Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Generative Models for Small Molecule Drug Discovery

Existing drug discovery pipelines take 5–10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space, which could be on the order of $10^{60}$. Deep generative models can model the underlying probability distribution of both the physical structures and property of drugs and relate them nonlinearly. By exploiting patterns in massive datasets, these models can distill salient features that characterize the molecules. Generative adversarial networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity toward binding with the receptor for a target disease. However, classical GANs cannot explore certain regions of the chemical space and suffer from training instabilities. The practical utility of such models is limited due to the vast size of the search space, characterized by millions of parameters. A full quantum GAN may require more than 90 qubits even to generate small molecules with up to nine heavy atoms. The proposed quantum GAN with a hybrid generator (QGAN-HG) model is composed of a hybrid quantum generator that supports various number of qubits and quantum circuit layers, and a classical discriminator. The QGAN-HG with less than 20% of the original parameters can learn molecular distributions as efficiently as its classical counterpart. Another extended version of the proposed QGAN-HG, which utilizes multiple quantum subcircuits, considerably accelerates our standard QGAN-HG training process and avoids the potential gradient vanishing issue of deep neural networks.
Visualizing the Financial Impact of Presidential Tweets on Stock Markets

As more and more data being created every day, all of it can help take better decisions with data analysis. It is not different with data generated in financial markets. Here we examine the process of how global economy is affected by the market sentiment influenced by the micro-blogging data (tweets) of the American President Donald Trump. The news feed is gathered from The Guardian and Bloomberg from the period between December 2016 and October 2019, which are used to further identify the potential tweets that influenced the markets as measured by changes in equity indices.
Energy cost bounded estimates for controlling complex social networks with conformity behavior

In order to understand controlling a complex system, an estimation of the required eﬀort needed to achieve control is vital. Previous works have addressed this issue by studying the scaling laws of energy cost in a general way with continuous-time dynamics. However, continuous-time modelling is unable to capture conformity behavior, which is common in many complex social systems. Therefore, to understand controlling social systems with conformity, discrete-time modelling is used and the energy cost scaling laws are derived. The results are validated numerically with model and real networks. In addition, it is shown through simulations that heterogeneous scale-free networks are less controllable, requiring a higher number of minimum drivers. Since the conformity-based model relates to various complex systems, such as ﬂocking, or evolutionary games, the results of this paper represent a step forward towards developing realistic control of complex social systems.
PHOSPHATE DEFICIENCY AFFECTS ACID PHOSPHATASE ACTIVITY AND GROWTH OF TWO WHEAT VARIETIES

The effects of phosphate deficiency on the plant growth and acid phosphatase activity in two wheat cultivars (Triticum aestivum L. cv. ‘Bryza’ and ‘Opatka’) were studied. Pi content decreased significantly in the shoots and roots of all plants grown for one to three weeks in the nutrient medium without phosphate (−P). Phosphate starvation affected growth of both wheat cultivars in a similar way: significantly decreased shoot and root mass, especially after three weeks growth on −P medium, even up to 8–11% of control. However the ratio of root/shoot fresh weight of −P plants increased (2–3 times more than control). The shoot height of −P wheat plants decreased to about 50–60% of the control after two to three weeks of culture; less affected by Pi deficiency was root elongation—no significant changes were observed for both wheat cultivars during one to three weeks of culture. Phosphate deficiency increased the activity of extracellular and intracellular acid phosphatases in comparison to phosphate-sufficient plants. The increase of intracellular acid phosphatase activity in leaves and roots was generally more significant in ‘Bryza’ cultivar as compared to ‘Opatka’, especially after two to three weeks of growth on −P medium. The increase of root surface acid phosphatase activity was the highest for Pi-deficient wheat cv. ‘Bryza’, after three weeks of culture in the −P medium. Generally, the increase of activity of acid phosphatases secreted by the roots of −P wheat plants was higher than those of intracellular acid phosphatases. These results indicate an important role of acid phosphatases in the acclimation of both wheat cultivars to early Pi deficiency.
Impact of Interventional Policies Including Vaccine on Covid-19 Propagation and Socio-Economic Factors

