Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Analysis of Stellar Populations in the Host Galaxies of SDSS Type I QSOs

We investigate the relationship between host galaxies’ stellar content and active galactic nuclei (AGNs) for optically selected QSOs with z < 0.5. There is a total of 82 QSOs that we select from the Sloan Digital Sky Survey. These 82 QSOs have both Wide-field Infrared Survey Explorer data and measurable stellar content. With the help of the stellar population synthesis code STARLIGHT, we determine the luminosity fractions of AGNs, stellar population ages, and star formation histories (SFHs) of host galaxies. We find that there is a correlation between the SFH and AGN properties, which suggests a possible delay from star formation to AGN. This probably indicates that the AGN activity correlates with the star formation activity, which consistent with a coevolution scheme for black hole and host galaxies.
On a class of distributions generated by stochastic mixture of the extreme order statistics of a sample of size two

Different methods may be used to introduce a new parameter to a family of distributions to increase flexibility for modeling purposes. Marshall and Olkin [9] introduced a method for adding a parameter to a family of distributions and applied it to the exponential and Weibull models. Jones [4] used the distribution of order statistics to provide new families of distributions with extra parameters. The well–known Farlie–Gumbel-Morgenstern (FGM, for short) family of bivariate distributions with the given univariate marginal distributions F1 and F2, is defined by
Antiferromagnetic magnons as highly squeezed Fock states underlying quantum correlations

Employing the concept of two-mode squeezed states from quantum optics, we demonstrate a revealing physical picture for the antiferromagnetic ground state and excitations. Superimposed on a Neel ordered configuration, a spin-flip restricted to one of the sublattices is called a sublattice-magnon. We show that an antiferromagnetic spin-up magnon is comprised by a quantum superposition of states with $n+1$ spin-up and $n$ spin-down sublattice-magnons, and is thus an enormous excitation despite its unit net spin. Consequently, its large sublattice-spin can amplify its coupling to other excitations. Employing von Neumann entropy as a measure, we show that the antiferromagnetic eigenmodes manifest a high degree of entanglement between the two sublattices, thereby establishing antiferromagnets as reservoirs for strong quantum correlations. Based on these novel insights, we outline strategies for exploiting the strong quantum character of antiferromagetic (squeezed-)magnons.
Photoluminescence Lifetime Based Investigations of Linker Mediated Electronic Connectivity Between Substrate and Nanoparticle

The evolution of systems based on nanoparticles as the main component seems to be a self-accelerating process during the last five decades. Hence, an overview across this field gets more and more challenging. It is sometimes rewarding to focus on the fundamental physical phenomenon of the electronic interconnection between the different building blocks of the obtained devices. Therefore, the investigation of charge transport among the utilized particles and their substrate is one of the mandatory steps in the development of semiconductor nanoparticle based devices like e.g., sensors and LEDs. The investigation of the influence of tunneling barriers on the properties of nanoparticle-functionalized surfaces is a challenging task. The different basic influences on the charge transport dynamics are often difficult to separate from each other. Non-invasive and easily viable experiments are still required to resolve the charge distributing mechanisms in the systems. In the presented work, we want to focus on thin and transparent indium tin oxide (ITO) layers covered glass slides since this substrate is frequently utilized in nanoelectronics. CdSe/CdS nanorods (NRs) are applied as an optically addressable probe for the electronic surface states of the conductive glass. The presented experimental design provides the proof of electronic interconnections in ITO coated glass/linker/NR electrodes via easy reproducible functionalization and polishing experiments. UV/Vis absorption and photoluminescence (PL) lifetime measurements revealed changes in the optical properties caused by differences in the charge carrier dynamics between the system. Our work is focused on the modification of charge carrier dynamics due to the application of linker molecules with different functional groups like (3-mercaptopropyl)methoxysilane (MPTMS) and (3-aminopropyl)trimethoxysilane (APTMS). The presented observations are explained with a simple kinetic model.
Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series

Novelty detection is a process for distinguishing the observations that differ in some respect from the observations that the model is trained on. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains observations that were not known at the training time. In other words, the novelty class is often is not presented during the training phase or not well defined. In light of the above, one-class classifiers and generative methods can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end model is a challenging task itself. Therefore, detecting the Novel classes in unsupervised and semi-supervised settings is a crucial step in such tasks. In this thesis, we propose several methods to model the novelty detection problem in unsupervised and semi-supervised fashion. The proposed frameworks applied to different related applications of anomaly and outlier detection tasks. The results show the superior of our proposed methods in compare to the baselines and state-of-the-art methods.
Twisted fracton models in three dimensions

