Miracle-dz/UniFAR-FTUs huggingface dataset:
Marked correlation functions in perturbation theory

We develop perturbation theory approaches to model the marked correlation function constructed to up-weight low density regions of the Universe, which might help distinguish modified gravity models from the standard cosmology model based on general relativity. Working within Convolution Lagrangian Perturbation Theory, we obtain that weighted correlation functions are expressible as double convolution integrals that we approximate using a combination of Eulerian and Lagrangian schemes. We find that different approaches agree within 1% on quasi non-linear scales. Compared with N-body simulations, the perturbation theory is found to provide accurate predictions for the marked correlation function of dark matter fields, dark matter halos as well as Halo Occupation Distribution galaxies down to 30 Mpc/h. These analytic approaches help to understand the degeneracy between the mark and the galaxy bias and find a way to maximize the differences among various cosmological models.
de Sitter Entanglement and Conformal Description of the Cosmological Horizon

In this thesis, we aim to understand the microscopic details and origin of the Cosmological Horizon, produced by a static observer in four-dimensional de Sitter (dS$_4$) spacetime. We consider a deformed extension of dS spacetime by means of a single $\mathbb Z_q$ quotient, which resembles an Orbifold geometry. The Orbifold parameter induces a pair of codimension two minimal surfaces given by 2-spheres in the Euclidean geometry. Using dimensional reduction on the two-dimensional plane where the minimal surfaces have support, we use the Liouville field theory and the Kerr/CFT mechanism in order to describe the underlying degrees of freedom of the Cosmological Horizon. We then show, that in the large $q$-limit, this pair of codimensions two surfaces can be realized as the conformal boundaries of dS$_3$. We notice that the central charge obtained using Liouville theory, in the latter limit, corresponds to the Strominger central charge obtained in the context of the dS/CFT correspondence. In addition, a formulation of entanglement entropy for de Sitter spacetimes is given in terms of dS holography and also a different approach in which the entanglement between two disconnected bulk observers is described in terms of the topology of the spacetime. Therefore, a quarter of the area formula is proposed, in which the area corresponds to the are of the set of fixed points of an $S^2/\mathbb Z_q$ orbifold.
PLOS ONE Identification of cyclobutane pyrimidine dimer-responsive genes using UVB-irradiated human keratinocytes transfected with in vitro-synthesized photolyase mRNA

21 Major biological effects of UVB are attributed to cyclobutane pyrimidine dimers (CPDs), the 22 most common photolesions formed on DNA. To investigate the contribution of CPDs to 23 UVB-induced changes of gene expression, a model system was established by transfecting 24 keratinocytes with pseudouridine-modified mRNA (Ψ-mRNA) encoding CPD-photolyase. 25 Microarray analyses of this model system demonstrated that more than 50% of the gene 26 expression altered by UVB was mediated by CPD photolesions. Functional classification of 27 the gene targets revealed strong effects of CPDs on the regulation of the cell cycle and 28 transcriptional machineries. To confirm the microarray data, cell cycle-regulatory genes, 29 CCNE1 and CDKN2B that were induced exclusively by CPDs were selected for further 30 investigation. Following UVB irradiation, expression of these genes increased significantly at 31 both mRNA and protein levels, but not in cells transfected with CPD-photolyase Ψ-mRNA 32 and exposed to photoreactivating light. Treatment of cells with inhibitors of c-Jun N-terminal 33 kinase (JNK) blocked the UVB-dependent upregulation of both genes suggesting a role for 34 JNK in relaying the signal of UVB-induced CPDs into transcriptional responses. Thus, 35 photolyase mRNA-based experimental platform demonstrates CPD-dependent and 36 independent events of UVB-induced cellular responses, and, as such, has the potential to 37 identify novel molecular targets for treatment of UVB-mediated skin diseases. 38
Prediction of H2S and CO2 Solubility in Aqueous MDEA and MDEA/PZ Solutions Using ELECNRTL and ACID GAS Packages

In this study, the solubility of acid gases of hydrogen sulfide and carbon dioxide in MDEA and MDEA/PZ aqueous solutions was evaluated by different thermodynamic packages. Comparison of modeling results with a series of laboratory and industrial data released from 1997 to 2010 indicates the high accuracy of ACID GAS thermodynamic package (Aspen HYSYS 8.3) to prediction of acid gases solubility in the mentioned solutions compared to the ELECNRTL thermodynamic package (Aspen plus V8.2), especially in the range of acid gases operational concentration in the gas refineries.
Modeling of a Contact-Less Electric-Vehicle Battery-Charging Station Fed from On-Grid Photovoltic Arrays

