Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Phase-Pure Zeolite Sodalite from Clear Solution Extracted from Coal Fly Ash

The formation of zeolite sodalite (SOD) as a by-product in the synthesis of zeolite X from clear solution extracted from fused South African coal fly ash has recently been studied. However, the present study shows that zeolite X as well as zeolite A should be seen as intermediate structures and that phase-pure sodalite can be obtained by increasing crystallization time and temperature. To investigate the transition from zeolites A and X into SOD, the crystallization was conducted at 70, 80, and 90°C for different times. In addition to structural and chemical analyses by XRD and ICP, respectively, a detailed morphological characterization of the synthesis products by scanning electron microscopy (SEM) was undertaken in order to investigate the effect of the crystallization conditions on different crystal morphologies of zeolite SOD.
Quantum integrable systems and concentration of plasmon resonance

We are concerned with the quantitative mathematical understanding of surface plasmon resonance (SPR) when $d \geq 3$. SPR is the resonant oscillation of conducting electrons at the interface between negative and positive permittivity materials and forms the fundamental basis of many cutting-edge applications of metamaterials. It is recently found that the SPR concentrates due to curvature effect. In this paper, we derive sharper and more explicit characterisations of the SPR concentration at high-curvature places in both the static and quasi-static regimes. The study can be boiled down to analyzing the geometries of the so-called Neumann-Poincar\'e (NP) operators, which are certain pseudo-differential operators sitting on the interfacial boundary. We propose to study the joint Hamiltonian flow of an integral system given by the moment map defined by the NP operator. Via considering the Heisenberg picture and lifting the joint flow to a joint wave propagator, we establish a more general version of quantum ergodicity on each leaf of the foliation of this integrable system, which can then be used to establish the desired SPR concentration results. The mathematical framework developed in this paper leverages the Heisenberg picture of quantization and extends some results of quantum integrable system via generalising the concept of quantum ergodicity, which can be of independent interest to the spectral theory and the potential theory.
Social Shaping for Transactive Energy Systems

This paper considers the problem of shaping agent utility functions in a transactive energy system to ensure the optimal energy price at a competitive equilibrium is always socially acceptable, that is, below a prescribed threshold. Agents in a distributed energy system aim to maximize their individual payoffs, as a combination of the utility of energy consumption and the income/expenditure from energy exchange. The utility function of each agent is parameterized by individual preference vectors, with the overall system operating at competitive equilibriums. We show the social shaping problem of the proposed transactive energy system is conceptually captured by a set decision problem. The set of agent preferences that guarantees a socially acceptable price is characterized by an implicit algebraic equation for strictly concave and continuously differentiable utility functions. We also present two analytical solutions where tight ranges for the coefficients of linear-quadratic utilities and piece-wise linear utilities are established under which optimal pricing is proven to be always socially acceptable.
IID Sampling from Intractable Multimodal and Variable-Dimensional Distributions

Bhattacharya (2021b) has introduced a novel methodology for generating iid realizations from any target distribution on the Euclidean space, irrespective of dimensionality. In this article, our purpose is two-fold. We first extend the method for obtaining iid realizations from general multimodal distributions, and illustrate with a mixture of two 50-dimensional normal distributions. Then we extend the iid sampling method for fixed-dimensional distributions to variable-dimensional situations and illustrate with a variable-dimensional normal mixture modeling of the well-known “acidity data”, with crucial application of the iid sampling method developed for multimodal distributions.
The emission properties, structure and stability of ionic liquid menisci undergoing electrically assisted ion evaporation

Abstract The properties and structure of electrically stressed ionic liquid menisci experiencing ion evaporation are simulated using an electrohydrodynamic model with field-enhanced thermionic emission in steady state for an axially symmetric geometry. Solutions are explored as a function of the external background field, meniscus dimension, hydraulic impedance and liquid temperature. Statically stable solutions for emitting menisci are found to be constrained to a set of conditions: a minimum hydraulic impedance, a maximum current output and a narrow range of background fields that maximizes at menisci sizes of 0.5–3 ${\rm \mu}{\rm m}$ in radius. Static stability is lost when the electric field adjacent to the electrode that holds the meniscus corresponds to an electric pressure that exceeds twice the surface tension stress of a sphere of the same size as the meniscus. Preliminary investigations suggest this limit to be universal, therefore, independent of most ionic liquid properties, reservoir pressure, hydraulic impedance or temperature and could explain the experimentally observed bifurcation of a steady ion source into two or more emission sites. Ohmic heating near the emission region increases the liquid temperature, which is found to be important to accurately describe stability boundaries. Temperature increase does not affect the current output when the hydraulic impedance is constant. This phenomenon is thought to be due to an improved interface charge relaxation enhanced by the higher electrical conductivity. Dissipated ohmic energy is mostly conducted to the electrode wall. The higher thermal diffusivity of the wall versus the liquid, allows the ion source to run in steady state without heating.
Statistical Representation of Spacetime

