Miracle-dz/UniFAR-FTUs huggingface dataset:
Sub Acute Toxicity Studies of Punicaflavone from the Methanolic Flower Extract of Punica granatum (LINN.) in Rats

DOI: 10.21276/sjm.2018. 3.6. 1 Abstract: In the present study, the isolated compound Punicaflavone (PF) from the methanolic flower extract of Punica granatum (Linn.) was subjected to toxicity assessment in Wistar albino rats. Sub-acute toxicity studies were carried out by oral administration of the PF from Punica granatum (Linn.) for a period of 28 days at different dose levels of 50, 100 and 200mg/kg body weight. No major changes in body and organ weight, behavioural, haematology and histology were observed at the end of 28 days of daily oral administration. Biochemical parameters such as the levels of Glucose, Protein, Urea and Creatinine in serum were found to be well within the normal limits. Histopathological examination of the major vital organs (heart, liver and kidney) revealed no significant pathological alterations in the treated group of rats. The subacute toxicity studies of the PF showed no mortality and no symptoms of toxicity or behavioral changes at the maximum dose (1000mg/kg). The results indicate that the PF is safe up to a dose of 1000 mg/kg.
Measurement-driven Security Analysis of Imperceptible Impersonation Attacks

The emergence of Internet of Things (IoT) brings about new security challenges at the intersection of cyber and physical spaces. One prime example is the vulnerability of Face Recognition (FR) based access control in IoT systems. While previous research has shown that Deep Neural Network(DNN)-based FR systems (FRS) are potentially susceptible to imperceptible impersonation attacks, the potency of such attacks in a wide set of scenarios has not been thoroughly investigated. In this paper, we present the first systematic, wide-ranging measurement study of the exploitability of DNN-based FR systems using a large scale dataset. We find that arbitrary impersonation attacks, wherein an arbitrary attacker impersonates an arbitrary target, are hard if imperceptibility is an auxiliary goal. Specifically, we show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim, to different extents. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face. Our results show that finding a universal perturbation is a much harder problem from the attacker's perspective. Finally, we find that the perturbed images do not generalize well across different DNN models. This suggests security countermeasures that can dramatically reduce the exploitability of DNN-based FR systems.
Proximity-induced hidden order transition in a correlated heterostructure Sr$_2$VO$_3$FeAs.

Symmetry is one of the most significant concepts in physics, and its importance has been largely manifested in phase transitions by its spontaneous breaking. In strongly correlated systems, however, mysterious and enigmatic phase transitions, inapplicable of the symmetry description, have been discovered and often dubbed hidden order transitions, as found in, $\it{e.g.}$, high-$T_C$ cuprates, heavy fermion superconductors, and quantum spin liquid candidates. Here, we report a new type of hidden order transition in a correlated heterostructure Sr$_2$VO$_3$FeAs, whose origin is attributed to an unusually enhanced Kondo-type proximity coupling between localized spins of V and itinerant electrons of FeAs. Most notably, a fully isotropic gap opening, identified by angle-resolved photoemission spectroscopy, occurs selectively in one of the Fermi surfaces below $T_{\rm HO}$ $\sim$ 150 K, associated with a singular behavior of the specific heat and a strong enhancement on the anisotropic magnetoresistance. These observations are incompatible with the prevalent broken-symmetry-driven scenarios of electronic gap opening and highlight a critical role of proximity coupling. Our findings demonstrate that correlated heterostructures offer a novel platform for design and engineering of exotic hidden order phases.
Multimodal Learning for Cardiovascular Risk Prediction using EHR Data

