Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetic Cardiomyopathy Pathophysiology and Update on Treatment

The presence of diabetes in HF patients portends worse prognosis than does HF without DM. Heart failure in diabetes mellitus (DM) in the absence of conventional risk factors such as hypertension, coronary artery disease (CAD), and congenital or valvular heart disease, has been defined in the literature as diabetic cardiomyopathy (DCM). The question what are the link between DM and DCM? The answer of this question is not well understood. This review will identify the link between diabetes mellitus and DCM, will also explore the possible mechanisms and triggering factors or associations with emphasis upon the implications of new anti-diabetic treatment on preventing development of heart failure in DM. The keywords used in the literature search were heart failure, diabetes mellitus, cardiovascular disease in DM, diabetic cardiomyopathy and treatment of diabetes with heart failure. These keywords were synced into midline and google search to acquire literature reviews and structured topics in order to deliver significant understanding of the mechanism and factors contributing to the development of DCM. All heart failure secondary to known etiology such as hypertension, valvuler heart disease, coronary artery disease or cardio-myopathy without diabetes mellitus excluded. This literature review identify several link between diabetes mellitus and heart failure in the absence of conventional risk factors (DCM), it also explore the possible mechanisms and triggering factors or associations with emphasis upon the implications of new anti-diabetic treatment on preventing development of heart failure in diabetic patients. It also give conception on what remedy can change the course of this disease. DCMhas a distinct substrate in DM patients with HF in the absence of known causes ofheart diseases and its factors. All proven evidence-based medicine for its treatment should be taken into consideration. Uses of new anti-diabetic medication may delay or prevent development of DCM. Further clinical studies and basic science advancement will recognize the context of DCM.
Relating Facebook to Consumer Behavior: Facebook Motivates to Purchase Experiential Products

Facebook has become an integrated part of life for over a billion consumers. Yet, little is known about the effect of Facebook on consumer behavior. This thesis addresses this literature gap by showing that passive browsers of the Facebook-news feed develop an increased preference for experiential purchases over material purchases when they focus on close friends. Three lab studies explore the mechanism causing the preference of this particular group of news feed browsers. Consumers are shown to use the news feed frequently for positive self-presentation by posting about positively valenced activities they have experienced. Encountering this type of social information leads to benign envy towards Facebook-friends in the domain of experienced activities. The evoked benign envy is argued to be the key motivator for consumers to purchase more experiential products in order to experience more positive activities oneself. However, a mediation effect is not found. The main findings have implications for marketingand social media policy.
Measles in Mexico

The relationship between measles and immunization is such that morbidity is almost a mathematical function of the number of doses of vaccine distributed. After Mexicos massive immunization campaign of 1973 a sharp decrease in measles morbidity and mortality was registered in 1974 and 1975. Unfortunately the campaign was not able to eliminate measles completely because of 2 factors: the large number of small villages with scattered populations that were not reached by immunization brigades and problems restricting vaccine distribution only to the targeted children 6-18 months of age. In 1976-79 the mass immunization strategy was abandoned in favor of routine immunization throughout the year for children who were brought to health centers as a result of vaccination promotional activities. Beginning in 1980 intensive phases of immunization were introduced into the routine year-long immunization program. It is hoped that the 1981-82 policy of routine year-long immunization in health centers as well as intensive house-by-house immunization activities will reach at least 70% of susceptible preschool children and reduce measles to the low incidence achieved in Mexico in 1974-75. Special attention will be paid to reinforcing levels of immunity in those states or zones where measles morbidity is greatest.
THE BACKSTEPPING METHOD FOR STABILIZING TIME FRACTIONAL ORDER PARTIAL DIFFERENTIAL EQUATION

In this article the nonlinear time fractional order partial differential equation (NTFPDE) subject to a boundary controller at the boundary is considered. The semi-discretized backstepping control technique is used for stabilize the partial differential equation with fractional order 0 1. q < ≤ To the author best knowledge, this is the first time in the literature that the backstepping method is being used for stabilizing NTFPDE. Illustrative example is given to demonstrate the effectiveness of the proposed control scheme. Simulation results show that the proposed design not only can stabilize the NTFPDE but performs better than an integer order as well.
Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic

In this paper, we describe our team's effort on the semantic text question similarity task of NSURL 2019. Our top performing system utilizes several innovative data augmentation techniques to enlarge the training data. Then, it takes ELMo pre-trained contextual embeddings of the data and feeds them into an ON-LSTM network with self-attention. This results in sequence representation vectors that are used to predict the relation between the question pairs. The model is ranked in the 1st place with 96.499 F1-score (same as the second place F1-score) and the 2nd place with 94.848 F1-score (differs by 1.076 F1-score from the first place) on the public and private leaderboards, respectively.
Microscopic theory of pseudogap phenomena and unconventional Bose-liquid superconductivity and superfluidity in high-$T_c$ cuprates and other systems