It is assumed that the spacetime is composed by events and can be explained by partially ordered set (causal set). The parent events born two kinds of children. Some children have a causal relation with their parents and other kinds have not. It is assumed that evolution of the population is only happen by the causal children. The assumed population can be modeled by ﬁnite (inﬁnite) dimension Leslie matrix. In both ﬁnite and inﬁnite cases, it is shown that the stationary state of the population always exists and the matrix has positive eigenvalues. By ﬁnding the relation between the statistical information of the population and the stationary state, a probability matrix and a Shannon-like entropy is deﬁned. It is shown that the change in entropy is always quantized and positive and in consequence, the world is inﬂating. We show that the vacuum energy can be attributed to the necessary done work for preserving the causal relation between the parents and the children (cohesive energy). By assuming that the sum of cohesive energy and kinetic energy of the denumerable causal spacetime is equal to the heat, which ﬂows across a causal horizon, we ﬁnd the relation between energy-momentum tensor and discrete Ricci tensor which can be called the Einstein state equation. Finally, it is shown that the constant of proportionality η between the entropy and the area is proportional to k B l 2 p at Planck scale which is in good agreement with the Hawking’s result.
A Video Summarization Method Using Temporal Interest Detection and Key Frame Prediction

—In this paper, a Video Summarization Method using Temporal Interest Detection and Key Frame Prediction is proposed for supervised video summarization, where video summarization is formulated as a combination of sequence labeling and temporal interest detection problem. In our method, we firstly built a flexible universal network frame to simultaneously predicts frame-level importance scores and temporal interest segments, and then combine the two components with different weights to achieve a more detailed video summarization. Extensive experiments and analysis on two benchmark datasets prove the effectiveness of our method. Specifically, compared with other state-of-the-art methods, its performance is increased by at least 2.6% and 4.2% on TVSum and SumMe respectively.
Multimodal Integration of Human-Like Attention in Visual Question Answering

Human-like attention as a supervisory signal to guide neural attention has shown significant promise but is currently limited to unimodal integration – even for inherently multimodal tasks such as visual question answering (VQA). We present the Multimodal Human-like Attention Network (MULAN) – the first method for multimodal integration of human-like attention on image and text during training of VQA models. MULAN integrates attention predictions from two state-of-the-art text and image saliency models into neural self-attention layers of a recent transformer-based VQA model. Through evaluations on the challenging VQAv2 dataset, we show that MULAN is competitive to state of the art in its model class – achieving 73.98% accuracy on test-std and 73.72% on test-dev with approximately 80% fewer trainable parameters than prior work. Overall, our work underlines the potential of integrating multimodal human-like attention into neural attention mechanisms for VQA.
Optimal Simulation of Quantum Measurements via the Likelihood POVMs

By developing a new framework of likelihood POVMs, analysis techniques and a new proof of the quantum covering lemma, we address the simulation of separable quantum measurement over bipartite states. In addition to a new one shot inner bound that naturally generalizes to the asymptotic case, we demonstrate the power, generality and universality of the developed techniques in the most general distributed measurement scenario by recovering all current known inner bounds. In addition to the above results, this framework is appealing in being the most natural and simple POVM simulation protocol.
Detecting Biosignatures in the Atmospheres of Gas Dwarf Planets with the James Webb Space Telescope

