Miracle-dz/UniFAR-FTUs huggingface dataset:
HDhuman: High-quality Human Performance Capture with Sparse Views

—In this paper, we introduce HDhuman, a method that addresses the challenge of novel view rendering of human performers that wear clothes with complex texture patterns using a sparse set of camera views. Although some recent works have achieved remarkable rendering quality on humans with relatively uniform textures using sparse views, the rendering quality remains limited when dealing with complex texture patterns as they are unable to recover the high-frequency geometry details that observed in the input views. To this end, the proposed HDhuman uses a human reconstruction network with a pixel-aligned spatial transformer and a rendering network that uses geometry-guided pixel-wise feature integration to achieve high-quality human reconstruction and rendering. The designed pixel-aligned spatial transformer calculates the correlations between the input views, producing human reconstruction results with high-frequency details. Based on the surface reconstruction results, the geometry-guided pixel-wise visibility reasoning provides guidance for multi-view feature integration, enabling the rendering network to render high-quality images at 2k resolution on novel views. Unlike previous neural rendering works that always need to train or ﬁne-tune an independent network for a different scene, our method is a general framework that is able to generalize to novel subjects. Experiments show that our approach outperforms all the prior generic or speciﬁc methods on both synthetic data and real-world data.
Superlinear Lower Bounds Based on ETH

We introduce techniques for proving superlinear conditional lower bounds for polynomial time problems. In particular, we show that CircuitSAT for circuits with m gates and log(m) inputs (denoted by log-CircuitSAT) is not decidable in essentially-linear time unless the exponential time hypothesis (ETH) is false and k-Clique is decidable in essentially-linear time in terms of the graph's size for all fixed k. Such conditional lower bounds have previously only been demonstrated relative to the strong exponential time hypothesis (SETH). Our results therefore offer significant progress towards proving unconditional superlinear time complexity lower bounds for natural problems in polynomial time.
Synthesizing images using parameterized models for automated optical inspection (AOI)

Abstract When designing or improving systems for automated optical inspection (AOI), systematic evaluation is an important but costly necessity to achieve and ensure high quality. Computer graphics methods can be used to quickly create large virtual sets of samples of test objects and to simulate image acquisition setups. We use procedural modeling techniques to generate virtual objects with varying appearance and properties, mimicking real objects and sample sets. Physical simulation of rigid bodies is deployed to simulate the placement of virtual objects, and using physically-based rendering techniques we create synthetic images. These are used as input to an AOI system instead of physically acquired images. This enables the development, optimization, and evaluation of the image processing and classification steps of an AOI system independently of a physical realization. We demonstrate this approach for shards of glass, as sorting glass is one challenging practical application for AOI.
The Role of Lookahead and Approximate Policy Evaluation in Reinforcement Learning with Linear Value Function Approximation

Function approximation is widely used in reinforcement learning to handle the computational difficulties associated with very large state spaces. However, function approximation introduces errors which may lead to instabilities when using approximate dynamic programming techniques to obtain the optimal policy. Therefore, techniques such as lookahead for policy improvement and m-step rollout for policy evaluation are used in practice to improve the performance of approximate dynamic programming with function approximation. We quantitatively characterize, for the first time, the impact of lookahead and m-step rollout on the performance of approximate dynamic programming (DP) with function approximation: (i) without a sufficient combination of lookahead and m-step rollout, approximate DP may not converge, (ii) both lookahead and m-step rollout improve the convergence rate of approximate DP, and (iii) lookahead helps mitigate the effect of function approximation and the discount factor on the asymptotic performance of the algorithm. Our results are presented for two approximate DP methods: one which uses least-squares regression to perform function approximation and another which performs several steps of gradient descent of the least-squares objective in each iteration.
Lagrangian analysis of bio-inspired vortex ring formation

Abstract Pulsatile jet propulsion is a highly energy-efficient swimming mode used by various species of aquatic animals that continues to inspire engineers of underwater vehicles. Here, we present a bio-inspired jet propulsor that combines the flexible hull of a jellyfish with the compression motion of a scallop to create individual vortex rings for thrust generation. Similar to the biological jetters, our propulsor generates a nonlinear time-varying exit velocity profile and has a finite volume capacity. The formation process of the vortices generated by this jet profile is analysed using time-resolved velocity field measurements. The transient development of the vortex properties is characterised based on the evolution of ridges in the finite-time Lyapunov exponent field and on local extrema in the pressure field derived from the velocity data. Special attention is directed toward the vortex merging observed in the trailing shear layer. During vortex merging, the Lagrangian vortex boundaries first contract in the streamwise direction before expanding in the normal direction to keep the non-dimensional energy at its minimum value, in agreement with the Kelvin–Benjamin variational principle. The circulation, diameter and translational velocity of the vortex increase due to merging. The vortex merging takes place because the velocity of the trailing vortex is higher than the velocity of the main vortex ring prior to merging. The comparison of the temporal evolution of the Lagrangian vortex boundaries and the pressure-based vortex delimiters confirms that features in the pressure field serve as accurate and robust observables for the vortex formation process.
EGGS: Eigen-Gap Guided Search For Automated Spectral Clustering

