Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of dietary sugars on metabolic risk factors associated with heart disease.

The results from studies in which the effects of the extended feeding of sugars such as sucrose and fructose as compared to starch and other glucose-based carbohydrates on metabolic risk factors associated with heart disease have been reviewed. In general, the feeding of the sugars as compared to starch produced undesirable changes in metabolic risk factors such as blood triglycerides, total cholesterol and its lipoprotein distribution, insulin and uric acid. Other dietary components (e.g., saturated fat) can magnify the adverse metabolic effects of the sugars. A finite segment of the population characterized by high levels of triglycerides and insulin may be at a substantially higher risk than is the general population from the present level of intake of sucrose or fructose.
Finite-frequency fault detection for two-dimensional Fornasini–Marchesini dynamical systems

ABSTRACT This paper is concerned with the fault detection problem for two-dimensional (2-D) discrete-time systems described by the Fornasini–Marchesini local state-space model. The goal of the paper is to design a fault detection filter to detect the occurrence of faults in finite-frequency domain. To this end, a finite-frequency H− index is used to describe fault sensitivity performance, and a finite-frequency H∞ index is used to describe disturbance attenuation performance. In light of the generalised Kalman–Yakubovich–Popov lemma for 2-D systems and matrix inequality techniques, convex conditions are derived for this fault detection problem. Based on these conditions, a numerical algorithm is put forward to construct a desired fault detection filter. Finally, a numerical example and an industrial example are given to illustrate the effectiveness of the proposed algorithm.
Koszul duality in exact categories

In this paper we establish Koszul duality type results in the setting of chain complexes in exact categories. In particular we prove generalisations of Vallette's cooperadic Koszul duality theorem, and operadic Koszul duality along the lines of Lurie. We also prove a connective version. We conclude with some applications, including our main example, the category of complete bornological spaces over a Banach field.
A Novel Metal Nanoparticles-Graphene Nanodisks-Quantum Dots Hybrid-System-Based Spaser

Active nanoplasmonics have recently led to the emergence of many promising applications. One of them is the spaser (surface plasmons amplification by stimulated emission of radiation) that has been shown to generate coherent and intense fields of selected surface plasmon modes that are strongly localized in the nanoscale. We propose a novel nanospaser composed of a metal nanoparticles-graphene nanodisks hybrid plasmonic system as its resonator and a quantum dots cascade stack as its gain medium. We derive the plasmonic fields induced by pulsed excitation through the use of the effective medium theory. Based on the density matrix approach and by solving the Lindblad quantum master equation, we analyze the ultrafast dynamics of the spaser associated with coherent amplified plasmonic fields. The intensity of the plasmonic field is significantly affected by the width of the metallic contact and the time duration of the laser pulse used to launch the surface plasmons. The proposed nanospaser shows an extremely low spasing threshold and operates in the mid-infrared region that has received much attention due to its wide biomedical, chemical and telecommunication applications.
Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition

Multi-view facial expression recognition (FER) is a challenging task because the appearance of an expression varies in poses. To alleviate the influences of poses, recent methods either perform pose normalization or learn separate FER classifiers for each pose. However, these methods usually have two stages and rely on good performance of pose estimators. Different from existing methods, we propose a pose-adaptive hierarchical attention network (PhaNet) that can jointly recognize the facial expressions and poses in unconstrained environment. Specifically, PhaNet discovers the most relevant regions to the facial expression by an attention mechanism in hierarchical scales, and the most informative scales are then selected to learn the pose-invariant and expression-discriminative representations. PhaNet is end-to-end trainable by minimizing the hierarchical attention losses, the FER loss and pose loss with dynamically learned loss weights. We validate the effectiveness of the proposed PhaNet on three multi-view datasets (BU-3DFE, Multi-pie, and KDEF) and two in-the-wild FER datasets (AffectNet and SFEW). Extensive experiments demonstrate that our framework outperforms the state-of-the-arts under both within-dataset and cross-dataset settings, achieving the average accuracies of 84.92\%, 93.53\%, 88.5\%, 54.82\% and 31.25\% respectively.
A Graph-Based Method for Anomaly Prediction in Functional Brain Network

