Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification of Mouse Cardiac Troponin I and Myosin Binding Protein C Phosphorylation by Liquid Chromatolography-Mass Spectrometry (LC-MS)

Heart failure is a major public health issue, with its prevalence estimated to be 6.5 million adults in the USA. Of the hospitalized heart failure (HF) cases, 50% are characterized by preserved ejection function (HFpEF). In HFpEF, the heart pumps a normal proportion of blood that enters it. However, thickening of the ventricular walls inhibits the chamber filling to normal volume. The direct basis of HFpEF is a slowed elongation of the cardiac muscle during the diastolic phase of the cardiac cycle. Elucidation of mechanisms that mediate relaxation of cardiac muscle could help understand the pathogenic mechanisms in HFpEF. Myocardial contraction and relaxation are tightly controlled processes that involve thick and thin filament regulatory proteins. β-Adrenergic signaling pathway is a major regulator of myocardial contraction and relaxation via the activation of protein kinase A (PKA). Two key myofilament proteins, troponin I (TnI) and myosin binding protein-C (MyBPC), are phosphorylated by PKA following β-adrenergic stimulation. The purpose of this thesis is to develop a liquid chromatography-mass spectrometry (LC-MS) method for the quantification of phosphorylation in TnI and MyBPC and measure the changes in the degree of phosphorylation in transverse-aortic constriction (TAC) mouse hearts, a model representing HFpEF, and sham (control) mouse hearts. The initial approach of the project was to develop a method for quantification of phosphopeptides using synthesized stable isotope labeled (SIL) peptides, both with and without phosphate modification. To accomplish this goal, a multiple reactions monitoring (MRM)-LC-MS method for the quantification of the synthesized SIL peptides was first developed. This method, using low picomole amounts, is applicable to researchers in the field using SIL peptides for quantification. However, when the SIL peptides were actually applied, we determined that there was a selective absorption of some phosphate peptides in the LC column, limiting the use of the SIL peptides for quantification. This result is also of general interest to others trying to identify phosphopeptides, not realizing that some peptides will go unmeasured. Thus, we returned to expanding an earlier method developed in our research group to quantify the degree of phosphorylation. Key to this work was the development of a quantification method directly from heart myofibrillar protein preparations without requiring isolation of individual proteins by gel electrophoresis. Using the LC-MS method developed, we quantified phosphorylation sites of TnI and MyBPC in the TAC and control mouse hearts. The phosphorylation measurements showed no significant difference in phosphorylation between the TAC and control mice, except for one site, S302 in MyBPC that had a 13% decrease in phosphorylation with TAC. We conclude that in our TAC model, PKA dysfunction may not play a role in the initial development of HFpEF.
Optimal control of multiple Markov switching stochastic system with application to portfolio decision

In this paper we set up an optimal control framework for a hybrid stochastic system with dual or multiple Markov switching diffusion processes, while Markov chains governing these switching diffusions are not identical as assumed by the existing literature. As an application and illustration of this model, we solve a portfolio choice problem for an investor facing financial and labor markets that are both regime switching. In continuous time context we combine two separate Markov chains into one synthetic Markov chain and derive its corresponding generator matrix, then state the HJB equations for the optimal control problem with the newly synthesized Markov switching diffusion. Furthermore, we derive explicit solutions and value functions under some reasonable specifications.
Few-shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning

Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB). However, the conventional neural program induction (NPI) approach exhibits uneven performance when the questions have different types, harboring inherently different characteristics, e.g., difficulty level. This paper proposes a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions. Our method quickly and effectively adapts the meta-learned programmer to new questions based on the most similar questions retrieved from the training data. The meta-learned policy is then used to learn a good programming policy, utilizing the trial trajectories and their rewards for similar questions in the support set. Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and meta-training on tasks constructed from only 1% of the training set. We have released our code at https://github.com/DevinJake/MRL-CQA.
A Greedy Algorithm for Quantizing Neural Networks

We propose a new computationally efficient method for quantizing the weights of pre-trained neural networks that is general enough to handle both multi-layer perceptrons and convolutional neural networks. Our method deterministically quantizes layers in an iterative fashion with no complicated re-training required. Specifically, we quantize each neuron, or hidden unit, using a greedy path-following algorithm. This simple algorithm is equivalent to running a dynamical system, which we prove is stable for quantizing a single-layer neural network (or, alternatively, for quantizing the first layer of a multi-layer network) when the training data are Gaussian. We show that under these assumptions, the quantization error decays with the width of the layer, i.e., its level of over-parametrization. We provide numerical experiments, on multi-layer networks, to illustrate the performance of our methods on MNIST and CIFAR10 data.
Graph Neural Network for Metal Organic Framework Potential Energy Approximation

Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal ions and organic linkers. MOFs play an important role in industrial applications such as gas separation, gas purification, and electrolytic catalysis. Important MOF properties such as potential energy are currently computed via techniques such as density functional theory (DFT). Although DFT provides accurate results, it is computationally costly. We propose a machine learning approach for estimating the potential energy of candidate MOFs, decomposing it into separate pair-wise atomic interactions using a graph neural network. Such a technique will allow high-throughput screening of candidates MOFs. We also generate a database of 50,000 spatial configurations and high-quality potential energy values using DFT.
Generating Radiology Reports via Memory-driven Transformer

Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment. Writing imaging reports is time-consuming and can be error-prone for inexperienced radiologists. Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is an essential task to apply artificial intelligence to the medical domain. In this paper, we propose to generate radiology reports with memory-driven Transformer, where a relational memory is designed to record key information of the generation process and a memory-driven conditional layer normalization is applied to incorporating the memory into the decoder of Transformer. Experimental results on two prevailing radiology report datasets, IU X-Ray and MIMIC-CXR, show that our proposed approach outperforms previous models with respect to both language generation metrics and clinical evaluations. Particularly, this is the first work reporting the generation results on MIMIC-CXR to the best of our knowledge. Further analyses also demonstrate that our approach is able to generate long reports with necessary medical terms as well as meaningful image-text attention mappings.
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data

Developing high-performing predictive models for large tabular data sets is a challenging task. Neural architecture search (NAS) is an AutoML approach that generates and evaluates multiple neural networks with different architectures concurrently to automatically discover an high performing model. A key issue in NAS, particularly for large data sets, is the large computation time required to evaluate each generated architecture. While data-parallel training has the potential to address this issue, a straightforward approach can result in significant loss of accuracy. To that end, we develop AgEBO-Tabular, which combines Aging Evolution (AE) to search over neural architectures and asynchronous Bayesian optimization (BO) to search over hyperparameters to adapt data-parallel training. We evaluate the efficacy of our approach on two large predictive modeling tabular data sets from the Exascale Computing Project-CANcer Distributed Learning Environment (ECP-CANDLE).
Eliciting Knowledge from Language Models Using Automatically Generated Prompts

The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the-blanks problems (e.g., cloze tests) is a natural approach for gauging such knowledge, however, its usage is limited by the manual effort and guesswork required to write suitable prompts. To address this, we develop AutoPrompt, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided search. Using AutoPrompt, we show that masked language models (MLMs) have an inherent capability to perform sentiment analysis and natural language inference without additional parameters or finetuning, sometimes achieving performance on par with recent state-of-the-art supervised models. We also show that our prompts elicit more accurate factual knowledge from MLMs than the manually created prompts on the LAMA benchmark, and that MLMs can be used as relation extractors more effectively than supervised relation extraction models. These results demonstrate that automatically generated prompts are a viable parameter-free alternative to existing probing methods, and as pretrained LMs become more sophisticated and capable, potentially a replacement for finetuning.
Data compression and covariance matrix inspection: Cosmic shear

Covariance matrices are among the most difficult pieces of end-to-end cosmological analyses. In principle, for two-point functions, each component involves a four-point function, and the resulting covariance often has hundreds of thousands of elements. We investigate various compression mechanisms capable of vastly reducing the size of the covariance matrix in the context of cosmic shear statistics. This helps identify which of its parts are most crucial to parameter estimation. We start with simple compression methods, by isolating and "removing" 200 modes associated with the lowest eigenvalues, then those with the lowest signal-to-noise ratio, before moving on to more sophisticated schemes like compression at the tomographic level and, finally, with the Massively Optimized Parameter Estimation and Data compression (MOPED). We find that, while most of these approaches prove useful for a few parameters of interest, like $\Omega_m$, the simplest yield a loss of constraining power on the intrinsic alignment (IA) parameters as well as $S_8$. For the case considered -- cosmic shear from the first year of data from the Dark Energy Survey -- only MOPED was able to replicate the original constraints in the 16-parameter space. Finally, we apply a tolerance test to the elements of the compressed covariance matrix obtained with MOPED and confirm that the IA parameter $A_{\mathrm{IA}}$ is the most susceptible to inaccuracies in the covariance matrix.
Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis

Abstract Background and study aims Recently, a growing body of evidence has been amassed on evaluation of artificial intelligence (AI) known as deep learning in computer-aided diagnosis of gastrointestinal lesions by means of convolutional neural networks (CNN). We conducted this meta-analysis to study pooled rates of performance for CNN-based AI in diagnosis of gastrointestinal neoplasia from endoscopic images. Methods Multiple databases were searched (from inception to November 2019) and studies that reported on the performance of AI by means of CNN in the diagnosis of gastrointestinal tumors were selected. A random effects model was used and pooled accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Pooled rates were categorized based on the gastrointestinal location of lesion (esophagus, stomach and colorectum). Results Nineteen studies were included in our final analysis. The pooled accuracy of CNN in esophageal neoplasia was 87.2 % (76–93.6) and NPV was 92.1 % (85.9–95.7); the accuracy in lesions of stomach was 85.8 % (79.8–90.3) and NPV was 92.1 % (85.9–95.7); and in colorectal neoplasia the accuracy was 89.9 % (82–94.7) and NPV was 94.3 % (86.4–97.7). Conclusions Based on our meta-analysis, CNN-based AI achieved high accuracy in diagnosis of lesions in esophagus, stomach, and colorectum.
Systolic Computing on GPUs for Productive Performance

We propose a language and compiler to productively build high-performance {\it software systolic arrays} that run on GPUs. Based on a rigorous mathematical foundation (uniform recurrence equations and space-time transform), our language has a high abstraction level and covers a wide range of applications. A programmer {\it specifies} a projection of a dataflow compute onto a linear systolic array, while leaving the detailed implementation of the projection to a compiler; the compiler implements the specified projection and maps the linear systolic array to the SIMD execution units and vector registers of GPUs. In this way, both productivity and performance are achieved in the same time. This approach neatly combines loop transformations, data shuffling, and vector register allocation into a single framework. Meanwhile, many other optimizations can be applied as well; the compiler composes the optimizations together to generate efficient code. We implemented the approach on Intel GPUs. This is the first system that allows productive construction of systolic arrays on GPUs. We allow multiple projections, arbitrary projection directions and linear schedules, which can express most, if not all, systolic arrays in practice. Experiments with 1- and 2-D convolution on an Intel GEN9.5 GPU have demonstrated the generality of the approach, and its productivity in expressing various systolic designs for finding the best candidate. Although our systolic arrays are purely software running on generic SIMD hardware, compared with the GPU's specialized, hardware samplers that perform the same convolutions, some of our best designs are up to 59\% faster. Overall, this approach holds promise for productive high-performance computing on GPUs.
What's in a Loss Function for Image Classification?

It is common to use the softmax cross-entropy loss to train neural networks on classification datasets where a single class label is assigned to each example. However, it has been shown that modifying softmax cross-entropy with label smoothing or regularizers such as dropout can lead to higher performance. This paper studies a variety of loss functions and output layer regularization strategies on image classification tasks. We observe meaningful differences in model predictions, accuracy, calibration, and out-of-distribution robustness for networks trained with different objectives. However, differences in hidden representations of networks trained with different objectives are restricted to the last few layers; representational similarity reveals no differences among network layers that are not close to the output. We show that all objectives that improve over vanilla softmax loss produce greater class separation in the penultimate layer of the network, which potentially accounts for improved performance on the original task, but results in features that transfer worse to other tasks.
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers

The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model. In order to maintain an up-to-date training set, there is a need to continuously collect benign and malicious files from a wide range of sources, providing an exploitable target to attackers. In this study, we show how an attacker can launch a sophisticated and efficient poisoning attack targeting the dataset used to train a malware classifier. The attacker's ultimate goal is to ensure that the model induced by the poisoned dataset will be unable to detect the attacker's malware yet capable of detecting other malware. As opposed to other poisoning attacks in the malware detection domain, our attack does not focus on malware families but rather on specific malware instances that contain an implanted trigger, reducing the detection rate from 99.23% to 0% depending on the amount of poisoning. We evaluate our attack on the EMBER dataset with a state-of-the-art classifier and malware samples from VirusTotal for end-to-end validation of our work. We propose a comprehensive detection approach that could serve as a future sophisticated defense against this newly discovered severe threat.
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data Augmentation.

