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Some van der Waals (vdW) materials exhibit ferroelectricity, making them promising for novel non-volatile memories (NVMs) such as ferroelectric diodes (FeDs). CuInP2S6 (CIPS) is a well-known vdW ferroelectric that has been integrated with graphene for memory devices. Here we demonstrate FeDs with self-rectifying, hysteretic current-voltage characteristics based on vertical heterostructures of 10-nm-thick CIPS and graphene. By using vdW indium-cobalt top electrodes and graphene bottom electrodes, we achieve high electroresistance (on- and off-state resistance ratios) of ~10^6, on-state rectification ratios of ~2500 for read/write voltages of 2 V/0.5 V and maximum output current densities of 100 A/cm^2. These metrics compare favourably with state-of-the-art FeDs. Piezoresponse force microscopy measurements show that stabilization of intermediate net polarization states in CIPS leads to stable multi-bit data retention at room temperature. The combination of two-terminal design, multi-bit memory, and low-power operation in CIPS-based FeDs is potentially interesting for compute-in-memory and neuromorphic computing applications., Comment: 17 Pages

Study of canonical entropy in electron-proton interactions at $\sqrt{s}$ = 300 GeV is presented. The precision data collected by the H1 experiment at the HERA in different ranges of invariant hadronic mass $W$ and the squared four-momentum exchange $Q^{2}$ in $ep$ interactions have been analyzed in the ensemble theory approach. The canonical partition function relates to the multiplicity distribution which is often studied in collider experiments. We use the canonical ensemble partition function to explore the dynamics of hadron production in $ep$ interactions by devising different methods to find the entropic parameter and the collision temperature. The inverse slope of the transverse momentum spectrum of produced hadrons also relates to the temperature. In the recent past, the CMS, ATLAS and ALICE experiments at the LHC have studied the charged hadron transverse momentum and particle distributions in proton-proton and proton-nucleus interactions by using the Tsallis function within this approach. A detailed investigation into the role of the system volume and relation amongst different dynamical parameters reveals interesting results., Comment: 13 pages,11 Figures, 8 Tables

With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases. Previous works have depicted biases that persist in KGs, as well as employed several metrics for measuring the biases. However, such studies lack the systematic exploration of the sensitivity of the bias measurements, through varying sources of data, or the embedding algorithms used. To address this research gap, in this work, we present a holistic analysis of bias measurement on the knowledge graph. First, we attempt to reveal data biases that surface in Wikidata for thirteen different demographics selected from seven continents. Next, we attempt to unfold the variance in the detection of biases by two different knowledge graph embedding algorithms - TransE and ComplEx. We conduct our extensive experiments on a large number of occupations sampled from the thirteen demographics with respect to the sensitive attribute, i.e., gender. Our results show that the inherent data bias that persists in KG can be altered by specific algorithm bias as incorporated by KG embedding learning algorithms. Further, we show that the choice of the state-of-the-art KG embedding algorithm has a strong impact on the ranking of biased occupations irrespective of gender. We observe that the similarity of the biased occupations across demographics is minimal which reflects the socio-cultural differences around the globe. We believe that this full-scale audit of the bias measurement pipeline will raise awareness among the community while deriving insights related to design choices of data and algorithms both and refrain from the popular dogma of ``one-size-fits-all''., Comment: 11 pages

Wikipedia has been turned into an immensely popular crowd-sourced encyclopedia for information dissemination on numerous versatile topics in the form of subscription free content. It allows anyone to contribute so that the articles remain comprehensive and updated. For enrichment of content without compromising standards, the Wikipedia community enumerates a detailed set of guidelines, which should be followed. Based on these, articles are categorized into several quality classes by the Wikipedia editors with increasing adherence to guidelines. This quality assessment task by editors is laborious as well as demands platform expertise. As a first objective, in this paper, we study evolution of a Wikipedia article with respect to such quality scales. Our results show novel non-intuitive patterns emerging from this exploration. As a second objective we attempt to develop an automated data driven approach for the detection of the early signals influencing the quality change of articles. We posit this as a change point detection problem whereby we represent an article as a time series of consecutive revisions and encode every revision by a set of intuitive features. Finally, various change point detection algorithms are used to efficiently and accurately detect the future change points. We also perform various ablation studies to understand which group of features are most effective in identifying the change points. To the best of our knowledge, this is the first work that rigorously explores English Wikipedia article quality life cycle from the perspective of quality indicators and provides a novel unsupervised page level approach to detect quality switch, which can help in automatic content monitoring in Wikipedia thus contributing significantly to the CSCW community.