Electronic health records (EHRs) contain structured and unstructured data of significant clinical and research value. Various machine learning approaches have been developed to employ information in EHRs for risk prediction. The majority of these attempts, however, focus on structured EHR fields and lose the vast amount of information in the unstructured texts. Deep neural networks, on the other hand, gained tremendous momentum in knowledge discovery from EHR texts, while there are very seldom studies that used of both free-texts and the structured information in EHRs for clinical prediction. To exploit the potential information captured in EHRs, in this study we propose MI-BiLSTM, a multimodal bidirectional long short-term memory-based framework for cardiovascular risk prediction that integrates medical texts and structured clinical information. The MI-BiLSTM framework concatenates word embeddings from x-ray reports to classical clinical predictors from the Second Manifestations of ARTerial disease (SMART) study [1], before applying them to a final fully connected neural network. In the experiments, by employing the proposed framework, we compared performances of different deep neural network architectures on data of 5603 patients using 5-fold cross validation. Evaluated on the SMART study, we demonstrate the clinical relevance of integrating text features and classical predictors for cardiovascular risk prediction for patients with manifest vascular disease or at high--risk for cardiovascular disease. Our results show that the MI-BiLSTM framework using text data in addition to laboratory values outperforms deep learning models using only known clinical predictors. In future, we will focus on expanding our multimodal framework to import knowledge from available medical ontologies to enhance the quality of clinical decision making in risk prediction models. An open-source implementation of the proposed framework is publicly available at https://github.com/bagheria/CardioRisk-TextMining
Routing and Wavelength Assignment with Protection: A QUBO and Digital Annealer Approach.

The routing and wavelength assignment with protection (RWA-P) is an important problem in telecommunications. Given an optical network and incoming connection requests, the problem aims to grant maximum number of requests by assigning lightpaths at minimum network resource usage level, while ensuring the provided services remain functional in case of a single-link failure. We consider a practically relevant case of RWA-P where alternative lightpaths for requests are assumed to be given as a precomputed set, and show that it is NP-hard. We formulate RWA-P as an integer programming (IP) model, then use it as a foundation to develop a novel quadratic unconstrained binary optimization (QUBO) model. Moreover, we present conditions on model parameters to achieve a desired objective prioritization, and to ensure the exactness of the QUBO model. We use a new technology, Digital Annealer (DA), to solve the QUBO model, and compare it with three different alternatives that employ GUROBI, namely providing the models directly to the solver, and applying a branch-and-cut method. We conduct computational experiments on a large suite of instances for performance comparison and sensitivity analysis on model parameters. The results show that the emerging solution technology DA outperforms or is comparable to the established techniques coupled with the state-of-the-art solvers in addressing the need of generating high-quality solutions quickly.
Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion

Hierarchical learning has been successful at learning generalizable locomotion skills on walking robots in a sample-efficient manner. However, the low-dimensional “latent” action used to communicate between two layers of the hierarchy is typically user-designed. In this letter, we present a fully-learned hierarchical framework, that is capable of jointly learning the low-level controller and the high-level latent action space. Once this latent space is learned, we plan over continuous latent actions in a model-predictive control fashion, using a learned high-level dynamics model. This framework generalizes to multiple robots, and we present results on a Daisy hexapod simulation, A1 quadruped simulation, and Daisy robot hardware. We compare a range of learned hierarchical approaches from literature, and show that our framework outperforms baselines on multiple tasks and two simulations. In addition to learning approaches, we also compare to inverse-kinematics (IK) acting on desired robot motion, and show that our fully-learned framework outperforms IK in adverse settings on both A1 and Daisy simulations. On hardware, we show the Daisy hexapod achieve multiple locomotion tasks, in an unstructured outdoor setting, with only 2000 hardware samples, reinforcing the robustness and sample-efficiency of our approach.
A Blast From the Past: Personalizing Predictions of Video-Induced Emotions using Personal Memories as Context

