acquired visuo-motor subroutines by using them as is for exploration, and as sub-policies in a hierarchical RL framework for reaching point goals and semantic goals. We also demonstrate behavior of our subroutines in the real world, by deploying them on a real robotic platform. Project website with videos, code and data: https://ashishkumar1993.github. io/subroutines/.
Beck Depression Inventory II: determination and comparison of its diagnostic accuracy in cardiac outpatients

Objectives To evaluate the impact of covariates on performance accuracy of the Beck Depression Inventory II (BDI-II) and to determine the optimal cut-off score for the BDI-II in cardiac outpatients. Differences of optimal cut-off scores were also verified across covariate subgroups. Design and setting Prospective cross-sectional study at the Department of Nuclear Medicine of the Montreal Heart Institute (Quebec, Canada). Methods A total of 750 adult cardiac outpatients (mean ± SD age 58 ± 10 years, 31% women) completed the BDI-II and the Primary Care Evaluation of Mental Disorders (PRIME-MD; a psychiatric interview used as the reference standard for determining diagnosis of major depressive disorder). The receiver operating characteristics (ROC) curve of the BDI-II was adjusted for age, sex, level of education, smoking status, obesity, anxiety disorder, psychotropic medication, and history of coronary artery disease. The ROC analyses were conducted to determine optimal cut-off scores. Results Forty-two (6%) patients met criteria for current major depressive disorder according to the PRIME-MD. After adjusted for covariates, the area under the ROC curve was significantly smaller than the unadjusted curve (0.76, 95% CI 0.66 to 0.85 vs. 0.84, 95% CI 0.77 to 0.89; ΔAUC = −0.07, 95% CI −0.13 to −0.02). While the optimal cut-off score was 10 for the total sample (sensitivity 83%, specificity 73%), the analyses indicated different cut-off scores across covariate subgroups: e.g. sex (women 13; men 10), and anxiety disorders (yes 15; no 10). Conclusions BDI-II is a good screening instrument for depression in cardiac outpatients. However, the present results suggest that covariates can affect the classification accuracy of the BDI-II’s original recommended cut-off score. Scholars and clinicians should be aware of the principle that a screening score established in one population may not be relevant to another.
Development and characterization of detectors for large area application in neutron scattering and small area application in neutron reflectometry

Neutron scattering techniques offer a unique combination of structural and the dynamic information of atomic and molecular systems over a wide range of distances and times. The increasing complexity in science investigations driven by technological advances is reflected in the studies of neutron scattering science, which enforces a diversification and an improvement of experimental tools, from the instrument design to the detector performance. It calls as well for more advanced data analysis and modelling. The improvements in resolution, count rate and signal-to-background ratio, achievable with the new instrumentations, also drive the research of alternative technologies to replace the 3He-based detector technology unable to fulfil the requirement of increasing performance. Two solution have been studied: a boron-10-based gaseous detector, the Multi-Blade and a solid-state Si-Gd detector. Both solution are suitable alternatives for neutron detection, able to meet the demands of high performance. It has been shown not only the technical characteristic of the devices, but how the science can profit from the better performance of these new detector technologies in real experimental condition.
Jordan chains of elliptic partial differential operators and Dirichlet-to-Neumann maps

Let $\Omega \subset {\bf R}^d$ be a bounded open set with Lipschitz boundary $\Gamma$. It will be shown that the Jordan chains of m-sectorial second-order elliptic partial differential operators with measurable coefficients and (local or non-local) Robin boundary conditions in $L_2(\Omega)$ can be characterized with the help of Jordan chains of the Dirichlet-to-Neumann map and the boundary operator from $H^{1/2}(\Gamma)$ into $H^{-1/2}(\Gamma)$. This result extends the Birman--Schwinger principle in the framework of elliptic operators for the characterization of eigenvalues, eigenfunctions and geometric eigenspaces to the complete set of all generalized eigenfunctions and algebraic eigenspaces.
Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings

Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution. Prior work has shown multi-relational embeddings can encode semantic knowledge graphs to promote generalizability and scalability, but only within a batched learning paradigm. We present Incremental Semantic Initialization (ISI), an incremental learning approach that enables novel semantic concepts to be initialized in the embedding in relation to previously learned embeddings of semantically similar concepts. We evaluate ISI on mined AI2Thor and MatterPort3D datasets; our experiments show that on average ISI improves immediate query performance by 41.4%. Additionally, ISI methods on average reduced the number of epochs required to approach model convergence by 78.2%.
Morphology and kinetics of asphalt binder microstructure at gas, liquid and solid interfaces