Extracting the desired speech from a mixture is a meaningful and challenging task. The end-to-end DNN-based methods, though attractive, face the problem of generalization. In this paper, we explore a sequential approach for target speech extraction by combining blind source separation (BSS) with the x-vector based speaker recognition (SR) module. Two promising BSS methods based on source independence assumption, independent low-rank matrix analysis (ILRMA) and multi-channel variational autoencoder (MVAE), are utilized and compared. ILRMA employs nonnegative matrix factorization (NMF) to capture spectral structures of source signals and MVAE utilizes the strong modeling power of deep neural networks (DNN). However, the investigation of MVAE has been limited to the training with very few speakers and the speech signals of test speakers are usually included. We extend the training of MVAE using clean speech signals of 500 speakers to evaluate its generalization to unseen speakers. To improve the correct extraction rate, two data augmentation strategies are implemented to train the SR module. The performance of the proposed cascaded approach is investigated with test data constructed with real room impulse responses under varied environments.
Cross-continental analysis of coastal biodiversity change

Whereas the anthropogenic impact on marine biodiversity is undebated, the quantification and prediction of this change are not trivial. Simple traditional measures of biodiversity (e.g. richness, diversity indices) do not capture the magnitude and direction of changes in species or functional composition. In this paper, we apply recently developed methods for measuring biodiversity turnover to time-series data of four broad taxonomic groups from two coastal regions: the southern North Sea (Germany) and the South African coast. Both areas share geomorphological features and ecosystem types, allowing for a critical assessment of the most informative metrics of biodiversity change across organism groups. We found little evidence for directional trends in univariate metrics of diversity for either the effective number of taxa or the amount of richness change. However, turnover in composition was high (on average nearly 30% of identities when addressing presence or absence of species) and even higher when taking the relative dominance of species into account. This turnover accumulated over time at similar rates across regions and organism groups. We conclude that biodiversity metrics responsive to turnover provide a more accurate reflection of community change relative to conventional metrics (absolute richness or relative abundance) and are spatially broadly applicable. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.
The ‘plantspec’ r package: A tool for spectral analysis of plant stoichiometry

Data on plant elemental stoichiometry are essential for ecologists investigating ecosystem function, nutrient cycling, plant–herbivore interactions and leaf economics. A cost‐effective approach to leaf elemental analysis is near‐infrared spectroscopy (NIRS), whereby components such as carbon (C), nitrogen (N), phosphorus (P) and potassium (K) can be predicted using NIR spectra from plant material. However, a major factor limiting the widespread use of NIRS by plant ecologists is the availability of free software and calibration data for developing plant chemistry datasets from spectra. Here, we present a pair of companion r packages (‘plantspec’ and ‘plantspecDB’) that satisfy this need by providing an entire workflow, from spectra to predicted elemental data. The main r package, called ‘plantspec’, allows users to manipulate spectral data and develop custom partial least square (PLS) models for predicting the elemental composition of their own datasets. The second package, ‘plantspecDB’, provides NIR spectra, and matched elemental data obtained with standard analytical techniques, for herbaceous samples collected from 18 grassland sites around the world, primarily sourced from the Nutrient Network experiment. The ‘plantspecDB’ data package also provides calibrations for C, N, P and K for bulk samples and separated by plant functional type: grasses, forbs and legumes. Finally, we provide an example of the ‘plantspec’ workflow, and external validation of these models, and an example application to the stoichiometry of East African grasses as it relates to their evolutionary relationships. The r package ‘plantspec’, and our associated workflow, provides a template to produce robust calibration models and increase the application of NIR in ecology. Although, we focus on plant stoichiometry, the workflow presented here can be applied broadly for a broad range of applications, including soils, remote sensing data, solutions and tissues. Finally, our global dataset provides unprecedented access to NIR calibration data as they pertain to tissue concentrations of key plant limiting elements.
Development of MR elastography method to characterize the elastic property of the sterno-cleido-mastoid (SCM) muscle

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Development of MR Elastography method to characterize the elastic property of the sterno-cleido-mastoid (SCM) muscle P. Pouletaut, F. Charleux, B. Devauchelle, J.-M. Constans, R. Ternifi, S Boussida, A Hamaoui, C Krzisch, S. Bensamoun