Miracle-dz/UniFAR-FTUs huggingface dataset:
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators

This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated shapes. Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy. We show how to develop the unsupervised loss via a spectral decomposition of the Hessian of the ARAP energy. Our loss nicely decouples pose and shape variations through a robust norm. The loss admits simple closed-form expressions. It is easy to train and can be plugged into any standard generation models, e.g., variational auto-encoder (VAE) and auto-decoder (AD). Experimental results show that our approach outperforms existing shape generation approaches considerably on public benchmark datasets of various shape categories such as human, animal and bone. Our code and data are available at https://github.com/GitBoSun/ARAPReg.
A strong polarizing field in thin-film paraelectrics

The surface charge associated with the spontaneous polarization in ferroelectrics is well known to cause a depolarizing field that can be particularly detrimental in the thin-film geometry desirable for microelectronic devices. Incomplete screening of the surface charge, for example by metallic electrodes or surface adsorbates, can lead to the formation of domains, suppression or reorientation of the polarization, or even stabilization of a higher energy non-polar phase . A huge amount of research and development effort has been invested in understanding the depolarizing behavior and minimizing its unfavorable effects. Here we demonstrate the opposite behavior: A strong polarizing field that drives thin films of materials that are centrosymmetric and paraelectric in their bulk form into a non-centrosymmetric, polar state. We illustrate the behavior using density functional computations for perovskite-structure potassium tantalate, KTaO$_3$, which is of considerable interest for its high dielectric constant, proximity to a quantum critical point and superconductivity. We then provide a simple recipe to identify whether a particular material and film orientation will exhibit the effect, and develop an electrostatic model to estimate the critical thickness of the induced polarization in terms of well-known material parameters. Our results provide practical guidelines for exploiting the electrostatic properties of thin-film ionic insulators to engineer novel functionalities for nanoscale devices.
Integrating Transductive and Inductive Embeddings Improves Link Prediction Accuracy

In recent years, inductive graph embedding models, viz., graph neural networks (GNNs) have become increasingly accurate at link prediction (LP) in online social networks. The performance of such networks depends strongly on the input node features, which vary across networks and applications. Selecting appropriate node features remains application-dependent and generally an open question. Moreover, owing to privacy and ethical issues, use of personalized node features is often restricted. In fact, many publicly available data from online social network do not contain any node features (e.g., demography). In this work, we provide a comprehensive experimental analysis which shows that harnessing a transductive technique (e.g., Node2Vec) for obtaining initial node representations, after which an inductive node embedding technique takes over, leads to substantial improvements in link prediction accuracy. We demonstrate that, for a wide variety of GNN variants, node representation vectors obtained from Node2Vec serve as high quality input features to GNNs, thereby improving LP performance.
Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies

Context. Computational techniques are essential for mining large databases produced in modern surveys with value-added products. Aims. This paper presents a machine learning procedure to carry out a galaxy morphological classification and photometric redshift estimates simultaneously. Currently, only a spectral energy distribution (SED) fitting has been used to obtain these results all at once. Methods. We used the ancillary data gathered in the OTELO catalog and designed a nonsequential neural network that accepts optical and near-infrared photometry as input. The network transfers the results of the morphological classification task to the redshift fitting process to ensure consistency between both procedures. Results. The results successfully recover the morphological classification and the redshifts of the test sample, reducing catastrophic redshift outliers produced by an SED fitting and avoiding possible discrepancies between independent classification and redshift estimates. Our technique may be adapted to include galaxy images to improve the classification.
QDEF and Its Approximations in OBDM

Given an input dataset (i.e., a set of tuples), query definability in Ontology-based Data Management (OBDM) amounts to find a query over the ontology whose certain answers coincide with the tuples in the given dataset. We refer to such a query as a characterization of the dataset with respect to the OBDM system. Our first contribution is to propose approximations of perfect characterizations in terms of recall (complete characterizations) and precision (sound characterizations). A second contribution is to present a thorough complexity analysis of three computational problems, namely verification (check whether a given query is a perfect, or an approximated characterization of a given dataset), existence (check whether a perfect, or a best approximated characterization of a given dataset exists), and computation (compute a perfect, or best approximated characterization of a given dataset).
Experimental investigation of the limitations of polarisation optics for future gravitational wave detectors based on the polarisation Sagnac speedmeter