Resting-state functional MRI (rs-fMRI) in functional neuroimaging techniques have accelerated progress in brain disorders and dysfunction studies. Since, there are the slight differences between healthy and disorder brain, investigation in the complex topology of functional brain networks in human is difficult and complicated task with the growth of evaluation criteria. Recently, graph theory and deep learning applications have spread widely to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns. Irregular graph analysis has been widely applied in many brain recognition domains, these applications might involve both node-centric and graph-centric tasks. In this paper, we performed a novel analysis of individual Variational Autoencoder (VAE) and Graph Convolutional Network (GCN) for the region of interest recognition areas of brain which not have normal connection when apply certain tasks. Here in, we identified a framework of Graph Auto-Encoder (GAE) with hypersphere distributer for functional data analysis in brain imaging studies that is underlying non-Euclidean structure, in learning of strong rigid graphs among large scale data. In addition, we distinguish the possible mode correlations in abnormal brain connections.
Quantum Period Finding with a Single Output Qubit - Factoring n-bit RSA with n/2 Qubits

We study quantum period finding algorithms such as Simon, Shor, and Ekera-Hastad. For a periodic function $f$ these algorithms produce -- via some quantum embedding of $f$ -- a quantum superposition $\sum_x \vert x\rangle\vert f(x)\rangle$, which requires a certain amount of output bits that represent $\vert f(x)\rangle$. We show that we can lower this amount to a single output qubit by hashing $f$ down to a single bit. Namely, we replace the embedding of $f$ in quantum period finding circuits by several embeddings of hashed versions of $f$. We show that on expectation this modification only doubles the required amount of quantum measurements, while significantly reducing the total number of qubits. For example, for Simon's period finding algorithm in some $n$-bit function $f: \mathbb{F}_2^n \rightarrow \mathbb{F}_2^n$ our hashing technique reduces the required qubits from $2n$ down to $n+1$. For the Ekera-Hastad algorithm for factoring $n$-bit RSA our hashing reduces the required qubits from $(\frac 3 2 + o(1))n$ down to $(\frac 1 2 + o(1))n$.
Monodromy representations of meromorphic projective structures

We determine the image of the monodromy map for meromorphic projective structures with poles of orders greater than two. This proves the analogue of a theorem of Gallo-Kapovich-Marden and answers a question of Allegretti and Bridgeland in this case. Our proof uses coordinates on the moduli space of framed representations arising from the work of Fock and Goncharov.
Bi-objective Framework for Sensor Fusion in RGB-D Multi-View Systems: Applications in Calibration

Complete and textured 3D reconstruction of dynamic scenes has been facilitated by mapped RGB and depth information acquired by RGB-D cameras based multi-view systems. One of the most critical steps in such multi-view systems is to determine the relative poses of all cameras via a process known as extrinsic calibration. In this work, we propose a sensor fusion framework based on a weighted bi-objective optimization for refinement of extrinsic calibration tailored for RGB-D multi-view systems. The weighted bi-objective cost function, which makes use of 2D information from RGB images and 3D information from depth images, is analytically derived via the Maximum Likelihood (ML) method. The weighting factor appears as a function of noise in 2D and 3D measurements and takes into account the affect of residual errors on the optimization. We propose an iterative scheme to estimate noise variances in 2D and 3D measurements, for simultaneously computing the weighting factor together with the camera poses. An extensive quantitative and qualitative evaluation of the proposed approach shows improved calibration performance as compared to refinement schemes which use only 2D or 3D measurement information.
μ-Forcing: Training Variational Recurrent Autoencoders for Text Generation

