Miracle-dz/UniFAR-FTUs huggingface dataset:
Significant changes in hepatic transcriptome and circulating miRNAs are associated with diet‐induced metabolic syndrome in apoE3L.CETP mice

Long‐term exposure to excess dietary fat leads to obesity and the metabolic syndrome (MetS). The purpose of the present study was to identify global changes in liver gene expression and circulating miRNAs in a humanized mouse model of diet‐induced MetS. Male apoE3L.CETP mice received a high‐fat diet (HFD) or a low‐fat diet (LFD) for different time periods and the progression of MetS pathology was monitored. A separate group of mice was divided into responders (R) or nonresponders (NR) and received HFD for 16 weeks. We found that mice receiving the HFD developed manifestations of MetS and displayed an increasing number of differentially expressed transcripts at 4, 8, and 12 weeks compared with mice receiving the LFD. Significantly changed genes were functionally annotated to metabolic diseases and pathway analysis revealed the downregulation of genes in cholesterol and fatty acid biosynthesis and upregulation of genes related to lipid droplet formation, which was in line with the development of hepatic steatosis. In the serum of the apoE3L.CETP mice we identified three miRNAs that were upregulated specifically in the HFD group. We found that responder mice have a distinct gene signature that differentiates them from nonresponders. Comparison of the two diet intervention studies revealed a limited number of common differentially expressed genes but the expression of these common genes was affected in a similar way in both studies. In conclusion, the characteristic hepatic gene signatures and serum miRNAs identified in the present study provide novel insights to MetS pathology and could be exploited for diagnostic or therapeutic purposes.
Genomes & Developmental Control R11: a cis-regulatory node of the sea urchin embryo gene network that controls early expression of SpDelta in micromeres

A gene regulatory network (GRN) controls the process by which the endomesoderm of the sea urchin embryo is specified. In this GRN, the program of gene expression unique to the skeletogenic micromere lineage is set in train by activation of the pmar1 gene. Through a double repression system, this gene is responsible for localization of expression of downstream regulatory and signaling genes to cells of this lineage. One of these genes, delta, encodes a Notch ligand, and its expression in the right place and time is crucial to the specification of the endomesoderm. Here we report a cis-regulatory element R11 that is responsible for localizing the expression of delta by means of its response to the pmar1 repression system. R11 was identified as an evolutionarily conserved genomic sequence located about 13 kb downstream of the last exon of the delta gene. We demonstrate here that this cis-regulatory element is able to drive the expression of a reporter gene in the same cells and at the same time that the endogenous delta gene is expressed, and that temporally, spatially, and quantitatively it responds to the pmar1 repression system just as predicted for the delta gene in the endomesoderm GRN. This work illustrates the application of cis-regulatory analysis to the validation of predictions of the GRN model. In addition, we introduce new methodological tools for quantitative measurement of the output of expression constructs that promise to be of general value for cisregulatory analysis in sea urchin embryos. D 2004 Elsevier Inc. All rights reserved.
Rapid Evaluation of Propulsion System Effects. Volume I

Abstract : This report presents the results of a research program to develop computerized preliminary analysis procedures for calculating propulsion system installation losses. These losses include inlet and nozzle internal losses and external drag losses for a wide variety of subsonic and supersonic aircraft configurations up to Mach 3.5. The calculation procedures used in the computer programs, which were largely developed from existing engineering procedures and experimental data, are suitable for preliminary studies of advanced aircraft configurations. Two interactive computer programs were developed during the contract: (1) A propulsion installation effects program that calculates installed performance, using input maps of inlet and nozzle/aftbody characteristics for specific configurations, and (2) a 'derivative' program that allows the user to generate new sets of input maps by perturbations to the geometries of the basic input maps. The work accomplished during the contract is documented in four separate volumes. Volume I is a Final Report discussing the analysis methods and data used to develop the programs, and major technical observations from the study. Volume II is a PIPSI Users Manual, containing documentation of the interactive propulsion installation program. Volume III is the Derivative Procedure Users Manual, documenting the methods and usage of the derivative procedure. Volume IV is the library of input maps.
The Value of Quick Sepsis-Related Organ Failure Assessment Scores in Patients With Acute Pancreatitis Who Present to Emergency Departments: A Three-Year Cohort Study