Electric vehicles (EVs) are environmental friendly due to no exhaust gases or carbon dioxide. In addition, there is no noise through operation. However, up to now, there are some challenges that facing its spread through all over the world. The main problem that these vehicles face is the fast charging process of the used batteries through neat and clean source without plugs. So, this chapter deals with a proposed method for a contactless battery charger of both electric and hybrid electric vehicles (HEVs) from renewable resources. The chapter proposes a public station for fast charging. This station implies off-board battery charger fed from on-grid (OG) photovoltaic (PV) arrays through inductively power transfer (IPT). This comfortable 100 kW contactless power station is designed, modeled, and simulated as a general software package reliable to be used for any other station design. The air gap of the air-core transformer (ACT) divides the station into two parts. The first part implies roof-mounted PV array with its intelligent-controlled maximum power point tracking (MPPT) technique, power converter, three-level power inverter, and resonant compensator converter that operates at high frequency. The second one includes rectifiers and switched mode power converter with smart controller. The chapter includes samples for simulation results that are obtained from the Matlab software package.
Examining Attitudinal Determinants of Startup Intention Among University Students of Hospitality and Tourism

Entrepreneurship is seen as a panacea to some of the societal challenges faced by economies nowadays. It is regarded as a source of employment, innovation, competitiveness, and wealth. Research emphasizes the intentionality of entrepreneurial behaviors as these behaviors are voluntary. The intention to be entrepreneur has been studied extensively. However, few studies considered studying hospitality and tourism students, particularly in Malaysia, knowing that tourism sector is an important contributor to the GDP of the country. Therefore, the main objective of the current study is to examine the effects of the attitudinal determinant on entrepreneurial intention. The sample utilized for the study comprised 206 students from one public university in Malaysia. Regression analysis indicated that self-efficacy is the strongest predictor of entrepreneurial intention followed by attitudes towards entrepreneurship and subjective norms. The model explained over 60% of variance in entrepreneurial intention.
ITERATION IN THE DESIGN OF THE HUMAN-COMPUTER INTERFACE1

Recent advances in the microelectronic industry have made wide-spread physical and economic access to high technology tools a reality. Before the full potential benefits of these tools can be realized , however, them must become accessible in the cognitive sense as well. This we can discuss from two points of view: that of the Buxton, W. & Sniderman, R. (1980). Iteration in the Design of the Human-Computer Interface. Proceedings of the 13th Annual Meeting, Human Factors Association of Canada, 72-81. Issues pertaining to designing effective human-computer interfaces are discussed. This presentation focuses on the special case of providing congenial computer-based tools to end users who are expert in their own area, but who may be technologically naive. In so doing, we draw examples from a particular study of designing computer systems for professional musicians. This experience brings to light many issues which have relevance beyond the specific application of music. These include the importance of effective prototyping tools, the use of test subjects during the design process, the importance of developing methods of performance evaluation, and more generally, the value of taking an iterative approach to design.
Human Annotations Improve GAN Performances

Generative Adversarial Networks (GANs) have shown great success in many applications. In this work, we present a novel method that leverages human annotations to improve the quality of generated images. Unlike previous paradigms that directly ask annotators to distinguish between real and fake data in a straightforward way, we propose and annotate a set of carefully designed attributes that encode important image information at various levels, to understand the differences between fake and real images. Specifically, we have collected an annotated dataset that contains 600 fake images and 400 real images. These images are evaluated by 10 workers from the Amazon Mechanical Turk (AMT) based on eight carefully defined attributes. Statistical analyses have revealed different distributions of the proposed attributes between real and fake images. These attributes are shown to be useful in discriminating fake images from real ones, and deep neural networks are developed to automatically predict the attributes. We further utilize the information by integrating the attributes into GANs to generate better images. Experimental results evaluated by multiple metrics show performance improvement of the proposed model.
Unlabeled sensing with local permutations

Unlabeled sensing is a linear inverse problem where the measurements are scrambled under an unknown permutation leading to loss of correspondence between the measurements and the rows of the sensing matrix. Motivated by practical tasks such as mobile sensor networks, target tracking and the pose and correspondence estimation between point clouds, we study a special case of this problem restricting the class of permutations to be local and allowing for multiple views. In this setting, namely unlabeled multi-view sensing with local permutation, previous results and algorithms are not directly applicable. In this paper, we propose a computationally efficient algorithm that creatively exploits the machinery of graph alignment and Gromov-Wasserstein alignment and leverages the multiple views to estimate the local permutations. Simulation results on synthetic data sets indicate that the proposed algorithm is scalable and applicable to the challenging regimes of low to moderate SNR.
Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams

Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems being designed according to the concept of stream processing. A common area of application is the processing of continuous data streams from sensors, for example, IoT devices or performance monitoring tools. In addition to analyzing pure sensor data, analyses of data for groups of sensors often need to be performed as well. Therefore, data streams of the individual sensors have to be continuously aggregated to a data stream for a group. Motivated by a real-world application scenario, we propose that such a stream aggregation approach has to allow for aggregating sensors in hierarchical groups, support multiple such hierarchies in parallel, provide reconfiguration at runtime, and preserve the scalability and reliability qualities induced by applying stream processing techniques. We propose a stream processing architecture fulfilling these requirements, which can be integrated into existing big data architectures. We present a pilot implementation of such an extended architecture and show how it is used in industry. Furthermore, in experimental evaluations we show that our solution scales linearly with the amount of sensors and provides adequate reliability in the case of faults.
Quantum experiments with single-photon spin-orbit lattice arrays

The thesis introduces two single-photon experiments with the lattice of spin-orbit arrays. The basic background knowledge has been introduced in Chapter 1. Chapters 2− 4 describe the detailed techniques used in these two experiments. In the first work, we implement a remote state preparation protocol on our singlephoton orbital angular momentum (OAM) lattice state via hybrid-entanglement. Remote state preparation is a variant of quantum state teleportation where the sender knows the transmitted state. It is known to require fewer classical resources and exhibit a nontrivial trade-off between the entanglement and classical communication compared with quantum teleportation. Here we propose a state preparation scheme between two spatially separated photons sharing a hybrid-entangled polarization-OAM state. By sending one of the polarization-entangled photon pairs through Lattice of Optical Vortex prism pairs, we generate a two-dimensional lattice of spin-orbit coupled single-photons. We show that the measurement taken by an electron-multiplying intensified CCD camera on the transformed photons can be remotely prepared by the polarization projection of the other. Our protocol could have a significant impact on long-distance quantum communication with higher channel capacity and lead to more efficient and compatible quantum information processing techniques. The second experiment investigates the single-photon Talbot Effect in spin-orbit arrays. The Talbot Effect is a near-field diffraction effect, that occurs with the propagation of periodically structured waves. It has enabled several unique applications in optical metrology, image processing, data transmission, and matter-wave interferometry. We observe that upon propagation, the wavefronts of the single photons manifest self-imaging whereby the OAM lattice intensity profile is recovered. Furthermore, we show that the intensity distribution at each fractional Talbot distance is indicative of the periodic helical phase structure corresponding to a lattice of OAM states. This phenomenon is a significant addition to the toolbox of orbital angular momentum and spin-orbit techniques that are becoming increasingly important in optical implementations of quantum information.
Flexible Functional Split and Power Control for Energy Harvesting Cloud Radio Access Networks

Functional split is a promising technique to flexibly balance the processing cost at remote ends and the fronthaul rate in cloud radio access networks (C-RAN). By harvesting renewable energy, remote radio units (RRUs) can save grid power and be flexibly deployed. However, the randomness of energy arrival poses a major design challenge. To maximize the throughput under the average fronthaul rate constraint in C-RAN with renewable powered RRUs, we first study the offline problem of selecting the optimal functional split modes and the corresponding durations, jointly with the transmission power. We find that between successive energy arrivals, at most two functional split modes should be selected. Then the optimal online problem is formulated as an Markov decision process (MDP). To deal with the curse of dimensionality of solving MDP, we further analyze the special case with one instance of energy arrival and two candidate functional split modes as inspired by the offline solution, and then a heuristic online policy is proposed. Numerical results show that with flexible functional split, the throughput can be significantly improved compared with fixed functional split. Also, the proposed heuristic online policy has similar performance with the optimal online one, as validated by simulations.
Predicting Drug-Drug Interactions from Molecular Structure Images

Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view. Almost all of the machine learning approaches have focused on text data or textual representation of the structural data of drugs. We present the first work that uses drug structure images as the input and utilizes a Siamese convolutional network architecture
An Energy Efficient D2D Model with Guaranteed Quality of Service for Cloud Radio Access Networks

This paper proposes a spectrum selection scheme and a transmit power minimization scheme for a device-to-device (D2D) network cross-laid with a cloud radio access network (CRAN). The D2D communications are allowed as an overlay to the CRAN as well as in the unlicensed industrial, scientific and medical radio (ISM) band. A link distance based scheme is proposed and closed-form approximations are derived for the link distance thresholds to select the operating band of the D2D users. Furthermore, analytical expressions are derived to calculate the minimum required transmit power to achieve a guaranteed level of quality of service in each operating band. The results demonstrate that the proposed scheme achieves nearly 50% power saving compared to a monolithic (purely overlay or purely ISM band) D2D network.
Towards a deeper understanding of the physics driving galaxy quenching – inferring trends in the gas content via extinction