DAYIHENG LIU, College of Computer Science, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, China YANG XUE∗, College of Computer Science, Sichuan University, China FENG HE, College of Computer Science, Sichuan University, China YUANYUAN CHEN†, College of Computer Science, Sichuan University, China JIANCHENG LV‡, College of Computer Science, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, China
Learning Cross-Domain Representation with Multi-Graph Neural Network

Learning effective embedding has been proved to be useful in many real-world problems, such as recommender systems, search ranking and online advertisement. However, one of the challenges is data sparsity in learning large-scale item embedding, as users' historical behavior data are usually lacking or insufficient in an individual domain. In fact, user's behaviors from different domains regarding the same items are usually relevant. Therefore, we can learn complete user behaviors to alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users' behaviors using graph, and graph neural networks (GNNs) have recently shown the great power for representation learning, which can be used to learn item embedding. However, it is challenging to transfer the information across domains and learn cross-domain representation using the existing GNNs. To address these challenges, in this paper, we propose a novel model - Deep Multi-Graph Embedding (DMGE) to learn cross-domain representation. Specifically, we first construct a multi-graph based on users' behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain representation in an unsupervised manner. Particularly, we present a multiple-gradient descent optimizer for efficiently training the model. We evaluate our approach on various large-scale real-world datasets, and the experimental results show that DMGE outperforms other state-of-art embedding methods in various tasks.
Detecting DOM-Sourced Cross-Site Scripting in Browser Extensions

In recent years, with the advances in JavaScript engines and the adoption of HTML5 APIs, web applications begin to show a tendency to shift their functionality from the server side towards the client side, resulting in dense and complex interactions with HTML documents using the Document Object Model (DOM). As a consequence, client-side vulnerabilities become more and more prevalent. In this paper, we focus on DOM-sourced Cross-site Scripting (XSS), which is a kind of severe but not well-studied vulnerability appearing in browser extensions. Comparing with conventional DOM-based XSS, a new attack surface is introduced by DOM-sourced XSS where the DOM could become a vulnerable source as well besides common sources such as URLs and form inputs. To discover such vulnerability, we propose a detecting framework employing hybrid analysis with two phases. The first phase is the lightweight static analysis consisting of a text filter and an abstract syntax tree parser, which produces potential vulnerable candidates. The second phase is the dynamic symbolic execution with an additional component named shadow DOM, generating a document as a proof-of-concept exploit. In our large-scale real-world experiment, 58 previously unknown DOM-sourced XSS vulnerabilities were discovered in user scripts of the popular browser extension Greasemonkey.
Neuro-Optimization: Learning Objective Functions Using Neural Networks

Mathematical optimization is widely used in various research fields. With a carefully-designed objective function, mathematical optimization can be quite helpful in solving many problems. However, objective functions are usually hand-crafted and designing a good one can be quite challenging. In this paper, we propose a novel framework to learn the objective function based on a neural net-work. The basic idea is to consider the neural network as an objective function, and the input as an optimization variable. For the learning of objective function from the training data, two processes are conducted: In the inner process, the optimization variable (the input of the network) are optimized to minimize the objective function (the network output), while fixing the network weights. In the outer process, on the other hand, the weights are optimized based on how close the final solution of the inner process is to the desired solution. After learning the objective function, the solution for the test set is obtained in the same manner of the inner process. The potential and applicability of our approach are demonstrated by the experiments on toy examples and a computer vision task, optical flow.
CASE RECORDS of the MASSACHUSETTS GENERAL HOSPITAL. Case 5-2016. A 43-Year-Old Man with Altered Mental Status and a History of Alcohol Use.