Investigating the competition between ferroelectric ordering and quantum fluctuations is essential to tailor the desired functionalities of mixed ferroelectric and incipient ferroelectric systems, like, (Ba,Sr)TiO3 and (Eu,Ba)TiO3. Recently, it has been shown that suppression of quantum fluctuations increases ferroelectric ordering in (Eu,Ba)TiO3 and since these phenomena are coupled to crystallographic phase transitions it is essential to understand the role of phonons. Here, we observe that the unusual temperature dependence of phonons in BaTiO3 gets suppressed when Ba2+ is replaced by Eu2+. This manifests in a decrease in the cubic-to-tetragonal (i.e., para-to-ferroelectric) phase transition temperature (by 150 K) and a complete suppression of tetragonality of the lattice (at room temperature by 40% replacement of Ba2+ by Eu2+). We have quantified the anharmonicity of the phonons and observed that the replacement of Ba2+ by Eu2+ suppresses it (by 93%) with a resultant lowering of the ferroelectric ordering temperature in the EuxBa1-xTiO3. This suggests that tuning phonon anharmonicity can be an important route to novel ferroelectric materials.

Success of planetary-scale online collaborative platforms such as Wikipedia is hinged on active and continued participation of its voluntary contributors. The phenomenal success of Wikipedia as a valued multilingual source of information is a testament to the possibilities of collective intelligence. Specifically, the sustained and prudent contributions by the experienced prolific editors play a crucial role to operate the platform smoothly for decades. However, it has been brought to light that growth of Wikipedia is stagnating in terms of the number of editors that faces steady decline over time. This decreasing productivity and ever increasing attrition rate in both newcomer and experienced editors is a major concern for not only the future of this platform but also for several industry-scale information retrieval systems such as Siri, Alexa which depend on Wikipedia as knowledge store. In this paper, we have studied the ongoing crisis in which experienced and prolific editors withdraw. We performed extensive analysis of the editor activities and their language usage to identify features that can forecast prolific Wikipedians, who are at risk of ceasing voluntary services. To the best of our knowledge, this is the first work which proposes a scalable prediction pipeline, towards detecting the prolific Wikipedians, who might be at a risk of retiring from the platform and, thereby, can potentially enable moderators to launch appropriate incentive mechanisms to retain such `would-be missing' valued Wikipedians.

Motivated by recent studies of circuit complexity in weakly interacting scalar field theory, we explore the computation of circuit complexity in $\mathcal{Z}_2$ Even Effective Field Theories ($\mathcal{Z}_2$ EEFTs). We consider a massive free field theory with higher-order Wilsonian operators such as $\phi^{4}$, $\phi^{6}$ and $\phi^8.$ To facilitate our computation we regularize the theory by putting it on a lattice. First, we consider a simple case of two oscillators and later generalize the results to $N$ oscillators. The study has been carried out for nearly Gaussian states. In our computation, the reference state is an approximately Gaussian unentangled state, and the corresponding target state, calculated from our theory, is an approximately Gaussian entangled state. We compute the complexity using the geometric approach developed by Nielsen, parameterizing the path ordered unitary transformation and minimizing the geodesic in the space of unitaries. The contribution of higher-order operators, to the circuit complexity, in our theory has been discussed. We also explore the dependency of complexity with other parameters in our theory for various cases., Comment: 35 pages, 5 figures, 3 tables, reference list updated and version modified, Accepted for publication in Symmetry (section: Physics and Symmetry/Asymmetry, Special issue: Symmetry and Geometry in Physics II)