Background Distinguishing sepsis from other inflammatory syndromes continues to be a clinical challenge. The goal of risk stratification tools is to differentiate sepsis from other conditions. We compare the ability of quick sepsis-related organ failure assessment (qSOFA) and systemic inflammatory responses syndrome (SIRS) scores to predict prolonged length of stay (LOS) among patients who presented to the emergency department and hospital ward with acute pancreatitis (AP). Methods We compiled a retrospective database of all adult patients hospitalized for AP during 2015 - 2018 at a single tertiary care center. Independent t-tests, Pearson’s correlation and multiple regressions were performed with hospital LOS as the dependent variable, versus demographic characteristics and etiology of the pancreatitis as independent variables. Prolonged LOS was defined as > 5 days. Results The sensitivity and specificity of an SIRS score of 2 or greater for the detection of patients with prolonged LOS were 61% and 80%, respectively. The qSOFA score of 2 or greater corresponded to a diagnosis of significant AP with a specificity of 99% and a sensitivity of 4%. Multiple regression analysis demonstrated that each point increase in an SIRS score is associated with 2.24 days in additional hospital LOS. Interestingly, SIRS scores were found to correlate with the LOS, but not qSOFA. Conclusion The qSOFA is a tool designed to identify patients at high risk of mortality due to sepsis. The data suggest that as with sepsis, patients with AP who are triaged with only qSOFA could be underrecognized and subsequently undertreated. Secondarily, the data suggest that SIRS scoring has the potential to promptly predict how long patients with AP will stay in the hospital.
Condition-transforming Variational Autoencoder for Conversation Response Generation

This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional variational autoencoders (CVAEs). In conventional CVAEs , the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tends to become condition-independent in practical application. In our proposed CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution $\mathcal{N}\left( {0,{\mathbf{I}}} \right)$. In our objective evaluations, the CTVAE model outperforms the CVAE model on fluency metrics and surpasses a sequence-to-sequence (Seq2Seq) model on diversity metrics. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and Seq2Seq models on generating fluency, informative and topic relevant responses.
Towards lattice-assisted hadron physics calculations based on QCD n-point functions

We present preliminary lattice results for the nonperturbative tensor structure of the vector and axial-vector quark-antiquark vertices in QCD. Our lattice calculations are for $N_f=2$ mass-degenerate Wilson fermion flavors whose quark mass values include an almost physical one. We compare our lattice results with the corresponding continuum solutions of the inhomogeneous Bethe-Salpeter equations in a rainbow-ladder truncation. We find similarities in the momentum dependencies of the form factors but also clear deviations at low momentum.
Prediction of Progression to Alzheimer's disease with Deep InfoMax

Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear way. In this paper, we investigate the use of variants of DIM in a setting of progression to Alzheimer's disease in comparison with supervised AlexNet and ResNet inspired convolutional neural networks. As a benchmark, we use a classification task between four groups: patients with stable, and progressive mild cognitive impairment (MCI), with Alzheimer's disease, and healthy controls. Our dataset is comprised of 828 subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. Our experiments highlight encouraging evidence of the high potential utility of DIM in future neuroimaging studies.
Atomic data from the IRON Project - XXIV. Electron excitation of Li-like Fe xxiv between the $n$ = 2 and $n'$ = 2, 3, 4 fine-structure levels

Collision strengths for electron induced tran- sitions in Li-like Fe xxiv are calculated using a Breit- Pauli R-matrix technique. The target has 15 ne struc- ture states corresponding to n 4. Autoionizing reso- nances are found to aect 18 of the 39 collision strengths, namely those for transitions 2lj! n 0 l 0 j 0 where n 0 =2 ; 3 : The present calculation is carried out only for electrons incident on the ground state with energies not exceeding 265 Ry. Beyond this we use the relativistic distorted wave collision strengths of Zhang et al. in order to complete our results. Thermally averaged collision strengths are com- puted and tabulated as functions of logT for transitions out of the 2s1=2 ,2 p 1 = 2 and 2p3=2 states. The temperature range considered is 6:2 log T 8:0, Fe xxiv having its maximum coronal abundance when logT =7 :2.
Towards Combining On-Off-Policy Methods for Real-World Applications

In this paper, we point out a fundamental property of the objective in reinforcement learning, with which we can reformulate the policy gradient objective into a perceptron-like loss function, removing the need to distinguish between on and off policy training. Namely, we posit that it is sufficient to only update a policy $\pi$ for cases that satisfy the condition $A(\frac{\pi}{\mu}-1)\leq0$, where $A$ is the advantage, and $\mu$ is another policy. Furthermore, we show via theoretic derivation that a perceptron-like loss function matches the clipped surrogate objective for PPO. With our new formulation, the policies $\pi$ and $\mu$ can be arbitrarily apart in theory, effectively enabling off-policy training. To examine our derivations, we can combine the on-policy PPO clipped surrogate (which we show to be equivalent with one instance of the new reformation) with the off-policy IMPALA method. We first verify the combined method on the OpenAI Gym pendulum toy problem. Next, we use our method to train a quadrotor position controller in a simulator. Our trained policy is efficient and lightweight enough to perform in a low cost micro-controller at a minimum update rate of 500 Hz. For the quadrotor, we show two experiments to verify our method and demonstrate performance: 1) hovering at a fixed position, and 2) tracking along a specific trajectory. In preliminary trials, we are also able to apply the method to a real-world quadrotor.
DELIVERABLE SUBMISSION SHEET

