Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Characteristics of Middle Level Schools. ERIC Digest.

THIS DIGEST WAS CREATED BY ERIC, THE EDUCATIONAL RESOURCES INFORMATION CENTER. FOR MORE INFORMATION ABOUT ERIC, CONTACT ACCESS ERIC 1-800-LET-ERIC Concerns about middle level education began early in this century and will continue to be discussed by researchers and observers well into the next century. This digest examines the evolving characteristics and goals of middle level education in all settings, especially in terms of the most recent statements of purpose by major organizations in
Handbook of Comparative Pharmacokinetics and Residues of Veterinary Antimicrobials

The major objective of this handbook is to compile-in tabular form-the pharmacokinetic parameters of antimicrobial drugs used in food animals. This unique publication represents data from the FARAD (Food Animal Residue Avoidance Databank) databank, established by the authors under the auspices of the U.S.D.A. and contains significant amounts of previously unavailable information. This updated, one-of-a-kind volume even features additional data on laboratory rodents, dogs, cats, and horses in order to facilitate broader interspecies extrapolations. This easy-to-use reference is timely as well as invaluable to animal scientists, veterinarians, pharmacologists, and toxicologists who work with antimicrobials in chickens, turkeys, dairy and beef cattle, swine, goats, and sheep.
Phenomenological Study of Type II Seesaw with $\Delta(27)$ Symmetry

We discuss the phenomenology of type-II seesaw by extending the Standard Model with additional Higgs doublets and scalar triplets additionally invoked with $\Delta(27)$ flavor symmetry for the explanation of non-zero neutrino masses and mixings, matter-antimatter asymmetry and lepton flavor violation. The non-zero neutrino masses can be realized via type-II seesaw mechanism by introducing scalar triplets transforming as triplets under $\Delta(27)$ while we add additional $SU(2)_L$ scalar doublets to have correct charge lepton masses. We further demonstrate with detailed numerical analysis in agreement with neutrino oscillation data like non-zero reactor mixing angle, $\delta_{CP}$, the sum of the light neutrino masses, two mass squared differences and its implication to neutrinoless double beta decay. We also discuss on the matter-antimatter asymmetry of the universe through leptogenesis with the decay of TeV scale scalar triplets and variation of CP-asymmetry with input model parameters. Finally, we comment on implication to lepton flavor violating decays like $\mu \to e \gamma$, $\mu \to 3 e$ processes.
On the Use of a New Class of Dissimilarity Measures for Nearest Neighbor Classification of High Dimension, Low Sample Size Data.

In high dimension, low sample size (HDLSS) settings, the nearest neighbor classifier based on the Euclidean distance yields poor performance if differences between the locations get masked by the scale differences. To rectify this problem, several modifications of the nearest neighbor classifier have been proposed in the literature. However, these existing methods often fail to discriminate among populations having same locations and scales. In this article, we propose some simple modifications of the nearest neighbor classifier using a new class of dissimilarity measures. The resulting classifiers perform quite well even when the underlying populations have no differences in their locations and scales. Some of these classifiers can also discriminate among populations having the same one-dimensional marginal distributions. High-dimensional behavior of the proposed classifiers are studied theoretically. Numerical experiments with a variety of simulated as well as real data sets clearly exhibit the usefulness of our classifiers.
Counting weighted independent sets beyond the permanent

Jerrum, Sinclair and Vigoda (2004) showed that the permanent of any square matrix can be estimated in polynomial time. This computation can be viewed as approximating the partition function of edge-weighted matchings in a bipartite graph. Equivalently, this may be viewed as approximating the partition function of vertex-weighted independent sets in the line graph of a bipartite graph. Line graphs of bipartite graphs are perfect graphs, and are known to be precisely the class of (claw, diamond, odd hole)-free graphs. So how far does the result of Jerrum, Sinclair and Vigoda extend? We first show that it extends to (claw, odd hole)-free graphs, and then show that it extends to the even larger class of (fork, odd hole)-free graphs. Our techniques are based on graph decompositions, which have been the focus of much recent work in structural graph theory, and on structural results of Chvatal and Sbihi (1988), Maffray and Reed (1999) and Lozin and Milanic (2008).
Satellite galaxies as better tracers of the Milky Way halo mass