Dr. Sarah K. Bourne (Neurosurgery): A 43-year-old man with a history of alcohol-use disorder was admitted to this hospital because of altered mental status. On the day of admission, the patient was found by emergency medical services personnel, who had been contacted by a neighbor. He was sitting on a mattress, was incontinent of feces and urine, and was surrounded by empty prescription containers for gabapentin and bupropion, along with empty bottles of vodka. He had last been seen by neighbors 4 days earlier, when he reportedly locked himself in his apartment after purchasing alcohol. No other drugs or drug paraphernalia were found. On examination, he was conscious and shaking and did not speak or follow commands. The initial score on the Glasgow Coma Scale was 10 (on a scale ranging from 3 to 15, with lower scores indicating reduced levels of consciousness). The blood pressure was 236/158 mm Hg, the pulse 90 beats per minute, and the respiratory rate 20 breaths per minute. The lungs were clear. Glucometer readings of fingerstick capillary blood specimens reported an error. He required assistance to stand and walk to the stretcher. He was brought by ambulance to the emergency department of this hospital. On examination, the temperature was 36.1°C, the blood pressure 216/100 mm Hg, the pulse 76 beats per minute, the respiratory rate 32 breaths per minute, and the oxygen saturation 100% while the patient was breathing ambient air. He was awake and alert but unable to give his name, answer questions, or follow commands. The score on the Glasgow Coma Scale was 11 (4 for spontaneous eye opening, 2 for incomprehensible sounds, and 5 for response to pain). The pupils were equal, round, and reactive to light. No papilledema was present. He was able to track and regard the examiner. The face was symmetric. Cough and gag reflex were present. He moved his limbs spontaneously and against gravity. Deep-tendon reflexes were normal, plantar reflexes were flexor bilaterally, and there was no Hoffman’s reflex (a relatively nonspecific sign in which tapping of the nail or flicking of the terminal phalanx of the middle or ring finger leads to flexion of the terminal phalanx of the thumb that can herald upper motor neuron disease). From the Department of Neurosurgery, Duke University School of Medicine and Duke Raleigh Hospital, Raleigh, NC (A.R.T.); the Departments of Neurosurgery and Pediatrics and Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT (K.T.K.); and the Departments of Radiology (M.L.), Medicine (J.M.V.), and Pathology (A.S.-R.), Massachusetts General Hospital, and the Departments of Radiology (M.L.), Medicine (J.M.V.), and Pathology (A.S.-R.), Harvard Medical School — both in Boston.
Mask-Guided Portrait Editing With Conditional GANs

Portrait editing is a popular subject in photo manipulation.The Generative Adversarial Network (GAN) advances the generating of realistic faces and allows more face editing. In this paper, we argue about three issues in existing techniques: diversity, quality, and controllability for portrait synthesis and editing. To address these issues, we propose a novel end-to-end learning framework that leverages conditional GANs guided by provided face masks for generating faces. The framework learns feature embeddings for every face component (e.g., mouth, hair, eye), separately, contributing to better correspondences for image translation, and local face editing. With the mask, our network is available to many applications, like face synthesis driven by mask, face Swap+ (including hair in swapping), and local manipulation. It can also boost the performance of face parsing a bit as an option of data augmentation.
How big should a Stress Shock be

Stress shocks are often calculated as multiples of the standard deviation of a history set. This paper investigates how many standard deviations are required to guarantee that this shock exceeds any observation within the history set, given the additional constraint of kurtosis. The results of this analysis are then used to validate the shocks produced by some stress test models, in particular that of Brace-Lauer-Rado. A secondary application of our results is to investigate three known extensions of Chebyshev's Inequality where the kurtosis is known. It is found that our results give a tighter bound than the well-known inequalities.
Utilisation d'invariants pour une médiation inter-domaines de modèles utilisateurs : ressources invariantes et invariants sémantiques