A novel coronavirus disease has emerged (later named COVID-19) and caused the world to enter a new reality, with many direct and indirect factors influencing it. Some are human-controllable (e.g. interventional policies, mobility and the vaccine);some are not (e.g. the weather). We have sought to test how a change in these human-controllable factors might influence two measures: the number of daily cases against economic impact. If applied at the right level and with up-to-date data to measure, policymakers would be able to make targeted interventions and measure their cost. This study aims to provide a predictive analytics framework to model, predict and simulate COVID-19 propagation and the socio-economic impact of interventions intended to reduce the spread of the disease such as policy and/or vaccine. It allows policymakers, government representatives and business leaders to make better-informed decisions about the potential effect of various interventions with forward-looking views via scenario planning. We have leveraged a recently launched open-source COVID-19 big data platform and used published research to find potentially relevant variables (features) and leveraged in-depth data quality checks and analytics for feature selection and predictions. An advanced machine learning pipeline has been developed armed with a self-evolving model, deployed on a modern machine learning architecture. It has high accuracy for trend prediction (back-tested with r-squared) and is augmented with interpretability for deeper insights.
Quantum Codes Do Not Fix Qubit Independent Errors

The goal of this work is to analyse the performance of quantum error correction codes in regard to fixing independent errors in several qubits. We say that a quantum code does not fix a quantum computing error if its application does not reduce the variance of the error. We restrict our study to the classical 5−qubit quantum error correcting code (proposed by Laflamme and some collaborators), which is able to correct arbitrary errors in a single qubit and is also fault-tolerant. We show that this code does not fix qubit independent errors, even assuming that the correction circuit does not introduce new errors. We also prove, for qubit independent errors, that if the correction circuit of the 5−qubit quantum code detects an error, then the corrected state has central symmetry and, as a consequence, its variance is maximal. We have been able to obtain these results thanks to the high symmetry of the 5−qubit quantum code. Although the calculations needed to extend our proofs to less symmetric codes seem to be extremely complicated, we nevertheless think that the results obtained for the 5−qubit quantum code reveal a general behavior pattern of quantum error correcting codes against qubit independent errors.
High-speed growth and photoluminescence of porous anodic alumina films with controllable interpore distances over a large range

Highly ordered porous anodic alumina (PAA) films are fabricated with high efficiency by stable high-field anodization in oxalic acid/ethanol/water electrolytes at 100–180V and sulfuric acid/oxalic acid/ethanol/water electrolytes at 30–80V, giving interpore distances in the range of 225–450nm and 70–140nm, respectively. The photoluminescence of PAA films prepared by high-field anodization shows remarkable redshift of the peak position and decrease of the intensity compared to that of PAA films formed by conventional low-field anodization.
Provably Approximated ICP

The goal of the \emph{alignment problem} is to align a (given) point cloud $P = \{p_1,\cdots,p_n\}$ to another (observed) point cloud $Q = \{q_1,\cdots,q_n\}$. That is, to compute a rotation matrix $R \in \mathbb{R}^{3 \times 3}$ and a translation vector $t \in \mathbb{R}^{3}$ that minimize the sum of paired distances $\sum_{i=1}^n D(Rp_i-t,q_i)$ for some distance function $D$. A harder version is the \emph{registration problem}, where the correspondence is unknown, and the minimum is also over all possible correspondence functions from $P$ to $Q$. Heuristics such as the Iterative Closest Point (ICP) algorithm and its variants were suggested for these problems, but none yield a provable non-trivial approximation for the global optimum. We prove that there \emph{always} exists a"witness"set of $3$ pairs in $P \times Q$ that, via novel alignment algorithm, defines a constant factor approximation (in the worst case) to this global optimum. We then provide algorithms that recover this witness set and yield the first provable constant factor approximation for the: (i) alignment problem in $O(n)$ expected time, and (ii) registration problem in polynomial time. Such small witness sets exist for many variants including points in $d$-dimensional space, outlier-resistant cost functions, and different correspondence types. Extensive experimental results on real and synthetic datasets show that our approximation constants are, in practice, close to $1$, and up to x$10$ times smaller than state-of-the-art algorithms.
Learning from Satisfying Assignments Using Risk Minimization

In this paper we consider the problem of Learning from Satisfying Assignments introduced by [1] of finding a distribution that is a close approximation to the uniform distribution over the satisfying assignments of a low complexity Boolean function f . In a later work [2] consider the same problem but with the knowledge of some continuous distribution D and the objective being to estimate Df , which is D restricted to the satisfying assignments of an unknown Boolean function f . We consider these problems from the point of view of parameter estimation techniques in statistical machine learning and prove similar results that are based on standard optimization algorithms for Risk Minimization.
gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correlation in high-dimensional geospatial datasets

Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts often want to be able to visualize observations and local relationships on a map. However, software dedicated to visualizing local spatial relations between multiple variables in high dimensional datasets remains undeveloped. This paper introduces gwpcorMapper, a newly developed software application for mapping geographically weighted correlation and partial correlation in large multivariate datasets. gwpcorMapper facilitates ESDA by giving researchers the ability to interact with map components that describe local correlative relationships. We built gwpcorMapper using the R Shiny framework. The software inherits its core algorithm from GWpcor, an R library for calculating the geographically weighted correlation and partial correlation statistics. We demonstrate the application of gwpcorMapper by using it to explore census data in order to find meaningful relationships that describe the work-life environment in the 23 special wards of Tokyo, Japan. We show that gwpcorMapper is useful in both variable selection and parameter tuning for geographically weighted statistics. gwpcorMapper highlights that there are strong statistically clear local variations in the relationship between the number of commuters and the total number of hours worked when considering the total population in each district across the 23 special wards of Tokyo. Our application demonstrates that the ESDA process with high-dimensional geospatial data using gwpcorMapper has applications across multiple fields.
An Early Look at the Parler Online Social Network

Parler is as an"alternative"social network promoting itself as a service that allows to"speak freely and express yourself openly, without fear of being deplatformed for your views. "Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider. This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon.
Target Detection and Segmentation in Circular-Scan Synthetic Aperture Sonar Images Using Semisupervised Convolutional Encoder–Decoders

In this article, we propose a framework for saliency-based multitarget detection and segmentation of circular-scan synthetic aperture sonar (CSAS) imagery. Our framework relies on a multibranch convolutional encoder–decoder network (MB-CEDN). The encoder portion of the MB-CEDN extracts visual contrast features from CSAS images. These features are fed into dual decoders that perform pixel-level segmentation to mask targets. Each decoder provides different perspectives as to what constitutes a salient target. These opinions are aggregated and cascaded into a deep parsing network to refine the segmentation. We evaluate our framework using real-world CSAS imagery consisting of five broad target classes and compare it with existing approaches from the computer vision literature. We show that our framework outperforms supervised deep saliency networks designed for natural imagery. It greatly outperforms unsupervised saliency approaches developed for natural imagery. This illustrates that natural-image-based models may need to be altered to be effective for this imaging sonar modality.
Shortest non-separating st-path on chordal graphs

Many NP-Hard problems on general graphs, such as maximum independence set, maximal cliques and graph coloring can be solved efficiently on chordal graphs. In this paper, we explore the problem of non-separating st-paths defined on edges: for a connected undirected graph and two vertices, a non-separating path is a path between the two vertices such that if we remove all the edges on the path, the graph remains connected. We show that on general graphs, checking the existence of non-separating st-paths is NP-Hard, but the same problem can be solved in linear time on chordal graphs. In the case that such path exists, we introduce an algorithm that finds the shortest non-separating st-path on a connected chordal graph of $n$ vertices and $m$ edges with positive edge lengths that runs in $O(n\log{n} + m)$ time.
Dispersion relations in nonlinear electrodynamics and the kinematics of the Compton effect in a magnetic background

Non-linear electrodynamic models are re-assessed in this paper to pursue an investigation of the kinematics of the Compton effect in a magnetic background. Before considering specific models, we start off by presenting a general non-linear Lagrangian built up in terms of the most general Lorentzand gauge-invariant combinations of the electric and magnetic fields. The extended Maxwell-like equations and the energy-momentum tensor conservation are presented and discussed in their generality. We next expand the fields around a uniform and time-independent electric and magnetic backgrounds up to second order in the propagating wave, and compute dispersion relations which account for the effect of the external fields. We obtain thereby the refraction index and the group velocity for the propagating radiation in different situations. In particular, we focus on the kinematics of the Compton effect in presence of external magnetic fields. This yields constraints that relate the derivatives of the general Lagrangian with respect to the field invariants and the magnetic background under consideration. We carry out our inspection by focusing on some specific nonlinear electrodynamic effective models: Hoffmann-Infeld, Euler-Heisenberg, generalized Born-Infeld and Logarithmic.
Optimized Multi-Level Prime Array Configurations

