Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular diffusivities of H2 16O,HD16O, and H2 18O in gases

Isotopic effects on the molecular diffusion coefficients of H2 16O, HD16O, and H2 18O have been determined by two independent methods. In the first one, the evaporation of liquid water under controlled laminar flow conditions was investigated. In the second one, the transport of water vapor through a diffusion tube was measured. The molecular diffusion coefficient of natural water vapor in nitrogen was redetermined. At 21 °C and P=760 Hg mm, D=0.244±0.004 cm2/sec. For the isotopic molecules we have DHD16O/DH216O=0.9757±0.0009 and DH218O/DH216O=0.9727±0.0007. Agreement between the data obtained in the pure gas phase experiment and the two‐phase experiment shows that the condensation coefficients of the isotopically substituted molecules are identical. Interpretation of the isotopic effects on molecule diffusivities indicates that the relative mass differences between H2 16O, HD16O, and H2 18O alone cannot account for the experimental results. This is more important for the D–H substitution in which the asy.
SoK: Diving into DAG-based Blockchain Systems

Blockchain plays an important role in cryptocurrency markets and technology services. However, limitations on high latency and low scalability retard their adoptions and applications in classic designs. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of Directed Acyclic Graph (DAG) and consequently re-build upper layer components including consensus, incentives, \textit{etc.} The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises the DAG technique. To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of DAG-based blockchain systems. Through deconstructing open-sourced systems and reviewing academic researches, we conclude the main components and featured properties of systems, and provide the approach to establish a DAG. With this in hand, we analyze the security and performance of several leading systems, followed by discussions and comparisons with concurrent (scaling blockchain) techniques. We further identify open challenges to highlight the potentiality of DAG-based solutions and indicate their promising directions for future research.
On the role of covalent strain in protein function

Strain has frequently been suggested to play an important role in the function of several proteins. In this paper, we review various definition of strain and discuss how it may be quantified. We show that covalent strain (i.e. strain caused by covalent interactions) plays a minor role for the function of all proteins we have studied, e.g. blue copper proteins, desulforedoxin, CuA in cytochrome c oxidase, and antibodies (the immuno­chromic effect). However, in small molecules, such as the haem group and macrocyclic model complexes covalent strain may have a pronounced effect. In general, significant strain is seen when a molecule is constrained by covalent bonds in rings, whereas forces present in proteins, i.e. torsional constraints and non-bonded interactions are too weak to distort a bound molecule or metal significantly.
Online Coresets for Clustering with Bregman Divergences

We present algorithms that create coresets in an online setting for clustering problems according to a wide subset of Bregman divergences. Notably, our coresets have a small additive error, similar in magnitude to the lightweight coresets Bachem et. al. 2018, and take update time $O(d)$ for every incoming point where $d$ is dimension of the point. Our first algorithm gives online coresets of size $\tilde{O}(\mbox{poly}(k,d,\epsilon,\mu))$ for $k$-clusterings according to any $\mu$-similar Bregman divergence. We further extend this algorithm to show existence of a non-parametric coresets, where the coreset size is independent of $k$, the number of clusters, for the same subclass of Bregman divergences. Our non-parametric coresets are larger by a factor of $O(\log n)$ ($n$ is number of points) and have similar (small) additive guarantee. At the same time our coresets also function as lightweight coresets for non-parametric versions of the Bregman clustering like DP-Means. While these coresets provide additive error guarantees, they are also significantly smaller (scaling with $O(\log n)$ as opposed to $O(d^d)$ for points in $\~R^d$) than the (relative-error) coresets obtained in Bachem et. al. 2015 for DP-Means. While our non-parametric coresets are existential, we give an algorithmic version under certain assumptions.
Strategies for Protein Separation