A key challenge in the accurate prediction of viewers' emotional responses to video stimuli in real-world applications is accounting for person- and situation-specific variation. An important contextual influence shaping individuals' subjective experience of a video is the personal memories that it triggers in them. Prior research has found that this memory influence explains more variation in video-induced emotions than other contextual variables commonly used for personalizing predictions, such as viewers' demographics or personality. In this article, we show that (1) automatic analysis of text describing their video-triggered memories can account for variation in viewers' emotional responses, and (2) that combining such an analysis with that of a video's audiovisual content enhances the accuracy of automatic predictions. We discuss the relevance of these findings for improving on state of the art approaches to automated affective video analysis in personalized contexts.
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI

Intelligent assistants that follow commands or answer simple questions, such as Siri and Google search, are among the most economically important applications of AI. Future conversational AI assistants promise even greater capabilities and a better user experience through a deeper understanding of the domain, the user, or the user's purposes. But what domain and what methods are best suited to researching and realizing this promise? In this article we argue for the domain of voice document editing and for the methods of model-based reinforcement learning. The primary advantages of voice document editing are that the domain is tightly scoped and that it provides something for the conversation to be about (the document) that is delimited and fully accessible to the intelligent assistant. The advantages of reinforcement learning in general are that its methods are designed to learn from interaction without explicit instruction and that it formalizes the purposes of the assistant. Model-based reinforcement learning is needed in order to genuinely understand the domain of discourse and thereby work efficiently with the user to achieve their goals. Together, voice document editing and model-based reinforcement learning comprise a promising research direction for achieving conversational AI.
Benford’s law in the Gaia universe

Context. Benford’s law states that for scale- and base-invariant data sets covering a wide dynamic range, the distribution of the first significant digit is biased towards low values. This has been shown to be true for wildly different datasets, including financial, geographical, and atomic data. In astronomy, earlier work showed that Benford’s law also holds for distances estimated as the inverse of parallaxes from the ESA HIPPARCOS mission. Aims. We investigate whether Benford’s law still holds for the 1.3 billion parallaxes contained in the second data release of Gaia (Gaia DR2). In contrast to previous work, we also include negative parallaxes. We examine whether distance estimates computed using a Bayesian approach instead of parallax inversion still follow Benford’s law. Lastly, we investigate the use of Benford’s law as a validation tool for the zero-point of the Gaia parallaxes. Methods. We computed histograms of the observed most significant digit of the parallaxes and distances, and compared them with the predicted values from Benford’s law, as well as with theoretically expected histograms. The latter were derived from a simulated Gaia catalogue based on the Besançon galaxy model. Results. The observed parallaxes in Gaia DR2 indeed follow Benford’s law. Distances computed with the Bayesian approach of Bailer-Jones et al. (2018, AJ, 156, 58) no longer follow Benford’s law, although low-value ciphers are still favoured for the most significant digit. The prior that is used has a significant effect on the digit distribution. Using the simulated Gaia universe model snapshot, we demonstrate that the true distances underlying the Gaia catalogue are not expected to follow Benford’s law, essentially because the interplay between the luminosity function of the Milky Way and the mission selection function results in a bi-modal distance distribution, corresponding to nearby dwarfs in the Galactic disc and distant giants in the Galactic bulge. In conclusion, Gaia DR2 parallaxes only follow Benford’s Law as a result of observational errors. Finally, we show that a zero-point offset of the parallaxes derived by optimising the fit between the observed most-significant digit frequencies and Benford’s law leads to a value that is inconsistent with the value that is derived from quasars. The underlying reason is that such a fit primarily corrects for the difference in the number of positive and negative parallaxes, and can thus not be used to obtain a reliable zero-point.
Random tree-weighted graphs