Abstract The inference of the Milky Way halo mass requires modelling the phase space structure of dynamical tracers, with different tracers following different models and having different levels of sensitivity to the halo mass. For steady-state models, deviations from steady-state in the tracer distribution lead to an irreducible stochastic bias. This bias is small for satellite galaxies and dark matter particles, but as large as a factor of 2 for halo stars. This is consistent with the picture that satellite galaxies closely trace the underlying phase space distribution of dark matter particles, while halo stars are less phase-mixed. As a result, the use of only ~100 satellite galaxies can achieve a significantly higher accuracy than that achievable with a much larger sample of halo stars.
Local generalizations of derivatives

From physical perspective, derivatives can be viewed as mathematical idealizations of the linear growth. The linear growth condition has special properties, which make it preferred. The manuscript investigates the general properties of the local generalizations of derivatives assuming the usual topology of the real line. The concept of derivative is generalized in terms of the class of the modulus of continuity of the primitive function. This definition is suitable for applications involving continuous but possibly non-absolutely continuous functions of a real variable. The main application of the approach is the generalization of the Lebesgue monotone differentiation theorem. On the second place, the conditions of continuity of generalized derivative are also demonstrated.
The fundamental relation between supermassive black holes and their host galaxies

We study the correlations between supermassive black holes (BHs) and their host galaxies, using a sample of 83 BH masses collected from the most recent and reliable spatially resolved estimates available from the literature. We confirm the mono- and bivariate correlations between SMBHs and the bulges of their host galaxies, confirming that the correlation with the effective velocity dispersion is not significantly improved by higher dimensionality. Instead, pseudo-bulges do not seem to correlate with their SMBHs, probably because their secular evolution is often unable to trigger accretion onto the central BH. We then present a novel approach aimed at finding the fundamental relation between SMBHs and their host galaxies. For the first time, we analytically combine BH masses with the Fundamental Plane (FP), showing that MBH–σe appears to be the fundamental relation rather than a putative ‘BH Fundamental Plane’ of the kind MBH–σe–Re. These results can be explained by a picture which sees the MBH–σe relation as a natural outcome of the change in AGN feedback from momentum-driven to energy-driven. The other scaling relations are then established through the FP.
SCREENING OF ANTENATAL MOTHERS AND PREVENTION OF PERINATAL GROUP B STREPTOCOCCAL INFECTION

The objectives of this study were: (1) to detect group B streptococcus (GBS) carriers among antenatal patients during pregnancy ( 35 to 37 weeks) in the University Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia, and (2) to give antibiotic prophylaxis during the intrapartum period for the GBS carrier patients. The aim is to prevent early onset of GBS infection in newborn. This pilot study was carried out from 1st July 2005 to 31st August 2005. It is a prospective study involving 56 pregnant women who were seen at the antenatal clinic in the UMMC between 35 to 37 weeks gestation. High vaginal swab and low vaginal together with endoanal swab were taken for culture and sensitivity. The antibiotic prophylaxis was started early in the intrapartum period for patients with positive culture for GBS. GBS was detected in 18 patients which contributes to about 32% of the study population. The proper choice of antibiotic is important in successful disease prevention.
Scattering of solutions to NLW by Inward Energy Decay