The evolution of AI-based system and applications had pervaded everyday life to make decisions that have momentous impact on individuals and society. With the staggering growth of online data, often termed as the Online Infosphere it has become paramount to monitor the infosphere to ensure social good as the AI-based decisions are severely dependent on it. The goal of this survey is to provide a comprehensive review of some of the most important research areas related to infosphere, focusing on the technical challenges and potential solutions. The survey also outlines some of the important future directions. We begin by discussions focused on the collaborative systems that have emerged within the infosphere with a special thrust on Wikipedia. In the follow up we demonstrate how the infosphere has been instrumental in the growth of scientific citations and collaborations thus fueling interdisciplinary research. Finally, we illustrate the issues related to the governance of the infosphere such as the tackling of the (a) rising hateful and abusive behavior and (b) bias and discrimination in different online platforms and news reporting., Comment: 29 pages

Electron Acoustic Solitary structures in Fermi Plasma with two temperature electrons have various applications in space and laboratory-made plasma. Formulation of an adequate theory is important to understand various physical systems with various physical parameters. The motion of two temperature electrons in a quantum Fermi plasma system highly affects the solitary profile of the system. We study the quantum Fermi plasma system with two temperature electrons where the streaming velocities of the two-electron population are opposite. We consider the quantum hydrodynamic model (QHD) and derive a linear dispersion relation for the system. For the non-linear study of the system, we use the standard perturbation technique to derive KdV-B equation and show the evolution of a solitary profile with different plasma parameters. We analyze the stable Rouge wave structure using NLSE and show simulation results. We study the dynamical properties and phase plot for a two-stream quantum Fermi plasma system with two temperature electrons., Comment: 8 pages, 14 figures. arXiv admin note: substantial text overlap with arXiv:2008.13021

Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia QualityMonitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies., Comment: EMNLP 2020: Long paper

Electrons commonly couple through Fr\"ohlich interactions with longitudinal optical phonons to form polarons. However, trions possess a finite angular momentum and should therefore couple instead to rotational optical phonons. This creates a polaronic trion whose binding energy is determined by the crystallographic orientation of the lattice. Here, we demonstrate theoretically within the Fr\"ohlich approach and experimentally by photoluminescence emission that the bare trion binding energy (20 meV) is significantly enhanced by the phonons at the interface between the two-dimensional semiconductor MoS$_2$ and the bulk transition metal oxide SrTiO$_3$. The low-temperature {binding energy} changes from 60 meV in [001]-oriented substrates to 90 meV for [111] orientation, as a result of the counter-intuitive interplay between the rotational axis of the MoS$_2$ trion and that of the SrTiO$_3$ phonon mode., Comment: 5+ pages (4 figures) + Suppl. Materials (7 figures), to appear in Phys. Rev. Lett. (August 2020)

Masking of data is a method to protect information by shielding it from a third party, however keeping it usable for further usages like application development, building program extensions to name a few. Whereas it is possible for classical information encoded in composite quantum states to be completely masked from reduced sub-systems, it has to be checked if quantum information can also be masked when the future possibilities of a quantum computer are increasing day by day. Newly proposed no-masking theorem [Phys. Rev. Lett. 120, 230501 (2018)], one of the no-go theorems, demands that except for some restricted sets of non-orthogonal states, it's impossible to mask arbitrary quantum states. Here, we explore the possibility of masking in the IBM quantum experience platform by designing the quantum circuits and running them on the 5-qubit quantum computer. We choose two particular states considering both the orthogonal and non-orthogonal basis states and illustrate their masking through both the theoretical calculation as well as verification in the quantum computer. By quantum state tomography, it is concluded that the experimental results are collected with high fidelity and hence the possibility of masking is realized., Comment: 12 figures, 14 pages