The performance of spectral clustering heavily relies on the quality of afﬁnity matrix. A variety of afﬁnity-matrix-construction (AMC) methods have been proposed but they have hyperparam-eters to determine beforehand, which requires strong experience and lead to difﬁculty in real applications especially when the inter-cluster similarity is high or/and the dataset is large. In addition, we often need to choose different AMC methods for different datasets, which still depends on experience. To solve these two challenging problems, in this paper, we present a simple yet effective method for automated spectral clustering. The main idea is to ﬁnd the most reliable afﬁnity matrix among a set of candidates given by different AMC methods with different hyperparameters, where the reliability is quan-tiﬁed by the relative-eigen-gap of graph Laplacian introduced in this paper. We also implement the method using Bayesian optimization.We extend the method to large-scale datasets such as MNIST, on which the time cost is less than 90s and the clustering accuracy is state-of-the-art. Extensive experiments of natural image clustering show that our method is more versatile, accurate, and efﬁcient than baseline methods.
QUBO formulations for numerical quantum computing

With the advent of quantum computers, many quantum computing algorithms are being developed. Solving linear systems is one of the most fundamental problems in almost all science and engineering. The Harrow-Hassidim-Lloyd algorithm, a monumental quantum algorithm for solving linear systems on gate model quantum computers, was invented and several advanced variations have been developed. For a given n by n matrix A and a vector b, we will find unconstrained binary optimization (QUBO) models for a vector x that satisfies Ax=b. To formulate QUBO models for a linear system solving problem, we make use of a linear least-square problem with binary representation of the solution. We validate those QUBO models on the D-Wave system and discuss the results. For a simple system, we provide a Python code to calculate the matrix characterizing the relationship between the variables and to print the test code that can be used directly in the D-Wave system.
Prepared for submission to JHEP

We find that the chiral phase transition (chiral crossover) in QCD at physical point is triggered by big imbalance among three fundamental quantities essential for the QCD vacuum structure: susceptibility functions for the chiral symmetry, axial symmetry, and the topological charge. The balance, dobbed the QCD trilemma, is unavoidably violated when one of the magnitudes among them is highly dominated, or suppressed. Based on a three-flavor Nambu-Jona-Lasinio model, we explicitly evaluate the amount of violation of the QCD trilemma at physical point, and show that the violation takes place not only at vacuum, but even in a whole temperature regime including the chiral crossover epoch. This work confirms and extends the suggestion recently reported from lattice QCD with 2 flavors on dominance of the axial and topological susceptibilities left in the chiral susceptibility at high temperatures. It turns out that the imbalance is essentially due to the flavor symmetry violation of the lightest three flavors, and the flavor breaking specifically brings enhancement of the axial anomaly contribution in the chiral order parameter, while the axial order parameter and the transition rate of the topological charge are fairly insensitive to the flavor symmetry. The violation of QCD trilemma and its flavor dependence can be tested by lattice simulations with 2 + 1 flavors in the future, and would also give a new guiding principle to explore the flavor dependence of the chiral phase transition, such as the Columbia plot, including possible extension with external fields. ar X iv :2 10 6. 05 67 4v 3 [ he pph ] 2 4 Ja n 20 22
Sex differences in the influence of toxoplasmosis on stress and perceived anxiety: Evidence for the stress-coping hypothesis