The proteome of a cell or tissue depends on cellular and environmental conditions, showing a dynamic system subject to large variations. To study these large changes of variability and quantity, proteomics has emerged, providing techniques dedicated to global characterization of all proteins simultaneously. The expectation is that this information will produce new insights into the biological function of proteins in different physiological states of a cell or tissue. The proteome has a dynamic and complex nature that is the result of many posttranslational modifications, molecular interactions, and a variety of proteins arising from alternative mRNA splicing. With this in mind, the number of modified and unmodified proteins found in any biological system is much bigger than the number of genes (Anderson et al., 2004), which is why mRNA expression may not correlate with protein content (Rogers et. al, 2008). In addition, not all proteins are expressed in the same or similar level in the proteome. For example, the enzyme Rubisco comprises 3050% of leaf proteome (Feller et al., 2008), which is a big issue in the proteomic assessment of low-abundance proteins. In fact, the majority of proteins are in the low-abundance level. To overcome these challenges, the proteome must be fractionated for effective detection and quantification by mass spectrometry (MS). Consequently, the analysis of proteins on the large or small scale is dependent on separation methods. As the ultimate goal in proteomics is to resolve all individual proteins in the cell, although it is quite difficult to find a separation method that could accommodate the diversity of proteins equally, protein separation methods directly affect the achievement of reliable results. Such methods are based on the physical or chemical properties of different proteins, such as their mass or net charge. The combination of sequential methods exploiting different properties can provide highresolution analysis of very complex protein mixtures. Then, current analytical strategies can reach different levels of resolution depending on the platform used. Two-dimensional gel electrophoresis (2DGE) and multidimensional liquid chromatography (MDLC) are the two methods that dominate the separation steps in proteomics. The differences of each strategy are basically related to sensitivity, automation, and high-throughput possibilities. In this chapter, the limitations and principles of these techniques will be discussed.
Synthesis and characterization of new alkali-metal oxometalates obtained via the azide-nitrate route

During this PhD thesis, azide/nitrate route has been confirmed to be an efficient tool in the synthesis of alkali-metal oxometalates. As a particularly beneficial feature, azide/nitrate route enables to approach such metastable oxometalates, which are not in reach via classical oxide-acid-base reactions. Especially, with this method it was possible to synthesize new ternary oxides containing low valences and uncommon coordination numbers (CN) of the respective alkali ions (K, Rb, Cs) and transition metal (Ni). Most significantly, this method allows to precisely fix the oxygen content of the target compound, and thus the valence state of transition metal, by the starting azide/nitrate ratio. Along this approach, we have been able to synthesize two families of low dimensional, intrinsically doped oxocuprates(II/III) and oxonickelates(II/III). In addition, the influence of the kind of alkalimetal on the secondary structure as well as physical properties of the one dimensional polyoxometalate anion was examined. Also, introduction of the alkaline earth metal azides in the azide/nitrate route has resulted in a the new phase displaying unusual structural features. Im Rahmen der vorliegenden Dissertation wurde gezeigt, dass die Azid-Nitrat-Route ein effizientes Werkzeug zur Synthese von Alkalimetal-oxometallaten darstellt. Ein besonders herauszustellender Vorteil dieser Methode ist die moglich gewordene Synthese metastabiler Oxometallate welche nicht durch die klassische Oxid-Saure-Base-Reaktion hergestellt werden konnen. So war es moglich neue ternare Oxide, mit ungewohnlicher Koordinationszahl (CN) der Alkali- (K, Rb, Cs) sowie der Ubergangsmetalle (Ni und Cu) und ungewohnlich niedriger Oxidationszahl des Nickels, darzustellen. Das Bedeutsame dieser Methode stellt vor allem die genaue Einstellung des Sauerstoffgehalts und somit der Oxidationszahl des Ubergangsmetalls, uber das Verhaltnis von Azid zu Nitrat, in der Zielverbindung dar. Anhand dieses Vorgehens war es uns moglich zwei Klassen niederdimensionaler, intrinsisch dotierter Oxocuprate (II/III) und -nickelate (II/III) darzustellen. Zusatzlich wurde der Einfluss des jeweiligen Alkalimetalls auf die Sekundarstruktur wie auch die physikalischen Eigenschaften des eindimensionalen Polyoxometallatanions untersucht. Ebenso fuhrte die Verwendung von Erdalkalimetallaziden in der Azid-Nitrat-Route zu einer neuen Phase mit ungewohnlichen strukturellen Eigenheiten.
Highly Curved Defects in Lyotropic Lamellar Phases

Abstract Lamellar phases of non ionic surfactant/water systems were investigated by two different experimental methods: (i) Spin labelling, which revealed the existence of highly curved defects, precursors of the high temperature phase, whose concentratin increased with temperature. (ii) Electron microscopy of replicas of freeze-fractured samples which showed the existence of screw dislocations, whose concentration increased with temperature. These screws are the components of dislocation loops crossing the layers. Parts of the highly curved areas indicated by ESR are attributed to the cores of these dislocations.
Experimental and theoretical investigation of nucleation and growth of atmospheric aerosols