With the ongoing debate on 'freedom of speech' vs. 'hate speech' there is an urgent need to carefully understand the consequences of the inevitable culmination of the two, i.e., 'freedom of hate speech' over time. An ideal scenario to understand this would be to observe the effects of hate speech in an (almost) unrestricted environment. Hence, we perform the first temporal analysis of hate speech on Gab.com, a social media site with very loose moderation policy. We first generate temporal snapshots of Gab from millions of posts and users. Using these temporal snapshots, we compute an activity vector based on DeGroot model to identify hateful users. The amount of hate speech in Gab is steadily increasing and the new users are becoming hateful at an increased and faster rate. Further, our analysis analysis reveals that the hate users are occupying the prominent positions in the Gab network. Also, the language used by the community as a whole seem to correlate more with that of the hateful users as compared to the non-hateful ones. We discuss how, many crucial design questions in CSCW open up from our work., Comment: 24 pages, 14 figures, 1 table. Accepted at CSCW 2020

Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects. This creates an immense scope, in the field of natural language processing towards developing automated tools for content moderation and review. In this paper we propose Self Attentive Revision Encoder (StRE) which leverages orthographic similarity of lexical units toward predicting the quality of new edits. In contrast to existing propositions which primarily employ features like page reputation, editor activity or rule based heuristics, we utilize the textual content of the edits which, we believe contains superior signatures of their quality. More specifically, we deploy deep encoders to generate representations of the edits from its text content, which we then leverage to infer quality. We further contribute a novel dataset containing 21M revisions across 32K Wikipedia pages and demonstrate that StRE outperforms existing methods by a significant margin at least 17% and at most 103%. Our pretrained model achieves such result after retraining on a set as small as 20% of the edits in a wikipage. This, to the best of our knowledge, is also the first attempt towards employing deep language models to the enormous domain of automated content moderation and review in Wikipedia., Comment: Accepted in ACL 2019 , 9 pages

Many scale-free networks exhibit a rich club structure, where high degree vertices form tightly interconnected subgraphs. In this paper, we explore the emergence of rich clubs in the context of shortest path based centrality metrics. We term these subgraphs of connected high closeness or high betweeness vertices as rich centrality clubs (RCC). Our experiments on real world and synthetic networks highlight the inter-relations between RCCs, expander graphs, and the core-periphery structure of the network. We show empirically and theoretically that RCCs exist, if the core-periphery structure of the network is such that each shell is an expander graph, and their density decreases from inner to outer shells. The main contributions of our paper are: (i) we demonstrate that the formation of RCC is related to the core-periphery structure and particularly the expander like properties of each shell, (ii) we show that the RCC property can be used to find effective seed nodes for spreading information and for improving the resilience of the network under perturbation and, finally, (iii) we present a modification algorithm that can insert RCC within networks, while not affecting their other structural properties. Taken together, these contributions present one of the first comprehensive studies of the properties and applications of rich clubs for path based centralities., Comment: Accepted at ACM CIKM 2018, 10 Pages

Recent advances in the field of network representation learning are mostly attributed to the application of the skip-gram model in the context of graphs. State-of-the-art analogues of skip-gram model in graphs define a notion of neighbourhood and aim to find the vector representation for a node, which maximizes the likelihood of preserving this neighborhood. In this paper, we take a drastic departure from the existing notion of neighbourhood of a node by utilizing the idea of coreness. More specifically, we utilize the well-established idea that nodes with similar core numbers play equivalent roles in the network and hence induce a novel and an organic notion of neighbourhood. Based on this idea, we propose core2vec, a new algorithmic framework for learning low dimensional continuous feature mapping for a node. Consequently, the nodes having similar core numbers are relatively closer in the vector space that we learn. We further demonstrate the effectiveness of core2vec by comparing word similarity scores obtained by our method where the node representations are drawn from standard word association graphs against scores computed by other state-of-the-art network representation techniques like node2vec, DeepWalk and LINE. Our results always outperform these existing methods, Comment: Accepted at ASONAM 2018, 4 Pages,4 Figures

Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minimal computation, whether the influential vertices of the current time step will retain their high centrality, in the future time steps, as the network evolves. In this paper, based on empirical evidences from several large real world time varying networks, we discover a certain class of networks where the highly central vertices are part of the innermost core of the network and this property is maintained over time. As a key contribution of this work, we propose novel heuristics to identify these networks in an optimal fashion and also develop a two-step algorithm for predicting high centrality vertices. Consequently, we show for the first time that for such networks, expensive shortest path computations in each time step as the network changes can be completely avoided; instead we can use time series models (e.g., ARIMA as used here) to predict the overlap between the high centrality vertices in the current time step to the ones in the future time steps. Moreover, once the new network is available in time, we can find the high centrality vertices in the top core simply based on their high degree., Comment: Accepted in Journal Track of ECML PKDD 2018