A consistent microscopic theory of pseudogap phenomena and novel Bose-liquid superconductivity (superfluidity) is presented, based on the fact that in high-$T_c$ cuprates and related systems the energy $\varepsilon_A$ of the effective attraction between fermions is comparable with their Fermi energy $\varepsilon_F$ and the bosonic Cooper pairs are formed above $T_c$ and then a part of such Cooper pairs condense into a Bose superfluid at $T_c$. High-$T_c$ cuprates and other systems with low Fermi energies ($\varepsilon_F\sim\varepsilon_A$) are bosonic superconductors/superfluids and exhibit pseudogap phases above $T_c$, $\lambda$-like superconducting transition at $T_c$ and Bose-liquid superconductivity below $T_c$. The relevant charge carriers in high-$T_c$ cuprates are polarons which are bound into bosonic Cooper pairs above $T_c$. Polaronic and pseudogap effects weaken with increasing the doping and disappear at a quantum critical point. The modified BCS-like theory describes another pseudogap regime but the superfluid transition in high-$T_c$ cuprates and other systems is neither BCS-like transition nor usual Bose-Einstein condensation. The criteria for bosonization of Cooper pairs are formulated. The mean-field theory of the coherent single particle and pair condensates of bosonic Cooper pairs describes fairly well the novel superconducting states and properties of high-$T_c$ cuprates in full agreement with the experimental findings. The unusual superconducting/superfluid states and properties of other exotic systems (e.g., heavy-fermion and organic compounds, $\rm{Sr_2RuO_4}$, $^3$He, $^4$He and atomic Fermi gases) are explained more clearly by the theory of Bose superfluids. Finally, the new criteria and principles of unconventional superconductivity and superfluidity are formulated.
Asymmetrical resonant oscillations in periodically forced spatially inhomogeneous media

Spatially localized oscillations in periodically forced systems are intriguing phenomena. They may occur in homogeneous media (oscillons), but quite often they emerge in heterogeneous media, such as the auditory system, where asymmetrical localized oscillations are believed to play an important role in frequency discrimination of incoming sound waves. In this paper, we use an amplitude-equation approach to study the asymmetry of the oscillations amplitude and the factors that affect it. More specifically, we use a variant of the forced complex Ginzburg-Landau (FCGL) equation that describes an oscillatory system below the Hopf bifurcation with space-dependent Hopf frequency, subjected to both parametric and additive forcing. We show that spatial heterogeneity combined with bistability of system states result in asymmetry of the localized oscillations. We further identify parameters that control that asymmetry, and characterize the spatial profile of the oscillations in terms of maximal amplitude, location, width and asymmetry. Our results bare qualitative similarities to empirical observation trends that have found in the auditory system.
Flatband Line States in Photonic Super‐Honeycomb Lattices

For the first time, a photonic super‐honeycomb lattice (sHCL) is established experimentally by use of a continuous‐wave laser writing technique, and thereby two distinct flatband line states that manifest as noncontractible loop states in an infinite flatband lattice are demonstrated. These localized states (“straight” and “zigzag” lines) observed in the sHCL with tailored boundaries cannot be obtained by the superposition of conventional compact localized states because they arise from real‐space topological property of certain flatband systems. In fact, the zigzag‐line states, unique to the sHCL, are in contradistinction with those previously observed in the Kagome and Lieb lattices. Their momentum‐space spectrum emerges in the high‐order Brillouin zone where the flatband touches the dispersive bands, revealing the characteristic of topologically protected band‐touching. The experimental results are corroborated by numerical simulations. This work may provide insight into Dirac‐like 2D materials beyond graphene.
oLMpics-On What Language Model Pre-training Captures

Abstract Recent success of pre-trained language models (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM representations are useful for symbolic reasoning tasks have been limited and scattered. In this work, we propose eight reasoning tasks, which conceptually require operations such as comparison, conjunction, and composition. A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data. To address this, we propose an evaluation protocol that includes both zero-shot evaluation (no fine-tuning), as well as comparing the learning curve of a fine-tuned LM to the learning curve of multiple controls, which paints a rich picture of the LM capabilities. Our main findings are that: (a) different LMs exhibit qualitatively different reasoning abilities, e.g., RoBERTa succeeds in reasoning tasks where BERT fails completely; (b) LMs do not reason in an abstract manner and are context-dependent, e.g., while RoBERTa can compare ages, it can do so only when the ages are in the typical range of human ages; (c) On half of our reasoning tasks all models fail completely. Our findings and infrastructure can help future work on designing new datasets, models, and objective functions for pre-training.
Uhlenbeck compactness for arbitrary $L^\infty$ connections and optimal regularity in General Relativity by the RT-equations