We combined optical and atomic force microscopy to observe morphology and kinetics of microstructures (typically referred to as bees) that formed at free surfaces of unmodified Performance Graded (PG) 64‐22 asphalt binders upon cooling from 150°C to room temperature (RT) at 5°C min–1, and changes in these microstructures when the surface was terminated with a transparent solid (glass) or liquid (glycerol) overlayer. The main findings are: (1) at free binder surfaces, wrinkled microstructures started to form near the crystallization temperature (∼45°C) of saturates such as wax observed by differential scanning calorimetry, then grew to ∼5 µm diameter, ∼25 nm wrinkle amplitude and 10–30% surface area coverage upon cooling to RT, where they persisted indefinitely without observable change in shape or density. (2) Glycerol coverage of the binder surface during cooling reduced wrinkled area and wrinkle amplitude three‐fold compared to free binder surfaces upon initial cooling to RT; continued glycerol coverage at RT eliminated most surface microstructures within ∼4 h. (3) No surface microstructures were observed to form at binder surfaces covered with glass. (4) Submicron bulk microstructures were observed by near‐infrared microscopy beneath the surfaces of all binder samples, with size, shape and density independent of surface coverage. No tendency of such structures to float to the top or sink to the bottom of mm‐thick samples was observed. (5) We attribute the dependence of surface wrinkling on surface coverage to variation in interface tension, based on a thin‐film continuum mechanics model.
Pre-training Graph Neural Networks

Many applications of machine learning in science and medicine, including molecular property and protein function prediction, can be cast as problems of predicting some properties of graphs, where having good graph representations is critical. However, two key challenges in these domains are (1) extreme scarcity of labeled data due to expensive lab experiments, and (2) needing to extrapolate to test graphs that are structurally different from those seen during training. In this paper, we explore pre-training to address both of these challenges. In particular, working with Graph Neural Networks (GNNs) for representation learning of graphs, we wish to obtain node representations that (1) capture similarity of nodes’ network neighborhood structure, (2) can be composed to give accurate graph-level representations, and (3) capture domain-knowledge. To achieve these goals, we propose a series of methods to pre-train GNNs at both the node-level and the graph-level, using both unlabeled data and labeled data from related auxiliary supervised tasks. We perform extensive evaluation on two applications, molecular property and protein function prediction. We observe that performing only graph-level supervised pre-training often leads to marginal performance gain or even can worsen the performance compared to non-pre-trained models. On the other hand, effectively combining both node-and graph-level pre-training techniques signiﬁcantly improves generalization to out-of-distribution graphs, consistently outperforming non-pre-trained GNNs across 8 datasets in molecular property prediction (resp. 40 tasks in protein function prediction), with the average ROC-AUC improvement of 7.2% (resp. 11.7%).
Attention Based Pruning for Shift Networks

In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods. However, it is often required to assemble a large number of CLs, each containing thousands of parameters, in order to reach state-of-the-art accuracy, thus resulting in complex and demanding systems that are poorly fitted to resource-limited devices. Recently, methods have been proposed to replace the generic convolution operator by the combination of a shift operation and a simpler $1\times 1$ convolution. The resulting block, called Shift Layer (SL), is an efficient alternative to CLs in the sense it allows to reach similar accuracies on various tasks with faster computations and fewer parameters. In this contribution, we introduce Shift Attention Layers (SALs), which extend SLs by using an attention mechanism that learns which shifts are the best at the same time the network function is trained. We demonstrate SALs are able to outperform vanilla SLs (and CLs) on various object recognition benchmarks while significantly reducing the number of float operations and parameters for the inference.
Towards Efficient Detection and Optimal Response against Sophisticated Opponents

Multiagent algorithms often aim to accurately predict the behaviors of other agents and find a best response accordingly. Previous works usually assume an opponent uses a stationary strategy or randomly switches among several stationary ones. However, an opponent may exhibit more sophisticated behaviors by adopting more advanced reasoning strategies, e.g., using a Bayesian reasoning strategy. This paper proposes a novel approach called Bayes-ToMoP which can efficiently detect the strategy of opponents using either stationary or higher-level reasoning strategies. Bayes-ToMoP also supports the detection of previously unseen policies and learning a best-response policy accordingly. We provide a theoretical guarantee of the optimality on detecting the opponent's strategies. We also propose a deep version of Bayes-ToMoP by extending Bayes-ToMoP with DRL techniques. Experimental results show both Bayes-ToMoP and deep Bayes-ToMoP outperform the state-of-the-art approaches when faced with different types of opponents in two-agent competitive games.
Taking the path computably traveled

We define a real $A$ to be low for paths in Baire space (or Cantor space) if every $\varPi ^0_1$ class with an $A$-computable element has a computable element. We prove that lowness for paths in Baire space and lowness for paths in Cantor space are equivalent and, furthermore, that these notions are also equivalent to lowness for isomorphism.
Impaired regulation of the firing pattern of single motor units

The firing pattern of single motor units from the anterior tibial muscle was studied during constant isometric contraction. Recordings were made with bipolar, fine‐wire electrodes from 4 normal subjects and from 10 patients with spasticity. A microcomputer was used to classify the motor unit potentials automatically and to measure the interspike intervals. The patients had reduced firing frequencies and reduced variability between neighbor intervals. Seven patients with spasticity due to supraspinal lesions had positive correlation between neighbor intervals.