FP7-ICT Strategic Targeted Research Project PHEME (No. 611233) Deliverable D5.2. 2 (WP 5) This deliverable summarizes work conducted in WP5 of the PHEME project. The goal of T5.4 is to develop a visual analytics dashboard based on a multiple coordinated view approach to explore the veracity intelligence extracted by the content analytics methods from WP2, WP3 and WP4. This requires modular and scalable update mechanisms, advanced query capabilities to reveal supportive and critical voices, charts to track the rise and decay of rumours across media channels and languages, and visual tools to reveal the context and diffusion of emerging rumours.
DeepSense: Enabling Carrier Sense in Low-Power Wide Area Networks Using Deep Learning

The last few years have seen the proliferation of low-power wide area networks like LoRa, Sigfox and 802.11ah, each of which use a different and sometimes proprietary coding and modulation scheme, work below the noise floor and operate on the same frequency band. We introduce DeepSense, which is the first carrier sense mechanism that enables random access and coexistence for low-power wide area networks even when signals are below the noise floor. Our key insight is that any communication protocol that operates below the noise floor has to use coding at the physical layer. We show that neural networks can be used as a general algorithmic framework that can learn the coding mechanisms being employed by such protocols to identify signals that are hidden within noise. Our evaluation shows that DeepSense performs carrier sense across 26 different LPWAN protocols and configurations below the noise floor and can operate in the presence of frequency shifts as well as concurrent transmissions. Beyond carrier sense, we also show that DeepSense can support multi bit-rate LoRa networks by classifying between 21 different LoRa configurations and flexibly adapting bitrates based on signal strength. In a deployment of a multi-rate LoRa network, DeepSense improves bit rate by 4x for nearby devices and provides a 1.7x increase in the number of locations that can connect to the campus-wide network.
Towards a Green and Self-Powered Internet of Things Using Piezoelectric Energy Harvesting

The Internet of Things (IoT) is a revolutionizing technology which aims to create an ecosystem of connected objects and embedded devices and provide ubiquitous connectivity between trillions of not only smart devices but also simple sensors and actuators. Although recent advancements in miniaturization of devices with higher computational capabilities and ultra-low power communication technologies have enabled the vast deployment of sensors and actuators everywhere, such an evolution calls for fundamental changes in hardware design, software, network architecture, data analytics, data storage, and power sources. A large portion of the IoT devices cannot be powered by batteries only anymore, as they will be installed in hard to reach areas and regular battery replacement and maintenance are infeasible. A viable solution is to scavenge and harvest energy from the environment and then provide enough energy to the devices to perform their operations. This will significantly increase the device life time and eliminate the need for the battery as an energy source. This survey aims at providing a comprehensive study on energy harvesting techniques as alternative and promising solutions to power the IoT devices. We present the main design challenges of the IoT devices in terms of energy and power and provide design considerations for a successful implementation of self-powered the IoT devices. We then specifically focus on piezoelectric energy harvesting as one of the most promising solutions to power the IoT devices and present the main challenges and research directions. We also shed lights on the hybrid energy harvesting for the IoT and security challenges of energy harvesting enabled the IoT systems.
A Novel Antifragility Measure Based on Satisfaction and Its Application to Random and Biological Boolean Networks

Antifragility is a property that enhances the capability of a system in response to external perturbations. Although the concept has been applied in many areas, a practical measure of antifragility has not been developed yet. Here we propose a simply calculable measure of antifragility, based on the change of “satisfaction” before and after adding perturbations, and apply it to random Boolean networks (RBNs). Using the measure, we found that ordered RBNs are the most antifragile. Also, we demonstrated that seven biological systems are antifragile. Our measure and results can be used in various applications of Boolean networks (BNs) including creating antifragile engineering systems, identifying the genetic mechanism of antifragile biological systems, and developing new treatment strategies for various diseases.
Latent Variable Session-Based Recommendation