We study novel three-dimensional quantum phases of matter which support particles with restricted mobility, including immobile "fracton" excitations. Many fracton models may be viewed as generalized Abelian lattice gauge theories. Here, by analogy with Dijkgraaf-Witten topological gauge theories, we discover a natural generalization of fracton models, obtained by twisting the gauge symmetries. Introducing generalized gauge transformation operators carrying extra phase factors depending on local configurations, we construct a large and rich family of exactly solvable three-dimensional models, which we dub "twisted fracton models. " A key result of our approach is to demonstrate explicitly the existence of non-Abelian fractons (as well as non-Abelian quasi-particles whose motion is restricted to some dimensions) in a three-dimensional system with local interactions. We further characterize these twisted fracton phases by embedding them on a three-torus and computing their ground state degeneracies, which depend sub-extensively on the system size. Moreover, as an important advance in the study of fracton order, we develop a general mathematical framework which systematically captures the fusion and braiding properties of fractons and other quasi-particles with restricted mobility.
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks

Deep networks consume a large amount of memory by their nature. A natural question arises can we reduce that memory requirement whilst maintaining performance. In particular, in this work we address the problem of memory efficient learning for multiple tasks. To this end, we propose a novel network architecture producing multiple networks of different configurations, termed deep virtual networks (DVNs), for different tasks. Each DVN is specialized for a single task and structured hierarchically. The hierarchical structure, which contains multiple levels of hierarchy corresponding to different numbers of parameters, enables multiple inference for different memory budgets. The building block of a deep virtual network is based on a disjoint collection of parameters of a network, which we call a unit. The lowest level of hierarchy in a deep virtual network is a unit, and higher levels of hierarchy contain lower levels’ units and other additional units. Given a budget on the number of parameters, a different level of a deep virtual network can be chosen to perform the task. A unit can be shared by different DVNs, allowing multiple DVNs in a single network. In addition, shared units provide assistance to the target task with additional knowledge learned from another tasks. This cooperative configuration of DVNs makes it possible to handle different tasks in a memory-aware manner. Our experiments show that the proposed method outperforms existing approaches for multiple tasks. Notably, ours is more efficient than others as it allows memory-aware inference for all tasks.
Diffusion of inertia-gravity waves by geostrophic turbulence

The scattering of inertia-gravity waves by large-scale geostrophic turbulence in a rapidly rotating, strongly stratified fluid leads to the diffusion of wave energy on the constant-frequency cone in wavenumber space. We derive the corresponding diffusion equation and relate its diffusivity to the wave characteristics and the energy spectrum of the turbulent flow. We check the predictions of this equation against numerical simulations of the three-dimensional Boussinesq equations in initial-value and forced scenarios with horizontally isotropic wave and flow fields. In the forced case, wavenumber diffusion results in a $k^{-2}$ wave energy spectrum consistent with as-yet-unexplained features of observed atmospheric and oceanic spectra.
Hierarchical Deep Feature Learning For Decoding Imagined Speech From EEG

We propose a mixed deep neural network strategy, incorporating parallel combination of Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep autoencoders and fully connected layers towards automatic identification of imagined speech from EEG. Instead of utilizing raw EEG channel data, we compute the joint variability of the channels in the form of a covariance matrix that provide spatio-temporal representations of EEG. The networks are trained hierarchically and the extracted features are passed onto the next network hierarchy until the final classification. Using a publicly available EEG based speech imagery database we demonstrate around 23.45% improvement of accuracy over the baseline method. Our approach demonstrates the promise of a mixed DNN approach for complex spatialtemporal classification problems.
Reliable and Efficient Image Cropping: A Grid Anchor Based Approach

Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruth, which cannot reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of cropping models, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the special properties and requirements (e.g., local redundancy, content preservation, aspect ratio) of image cropping. Our formulation reduces the searching space of candidate crops from millions to less than one hundred. Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined. We also design an effective and lightweight network module, which simultaneously considers the region of interest and region of discard for more accurate image cropping. Our model can stably output visually pleasing crops for images of different scenes and run at a speed of 125 FPS.
Merged H/H2 and C+/C/CO transitions in the Orion Bar