For each $n \ge 1$, let $\mathrm{d}^n=(d^{n}(i),1 \le i \le n)$ be a sequence of positive integers with even sum $\sum_{i=1}^n d^n(i) \ge 2n$. Let $(G_n,T_n,\Gamma_n)$ be uniformly distributed over the set of simple graphs $G_n$ with degree sequence $\mathrm{d}^n$, endowed with a spanning tree $T_n$ and rooted along an oriented edge $\Gamma_n$ of $G_n$ which is not an edge of $T_n$. Under a finite variance assumption on degrees in $G_n$, we show that, after rescaling, $T_n$ converges in distribution to the Brownian continuum random tree as $n \to \infty$. Our main tool is a new version of Pitman's additive coalescent (this https URL), which can be used to build both random trees with a fixed degree sequence, and random tree-weighted graphs with a fixed degree sequence. As an input to the proof, we also derive a Poisson approximation theorem for the number of loops and multiple edges in the superposition of a fixed graph and a random graph with a given degree sequence sampled according to the configuration model; we find this to be of independent interest.
Output Feedback Control of Coupled Linear Parabolic ODE–PDE–ODE Systems

This article deals with the backstepping design of observer-based compensators for parabolic ODE–PDE–ODE systems. The latter consist of $\boldsymbol {n}$ coupled parabolic PDEs with distinct diffusion coefficients and spatially-varying coefficients, that are bidirectionally coupled to ODEs at both boundaries. The actuation and sensing appears through these ODEs resulting in a challenging control problem. For this setup a systematic backstepping approach is proposed, in order to determine a state feedback controller and an observer. In particular, the state feedback loop and the observer error dynamics are mapped into stable ODE–PDE–ODE cascades by making use of a sequence of transformations. With this, the design can be traced back to the solution of kernel equations already found in the literature as well as initial and boundary value problems, that can be solved numerically. Exponential stability of the closed-loop system is verified, wherein the decay rate can be directly specified in the design. The results of the article are illustrated by the output feedback control of an unstable ODE–PDE–ODE system with two coupled parabolic PDEs.
Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests

We study correlations between the structure and properties of a free association network of the English language, and solutions of psycholinguistic Remote Association Tests (RATs). We show that average hardness of individual RATs is largely determined by relative positions of test words (stimuli and response) on the free association network. We argue that the solution of RATs can be interpreted as a first passage search problem on a network whose vertices are words and links are associations between words. We propose different heuristic search algorithms and demonstrate that in “easily-solving” RATs (those that are solved in 15 seconds by more than 64% subjects) the solution is governed by “strong” network links (i.e. strong associations) directly connecting stimuli and response, and thus the efficient strategy consist in activating such strong links. In turn, the most efficient mechanism of solving medium and hard RATs consists of preferentially following sequence of “moderately weak” associations.
Design of a sensor integrated adaptive gripper

In this paper, design and development of a sensor integrated adaptive gripper is presented. Adaptive grippers are useful for grasping objects of varied geometric shapes by wrapping fingers around the object. The finger closing sequence in adaptive grippers may lead to ejection of the object from the gripper due to any unbalanced grasping force and such grasp failure is common for lightweight objects. Designing of the proposed gripper is focused on ensuring stable grasp on a wide variety of objects, especially, for lightweight objects (e.g., empty plastic bottles). The actuation mechanism is based on movable pulleys and tendon wires which ensure that once a link stops moving, the other links continue to move and wrap around the object. Further, optimisation is used to improve the design of the adaptive gripper. Finally, validation is done by executing object grasping in a simulated environment PyBullet for simple pick and place tasks on common household objects.
On the High Accuracy Limitation of Adaptive Property Estimation

Recent years have witnessed the success of adaptive (or unified) approaches in estimating symmetric properties of discrete distributions, where one first obtains a distribution estimator independent of the target property, and then plugs the estimator into the target property as the final estimator. Several such approaches have been proposed and proved to be adaptively optimal, i.e. they achieve the optimal sample complexity for a large class of properties within a low accuracy, especially for a large estimation error $\varepsilon\gg n^{-1/3}$ where $n$ is the sample size. In this paper, we characterize the high accuracy limitation, or the penalty for adaptation, for all such approaches. Specifically, we show that under a mild assumption that the distribution estimator is close to the true sorted distribution in expectation, any adaptive approach cannot achieve the optimal sample complexity for every $1$-Lipschitz property within accuracy $\varepsilon \ll n^{-1/3}$. In particular, this result disproves a conjecture in [Acharya et al. 2017] that the profile maximum likelihood (PML) plug-in approach is optimal in property estimation for all ranges of $\varepsilon$, and confirms a conjecture in [Han and Shiragur, 2020] that their competitive analysis of the PML is tight.
A principled analysis of Behavior Trees and their generalisations