Experimental and Theoretical Investigation of Nucleation and Growth of Atmospheric Aerosols. (December 2007) Jun Zhao, B.E. , South China University of Technology Chair of Advisory Committee: Dr. Renyi Zhang Aerosol particles have profound impacts on human health, atmospheric radiation, and cloud microphysics and these impacts are strongly dependent on particle sizes. However, formation and growth of atmospheric particles are currently not well understood. In this work, laboratory and theoretical studies have been performed to investigate the formation and growth of atmospheric particles. The first two parts of the dissertation are a laboratory investigation of new particle formation and growth, and a theoretical study of atmospheric molecular complexes and clusters. The nucleation rate was considerably enhanced in the presence of cis-pinonic acid and ammonia. The composition of the critical cluster was estimated from the dependence of the nucleation rate on the precursor concentration and the time evolution of the clusters was then simulated using molecular dynamic simulations. Results from quantum chemical calculations and quantum theory of atoms in molecules (QTAIM) reveal that formation of strong hydrogen bonding between an organic acid and sulfuric acid is likely responsible for a reduction of the nucleation barrier by modifying the hydrophobic properties of the organic acid and allowing further addition of hydrophilic species (e.g., H2SO4, H2O, and possibly NH3) to the hydrophilic side of the clusters. This promotes
Polymer thin films for antireflection coatings

Polymer thin films for antireflection (AR) coatings are reviewed, with particular emphasis on making different kinds of structures and establishing the relationships between structures and antireflective properties. Three kinds of main structures with antireflective properties, namely, homogeneous, inhomogeneous porous structures, and gradient-refractive-index structures, fabricated by “bottom up” or “top down” approaches are summarized. The transmittance of the polymer thin film can be increased to almost 100% in the single or broadband wavelength region (both of the visible and near-infrared region) by properly manipulating its structures. However, further studies are still needed to achieve reversible transmittance and multifunctional polymer AR due to the soft matter characteristic of polymers. In addition, the challenges of using polymer AR coatings still remain, such as poor solvent resistance, poor mechanical strength, and so on, which gives us the direction of future development for practical applications.
Transport of thermal-energy ionospheric oxygen (O+) ions between the ionosphere and the plasma sheet

in the quiet time high-altitude (>7000 km) polar ionosphere on Akebono, at temperatures of � 0.2–0.3 eV and flow velocities of a few km/s. In this paper, we use single-particle trajectory simulation to study the transport of these ions in the periods preceding a number of large magnetic storms (Dst < � 100 nT). Our simulation shows that due to centrifugal ion acceleration at higher altitudes (above � 3R E altitude), about 10–20% of polar wind and other low-energy O + ions reaches the plasma sheet during such periods; the actual percentage is a factor of � 3 larger in the dusk sector on average compared with the dawn sector and dependent on the IMF and the O + ion temperature. This provides a low but non-negligible flux of O + ions between the ionosphere and the plasma sheet and ring current, which is believed to constitute a significant “in-transit” oxygen ion population over a period of a few (� 4) hours preceding a magnetic storm. Such a population could explain the presence of energetic O + ions at the onset of the main phase of the storm,
Conformational Analysis of Macromolecules. II. The Rotational Isomeric States of the Normal Hydrocarbons

The conformational potential energy of the normal hydrocarbon molecules is considered to be a function of the dihedral angles of internal rotation about the carbon—carbon single bonds, the bond lengths and valence angles being held fixed. The energy equation contains terms of two kinds, a sum over torsional or internal‐rotation terms, one for each carbon—carbon bond, which are attributed to exchange interactions of electrons in bonds adjacent to the bond about which internal rotation occurs, and a sum over all the pairwise nonbonded interactions between all of the atoms in the molecule. The torsional terms are dependent on the dihedral angles of internal rotation and on the choice of fixed valence angles, but each torsional term reduces to the form ½U0(1+cos3ωi) when all the fixed valence angles are selected to be tetrahedral. The nonbonded‐interaction terms are represented by Lennard‐Jones ``6–12'' potential functions, Uij=(dij/rij12) − (eij/rij6), the parameters eij being calculated from the Slater—Kirk.
The chemistry and biochemistry of wheat

Abstract: This chapter discusses the components of the wheat kernel, its chemical composition and its properties that are important to breadmaking and nutrition. The themes presented include wheat flour and its carbohydrates, proteins, lipids, vitamins, enzymes and minerals. Presentation of the physical and chemical properties of the major components follows, as well as analytical techniques for their characterisation. Minor components that play an important role in breadmaking, particularly lipids and enzymes, are discussed together with some recent developments, which have introduced molecular changes designed to improve the properties of wheat-based products. Finally, there are sections on future trends and sources of further information.
Deep-Learning-Based Kinematic Reconstruction for DUNE