Life-long infection with Toxoplasma, which affects 30% of the human population, has specific behavioral effects. The stress-coping hypothesis explains why the toxoplasmosis-associated behavioral changes go in opposite directions in men and women. It suggests that toxoplasmosis impairs the health of humans, which results in chronic stress. Men and women are known to cope with stress in opposite ways. The first presumption of the hypothesis, impaired health, was confirmed in many studies. The second, higher level of stress, was tested only rarely. Levels of stress and anxiety, measured with the Perceived Stress Scale, and the State-Trait Anxiety Inventory X-2, respectively, were compared in a population of 614 Toxoplasma-free and 162 Toxoplasma-infected subjects. Higher stress was detected in the infected men, but not women. We also found that physical health had a positive rather than negative effect on stress when mental health is controlled, which seems to contradict the prediction of the stress-coping hypothesis. No differences were found in the anxiety of infected and noninfected subjects. Subjects who have objective reasons for stress (those with worse physical health) are less stressed than those without such reasons.
A comparative study of carbon nanotube characteristics synthesized from various biomass precursors through hydrothermal techniques and their potential applications

Abstract Preparing low-cost carbon nanotubes (CNTs) from renewable biomass resources as cheap carbon precursors will potentially benefit industries and is given extraordinary importance. This article describes CNT synthesis from biomass precursors using the hydrothermal technique using ferrocene as a catalyst for energy storage and decontamination of the atmosphere. The prepared CNTs were characterized using XRD, Raman, and SEM analysis. Among the synthesized products, TGSD-CNT was further taken for application studies. TGSD-CNT with a band gap (2.01 eV) has become a hot spot in various scientific achievements such as mitigation of environmental pollution, generation of energy, and storage in the present study. The emission spectrum was studied using the 1931 chromaticity diagram of the Commission Internationale de I'eclairage (CIE) to investigate the TGSD-CNT dominant emission. Batch adsorption experiments were conducted for different operating parameters and a comparative analysis was performed to study the performance between CAC and the synthesized TGSD-CNT. Therefore, the conversion of available cheap biomass into high value-added materials such as CNT becomes practical, feasible and this reduces CNT's cost of production. The synthesized TGSD-CNT is therefore a useful material for applications such as sorption of heavy metal and photovoltaic devices.
Antifungal Potential of Dill (Anethum graveolens L.) Seed Essential Oil, its Extracts and Components Against Phytopathogenic Maize Fungi

Abstract Dill (Anethum graveolens L.) is an aromatic herb that is mainly used as a flavoring agent in the food industry. It has been also used to treat various ailments. Many reports are available in the literature regarding its biological activities. So, the present study reports the fungitoxic impact of dill (Anethum graveolens L.) seed extracts, essential oil, isolated and derivatized compounds using the poisoned food technique against phytopathogenic maize fungi (Fusarium verticillioides and Drechslera maydis). GC/MS analysis of hydro distilled essential oil (yield 1.5%) revealed carvone (42.2%) and limonene (30.0%) to be the major compounds that were further isolated using column chromatography and were chemically modified. Dill seed extracts were prepared by different extraction methods (maceration, sonication, and soxhlet) using methanol and hexane solvents and maximum yield was observed for soxhlet extraction (5.7%). The antifungal activity of all the components (essential oil, extracts, isolated compounds and derivatives) was evaluated at different concentrations (125-1000 μg/mL) using Carbendazim 50 WP and Mancozeb 75 WP as the positive control. Overall, the isolated compounds and their derivatives were more effective in comparison to the extracts and essential oil. Further, the activity was increased with increasing the concentration as well as the polarity of the molecule.
The Connection and Underlying Cause between Financial Statement Fraud Cases

There are more and more cases of fraud associated with manipulation of financial statements today. Various schemes and management motivations for manipulating financial statements. However, is there a common thread between these cases that can be studied, so that it can provide a conclusion about prevention and improvement in the future? Our study seeks to present an analysis of cases of financial reporting fraud that occur in the world from a financial audit point of view. This research is a qualitative research in the form of inductive case study analysis. We present case studies using the background flow, financial statement fraud schemes, management motivation and financial auditor involvement. At the end we made a conclusion, drew a connection and underlying discussion along with lessons learned about the case. The results of our study found that the existence of pressure in the form of high expectations, failure of expansion and overly optimistic investment and misuse of company assets for personal use became the common underlying cause among these financial fraud cases. The implication of our study is that financial auditor or forensic accountant should pay more attention when those factors occurs or identified when they conducting audit or examination.
Distributed Algorithms for Connectivity and MST in Large Graphs with Efficient Local Computation