We resolve two problems in Mathematical Physics. First, we prove that any $L^{\infty}$ connection $\Gamma$ on the tangent bundle of an arbitrary differentiable manifold with $L^\infty$ Riemann curvature can be smoothed by coordinate transformation to optimal regularity, $\Gamma \in W^{1,p}$, (one derivative smoother than the curvature), any $p<\infty$. For Lorentzian metrics in General Relativity this implies that shock wave solutions of the Einstein-Euler equations are non-singular---geodesic curves, locally inertial coordinates and the Newtonian limit all exist in a classical sense. The proof is based on extending authors' existence theory for the RT-equations by one order, to the level of $L^{\infty}$ connections, and to accomplish this we introduce the $reduced$ RT-equations, a system of $elliptic$ partial differential equations for the Jacobians of the regularizing coordinate transformations. Secondly, we prove that this existence theory suffices to extend $Uhlenbeck$ $compactness$ from the case of connections on vector bundles over Riemannian manifolds (2019 Abel Prize and 2007 Steele Prize), to the case of connections on the tangent bundle of arbitrary differentiable manifolds, including Lorentzian manifolds of relativistic Physics. By this, Uhlenbeck compactness and optimal regularity are understood to be pure logical consequences of the rule which defines how connections transform from one coordinate system to another.
Silhouette-Net: 3D Hand Pose Estimation from Silhouettes

3D hand pose estimation has received a lot of attention for its wide range of applications and has made great progress owing to the development of deep learning. Existing approaches mainly consider different input modalities and settings, such as monocular RGB, multi-view RGB, depth, or point cloud, to provide sufficient cues for resolving variations caused by self occlusion and viewpoint change. In contrast, this work aims to address the less-explored idea of using minimal information to estimate 3D hand poses. We present a new architecture that automatically learns a guidance from implicit depth perception and solves the ambiguity of hand pose through end-to-end training. The experimental results show that 3D hand poses can be accurately estimated from solely {\em hand silhouettes} without using depth maps. Extensive evaluations on the {\em 2017 Hands In the Million Challenge} (HIM2017) benchmark dataset further demonstrate that our method achieves comparable or even better performance than recent depth-based approaches and serves as the state-of-the-art of its own kind on estimating 3D hand poses from silhouettes.
Megapixel time-gated SPAD image sensor for 2D and 3D imaging applications

We present the first 1Mpixel SPAD camera ever reported. The camera features 3.8ns time gating and 24kfps frame rate; it was fabricated in 180nm CIS technology. Two pixels have been designed with a pitch of 9.4$\mu$m in 7T and 5.75T configurations, respectively, achieving a maximum fill factor of 13.4%. The maximum PDP is 27%, median DCR 2.0cps, variation in gating length 120ps, position skew 410ps, and rise/fall time <550ps, all FWHM at 3.3V of excess bias. The sensor was used to capture 2D/3D scenes over 2m with an LSB of 5.4mm and a precision better than 7.8mm. Extended dynamic range is demonstrated in dual exposure operation mode. Spatially overlapped multi-object detection is experimentally demonstrated in single-photon time-gated ToF for the first time.
Full of Orions: a 200-pc mapping of the interstellar medium in the redshift-3 lensed dusty star-forming galaxy SDP.81

We present a sub-kpc resolved study of the interstellar medium properties in SDP.81, a $z$ = 3.042 strongly gravitationally lensed, dusty star-forming galaxy, based on high-resolution, multiband ALMA observations of the far-infrared (FIR) continuum, CO ladder, and the [C ii] line. Using a visibility-plane lens modelling code, we achieve a median source-plane resolution of ∼200 pc. We use photon-dominated region (PDR) models to infer the physical conditions – far-ultraviolet (FUV) field strength, density, and PDR surface temperature – of the star-forming gas on 200-pc scales, finding a FUV field strength of ∼103−104G0, gas density of ∼105 cm−3, and cloud surface temperatures up to 1500 K, similar to those in the Orion Trapezium region. The [C ii] emission is significantly more extended than that FIR continuum: ∼50 per cent of [C ii] emission arises outside the FIR-bright region. The resolved [C ii]/FIR ratio varies by almost 2 dex across the source, down to ∼2 × 10−4 in the star-forming clumps. The observed [C ii]/FIR deficit trend is consistent with thermal saturation of the C+ fine-structure-level occupancy at high gas temperatures. We make the source-plane reconstructions of all emission lines and continuum data publicly available.
LayoutLM: Pre-training of Text and Layout for Document Image Understanding

Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding. In this paper, we propose the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42). The code and pre-trained LayoutLM models are publicly available at https://aka.ms/layoutlm.
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection

This paper proposes an autoencoder (AE) that is used for improving the performance of once-class classifiers for the purpose of detecting anomalies. Traditional one-class classifiers (OCCs) perform poorly under certain conditions such as high-dimensionality and sparsity. Also, the size of the training set plays an important role on the performance of one-class classifiers. Autoencoders have been widely used for obtaining useful latent variables from high-dimensional datasets. In the proposed approach, the AE is capable of deriving meaningful features from high-dimensional datasets while doing data augmentation at the same time. The augmented data is used for training the OCC algorithms. The experimental results show that the proposed approach enhance the performance of OCC algorithms and also outperforms other well-known approaches.
Stabilizing Effect of Magnetic Helicity on Magnetic Cavities in the Intergalactic Medium

We investigate the effect of magnetic helicity on the stability of buoyant magnetic cavities as found in the intergalactic medium. In these cavities we insert helical magnetic fields and test whether or not helicity can increase their stability to shredding through the Kelvin–Helmholtz instability and, with that, their lifetime. This is compared to the case of an external vertical magnetic field that is known to reduce the growth rate of the Kelvin–Helmholtz instability. By comparing a low-helicity configuration with a high-helicity one with the same magnetic energy, we find that an internal helical magnetic field stabilizes the cavity. This effect increases as we increase the helicity content. Stabilizing the cavity with an external magnetic field requires instead a significantly stronger field at higher magnetic energy. We conclude that the presence of helical magnetic fields is a viable mechanism to explain the stability of intergalactic cavities on timescales longer than 100 Myr.
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embedding operations with unique irregular memory access patterns that pose a fundamental challenge to accelerate. This paper proposes a lightweight, commodity DRAM compliant, near-memory processing solution to accelerate personalized recommendation inference. The in-depth characterization of production-grade recommendation models shows that embedding operations with high model-, operator- and data-level parallelism lead to memory bandwidth saturation, limiting recommendation inference performance. We propose RecNMP which provides a scalable solution to improve system throughput, supporting a broad range of sparse embedding models. RecNMP is specifically tailored to production environments with heavy co-location of operators on a single server. Several hardware/software co-optimization techniques such as memory-side caching, table-aware packet scheduling, and hot entry profiling are studied, providing up to $9.8 \times$ memory latency speedup over a highly-optimized baseline. Overall, RecNMP offers $4.2 \times$ throughput improvement and 45.8% memory energy savings.
Strong-coupling effects of pairing fluctuations and Anderson-Bogoliubov and Higgs modes in neutron $^1$S$_0$ superfluids in neutron stars

We investigate effects of thermal and quantum fluctuations of the superfluid order parameter in $^1\mathrm{S}_{0}$ superfluids in neutron stars. We construct a separable potential to reproduce the $^1\mathrm{S}_{0}$ phase shift reconstructed by using the partial wave analysis from nucleon scattering data. We include superfluid fluctuations within a strong-coupling approximation developed by Nozi\`eres and Schmitt-Rink and determine self-consistently the superfluid order parameter as well as the chemical potential. We show that the quantum depletion, which gives a fraction of non-condensed neutrons at zero temperature due to quantum pairing fluctuations, plays an important role not only near the critical temperature from superfluid states to normal states but also at zero temperature. We derive the dispersion relation of Anderson-Bogoliubov and Higgs modes associated with phase and amplitude fluctuations, respectively, and show also that there is a nonzero fraction of non-condensed components in the neutron number as a result of the strong-coupling effect. Our results indicate that superfluid fluctuations are important for thermodynamic properties in neutron stars.
The tale of the tail – disentangling the high transverse velocity stars in Gaia DR2

Although the stellar halo accounts for just ~1% of the total stellar mass of the Milky Way, the kinematics of halo stars encode valuable information about the origins and evolution of our Galaxy. It has been shown that the high transverse velocity stars in Gaia DR2 reveal a double sequence in the Hertzsprung-Russell (HR) diagram, indicating a bifurcation in the local stellar halo within 1 kpc. We fit these stars by updating the popular Besancon/Galaxia model, incorporating the latest observational results for the stellar halo and an improved kinematic description for the thick-disk from Schonrich & Binney (2012). We are able to obtain a good match to the Gaia data and provide new constraints on the properties of the Galactic disc and stellar halo. In particular, we show that the kinematically defined thick disc contribution to this high velocity tail is ~13%. We look in greater detail using chemistry from LAMOST DR5, identifying a population of retrograde stars with thick-disc chemistry. Our thick disc kinematic model cannot account for this population and so we conclude there is likely to be a contribution from heated or accreted stars in the Solar Neighbourhood. We also investigate proposed dynamical substructures in this sample, concluding that they are probably due to resonant orbits rather than accreted populations. Finally we provide new insights on the nature of the two sequences and their relation with past accretion events and the primordial Galactic disc.