The polarisation Sagnac speedmeter interferometer has the potential to replace the Michelson interferometer as the instrumental basis for future generations of ground-based gravitational wave detectors. The quantum noise benefit of this speedmeter is dependent on high-quality polarisation optics, the polarisation beam-splitter (PBS) and quarter-waveplate (QWP) optics that are key to this detector configuration and careful consideration of the effect of birefringence in the arm cavities of the interferometer. A PBS with an extinction ratio of better than 4000 in transmission and 700 in reflection for a 41° angle of incidence was characterised along with a QWP of birefringence of λ4+λ324 . The cavity mirror optics of a 10 m prototype polarisation Sagnac speedmeter were measured to have birefringence in the range 1 × 10−3 to 2 × 10−5 radians. This level of birefringence, along with the QWP imperfections, can be cancelled out by careful adjustment of the QWP angle, to the extent that the extinction ratio of the PBS is the leading limitation for the polarisation Sagnac speedmeter in terms of polarisation effects.
Performance Analysis of Mixed RF/FSO Relaying under HPA Nonlinearity and IQ Imbalance

In this paper, we present the performance analysis of asymmetric dual-hop RF/FSO system with multiple relays. The RF channels follow the correlated Rayleigh fading while the optical links are subject to the Gamma-Gamma fading. To select the candidate relay to forward the communication, we assume Partial Relay Selection (PRS) with outdated Channel State Information (CSI). Unlike the vast majority of work in this area, we introduce the impairments to the relays and the destination. We will propose three impairment models called Soft Envelope Limiter (SEL), Traveling Wave Tube Amplifier (TWTA) and IQ Imbalance in order to compare the resilience of our system with the RF one against the hardware impairments. Closed-from of the outage probability (OP) is derived in terms of Meijer's G function as well as the upper bound of the ergodic capacity (EC). The Bit Error Rate (BER) and the exact EC are evaluated numerically. Finally, analytical and numerical results are presented and validated by Monte Carlo simulation.
Planetary nebulae with Wolf–Rayet-type central stars – II. Dissecting the compact planetary nebula M 2-31 with GTC MEGARA

We present a comprehensive analysis of the compact planetary nebula M 2-31 investigating its spectral properties, spatio-kinematical structure, and chemical composition using Gran Telescopio Canarias (GTC) Multi-Espectrógrafo en GTC de Alta Resolución para Astronomía (MEGARA) integral field spectroscopic observations and Nordic Optical Telescope(NOT) Alhambra Faint Object Spectrograph and Camera (ALFOSC) medium-resolution spectra and narrow-band images. The GTC MEGARA high-dispersion observations have remarkable tomographic capabilities, producing an unprecedented view of the morphology and kinematics of M 2-31 that discloses a fast spectroscopic bipolar outflow along position angles 50○ and 230○, an extended shell, and a toroidal structure or waist surrounding the central star perpendicularly aligned with the fast outflows. These observations also show that the C ii emission is confined in the central region and enclosed by the [N ii] emission. This is the first time that the spatial segregation revealed by a two-dimensional map of the C ii line implies the presence of multiple plasma components. The deep NOT ALFOSC observations allowed us to detect broad Wolf–Rayet (WR) features from the central star of M 2-31, including previously undetected broad O vi lines that suggest a reclassification as a [WO4]-type star.
A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents

Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations. In this work, we propose cross-document relation extraction, where the two entities of a relation tuple appear in two different documents that are connected via a chain of common entities. Following this idea, we create a dataset for two-hop relation extraction, where each chain contains exactly two documents. Our proposed dataset covers a higher number of relations than the publicly available sentence-level datasets. We also propose a hierarchical entity graph convolutional network (HEGCN) model for this task that improves performance by 1.1% F1 score on our two-hop relation extraction dataset, compared to some strong neural baselines.
Nonlinear interferometry enables coherent heat machine operation

Tomáš Opatrný, Šimon Bräuer, Avijit Misra, 3 Nilakantha Meher, Ofer Firstenberg, Eilon Poem, and Gershon Kurizki Department of Optics, Faculty of Science, Palacký University, 17. listopadu 50, 77146 Olomouc, Czech Republic International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, 200444 Shanghai, China Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel Physics of Complex Systems, Weizmann Institute of Science, Rehovot 7610001, Israel (Dated: August 24, 2021)
Spectrophotometric Determination of Clonazepam in Pure and Dosage forms using Charge Transfer Reaction