The topic of this paper is a semi-linear, energy sub-critical, defocusing wave equation $\partial_t^2 u - \Delta u = - |u|^{p -1} u$ in the 3-dimensional space ($3\leq p<5$) whose initial data are radial and come with a finite energy. In this work we prove scattering in the positive time direction by only assuming the inward part of the energy decays at a certain rate, as long as the total energy is finite, regardless of the decay rate or size of the outward energy. More precisely, we assume the initial data comes with a finite energy and \[ \int_{{\mathbb R}^3} \max\{1,|x|^\kappa\}\ (\ |\nabla u_0(x)\cdot \frac{x}{|x|} + \frac{u_0(x)}{|x|} + u_1(x)\ |^2 + \frac{2}{p+1}|u_0(x)|^{p+1}\ ) dx \kappa_0(p)$, we can also prove $\|u\|_{L^p L^{2p}}({\mathbb R}^+ \times {\mathbb R}^3)< +\infty$ and give an explicit rate of $u$'s convergence to a free wave.
Certified Side Channels

We demonstrate that the format in which private keys are persisted impacts Side Channel Analysis (SCA) security. Surveying several widely deployed software libraries, we investigate the formats they support, how they parse these keys, and what runtime decisions they make. We uncover a combination of weaknesses and vulnerabilities, in extreme cases inducing completely disjoint multi-precision arithmetic stacks deep within the cryptosystem level for keys that otherwise seem logically equivalent. Exploiting these vulnerabilities, we design and implement key recovery attacks utilizing signals ranging from electromagnetic (EM) emanations, to granular microarchitecture cache timings, to coarse traditional wall clock timings.
Breaking the dichotomy between typical and anomalous globular clusters: the case of NGC 3201

Abstract We recently discovered that NGC 3201 has characteristics that set it outside the current twofold classification scheme for Galactic globular clusters (GCs). Most GCs are mono-metallic and show light-element abundance variations (e.g., Na-O and C-N anti-correlations); but a minority of clusters also present variations in Fe correlating with s-process element and C+N+O abundances, and they possess multiple C-N sequences. These anomalous GCs also have a broad sub-giant branch (SGB) and follow the same mass-size relation as dwarf galaxies possibly evolving into GCs. We now revealed that NGC 3201 belongs to neither group. It has multiple C-N sequences, but no broad SGB, no strong evidence of a Fe-spread, and it does not follow the mass-size relation.
Hard properties with (very) short PCPPs and their applications

We show that there exist properties that are maximally hard for testing, while still admitting PCPPs with a proof size very close to linear. Specifically, for every fixed $\ell$, we construct a property $\mathcal{P}^{(\ell)}\subseteq\{0,1\}^n$ satisfying the following: Any testing algorithm for $\mathcal{P}^{(\ell)}$ requires $\Omega(n)$ many queries, and yet $\mathcal{P}^{(\ell)}$ has a constant query PCPP whose proof size is $O(n\cdot \log^{(\ell)}n)$, where $\log^{(\ell)}$ denotes the $\ell$ times iterated log function (e.g., $\log^{(2)}n = \log \log n$). The best previously known upper bound on the PCPP proof size for a maximally hard to test property was $O(n \cdot \mathrm{poly}\log{n})$. As an immediate application, we obtain stronger separations between the standard testing model and both the tolerant testing model and the erasure-resilient testing model: for every fixed $\ell$, we construct a property that has a constant-query tester, but requires $\Omega(n/\log^{(\ell)}(n))$ queries for every tolerant or erasure-resilient tester.
Commonsense Knowledge + BERT for Level 2 Reading Comprehension Ability Test

Commonsense knowledge plays an important role when we read. The performance of BERT on SQuAD dataset shows that the accuracy of BERT can be better than human users. However, it does not mean that computers can surpass the human being in reading comprehension. CommonsenseQA is a large-scale dataset which is designed based on commonsense knowledge. BERT only achieved an accuracy of 55.9% on it. The result shows that computers cannot apply commonsense knowledge like human beings to answer questions. Comprehension Ability Test (CAT) divided the reading comprehension ability at four levels. We can achieve human like comprehension ability level by level. BERT has performed well at level 1 which does not require common knowledge. In this research, we propose a system which aims to allow computers to read articles and answer related questions with commonsense knowledge like a human being for CAT level 2. This system consists of three parts. Firstly, we built a commonsense knowledge graph; and then automatically constructed the commonsense knowledge question dataset according to it. Finally, BERT is combined with the commonsense knowledge to achieve the reading comprehension ability at CAT level 2. Experiments show that it can pass the CAT as long as the required common knowledge is included in the knowledge base.
Juxtaposing Controlled Empirical Studies in Visualization with Topic Developments in Psychology