Exoplanets with radii between those of Earth and Neptune have stronger surface gravity than Earth, and can retain a sizable hydrogen-dominated atmosphere. In contrast to gas giant planets, we call these planets gas dwarf planets. The James Webb Space Telescope (JWST) will offer unprecedented insight into these planets. Here, we investigate the detectability of ammonia (NH3, a potential biosignature) in the atmospheres of seven temperate gas dwarf planets using various JWST instruments. We use petitRadTRANS and PandExo to model planet atmospheres and simulate JWST observations under different scenarios by varying cloud conditions, mean molecular weights (MMWs), and NH3 mixing ratios. A metric is defined to quantify detection significance and provide a ranked list for JWST observations in search of biosignatures in gas dwarf planets. It is very challenging to search for the 10.3–10.8 μm NH3 feature using eclipse spectroscopy with the Mid-Infrared Instrument (MIRI) in the presence of photon and a systemic noise floor of 12.6 ppm for 10 eclipses. NIRISS, NIRSpec, and MIRI are feasible for transmission spectroscopy to detect NH3 features from 1.5–6.1 μm under optimal conditions such as a clear atmosphere and low MMWs for a number of gas dwarf planets. We provide examples of retrieval analyses to further support the detection metric that we use. Our study shows that searching for potential biosignatures such as NH3 is feasible with a reasonable investment of JWST time for gas dwarf planets given optimal atmospheric conditions.
Disentangling the positive partisanship from the negative in affective polarization using a coevolving latent space network with attractors (CLSNA) model

We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. The models are estimated using Bayesian inference. We apply the CLSNA models to the question of affective polarization, which expects Republicans and Democrats to cohere, favor and interact with their own party and to distance, repel and interact less with the opposing party. Using two different longitudinal social networks from the social media platforms, Twitter and Reddit, we investigate the relative contributions of positive (attractive) and negative (repulsive) affect among political elites and the public, respectively. Our goal is to uncover and quantify polarization—and disentangle the positive and negative forces within and between parties, in particular. Our analysis confirms the existence of affective polarization among both political elites and the public. While positive partisanship remains dominant across the full period of study for both Democratic elites and the public, a notable decrease in Republicans’ strength of in-group affect since 2015 has led to the dominance of negative partisanship in their behavior.
The DNA Storage Channel: Capacity and Error Probability Bounds

We consider the DNA storage channel, in which M Deoxyribonucleic acid (DNA) molecules comprising each codeword, are stored without order, then sampled N times with replacement, and then sequenced over a discrete memoryless channel. For a constant coverage depth, M/N, and molecule length scaling Θ(log M), lower (achievability) and upper (converse) bounds on the capacity of the channel, as well as a lower (achievability) bound on the reliability function of the channel are provided. Both the lower and upper bounds on the capacity generalize a bound which was previously known to hold only for the binary symmetric sequencing channel, and only under certain restrictions on the molecule length scaling and the crossover probability parameters. When specified to binary symmetric sequencing channel, these restrictions are completely removed for the lower bound and are significantly relaxed for the upper bound. The lower bound on the reliability function is achieved under a universal decoder, and reveals that the dominant error event is that of outage – the event in which the capacity of the channel induced by the DNA molecule sampling operation does not support the target rate.
Know it to Defeat it: Exploring Health Rumor Characteristics and Debunking Efforts on Chinese Social Media during COVID-19 Crisis

Health-related rumors being spread online during a public crisis may pose a serious threat to people's well-being. Existing crisis informatics research lacks in-depth insights into the characteristics of health rumors and the efforts to debunk them on social media in a pandemic. To fill this gap, we conduct a comprehensive analysis of four months of rumor-related online discussion during COVID-19 on Weibo, a Chinese microblogging site. Results suggest that the dread (cause fear) type of health rumors provoked significantly more discussions and lasted longer than the wish (raise hope) type. We further explore how four kinds of social media users (i.e., government, media, organization, and individual) combat health rumors, and identify their preferred way of sharing the debunking information and the key rhetoric strategies used in the process. We examine the relationship between debunking and rumor discussions using a Granger causality approach, and show the efficacy of debunking in suppressing rumor discussions, which is time-sensitive and varies according to rumor type and debunker. Our results can provide insights into crisis informatics and risk management on social media in pandemic settings.
Isochrone fitting of Galactic globular clusters – III. NGC 288, NGC 362, and NGC 6218 (M12)