High-resolution ALMA images towards the Orion Bar show no discernible offset between the peak of H2 emission in the photodissociation region (PDR) and the CO(3-2) and HCO+(4-3) emission in the molecular region. This implies that positions of H2 and CO dissociation fronts are indistinguishable in the limit of ALMA resolution. We use the chemo-dynamical model MARION to show that the ALMA view of the Orion Bar, namely, no appreciable offset between the CO(3-2) and HCO+(4-3) peaks, merged H2 and CO dissociation fronts, and high intensity of HCO+(4-3) emission, can only be explained by the ongoing propagation of the dissociation fronts through the molecular cloud, coupled to the dust motion driven by the stellar radiation pressure, and are not reproduced in the model where the dissociation fronts are assumed to be stationary. Modelling line intensities, we demonstrate that after the fronts have merged, the angular separation of the CO(3-2) and HCO+(4-3) peaks is indeed unresolvable with the ALMA observations. Our model predictions are consistent with the results of the ALMA observations about the relation of the bright HCO+(4-3) emission to the compressed gas at the border of the PDR in the sense that the theoretical HCO+(4-3) peak does correspond to the gas density enhancement, which naturally appears in the dynamical simulation, and is located near the H2 dissociation front at the illuminated side of the CO dissociation front.
Learning latent variable dynamic models for integrated production scheduling and control

With the increasing need for dynamic decisions in fast-changing markets, the integration of scheduling and control is an important consideration in chemical processes. Nevertheless, computing optimal production schedules with dynamic process models explicitly embedded remains challenging, due to their nonlinearity and high-dimensionality. In this paper, we observe that the intrinsic dimensionality of process dynamics (as relevant to scheduling) is often much lower than the number of variables. We introduce a data mining approach to learn closed-loop process dynamics on a low-dimensional, latent manifold. The manifold dimensionality is selected based on a tradeoff between model accuracy and complexity. After projecting process data, system identification and optimal scheduling calculations can be performed in the low-dimensional, latent space. We apply these concepts to schedule an air separation unit under model predictive control operating with time-varying electricity prices; our approach reduces the computational effort required while offering more detailed dynamic information compared to previous works.
End-to-End Learning of Proactive Handover Policy for Camera-Assisted mmWave Networks Using Deep Reinforcement Learning

For mmWave networks, this paper proposes an image-to-decision proactive handover framework, which directly maps camera images to a handover decision. With the help of camera images, the proposed framework enables a proactive handover, i.e., a handover is triggered before a temporal variation in the received power induced by obstacles even if the variation is extremely rapid such that it cannot be predicted from a time series of received power. Furthermore, direct mapping allows scalability for the number of obstacles. This paper shows that optimal mapping is learned via deep reinforcement learning (RL) by proving that the decision process in our proposed framework is a Markov decision process. While performing deep RL, this paper designs a neural network (NN) architecture for a network controller to successfully learn the use of lower-dimensional observations in state information and higher-dimensional image observations. The evaluations based on experimentally obtained camera images and received powers indicate that the learned handover policy in the proposed framework outperforms the learned policy in a received power-based handover framework.
Role of Hippo Pathway-YAP/TAZ Signaling in Angiogenesis

Angiogenesis is a highly coordinated process of formation of new blood vessels from pre-existing blood vessels. The process of development of the proper vascular network is a complex process that is crucial for the vertebrate development. Several studies have defined essential roles of Hippo pathway-YAP/TAZ in organ size control, tissue regeneration, and self-renewal. Thus Hippo pathway is one of the central components in tissue homeostasis. There are mounting evidences on the eminence of Hippo pathway-YAP/TAZ in angiogenesis in multiple model organisms. Hippo pathway-YAP/TAZ is now demonstrated to regulate endothelial cell proliferation, migration and survival; subsequently regulating vascular sprouting, vascular barrier formation, and vascular remodeling. Major intracellular signaling programs that regulate angiogenesis concomitantly activate YAP/TAZ to regulate key events in angiogenesis. In this review, we provide a brief overview of the recent findings in the Hippo pathway and YAP/TAZ signaling in angiogenesis.
Advantage in two-way communication using non-classical states of light

The advantage of using a single-photon two-mode entangled state in two-way communication via maximal violation of an inequality associated with the `Guess Your Neighbour's Input' (GYNI) game has been theoretically [Phys. Rev. Lett. 120, 060503 (2018)] as well as experimentally [CLEO FID.4 (OSA, 2018)] established quite recently. We show that such an advantage can also be obtained using any single-mode pure non-classical state embedded in a two-mode pure entangled state, wherein the other mode is the vacuum (henceforth referred to as a generalized NOON state), regardless of the average photon number of the single mode state. For the special cases of the even-coherent, odd-coherent, and squeezed vacuum NOON states, we establish that the advantage is also maximal. We show that the usage of the even-coherent NOON states can provide an advantage under noisy apparatuses (beam splitters and photo detectors). As an aside, we study how some of these states fare in terms of violation of a reference-frame independent Bell-type inequality.
Probing the quantum phase transition in Mott insulator 
BaCoS2
 tuned by pressure and Ni substitution