Empirical studies form an integral part of visualization research. Not only can they facilitate the evaluation of various designs, techniques, systems, and practices in visualization, but they can also enable the discovery of the causalities explaining why and how visualization works. This state-of-the-art report focuses on controlled and semi-controlled empirical studies conducted in laboratories and crowd-sourcing environments. In particular, the survey provides a taxonomic analysis of over 129 empirical studies in the visualization literature. It juxtaposes these studies with topic developments between 1978 and 2017 in psychology, where controlled empirical studies have played a predominant role in research. To help appreciate this broad context, the paper provides two case studies in detail, where specific visualization-related topics were examined in the discipline of psychology as well as the field of visualization. Following a brief discussion on some latest developments in psychology, it outlines challenges and opportunities in making new discoveries about visualization through empirical studies.
Deep Context-Aware Recommender System Utilizing Sequential Latent Context

Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges. We suggest a new latent modeling of sequential context by generating sequences of contextual information and reducing their contextual space to a compressed latent space.We train a long short-term memory (LSTM) encoder-decoder network on sequences of contextual information and extract sequential latent context from the hidden layer of the network in order to represent a compressed representation of sequential data. We propose new context-aware recommendation models that extend the neural collaborative filtering approach and learn nonlinear interactions between latent features of users, items, and contexts which take into account the sequential latent context representation as part of the recommendation process. We deployed our approach using two context-aware datasets with different context dimensions. Empirical analysis of our results validates that our proposed sequential latent context-aware model (SLCM), surpasses state of the art CARS models.
Connected Assembly and Reconfiguration by Finite Automata

We consider methods for connected reconfigurations by finite automate in the so-called \emph{hybrid} or \emph{Robot-on-Tiles} model of programmable matter, in which a number of simple robots move on and rearrange an arrangement of passive tiles in the plane that form \emph{polyomino} shapes, making use of a supply of additional tiles that can be placed. We investigate the problem of reconfiguration under the constraint of maintaining connectivity of the tile arrangement; this reflects scenarios in which disconnected subarrangements may drift apart, e.g., in the absence of gravity in space. We show that two finite automata suffice to mark a bounding box, which can then be used as a stepping stone for more complex operations, such as scaling a tile arrangement by a given factor, rotating arrangements, or copying arrangements to a different location. We also describe an algorithm for scaling monotone polyominoes without the help of a bounding box.
√n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank

In this paper, we consider the problem of online learning of Markov decision processes (MDPs) with very large state spaces. Under the assumptions of realizable function approximation and low Bellman ranks, we develop an online learning algorithm that learns the optimal value function while at the same time achieving very low cumulative regret during the learning process. Our learning algorithm, Adaptive Value-function Elimination (AVE), is inspired by the policy elimination algorithm proposed in (Jiang et al., 2017), known as OLIVE. One of our key technical contributions in AVE is to formulate the elimination steps in OLIVE as contextual bandit problems. This technique enables us to apply the active elimination and expert weighting methods from (Dudik et al., 2011), instead of the random action exploration scheme used in the original OLIVE algorithm, for more efficient exploration and better control of the regret incurred in each policy elimination step. To the best of our knowledge, this is the first $\sqrt{n}$-regret result for reinforcement learning in stochastic MDPs with general value function approximation.
A New GNG Graph-Based Hand Gesture Recognition Approach