Session based recommendation provides an attractive alternative to the traditional feature engineering approach to recommendation. Feature engineering approaches require hand tuned features of the users history to be created to produce a context vector. In contrast a session based approach is able to dynamically model the users state as they act. We present a probabilistic framework for session based recommendation. A latent variable for the user state is updated as the user views more items and we learn more about their interests. The latent variable model is conceptually simple and elegant; yet requires sophisticated computational technique to approximate the integral over the latent variable. We provide computational solutions using both the re-parameterization trick and also using the Bouchard bound for the softmax function, we further explore employing a variational auto-encoder and a variational Expectation-Maximization algorithm for tightening the variational bound. The model performs well against a number of baselines. The intuitive nature of the model allows an elegant formulation combining correlations between items and their popularity and that sheds light on other popular recommendation methods. An attractive feature of the latent variable approach is that, as the user continues to act, the posterior on the user's state tightens reflecting the recommender system's increased knowledge about that user.
A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition

Current researches of action recognition mainly focus on single-view and multi-view recognition, which can hardly satisfies the requirements of human-robot interaction (HRI) applications to recognize actions from arbitrary views. The lack of datasets also sets up barriers. To provide data for arbitrary-view action recognition, we newly collect a large-scale RGB-D action dataset for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences. The dataset includes action samples captured in 8 fixed viewpoints and varying-view sequences which covers the entire 360 degree view angles. In total, 118 persons are invited to act 40 action categories, and 25,600 video samples are collected. Our dataset involves more participants, more viewpoints and a large number of samples. More importantly, it is the first dataset containing the entire 360 degree varying-view sequences. The dataset provides sufficient data for multi-view, cross-view and arbitrary-view action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition. Experiment results show that the VS-CNN achieves superior performance.
Tackling the Cornell NLVR task using seq 2 seq model with weak supervision

Natural language understanding is an ongoing challenge in the machine learning community. We experiment with the newly published CNLVR dataset of True/False statements regarding an image, in the framework of semantic parsing to logical forms using weak supervision that was proposed by Guu et al. 2016. The small size of the dataset along with complex linguistic structures it contains provide a reasonable challenge that required us to add several modifications to the model. We improve the achieved accuracy on the dev and test sets to 85% and 84%, as compared to 68% accuracy baseline presented by the dataset authors.
Extra invariance of principal shift invariant spaces and the Zak transform

We prove a necessary and sufficient condition for a principal shift invariant space of $L^2(\mathbb{R})$ to be invariant under translations by the subgroup $\frac{1}{N} \mathbb{Z}, N>1$. This condition is given in terms of the Zak transform of the group $\frac{1}{N} \mathbb{Z}. $ This result is extended to principal shift invariant spaces generated by a lattice in a general locally compact abelian (LCA) group.
A Rapid Fault Reconstruction Strategy Using a Bank of Sliding Mode Observers

This paper deals with the design of a model-based rapid fault detection and isolation strategy using sliding mode observers. To address this problem, a new scheme is proposed by adaptively combining the information provided by a bank of observers. In this regard, a new structure for sliding mode observers is considered. Then, the well-known recursive least square algorithm is utilized to merge individual state estimations suitably such that the system fault is detected faster. The required condition for enhancing perfect state estimation is derived, and the stability of the overall system is proven via Lyapunov's direct method. The supremacy of proposed scheme is fully discussed through mathematical analyses as well as simulations.
Learning big Gaussian Bayesian networks: partition, estimation, and fusion

Structure learning of Bayesian networks has always been a challenging problem. Nowadays, massive-size networks with thousands or more of nodes but fewer samples frequently appear in many areas. We develop a divide-and-conquer framework, called partition-estimation-fusion (PEF), for structure learning of such big networks. The proposed method first partitions nodes into clusters, then learns a subgraph on each cluster of nodes, and finally fuses all learned subgraphs into one Bayesian network. The PEF method is designed in a flexible way so that any structure learning method may be used in the second step to learn a subgraph structure as either a DAG or a CPDAG. In the clustering step, we adapt the hierarchical clustering method to automatically choose a proper number of clusters. In the fusion step, we propose a novel hybrid method that sequentially add edges between subgraphs. Extensive numerical experiments demonstrate the competitive performance of our PEF method, in terms of both speed and accuracy compared to existing methods. Our method can improve the accuracy of structure learning by 20% or more, while reducing running time up to two orders-of-magnitude.
Fine-Grained Named Entity Recognition using ELMo and Wikidata

Fine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types. These types can span diverse domains such as finance, healthcare, and politics. We observe that when the type set spans several domains the accuracy of the entity detection becomes a limitation for supervised learning models. The primary reason being the lack of datasets where entity boundaries are properly annotated, whilst covering a large spectrum of entity types. Furthermore, many named entity systems suffer when considering the categorization of fine grained entity types. Our work attempts to address these issues, in part, by combining state-of-the-art deep learning models (ELMo) with an expansive knowledge base (Wikidata). Using our framework, we cross-validate our model on the 112 fine-grained entity types based on the hierarchy given from the Wiki(gold) dataset.