Miracle-dz/UniFAR-FTUs huggingface dataset:
Amelioration of Cigarette Smoke-Induced Mucus Hypersecretion and Viscosity by Dendrobium officinale Polysaccharides In Vitro and In Vivo

Chronic obstructive pulmonary disease (COPD), characterized by oxidative stress and inflammation, is one of the leading causes of death worldwide, in which cigarette smoke (CS) is the major risk factor. Dendrobium officinale polysaccharides (DOPs) are the main active ingredients extracted from Dendrobium officinale, which have been reported to have antioxidant and anti-inflammatory activity as well as inhibition of mucin gene expression. This study is aimed at investigating the effect of DOPs on CS-induced mucus hypersecretion and viscosity in vitro and in vivo. For in vitro study, primary normal human bronchial epithelial cells (HBECs) differentiated at the air-liquid interface (ALI) culture for 28 days were stimulated with cigarette smoke medium (CSM) in the absence or presence of various concentrations of DOPs or N-acetylcysteine (NAC) for 24 hours. For in vivo study, male Sprague-Dawley rats were randomized to sham air (SA) as control group or CS group for 56 days. At day 29, rats were subdivided and given water as control, DOPs, or NAC as positive control as a mucolytic drug via oral gavage for the remaining duration. Samples collected from apical washing, cell lysates, bronchoalveolar lavage (BAL), and lung tissues were evaluated for mucin gene expression, mucus secretion, and viscosity. DOPs ameliorated the CS-induced mucus hypersecretion and viscosity as shown by the downregulation of MUC5AC mRNA, MUC5AC secretary protein, and mucus viscosity via inhibition of mucus secretory granules in both in vitro and in vivo models. DOPs produced its effective effects on the CS-induced mucus hypersecretion and viscosity via the inhibition of the mucus secretory granules. These findings could be a starting point for considering the potential role of DOPs in the management of the smoking-mediated COPD. However, further research is needed.
Mir: Automated Quantifiable Privilege Reduction Against Dynamic Library Compromise in JavaScript

Third-party libraries ease the development of large-scale software systems. However, they often execute with significantly more privilege than needed to complete their task. This additional privilege is often exploited at runtime via dynamic compromise, even when these libraries are not actively malicious. Mir addresses this problem by introducing a fine-grained read-write-execute (RWX) permission model at the boundaries of libraries. Every field of an imported library is governed by a set of permissions, which developers can express when importing libraries. To enforce these permissions during program execution, Mir transforms libraries and their context to add runtime checks. As permissions can overwhelm developers, Mir's permission inference generates default permissions by analyzing how libraries are used by their consumers. Applied to 50 popular libraries, Mir's prototype for JavaScript demonstrates that the RWX permission model combines simplicity with power: it is simple enough to automatically infer 99.33% of required permissions, it is expressive enough to defend against 16 real threats, it is efficient enough to be usable in practice (1.93% overhead), and it enables a novel quantification of privilege reduction.
Partial Differential Equations, Spectral Theory, and Mathematical Physics

We investigate spectral and asymptotic properties of the particular Schrodinger operator (also known as the Bloch-Torrey operator), $-\Delta + i g x$, in infinite periodically perforated domains of $\mathbb R^d$. We consider Dirichlet realizations of this operator and formalize a numerical approach proposed in [J. Phys. A: Math. Theor. 53, 325201 (2020)] for studying such operators. In particular, we discuss the existence of the spectrum of this operator and its asymptotic behavior as $g\to \infty$.
Cheap IR evaluation

To evaluate Information Retrieval (IR) effectiveness, a possible approach is to use test collections, which are composed of a collection of documents, a set of description of information needs (called topics), and a set of relevant documents to each topic. Test collections are modelled in a competition scenario: for example, in the well known TREC initiative, participants run their own retrieval systems over a set of topics and they provide a ranked list of retrieved documents; some of the retrieved documents (usually the first ranked) constitute the so called pool, and their relevance is evaluated by human assessors; the document list is then used to compute effectiveness metrics and rank the participant systems. Private Web Search companies also run their in-house evaluation exercises; although the details are mostly unknown, and the aims are somehow different, the overall approach shares several issues with the test collection approach. The aim of this work is to: (i) develop and improve some state-of-the-art work on the evaluation of IR effectiveness while saving resources, and (ii) propose a novel, more principled and engineered, overall approach to test collection based effectiveness evaluation. In this thesis we focus on three main directions: the first part details the usage of few topics (i.e., information needs) in retrieval evaluation and shows an extensive study detailing the effect of using fewer topics for retrieval evaluation in terms of number of topics, topics subsets, and statistical power. The second part of this thesis discusses the evaluation without relevance judgements, reproducing, extending, and generalizing state-of-the-art methods and investigating their combinations by means of data fusion techniques and machine learning. Finally, the third part uses crowdsourcing to gather relevance labels, and in particular shows the effect of using fine grained judgement scales; furthermore, explores methods to transform judgements between different relevance scales. Awarded by: University of Udine, Udine, Italy on 19 March 2020. Supervised by: Professor Stefano Mizzaro. Available at: https://kevinroitero.com/resources/kr-phd-thesis.pdf.
Point Transformer