Hand Gesture Recognition (HGR) is of major importance for Human-Computer Interaction (HCI) applications. In this paper, we present a new hand gesture recognition approach called GNG-IEMD. In this approach, first, we use a Growing Neural Gas (GNG) graph to model the image. Then we extract features from this graph. These features are not geometric or pixel-based, so do not depend on scale, rotation, and articulation. The dissimilarity between hand gestures is measured with a novel Improved Earth Mover\textquotesingle s Distance (IEMD) metric. We evaluate the performance of the proposed approach on challenging public datasets including NTU Hand Digits, HKU, HKU multi-angle, and UESTC-ASL and compare the results with state-of-the-art approaches. The experimental results demonstrate the performance of the proposed approach.
Improving Neural Question Generation using World Knowledge

In this paper, we propose a method for incorporating world knowledge (linked entities and fine-grained entity types) into a neural question generation model. This world knowledge helps to encode additional information related to the entities present in the passage required to generate human-like questions. We evaluate our models on both SQuAD and MS MARCO to demonstrate the usefulness of the world knowledge features. The proposed world knowledge enriched question generation model is able to outperform the vanilla neural question generation model by 1.37 and 1.59 absolute BLEU 4 score on SQuAD and MS MARCO test dataset respectively.
On the Non-Adaptive Zero-Error Capacity of the Discrete Memoryless Two-Way Channel

the problem of communicating over a discrete memoryless two-way channel using non-adaptive schemes, under a zero probability of error criterion. We derive inner and outer bounds on the zero-error capacity region, based on random coding, linear programming, and linear codes. Our work generalizes arguments of Holzman and Korner, and of Tolhuizen, obtained in the special case of the binary multiplying channel.
Aligning daily activities with personality: towards a recommender system for improving wellbeing

Recommender Systems have not been explored to a great extent for improving health and subjective wellbeing. Recent advances in mobile technologies and user modelling present the opportunity for delivering such systems, however the key issue is understanding the drivers of subjective wellbeing at an individual level. In this paper we propose a novel approach for deriving personalized activity recommendations to improve subjective wellbeing by maximizing the congruence between activities and personality traits. To evaluate the model, we leveraged a rich dataset collected in a smartphone study, which contains three weeks of daily activity probes, the Big-Five personality questionnaire and subjective wellbeing surveys. We show that the model correctly infers a range of activities that are 'good' or 'bad' (i.e. that are positively or negatively related to subjective wellbeing) for a given user and that the derived recommendations greatly match outcomes in the real-world.
On two-dimensional steady hypersonic-limit Euler flows passing ramps and radon measure solutions of compressible Euler equations

We proposed rigorous definitions of Radon measure solutions for boundary value problems of steady compressible Euler equations which modeling hypersonic-limit inviscid flows passing two-dimensional ramps, and their interactions with still gas and pressureless jets. We proved the Newton-Busemann pressure law of drags of body in hypersonic flow, and constructed various physically interesting measure solutions with density containing Dirac measures supported on curves, also exhibited examples of blow up of certain measure solutions. This established a mathematical foundation for applications in engineering and further studies of measure solutions of compressible Euler equations.
TYC 8606-2025-1: a mild barium star surrounded by the ejecta of a very late thermal pulse

We report the discovery of a spiral-like nebula with the Wide-field Infrared Survey Explorer and the results of optical spectroscopy of its associated star TYC 8606-2025-1 with the Southern African Large Telescope. We find that TYC 8606-2025-1 is a G8 III star of $\approx 3 \, \rm \, M_{\odot }$, showing a carbon depletion by a factor of 2 and a nitrogen enhancement by a factor of 3. We also derived an excess of s-process elements, most strongly for barium, which is a factor of 3 overabundant, indicating that TYC 8606-2025-1 is a mild barium star. We thereby add a new member to the small group of barium stars with circumstellar nebulae. Our radial velocity measurements indicate that TYC 8606-2025-1 has an unseen binary companion. The advanced evolutionary stage of TYC 8606-2025-1, together with the presence of a circumstellar nebula, implies an initial mass of the companion of also about $3 \, \rm \, M_{\odot }$. We conclude that the infrared nebula, due to its spiral shape, and because it has no optical counterpart, was ejected by the companion as a consequence of a very late thermal pulse, during about one orbital rotation.