Abstract In order to investigate the importance of different proposed quenching mechanisms, we use an indirect method to estimate gas masses for ∼62,000 SDSS DR7 galaxies. We infer gas surface densities from dust column densities as traced by extinction within the fibre, applying a metallicity correction to account for varying dust-to-gas ratios. We find that both gas fraction and star formation efficiency (SFE) decrease moving away from the star forming main sequence (MS)towards quiescence for all galaxy masses. We further show that both quantities correlate similarly strongly with the departure from the MS, implying the need for any physical model of quenching to invoke a change in both gas fraction and SFE. Our results call for a better understanding of the physical processes driving the decrease in star formation efficiency, which has received relatively little attention in the theory of quenching until now.
Pseudo-linear Convergence of an Additive Schwarz Method for Dual Total Variation Minimization

In this paper, we propose an overlapping additive Schwarz method for total variation minimization based on a dual formulation. The $O(1/n)$-energy convergence of the proposed method is proven, where $n$ is the number of iterations. In addition, we introduce an interesting convergence property called pseudo-linear convergence of the proposed method; the energy of the proposed method decreases as fast as linearly convergent algorithms until it reaches a particular value. It is shown that such the particular value depends on the overlapping width $\delta$, and the proposed method becomes as efficient as linearly convergent algorithms if $\delta$ is large. As the latest domain decomposition methods for total variation minimization are sublinearly convergent, the proposed method outperforms them in the sense of the energy decay. Numerical experiments which support our theoretical results are provided.
Morphological signatures induced by dust back reaction in discs with an embedded planet

Recent observations have revealed a gallery of substructures in the dust component of nearby protoplanetary discs, including rings, gaps, spiral arms, and lopsided concentrations. One interpretation of these substructures is the existence of embedded planets. Not until recently, however, most of the modelling effort to interpret these observations ignored the dust back reaction to the gas. In this work, we conduct local-shearing-sheet simulations for an isothermal, inviscid, non-self-gravitating, razor-thin dusty disc with a planet on a fixed circular orbit. We systematically examine the parameter space spanned by planet mass (0.1Mth ≤ Mp ≤ 1Mth, where Mth is the thermal mass), dimensionless stopping time (10−3 ≤ τs ≤ 1), and solid abundance (0 < Z ≤ 1). We find that when the dust particles are tightly coupled to the gas (τs < 0.1), the spiral arms are less open and the gap driven by the planet becomes deeper with increasing Z, consistent with a reduced speed of sound in the approximation of a single dust–gas mixture. By contrast, when the dust particles are marginally coupled (0.1 ≲ τs ≲ 1), the spiral structure is insensitive to Z and the gap structure in the gas can become significantly skewed and unidentifiable. When the latter occurs, the pressure maximum radially outside of the planet is weakened or even extinguished, and hence dust filtration by a low-mass (Mp < Mth) planet could be reduced or eliminated. Finally, we find that the gap edges where the dust particles are accumulated as well as the lopsided large-scale vortices driven by a massive planet, if any, are unstable, and they are broken into numerous small-scale dust–gas vortices.
Universal composition operators

A Hilbert space operator U is called \textit{universal} (in the sense of Rota) if every Hilbert space operator is similar to a multiple of U restricted to one of its invariant subspaces. It follows that the \textit{invariant subspace problem} for Hilbert spaces is equivalent to the statement that all minimal invariant subspaces for U are one dimensional. In this article we characterize all linear fractional composition operators Cϕf=f∘ϕ that have universal translates on both the classical Hardy spaces H2(C+) and H2(D) of the half-plane and the unit disk, respectively. The new example here is the composition operator on H2(D) with affine symbol ϕa(z)=az+(1−a) for $0
DNNRE: A Dynamic Neural Network for Distant Supervised Relation Extraction

Distant Supervised Relation Extraction (DSRE) is usually formulated as a problem of classifying a bag of sentences that contain two query entities, into the predefined relation classes. Most existing methods consider those relation classes as distinct semantic categories while ignoring their potential connections to each other and query entities. In this paper, we propose to leverage those connections to improve the relation extraction accuracy. Our key ideas are twofold: (1) For sentences belonging to the same relation class, the expression style, i.e. words choice, can vary according to the query entities. To account for this style shift, the model should adjust its parameters in accordance with entity types. (2) Some relation classes are semantically similar, and the mutual relationship of classes can be adopted to enhance the relation predictor. This is especially beneficial for those classes with few samples, i.e., long-tail classes. To unify these two ideas, we developed a novel Dynamic Neural Network for Relation Extraction (DNNRE). The network adopts a novel dynamic parameter generator which dynamically generates the network parameters according to the query entity types, relation classes, and the class similarity matrix. By using this mechanism, the network can simultaneously handle the style shift problem and leverage mutual class relationships to enhance the prediction accuracy for long-tail classes. Through our experimental study, we demonstrate the effectiveness of the proposed method and show that it can achieve superior performance over the state-of-the-art methods.