As complex autonomous robotic systems become more widespread, the goals of transparent and reusable Artificial Intelligence (AI) become more important. In this paper we analyse how the principles behind Behavior Trees (BTs), an increasingly popular tree-structured control architecture, are applicable to these goals. Using structured programming as a guide, we analyse the BT principles of reactiveness and modularity in a formal framework of action selection. Proceeding from these principles, we review a number of challenging use-cases of BTs in the literature, and show that reasoning via these principles leads to compatible solutions. Extending these arguments, we introduce a new class of control architectures we call generalised BTs or $k$-BTs and show how they can extend the applicability of BTs to some of the aforementioned challenging BT use-cases while preserving the BT principles. We compare BTs to a number of other control architectures within this framework, and show which forms of decision-making can and cannot be equivalently represented by BTs. This allows us to construct a hierarchy of architectures and to show how BTs fit into such a hierarchy.
Stochastic approximation for CVaR-based variational inequalities

In this paper we study variational inequalities (VI) defined by the conditional value-at-risk (CVaR) of uncertain functions. We introduce stochastic approximation schemes that employ an empirical estimate of the CVaR at each iteration to solve these VIs. We investigate convergence of these algorithms under various assumptions on the monotonicity of the VI and accuracy of the CVaR estimate. Our first algorithm is shown to converge to the exact solution of the VI when the estimation error of the CVaR becomes progressively smaller along any execution of the algorithm. When the estimation error is nonvanishing, we provide two algorithms that provably converge to a neighborhood of the solution of the VI. For these schemes, under strong monotonicity, we provide an explicit relationship between sample size, estimation error, and the size of the neighborhood to which convergence is achieved. A simulation example illustrates our theoretical findings.
Long-distance heat transfer between molecular systems through a hybrid plasmonic-photonic nanoresonator

We theoretically study a hybrid plasmonic-photonic cavity setup that can be used to induce and control long-distance heat transfer between molecular systems through optomechanical interactions. The structure we propose consists of two separated plasmonic nanoantennas coupled to a dielectric cavity. The hybrid modes of this resonator can combine the large optomechanical coupling of the sub-wavelength plasmonic modes with the large quality factor and delocalized character of the cavity mode that extends over a large distance (∼µm). We show that this can lead to effective long-range heat transport between molecular vibrations that can be actively controlled through an external driving laser.
Simple Reductions from Formula-SAT to Pattern Matching on Labeled Graphs and Subtree Isomorphism

The CNF formula satisfiability problem (CNF-SAT) has been reduced to many fundamental problems in P to prove tight lower bounds under the Strong Exponential Time Hypothesis (SETH). Recently, the works of Abboud, Hansen, Vassilevska W. and Williams (STOC 16), and later, Abboud and Bringmann (ICALP 18) have proposed basing lower bounds on the hardness of general boolean formula satisfiability (Formula-SAT). Reductions from Formula-SAT have two advantages over the usual reductions from CNF-SAT: (1) conjectures on the hardness of Formula-SAT are arguably much more plausible than those of CNF-SAT, and (2) these reductions give consequences even for logarithmic improvements in a problems upper bounds. Here we give tight reductions from Formula-SAT to two more problems: pattern matching on labeled graphs (PMLG) and subtree isomorphism. Previous reductions from Formula-SAT were to sequence alignment problems such as Edit Distance, LCS, and Frechet Distance and required some technical work. This paper uses ideas similar to those used previously, but in a decidedly simpler setting, helping to illustrate the most salient features of the underlying techniques.