In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information. For that purpose, we propose SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score. The output of Point Transformer is a sorted and permutation invariant feature list that can directly be incorporated into common computer vision applications. We evaluate our approach on standard classification and part segmentation benchmarks to demonstrate competitive results compared to the prior work.
Adaptive invariant density estimation for continuous-time mixing Markov processes under sup-norm risk

Up to now, the nonparametric analysis of multidimensional continuous-time Markov processes has focussed strongly on specific model choices, mostly related to symmetry of the semigroup. While this approach allows to study the performance of estimators for the characteristics of the process in the minimax sense, it restricts the applicability of results to a rather constrained set of stochastic processes. In particular, among other drawbacks, it hardly allows incorporating jump structures. As a consequence, for many models of applied and theoretical interest, no statement can be made about the robustness of typical statistical procedures beyond the beautiful, but limited framework available in the literature. This motivates us to go a different route, by not asking what rate-optimal results can be obtained for a specific type of Markov process, but what are the essential stability properties required in general that allow building a rigorous and robust statistical theory upon. We provide an answer to this question by showing that mixing properties are sufficient to obtain deviation and moment bounds for integral functionals of general Markov processes. Together with some unavoidable technical but not structural assumptions on the semigroup, these allow to derive convergence rates for kernel invariant density estimation which are known to be optimal in the much more restrictive context of reversible continuous diffusion processes, thus indicating the potential comprehensiveness of the mixing framework for various statistical purposes. We underline the usefulness of our general modelling idea by establishing new upper bounds on convergence rates of kernel invariant density estimation for scalar Levy-driven OU processes and multivariate Levy jump diffusions, both for the pointwise $L^2$-risk and the $\sup$-norm risk, by showing how they can be seamlessly integrated into our framework.
At most 4.47 n stable matchings.

We improve the upper bound for the maximum possible number of stable matchings among $n$ men and $n$ women from $O(131072^n)$ to $O(4.47^n)$. To establish this bound, we develop a novel formulation of a probabilistic technique that is easy to apply and may be of independent interest in counting other combinatorial objects.
Character Randomized Benchmarking for Non-Multiplicity-Free Groups With Applications to Subspace, Leakage, and Matchgate Randomized Benchmarking

Randomized benchmarking (RB) is a powerful method for determining the error rate of experimental quantum gates. Traditional RB, however, is restricted to gatesets, such as the Clifford group, that form a unitary 2-design. The recently introduced character RB can benchmark more general gates using techniques from representation theory; up to now, however, this method has only been applied to "multiplicity-free" groups, a mathematical restriction on these groups. In this paper, we extend the original character RB derivation to explicitly treat non-multiplicity-free groups, and derive several applications. First, we derive a rigorous version of the recently introduced subspace RB, which seeks to characterize a set of one- and two-qubit gates that are symmetric under SWAP. Second, we develop a new leakage RB protocol that applies to more general groups of gates. Finally, we derive a scalable RB protocol for the matchgate group, a group that like the Clifford group is non-universal but becomes universal with the addition of one additional gate. This example provides one of the few examples of a scalable non-Clifford RB protocol. In all three cases, compared to existing theories, our method requires similar resources, but either provides a more accurate estimate of gate fidelity, or applies to a more general group of gates. In conclusion, we discuss the potential, and challenges, of using non-multiplicity-free character RB to develop new classes of scalable RB protocols and methods of characterizing specific gates.
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering

In many applications, there is interest in clustering very high-dimensional data. A common strategy is first stage dimensionality reduction followed by a standard clustering algorithm, such as k-means. This approach does not target dimension reduction to the clustering objective, and fails to quantify uncertainty. Model-based Bayesian approaches provide an appealing alternative, but often have poor performance in high-dimensions, producing too many or too few clusters. This article provides an explanation for this behavior through studying the clustering posterior in a non-standard setting with fixed sample size and increasing dimensionality. We show that the finite sample posterior tends to either assign every observation to a different cluster or all observations to the same cluster as dimension grows, depending on the kernels and prior specification but not on the true data-generating model. To find models avoiding this pitfall, we define a Bayesian oracle for clustering, with the oracle clustering posterior based on the true values of low-dimensional latent variables. We define a class of LAtent Mixtures for Bayesian (Lamb) clustering that have equivalent behavior to this oracle as dimension grows. Lamb is shown to have good performance in simulation studies and an application to inferring cell types based on scRNAseq.
MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis

Representation learning from 3D point clouds is challenging due to their inherent nature of permutation invariance and irregular distribution in space. Existing deep learning methods follow a hierarchical feature extraction paradigm in which high-level abstract features are derived from low-level features. However, they fail to exploit different granularity of information due to the limited interaction between these features. To this end, we propose Multi-Abstraction Refinement Network (MARNet) that ensures an effective exchange of information between multi-level features to gain local and global contextual cues while effectively preserving them till the final layer. We empirically show the effectiveness of MARNet in terms of state-of-the-art results on two challenging tasks: Shape classification and Coarse-to-fine grained semantic segmentation. MARNet significantly improves the classification performance by 2% over the baseline and outperforms the state-of-the-art methods on semantic segmentation task.
Compact stellar model in Tolman space-time in presence of pressure anisotropy

In this paper, we develop a new relativistic compact stellar model for a spherically symmetric anisotropic matter distribution. The model has been obtained through generating a new class of solutions by invoking the Tolman {\em ansatz} for one of the metric potentials $g_{rr}$ and a physically reasonable selective profile of radial pressure. We have matched our obtained interior solution to the Schwarzschild exterior spacetime over the bounding surface of the compact star. These matching conditions together with the condition of vanishing the radial pressure across the boundary of the star have been utilized to determine the model parameters. We have shown that the central pressure of the star depends on the parameter $p_0$. We have estimated the range of $p_0$ by using the recent data of compact stars 4U 1608-52 and Vela X-1. The effect of $p_0$ on different physical parameters e.g., pressure anisotropy, the subliminal velocity of sound, relativistic adiabatic index etc. have also been discussed. The developed model of the compact star is elaborately discussed both analytically and graphically to justify that it satisfies all the criteria demanded a realistic star. From our analysis, we have shown that the effect of anisotropy becomes small for higher values of $p_0$. The mass-radius (M-R) relationship which indicates the maximum mass admissible for observed pulsars for a given surface density has also been investigated in our model. Moreover, the variation of radius and mass with central density has been shown which allow us to estimate central density for a given radius (or mass) of a compact star.
Neural Coreference Resolution for Arabic

No neural coreference resolver for Arabic exists, in fact we are not aware of any learning-based coreference resolver for Arabic since (Björkelund and Kuhn, 2014). In this paper, we introduce a coreference resolution system for Arabic based on Lee et al’s end-to-end architecture combined with the Arabic version of bert and an external mention detector. As far as we know, this is the first neural coreference resolution system aimed specifically to Arabic, and it substantially outperforms the existing state-of-the-art on OntoNotes 5.0 with a gain of 15.2 points conll F1. We also discuss the current limitations of the task for Arabic and possible approaches that can tackle these challenges.
Scene Flow from Point Clouds with or without Learning

Scene flow is the three-dimensional (3D) motion field of a scene. It provides information about the spatial arrangement and rate of change of objects in dynamic environments. Current learning-based approaches seek to estimate the scene flow directly from point clouds and have achieved state-of-the-art performance. However, supervised learning methods are inherently domain specific and require a large amount of labeled data. Annotation of scene flow on real-world point clouds is expensive and challenging, and the lack of such datasets has recently sparked interest in self-supervised learning methods. How to accurately and robustly learn scene flow representations without labeled real-world data is still an open problem. Here we present a simple and interpretable objective function to recover the scene flow from point clouds. We use the graph Laplacian of a point cloud to regularize the scene flow to be “asrigid-as-possible”. Our proposed objective function can be used with or without learning—as a self-supervisory signal to learn scene flow representations, or as a non-learning-based method in which the scene flow is optimized during runtime. Our approach outperforms related works in many datasets. We also show the immediate applications of our proposed method for two applications: motion segmentation and point cloud densification.
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey

In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user’s needs in task-oriented dialogue systems. We focus on two core tasks, slot filling (SF) and intent classification (IC), and survey how neural based models have rapidly evolved to address natural language understanding in dialogue systems. We introduce three neural architectures: independent models, which model SF and IC separately, joint models, which exploit the mutual benefit of the two tasks simultaneously, and transfer learning models, that scale the model to new domains. We discuss the current state of the research in SF and IC, and highlight challenges that still require attention.
N=1
 Supergravity with loop quantum gravity methods and quantization of the SUSY constraint