Antenna arrays have many applications in direction-of-arrival (DOA) estimation. Sparse arrays such as nested arrays, super nested arrays, and coprime arrays have large degrees of freedom (DOFs). They can estimate large number of sources greater than the number of elements. They also have closed form expressions for antenna locations and the achievable DOFs. The multi-level prime array (MLPA) uses multiple uniform linear subarrays where the number of elements in the subarrays are pairwise coprime integers. The array achieves large DOFs and it has closed form expressions for the antenna locations and the required aperture size. For a given number of subarrays and total number of elements, there are different design alternatives. This paper finds the optimum number of elements within each subarray and the optimized ordered inter-element spacing. In almost all cases, we have found that a unique configuration jointly realizes the maximum number of unique lags and the maximum number of consecutive lags.
The discrepancy of random rectangular matrices

A recent approach to the Beck–Fiala conjecture, a fundamental problem in combinatorics, has been to understand when random integer matrices have constant discrepancy. We give a complete answer to this question for two natural models: matrices with Bernoulli or Poisson entries. For Poisson matrices, we further characterize the discrepancy for any rectangular aspect ratio. These results give sharp answers to questions of Hoberg and Rothvoß (SODA 2019) and Franks and Saks (Random Structures Algorithms 2020). Our main tool is a conditional second moment method combined with Stein's method of exchangeable pairs. While previous approaches are limited to dense matrices, our techniques allow us to work with matrices of all densities. This may be of independent interest for other sparse random constraint satisfaction problems.
Horizontal-to-Vertical Video Conversion

At this blooming age of social media and mobile platform, mass consumers are migrating from horizontal video to vertical contents delivered on hand-held devices. Accordingly, revitalizing the exposure of horizontal video becomes vital and urgent, which is hereby tackled by our automated horizontal-to-vertical (abbreviated as H2V) video conversion framework. Essentially, the H2V framework performs subject-preserving video cropping instantiated in the proposed Rank-SS module. Rank-SS incorporates object detection to discover the candidate subjects, from which we select the primary subject-to-preserve leveraging location, appearance, and salient cues in a convolutional neural network. In addition to converting horizontal videos vertically by cropping around the selected subject, automatic shot detection and multi-object tracking are integrated into the H2V framework to accommodate long and complex videos. To develop H2V systems, we collect an H2V-142 K dataset containing 125 videos (132 K frames) and 9500 cover images annotated with primary subject bounding boxes. On H2V-142 K and public object detection datasets, our method demonstrates promising results on the subject selection comparing to the related solutions. Furthermore, our H2V framework is industrially deployed hosting millions of daily active users and exhibits favorable H2V conversion performance. By making this dataset as well as our approach publicly available, we wish to pave the way for more horizontal-to-vertical video conversion research. Our collected H2V-142 K dataset is available at https://tianchi.aliyun. com/dataset/dataDetail? dataId=93339.
Asymptotic Absence of Poles of Ihara Zeta Function of Large Erdős–Rényi Random Graphs

Using recent results on the concentration of the largest eigenvalue and maximal vertex degree of large random graphs, we prove a version of the graph theory Riemann Hypothesis for large Erdős-Rényi graphs G(n, pn) in the limit n → ∞, npn ≫ logn. 1 Ihara zeta function and graph theory Riemann hypothesis Given a finite connected non-oriented graph Γ = (V,E) with the vertex set V = (α1, . . . , αn) and the edge set E, the Ihara zeta function (IZF) ZΓ(u) introduced by Ihara in the algebraic context [15] can be determined as the generating function ZΓ(u) = exp 
The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications

In this work, we inspect different data sources for browser fingerprints. We show which disadvantages and limitations browser statistics have and how this can be avoided with other data sources. Since human visual behavior is a rich source of information and also contains person specific information, it is a valuable source for browser fingerprints. However, human gaze acquisition in the browser also has disadvantages, such as inaccuracies via webcam and the restriction that the user must first allow access to the camera. However, it is also known that the mouse movements and the human gaze correlate and therefore, the mouse movements can be used instead of the gaze signal. In our evaluation, we show the influence of all possible combinations of the three information sources for user recognition and describe our simple approach in detail. Link: https://es-cloud.cs. uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/? p=%2FThe%20Gaze%20and%20Mouse%20Signal%20as%20additional%20Source%20. &mode=list