A rapid, sensitive and without extraction spectrophotometric method for determination of clonazepam (CLO) in pure and pharmaceutical dosage forms has been described. The proposed method was simply depended on charge transfer reaction between reduced CLO (n-donor) and metol (N-methyl-p-aminophenol sulfate) as a chromogenic reagent (Ï€- acceptor). The reduced drug, with zinc and concentrated hydrochloric acid, produced a purple colored soluble charge-transfer complex with metol in the presence of sodium metaperiodate in neutral medium, which has been measured at Î»max 532 nm. All the variables which affected the developed and the stability of the colored product such as concentration of reagent and oxidant, temperature and time of reaction were investigated and optimized. The linearity of the method was observed within a concentration range 5-40 Î¼g ml-1 CLO and with a correlation coefficient not less than 0.998, while the molar absorbitivity and sandell sensitivity were 3. 473Ã—103 L.mole-1.cm-1 and 0.0909 Î¼g cm-2 respectively. The present work includes also the usage of the Benesiâ€“Hildebrand equation for the evaluation of the association constant and molar absorptivity of the colored complex . Finally the proposed method was successfully applied for the determination of CLO in tablets. Keywords: Spectrophotometric ; Clonazepam ; Metol ; Charge-transfer reaction.
Gendering of Smartphone Ownership and Autonomy among Youth: Narratives from Rural India

This study delves into the research question: how does gender influence smartphone ownership and autonomy in using the internet among the youth in rural India? This paper explores the influence of local culture on smartphone ownership and autonomy through an ethnographic study among rural Indian youth by analysing the intersection of gender with other identity axes. The findings show that young people’s smartphone ownership and autonomy is shaped by their social and cultural setting, and could lead to various inequalities in their internet usage. This study shows that gender paves way for various disparities with regard to smartphone ownership and internet usage. Decolonisation of the understanding of smartphone ownership and internet usage patterns of the youth in the Global South suggests a reconsideration of the user experience designs and platform policies.
Apache submarine: a unified machine learning platform made simple

As machine learning is applied more widely, it is necessary to have a machine-learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists [24] to improve their productivity. However, existing machine-learning platforms are ill-equipped to address the "Machine Learning tech debts" [36] such as glue code, reproducibility, and portability. Furthermore, existing platforms only take expert data scientists into consideration, and thus they are inflexible for infrastructure administrators and non-user-friendly for citizen data scientists. We propose Submarine, a unified machine-learning platform, and takes all infrastructure administrators, expert data scientists, and citizen data scientists into consideration. Submarine has been widely used in many technology companies, including Ke.com and LinkedIn. We present two use cases in Section 5.
Geometric Amortization of Enumeration Algorithms

In this paper, we introduce the technique of geometric amortization for enumeration algorithms. This technique can be used to make the delay of enumeration algorithms more regular without much overhead on the space it uses. More precisely, we are interested in enumeration algorithms having incremental linear delay, that is, algorithms enumerating a set $A$ of size $K$ such that for every $t \leq K$, it outputs at least $t$ solutions in time $O(tp)$, where $p$ is the incremental delay of the algorithm. While it is folklore that one can transform such an algorithm into an algorithm with delay $O(p)$, the naive transformation may blow the space exponentially. We show that, using geometric amortization, such an algorithm can be transformed into an algorithm with delay $O(p\log K)$ and $O(s\log K)$ space, where $s$ is the space used by the original algorithm. We apply geometric amortization to show that one can trade the delay of flashlight search algorithms for their average delay modulo a factor of $O(\log K)$. We illustrate how this tradeoff may be advantageous for the enumeration of solutions of DNF formulas.
Visualizing superconductivity in an inversion-symmetry-broken doped Weyl semimetal

The Weyl semimetal MoTe$_2$ offers a rare opportunity to study the interplay between Weyl physics and superconductivity. Recent studies have found that Se substitution can boost the superconductivity up to 1.5K, but suppress the Td structure phase that is essential for the emergence of Weyl state. A microscopic understanding of possible coexistence of enhanced superconductivity and the Td phase has not been established so far. Here, we use scanning tunneling microscopy (STM) to study a optimally doped new superconductor MoTe$_{1.85}$Se$_{0.15}$ with bulk Tc ~ 1.5K. By means of quasiparticle interference imaging, we identify the existence of low temperature Td phase with broken inversion symmetry where superconductivity globally coexists. Consistently, we find that the superconducting coherence length, extracted from both the upper critical field and the decay of density of states near a vortex, is much larger than the characteristic length scale of existing dopant derived chemical disorder. Our findings of robust superconductivity arising from a Weyl semimetal normal phase in MoTe$_{1.85}$Se$_{0.15}$, makes it a promising candidate for realizing topological superconductivity.
A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion

Many social phenomena are triggered by public opinion that is formed in the process of opinion exchange among individuals. To date, from the engineering point of view, a large body of work has been devoted to studying how to manipulate individual opinions so as to guide public opinion towards the desired state. Recently, Abebe et al. (KDD 2018) have initiated the study of the impact of interventions at the level of susceptibility rather than the interventions that directly modify individual opinions themselves. For the model, Chan et al. (The Web Conference 2019) designed a local search algorithm to find an optimal solution in polynomial time. However, it can be seen that the solution obtained by solving the above model might not be implemented in real-world scenarios. In fact, as we do not consider the amount of changes of the susceptibility, it would be too costly to change the susceptibility values for agents based on the solution. In this paper, we study an opinion optimization model that is able to limit the amount of changes of the susceptibility in various forms. First we introduce a novel opinion optimization model, where the initial susceptibility values are given as additional input and the feasible region is defined using the ℓp-ball centered at the initial susceptibility vector. For the proposed model, we design a projected gradient method that is applicable to the case where there are millions of agents. Finally we conduct thorough experiments using a variety of real-world social networks and demonstrate that the proposed algorithm outperforms baseline methods.
Schwinger-Keldysh effective action for a relativistic Brownian particle in the 
AdS/CFT
 correspondence

We compute Schwinger-Keldysh effective action for a relativistic heavy quark (with constant background velocity) in strongly coupled N = 4 supersymmetric Yang-Mills plasma. The holographic dual description involves a noisy trailing string moving in SchwarzschildAdS5 black brane. The noise is caused by Hawking radiation emitted from string worldsheet horizon. Besides quadractic terms, the effective action contains cubic interactions, which are entirely induced by the constant background velocity. The nonlinear fluctuation-dissipation relations for three-point functions are discussed based on holographic results. ∗ yybu@hit.edu. cn † zhangbiye@hit.edu. cn (correspondence author) ar X iv :2 10 8. 10 06 0v 1 [ he pth ] 2 3 A ug 2 02 1
Uniform explicit Stewart theorem on prime factors of linear recurrences

Stewart (2013) proved that the biggest prime divisor of the $n$th term of a Lucas sequence of integers grows quicker than $n$, answering famous questions of Erd\H{o}s and Schinzel. In this note we obtain a fully explicit and, in a sense, uniform version of Stewart's result.
Graph2Pix: A Graph-Based Image to Image Translation Framework

In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder1, where users interpolate multiple GAN-generated images to create artworks. This unique approach of creating new images leads to a tree-like structure where one can track historical data about the creation of a particular image. Inspired by this structure, we propose a novel graph-to-image translation model called Graph2Pix, which takes a graph and corresponding images as input and generates a single image as output. Our experiments show that Graph2Pix is able to outperform several image-to-image translation frameworks on benchmark metrics, including LPIPS (with a 25% improvement) and human perception studies (n = 60), where users preferred the images generated by our method 81.5% of the time. Our source code and dataset are publicly available at https://github.com/catlab-team/graph2pix.
ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation

This article presents an innovative deep learning pipeline, which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory units in modeling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funneled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with electrooptical red–green–blue inputs, thus mitigating the effects of artifacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data.
Expressing and Executing Informed Consent Permissions Using SWRL: The All of Us Use Case

The informed consent process is a complicated procedure involving permissions as well a variety of entities and actions. In this paper, we discuss the use of Semantic Web Rule Language (SWRL) to further extend the Informed Consent Ontology (ICO) to allow for semantic machine-based reasoning to manage and generate important permission-based information that can later be viewed by stakeholders. We present four use cases of permissions from the All of Us informed consent document and translate these permissions into SWRL expressions to extend and operationalize ICO. Our efforts show how SWRL is able to infer some of the implicit information based on the defined rules, and demonstrate the utility of ICO through the use of SWRL extensions. Future work will include developing formal and generalized rules and expressing permissions from the entire document, as well as working towards integrating ICO into software systems to enhance the semantic representation of informed consent for biomedical research.
Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network

This research features a deep learning based framework to address the problem of matching a given face sketch image against a face photo database. The photo-sketch matching problem is challenging because 1) modality gap between photo and sketch is very large, and 2) the number of paired photo/ sketch data is insufficient to train deep network. To circumvent the problem of large modality gap, our approach is to use an intermediate latent space between the two modalities. We effectively align the distributions of the two modalities in this latent space by employing a bidirectional (photo → sketch and sketch → photo) collaborative synthesis network. A StyleGAN-like architecture is utilized to make the intermediate latent space be equipped with rich representation power. To resolve the problem of insufficient training samples, we introduce a three-step training scheme. Extensive evaluation on public composite face sketch database confirms superior performance of our method compared to existing state-of-the-art methods. The proposed methodology can be employed in matching other modality pairs.