Anisotropic flow of hadronic matter is considered as a sensitive tool to detect the early stage dynamics of high-energy heavy-ion collisions. Taking the event by event fluctuations of the collision geometry into account, the elliptic flow parameter and the triangular flow parameter derived from the azimuthal distribution of produced hadrons, are investigated within the framework of a multiphase transport (AMPT) model, at a collision energy that in near future will typically be available at the Facility for Antiproton and Ion Research. The dependence of elliptic and triangular flow parameters on initial fluctuations, on parton scattering cross-sections, their mass ordering on different hadron species and on the constituent quark number scaling are examined. The AMPT simulation can not exactly match the elliptic flow results on Pb + Pb collision at 40A GeV of the NA49 experiment. The simulation results presented in this work are expected to provide us with an insight to study flow properties at high baryonic density but moderate temperature, and also with an opportunity to compare similar results available from RHIC and LHC experiments., Comment: 10 pages, 19 figures

Centrality and system geometry dependence of azimuthal anisotropy of charged hadrons measured in terms of the elliptic flow parameter are investigated using Au+Au event samples at incident beam energy $20$A GeV and $40$A GeV generated by Ultrarelativistic Quantum Molecular Dynamics and A Multiphase Transport models. The Monte Carlo Glauber model is employed to estimate the eccentricity of the overlapping zone at an early stage of the collisions. Anisotropies present both in the particle multiplicity distribution and in the kinetic radial expansion are examined by using standard statistical and phenomenological methods. In the context of upcoming Compressed Baryonic Matter experiment to be held at the Facility for Antiproton and Ion Research, the present set of simulated results provide us not only with an opportunity to examine the expected collective behavior of hadronic matter at high baryon density and moderate temperature environment, but when compared with similar results obtained from Relativistic Heavy Ion Collider and Large Hadron Collider experiments, they also allow us to investigate how anisotropy of hadronic matter may differ or agree with its low baryon density and high temperature counterpart., Comment: 13 pages, 17 figures

Azimuthal distributions of radial velocities of charged hadrons produced in nucleus-nucleus $(AB)$ collisions are compared with the corresponding azimuthal distribution of charged hadron multiplicity in the framework of a multiphase transport (AMPT) model at two different collision energies. The mean radial velocity seems to be a good probe for studying radial expansion. While the anisotropic part of the distributions indicates a kind of collective nature of radial expansion, the isotropic part characterizes a thermal motion. The present investigation is carried out keeping the upcoming Compressed Baryonic Matter (CBM) experiment to be held at the Facility for Anti-proton Ion Research (FAIR) in mind. As far as high-energy heavy-ion interactions are concerned, CBM will supplement the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) experiments. In this context our simulation results at high baryochemical potential would be interesting, when scrutinized from the perspective of almost a baryon free environment achieved at RHIC and LHC., Comment: 6 pages, 8 figures

In the era of big data, graph sampling is indispensable in many settings. Existing sampling methods are mostly designed for static graphs, and aim to preserve basic structural properties of the original graph (such as degree distribution, clustering coefficient etc.) in the sample. We argue that for any sampling method it is impossible to produce an universal representative sample which can preserve all the properties of the original graph; rather sampling should be application specific (such as preserving hubs - needed for information diffusion). Here we consider community detection as an application scenario. We propose ComPAS, a novel sampling strategy that unlike previous methods, is not only designed for streaming graphs (which is a more realistic representation of a real-world scenario) but also preserves the community structure of the original graph in the sample. Empirical results on both synthetic and different real-world graphs show that ComPAS is the best to preserve the underlying community structure with average performance reaching 73.2% of the most informed algorithm for static graphs., Comment: Accepted at AAMAS 2018