Les services de personnalisation du Web 2.0 reposent sur l'exploita- tion de modeles utilisateurs. Schematiquement, plus la quantite d'informations sur les utilisateurs est grande, meilleures sont la modelisation et la qualite du ser- vice. En pratique, nombre de services rencontrent un probleme de manque d'in- formations sur les utilisateurs. Dans cet article, nous y repondons par mediation inter-domaines de modeles utilisateurs, c'est-a-dire la completion de modeles en exploitant des donnees d'un autre domaine. La mediation que nous proposons repose sur un transfert d'informations inter-domaines. Ce transfert consiste en l'utilisation de couples invariants ou tres correles pouvant etre des couples de ressources ou de descripteurs semantiques, identifies apres enrichissement se- mantique des modeles. Nous montrons que le transfert sous forme de couple de ressources permet une completion de qualite et que l'exploitation de descripteurs semantiques augmente la couverture a qualite egale. Enrichir semantiquement est donc benefique pour le transfert inter-domaines.
Optimizing Shallow Networks for Binary Classification

Data driven classification that relies on neural networks is based on optimization criteria that involve some form of distance between the output of the network and the desired label. Using the same mathematical analysis, for a multitude of such measures, we can show that their optimum solution matches the ideal likelihood ratio test classifier. In this work we introduce a different family of optimization problems which is not covered by the existing approaches and, therefore, opens possibilities for new training algorithms for neural network based classification. We give examples that lead to algorithms that are simple in implementation, exhibit stable convergence characteristics and are antagonistic to the most popular existing techniques.
Secure Routing in IoT: Evaluation of RPL's Secure Mode under Attacks

As the Routing Protocol for Low Power and Lossy Networks (RPL) became the standard for routing in the Internet of Things (IoT) networks, many researchers had investigated the security aspects of this protocol. However, no work (to the best of our knowledge) has investigated the use of the security mechanisms included in the protocolâ€™s standard, due to the fact that there was no implementation for these features in any IoT operating system yet. A partial implementation of RPLâ€™s security mechanisms was presented recently for Contiki operating system (by Perazzo et al.), which provided us with the opportunity to examine RPLâ€™s security mechanisms. In this paper, we investigate the effects and challenges of using RPLâ€™s security mechanisms under common routing attacks. First, a comparison of RPLâ€™s performance, with and without its security mechanisms, under three routing attacks (Blackhole, Selective-Forward, and Neighbor attacks) is conducted using several metrics (e.g., average data packet delivery rate, average data packet delay, average power consumption. etc.) Based on the observations from this comparison, we come up with few suggestions that could reduce the effects of such attacks, without having added security mechanisms for RPL.
Shadow of complex fixed point: Approximate conformality of 
Q>4
 Potts model

We study the famous example of weakly first order phase transitions in the 1+1D quantum Q-state Potts model at Q>4. We numerically show that these weakly first order transitions have approximately conformal invariance. Specifically, we find entanglement entropy on considerably large system sizes fits perfectly with the universal scaling law of this quantity in the conformal field theories (CFTs). This supports that the weakly first order transitions is proximate to complex fixed points, which are described by recent conjectured complex CFTs. Moreover, the central charge extracted from this fitting is close to the real part of the complex central charge of these complex CFTs. We also study the conformal towers and the drifting behaviors of these conformal data (e.g., central charge and scaling dimensions).
Complicated grief as a disorder distinct from bereavement-related depression and anxiety: a replication study.

OBJECTIVE This study sought to confirm in an independent, nonclinical study group previous work which demonstrated that the symptoms of complicated grief were distinct from the symptoms of bereavement related depression and anxiety. METHOD Data used in the analyses were derived from a group of 150 widowed individuals who were interviewed 6 months after their deceased spouses' hospital admission (study entry). Complicated grief was measured with a modified version of the Grief Measurement Scale. Principal axis factoring was used to determine the distinctiveness of complicated grief, depression, and anxiety. RESULTS The principal axis factoring showed that the symptoms of complicated grief loaded quite highly on the first (complicated grief) factor and loaded very poorly on the anxiety and depression factors. CONCLUSIONS The results confirmed the authors' previous findings demonstrating the distinction between symptoms of complicated grief and symptoms of bereavement-related depression and anxiety.