We present a muon spin relaxation study of the Mott transition in BaCoS_2 using two independent control parameters: (i) pressure p to tune the electronic bandwidth and (ii) Ni-substitution x on the Co site to tune the band filling. For both tuning parameters, the antiferromagnetic insulating state first transitions to an antiferromagnetic metal and finally to a paramagnetic metal without undergoing any structural phase transition. BaCoS_2 under pressure displays minimal change in the ordered magnetic moment S_ord until it collapses abruptly upon entering the antiferromagnetic metallic state at p_cr ~ 1.3 GPa. In contrast, S_ord in the Ni-doped system Ba(Co_{1-x}Ni_{x})S_{2} steadily decreases with increasing x until the antiferromagnetic metallic region is reached at x_cr ~ 0.22. In both cases, significant phase separation between magnetic and nonmagnetic regions develops when approaching p_cr or x_cr, and the antiferromagnetic metallic state is characterized by weak, random, static magnetism in a small volume fraction. No dynamical critical behavior is observed near the transition for either tuning parameter. These results demonstrate that the quantum evolution of both the bandwidth- and filling-controlled metal-insulator transition at zero temperature proceeds as a first-order transition. This behavior is common to magnetic Mott transitions in RENiO_3 and V_2O_3, which are accompanied by structural transitions without the formation of an antiferromagnetic metal phase.
Convolutional Temporal Attention Model for Video-Based Person Re-Identification

The goal of video-based person re-identification is to match two input videos, so that the distance of the two videos is small if two videos contain the same person. A common approach for person re-identification is to first extract image features for all frames in the video, then aggregate all the features to form a video-level feature. The video-level features of two videos can then be used to calculate the distance of the two videos. In this paper, we propose a temporal attention approach for aggregating frame-level features into a video-level feature vector for re-identification. Our method is motivated by the fact that not all frames in a video are equally informative. We propose a fully convolutional temporal attention model for generating the attention scores. Fully convolutional network (FCN) has been widely used in semantic segmentation for generating 2D output maps. In this paper, we formulate video based person reidentification as a sequence labeling problem like semantic segmentation. We establish a connection between them and modify FCN to generate attention scores to represent the importance of each frame. Extensive experiments on three different benchmark datasets (i.e. iLIDS-VID, PRID-2011 and SDU-VID) show that our proposed method outperforms other state-of-the-art approaches.
Comparing Hydrolysis and Transglycosylation Reactions Catalyzed by Thermus thermophilus β-Glycosidase. A Combined MD and QM/MM Study

The synthesis of oligosaccharides and other carbohydrate derivatives is of relevance for the advancement of glycosciences both at the fundamental and applied level. For many years, glycosyl hydrolases (GHs) have been explored to catalyze the synthesis of glycosidic bonds. In particular, retaining GHs can catalyze a transglycosylation (T) reaction that competes with hydrolysis (H). This has been done either employing controlled conditions in wild type GHs or by engineering new mutants. The goal, which is to increase the T/H ratio, has been achieved with moderate success in several cases despite the fact that the molecular basis for T/H modulation are unclear. Here we have used QM(DFT)/MM calculations to compare the glycosylation, hydrolysis and transglycosylation steps catalyzed by wild type Thermus thermophilus β-glycosidase (family GH1), a retaining glycosyl hydrolase for which a transglycosylation yield of 36% has been determined experimentally. The three transition states have a strong oxocarbenium character and ring conformations between 4H3 and 4E. The atomic charges at the transition states for hydrolysis and transglycosylation are very similar, except for the more negative charge of the oxygen atom of water when compared to that of the acceptor Glc. The glycosylation transition state has a stronger SN2 character than the deglycosylation ones and the proton transfer is less advanced. At the QM(PBE0/TZVP)/MM level, the TS for transglycosylation has shorter O4GLC-C1FUC (forming bond) distance and longer OE2GLU338-C1FUC (breaking) distance than the hydrolysis one, although the HACC proton is closer to the Glu164 base in the hydrolysis TS. The QM(SCC-DFTB)/MM free energy maxima show the inverted situation, although the hydrolysis TS presents significant structural fluctuations. The 3-OHGLC group of the acceptor Glc (transglycosylation) and WAT432 (neighbor water in hydrolysis) are identified to stabilize the oxocarbenium transition states through interaction with O5FUC and O4FUC. The analysis of interaction suggests that perturbing the Glu392-Fuc interaction could increase the T/H ratio, either by direct mutation of this residue or indirectly as reported experimentally in the Asn390I and Phe401S cases. The molecular understanding of similarities and differences between hydrolysis and transglycosylation steps may be of help in the design of new biocatalysts for glycan synthesis.