We present new isochrone fits to colour–magnitude diagrams of the Galactic globular clusters NGC 288, NGC 362, and NGC 6218 (M12). We utilize a lot of photometric bands from the ultraviolet to mid-infrared by use of data from the HST, Gaia, unWISE, Pan-STARRS, and other photometric sources. In our isochrone fitting, we use theoretical models and isochrones from the Dartmouth Stellar Evolution Program and Bag of Stellar Tracks and Isochrones for α-enhanced abundance [α/Fe] = +0.40, different helium abundances, and a metallicity of about [Fe/H] = −1.3 adopted from the literature. We derive the most probable distances 8.96 ± 0.05, 8.98 ± 0.06, and 5.04 ± 0.05 kpc, ages 13.5 ± 1.1, 11.0 ± 0.6, and 13.8 ± 1.1 Gyr, extinctions AV = 0.08 ± 0.03, 0.11 ± 0.04, and 0.63 ± 0.03 mag, and reddenings E(B − V) = 0.014 ± 0.010, 0.028 ± 0.011, and 0.189 ± 0.010 mag for NGC 288, NGC 362, and NGC 6218, respectively. The distance estimates from the different models are consistent, while those of age, extinction, and reddening are not. The uncertainties of age, extinction, and reddening are dominated by some intrinsic systematic differences between the models. However, the models agree in their relative age estimates: NGC 362 is 2.6 ± 0.5 Gyr younger than NGC 288 and 2.8 ± 0.5 Gyr younger than NGC 6218, confirming age as the second parameter for these clusters. We provide reliable lists of the cluster members and precise cluster proper motions from the Gaia Early Data Release 3.
TRAINING SPIKING NEURAL NETWORKS USING LESSONS

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper shows how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks. This paper explores the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to spiking neural networks; the subtle link between temporal backpropagation and spike timing dependent plasticity, and how deep learning might move towards biologically plausible online learning. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here.
A Sierpinski carpet like fractal without standard self-similar energy

We construct a Sierpinski carpet like fractal, on which a diﬀusion with sub-Gaussian heat kernel estimate does not exist, in contrast to previous researches on the existence of such diﬀusions, on the generalized Sierpinski carpets and recently introduced unconstrained Sierpinski carpets.
Irrelevant deformations with boundaries and defects

We initiate the study of TT¯ -like irrelevant solvable deformations in quantum field theory with boundaries and defects. For this purpose, we employ a general formalism developed in the context of spin chains, which allows us to derive both, the deformed bulk and boundary/defect scattering matrices of integrable models. Using the deformed scattering matrices, we derive the flow equation for the deformed finite volume spectrum, as well as the cylinder partition function and the exact g-function.
DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings

Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability. Conventional approaches employ the siamese-network for this task, which obtains the sentence embeddings through modeling the context-response semantic relevance by applying a feed-forward network on top of the sentence encoders. However, as the semantic textual similarity is commonly measured through the element-wise distance metrics (e.g. cosine and L2 distance), such architecture yields a large gap between training and evaluating. In this paper, we propose DialogueCSE, a dialogue-based contrastive learning approach to tackle this issue. DialogueCSE first introduces a novel matching-guided embedding (MGE) mechanism, which generates a context-aware embedding for each candidate response embedding (i.e. the context-free embedding) according to the guidance of the multi-turn context-response matching matrices. Then it pairs each context-aware embedding with its corresponding context-free embedding and finally minimizes the contrastive loss across all pairs. We evaluate our model on three multi-turn dialogue datasets: the Microsoft Dialogue Corpus, the Jing Dong Dialogue Corpus, and the E-commerce Dialogue Corpus. Evaluation results show that our approach significantly outperforms the baselines across all three datasets in terms of MAP and Spearman’s correlation measures, demonstrating its effectiveness. Further quantitative experiments show that our approach achieves better performance when leveraging more dialogue context and remains robust when less training data is provided.
Log Calabi-Yau surfaces and Jeffrey-Kirwan residues

We prove an equality, predicted in the physical literature, between the Jeffrey-Kirwan residues of certain explicit meromorphic forms attached to a quiver without loops or oriented cycles and its Donaldson-Thomas type invariants. In the special case of complete bipartite quivers we also show independently, using scattering diagrams and theta functions, that the same Jeffrey-Kirwan residues are determined by the the Gross-Hacking-Keel mirror family to a log Calabi-Yau surface.
Cohomology and deformations of Jacobi-Jordan algebras

In this paper, we define and develop a cohomology and deformation theories of Jacobi-Jordan algebras. We construct a cohomology based on two operators, called zigzag cohomology, and detail the low degree cohomology spaces. We describe the relationships between first and second cohomology groups with extensions and deformations. Moreover, we consider cohomology properties of pseudo-euclidean Jacobi-Jordan algebras and provide a deformation theory that fits with our zigzag cohomology of Jacobi-Jordan algebras. Furthermore, the paper includes several examples and applications.
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents

We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based contexts in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.