In this paper, the classical and quantum theory of $\mathcal{N}=1$ supergravity in four spacetime dimensions will be studied in the framework of loop quantum gravity. We discuss the canonical analysis of the supergravity Holst action as first introduced by Tsuda. In this way, we also derive a compact expression of the supersymmetry constraint, which plays a crucial role in canonical supergravity theories, akin to the role of the Hamiltonian constraint in non-supersymmetric generally covariant theories. The resulting theory is then quantized using loop quantum gravity methods. In particular, we propose and discuss a quantization of the supersymmetry constraint and derive explicit expressions of the action of the resulting operator. This is important as it is the first step on the way of analyzing the Dirac algebra generated by supersymmetry and Hamiltonian constraint in the quantum theory and for finding physical states. We also discuss some qualitative properties of such solutions of the SUSY constraint.
Multimodal Continuous Emotion Recognition using Deep Multi-Task Learning with Correlation Loss

In this study, we focus on continuous emotion recognition using body motion and speech signals to estimate Activation, Valence, and Dominance (AVD) attributes. Semi-End-To-End network architecture is proposed where both extracted features and raw signals are fed, and this network is trained using multi-task learning (MTL) rather than the state-of-the-art single task learning (STL). Furthermore, correlation losses, Concordance Correlation Coefficient (CCC) and Pearson Correlation Coefficient (PCC), are used as an optimization objective during the training. Experiments are conducted on CreativeIT and RECOLA database, and evaluations are performed using the CCC metric. To highlight the effect of MTL, correlation losses and multi-modality, we respectively compare the performance of MTL against STL, CCC loss against root mean square error (MSE) loss and, PCC loss, multi-modality against single modality. We observe significant performance improvements with MTL training over STL, especially for estimation of the valence. Furthermore, the CCC loss achieves more than 7% CCC improvements on CreativeIT, and 13% improvements on RECOLA against MSE loss.
Quantitative particle approximation of nonlinear Fokker-Planck equations with singular kernel

We propose a new approach to obtain quantitative convergence of moderately interacting particle systems to solutions of nonlinear Fokker-Planck equations with singular kernels. Our result only requires very weak regularity on the interaction kernel, including the Biot-Savart kernel, the family of Keller-Segel kernels in arbitrary dimension, and more generally singular Riesz kernels. This seems to be the first time that such quantitative convergence results are obtained in Lebesgue and Sobolev norms for the aforementioned kernels. In particular, this convergence holds locally in time for PDEs exhibiting a blow-up in finite time. The proof is based on a semigroup approach combined with stochastic calculus techniques, and we also exploit the regularity of the solutions of the limiting equation. Furthermore, we obtain well-posedness for the McKean-Vlasov SDEs involving these singular kernels and we prove the trajectorial propagation of chaos for the associated moderately interacting particle systems
Many-body wavefunctions for quantum impurities out of equilibrium

We present a new method for calculating the time-dependent many-body wavefunction that follows a local quench, and we use it to find the exact wavefunction of the nonequilibrium Kondo model driven by a bias voltage. The Bethe ansatz is not used, and integrability does not appear to play any role. The method also works in other quantum impurity models (we present results in the interacting resonant level model and Anderson impurity model) and may be of wider applicability. In the case of the Kondo model, we show that the long time limit (with the system size taken to infinity first) of the time-evolving wavefunction is a current-carrying nonequilibrium steady state. We find a series expression for the average electric current that can be expanded either in weak coupling or in strong coupling, converging to all orders in the steady state limit in either case. After checking with known results in the weak antiferromagnetic regime, we find a new universal regime of strong ferromagnetic coupling with Kondo temperature $T_K^{(F)} = D e^{-\frac{3\pi^2}{8} \rho |J|}$ ($J<0$, $\rho|J|\to\infty$). In this regime, the differential conductance $dI/dV$ reaches the unitarity limit $2e^2/h$ asymptotically at large voltage or temperature.
Adaptive Variational Quantum Dynamics Simulations

We propose a general-purpose, self-adaptive approach to construct variational wavefunction ans\"atze for highly accurate quantum dynamics simulations. The McLachlan variational principle for real-time dynamics governs the equations of motion of variational parameters, where the variational ansatz is automatically generated and dynamically expanded along the time-evolution path, such that the "McLachlan distance", which is a measure of the simulation accuracy, remains below a fixed threshold. The adaptive variational quantum dynamics simulation (AVQDS) approach is applied to the Lieb-Schultz-Mattis spin chain model, where it captures the finite-rate quantum quench dynamics to high accuracy. The AVQDS circuits that prepare the quantum states along the dynamical path are shallower than those obtained from naive Trotterization by over an order of magnitude. We envision that a wide range of dynamical simulations of quantum many-body systems on near-term quantum devices will be made possible through the AVQDS framework.