In the framework of three-active-neutrino mixing, the charge parity phase, the neutrino mass ordering, and the octant of $\theta_{23}$ remain unknown. The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment, which aims to address these questions by measuring the oscillation patterns of $\nu_\mu/\nu_e$ and $\bar\nu_\mu/\bar\nu_e$ over a range of energies spanning the first and second oscillation maxima. DUNE far detector modules are based on liquid argon TPC (LArTPC) technology. A LArTPC offers excellent spatial resolution, high neutrino detection efficiency, and superb background rejection, while reconstruction in LArTPC is challenging. Deep learning methods, in particular, Convolutional Neural Networks (CNNs), have demonstrated success in classification problems such as particle identification in DUNE and other neutrino experiments. However, reconstruction of neutrino energy and final state particle momenta with deep learning methods is yet to be developed for a full AI-based reconstruction chain. To precisely reconstruct these kinematic characteristics of detected interactions at DUNE, we have developed and will present two CNN-based methods, 2-D and 3-D, for the reconstruction of final state particle direction and energy, as well as neutrino energy. Combining particle masses with the kinetic energy and the direction reconstructed by our work, the four-momentum of final state particles can be obtained. Our models show considerable improvements compared to the traditional methods for both scenarios.
Learning Order Parameters from Videos of Dynamical Phases for Skyrmions with Neural Networks

The ability to recognize dynamical phenomena (e.g., dynamical phases) and dynamical processes in physical events from videos, then to abstract physical concepts and reveal physical laws, lies at the core of human intelligence. The main purposes of this paper are to use neural networks for classifying the dynamical phases of some videos and to demonstrate that neural networks can learn physical concepts from them. To this end, we employ multiple neural networks to recognize the static phases (image format) and dynamical phases (video format) of a particle-based skyrmion model. Our results show that neural networks, without any prior knowledge, can not only correctly classify these phases, but also predict the phase boundaries which agree with those obtained by simulation. We further propose a parameter visualization scheme to interpret what neural networks have learned. We show that neural networks can learn two order parameters from videos of dynamical phases and predict the critical values of two order parameters. Finally, we demonstrate that only two order parameters are needed to identify videos of skyrmion dynamical phases. It shows that this parameter visualization scheme can be used to determine how many order parameters are needed to fully recognize the input phases. Our work sheds light on the future use of neural networks in discovering new physical concepts and revealing unknown yet physical laws from videos.
A Comprehensive Study of Deep Video Action Recognition

Video action recognition is one of the representative tasks for video understanding. Over the last decade, we have witnessed great advancements in video action recognition thanks to the emergence of deep learning. But we also encountered new challenges, including modeling long-range temporal information in videos, high computation costs, and incomparable results due to datasets and evaluation protocol variances. In this paper, we provide a comprehensive survey of over 200 existing papers on deep learning for video action recognition. We first introduce the 17 video action recognition datasets that influenced the design of models. Then we present video action recognition models in chronological order: starting with early attempts at adapting deep learning, then to the two-stream networks, followed by the adoption of 3D convolutional kernels, and finally to the recent compute-efficient models. In addition, we benchmark popular methods on several representative datasets and release code for reproducibility. In the end, we discuss open problems and shed light on opportunities for video action recognition to facilitate new research ideas.
First principles study of ruthenium(II) sensitizer adsorption on anatase TiO2 (001) surface

We present a systematic investigation of the adsorption behavior of the highly efficient ruthenium(II) sensitizer (N3) on the anatase TiO2 (001) surface based on density functional theory. Three preferable configurations can be formed by exploiting two or three carboxylic groups attached to the TiO2 surface, with their adsorption energies differing slightly. The interplay of N3 with the (001) surface is considerably stronger than that of N3 with the (101) surface, resulting in a larger dye coverage on the (001) surface. The energy gap of the N3 sensitizer, determining the absorption spectrum, decreases about 0.12 eV upon adsorption, suggesting an even larger range for the absorption spectrum than for the isolated N3 molecule. Moreover, the higher conduction band minimum of the TiO2 (001) surface with N3 adsorption, compared with that of the (101) surface, indicates the higher open circuit potential. These results provide a clue to understand the high solar light-to-electricity conversion efficiency of dye sensitized solar cells with TiO2 nanocrystals exposing a high percentage of {001} faces.