We study distributed algorithms for large-scale graphs, focusing on the fundamental problems of connectivity and minimum spanning tree (MST). We consider the k-machine model, a well-studied model for distributed computing for large-scale graph computations, where k ≥ 2 machines jointly perform computations on graphs with n nodes (typically, n ≫ k). The input graph is assumed to be initially randomly partitioned among the k machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication rounds (denoted Tc) of the computation. While communication is a significant factor that affects the time needed for large-scale computations, the computation cost incurred by the individual machines also contributes to the overall time complexity of the distributed algorithm. We posit a complexity measure called the local computation cost (denoted Tℓ) that measures the worst-case local computation cost among the machines. A lower bound for Tℓ in our model is Ω((m + n)/k + Δ + k), while a lower bound on Tc is Ω(n/k2) [Klauck et al., SODA 2015], where m is the number of edges and Δ is the maximum degree. Prior algorithms for connectivity and MST in the k-machine model [Klauck et al., SODA 2015, Pandurangan et al., SPAA 2016] do not take into account local computation; a straightforward local implementation of these algorithms is not optimal with respect to local computation. In this paper, we study several distributed algorithms for connectivity and MST and analyze their performance with respect to both the computation and communication cost. In particular, we analyze a well-studied flooding algorithm for connectivity and connected components that takes rounds and local computation time.1 We then present a deterministic filtering algorithm that has an improved round complexity of but local computation complexity of . Next, we present two deterministic algorithms which are increasingly sophisticated implementations of the classical Borůvka’s algorithm, the last of which has round complexity and local computation complexity . We finally present a randomized algorithm to find connected components with round complexity and local computation complexity that are both essentially optimal (up to polylogarithmic factors).
Real-Time Identification from Gait Features Using Cascade Voting Method

Abstract There are several biometric methods for identification. These are generally classified under two main groups as physiological and behavioural biometric methods. Recently, methods using behavioural biometric features have gained popularity. Identification made using gait pattern is also one of these methods. The present study proposes a machine learning based system performing identification in real time via gait features using a Kinect device. The data set is composed of 23 individuals’ skeleton model data obtained by the authors. From these data, 147 handcrafted features have been extracted. Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting (GB), XG-Boost (XGB) and K-Nearest Neighbour (KNN) classifiers have been trained with these features. Furthermore, the output of these five machine learning models has been combined with a voting approach. The highest classification has been obtained with 97.5 % accuracy via a voting approach. The classification accuracies of the RF, DNN, XGB, GB and KNN classifiers are 95 %, 87.5 %, 85 %, 80 % and 65 %, respectively. The classification accuracy obtained via a voting approach is higher than in the previous studies. The developed system successfully performs real-time identification.
Knowledge Graph Curation: A Practical Framework

Knowledge Graphs (KGs) have shown to be very important for applications such as personal assistants, question-answering systems, and search engines. Therefore, it is crucial to ensure their high quality. However, KGs inevitably contain errors, duplicates, and missing values, which may hinder their adoption and utility in business applications, as they are not curated, e.g., low-quality KGs produce low-quality applications that are built on top of them. In this vision paper, we propose a practical knowledge graph curation framework for improving the quality of KGs. First, we define a set of quality metrics for assessing the status of KGs, Second, we describe the verification and validation of KGs as cleaning tasks, Third, we present duplicate detection and knowledge fusion strategies for enriching KGs. Furthermore, we give insights and directions toward a better architecture for curating KGs.
QS-Dijkstra: A Safety-Enhanced Routing Algorithm on SDN Framework

We consider the security problems in Dijkstra caused by routing on the vulnerable communication links. This work employs a SDN framework to mitigate the vulnerable links while maintaining satisfactory QoS request. SDN is able to maintain an overall network awareness, through which a controller is capable of optimizing routing based on metrics of safety interest. A lot of useful link information, like link type, link ownership, interface bandwidth, transition delay and historical record, can be collected and computed by the SDN controller to enable more safe, reliable and efficient paths. In this way, we can consider the varying delay status of the communication network along with other network security parameters and get a presence of a passive/active adversary in the network routing area. The implement and experiment indicate that QS-Dijkstra algorithm is able to minimize traffic routing through vulnerable links while satisfying the QoS constraints of the network.
Prevention of SQL Injection Attack by Using Black Box Testing

SQL injection attacks are diverse and have fast variations, making it convenient to handle them. An SQL attack can affect the entire system to a great extent, adversely affecting the website, as well as data loss and leaking. In the past, enormous efforts have been put forth into handling the attack. This attack consists of a variety of heterogeneous components, which has always made research a challenging attempt. A web application injection attack allows hackers to gain control of a web application by entering invalid input. In the past, researchers found that many applications were vulnerable to such attacks as a result of less efficient entry validation. We propose an automated black box testing program for automating the task of SQL Injection Vulnerability to eradicate SQL injection attacks. The SQLIV is automatically assessed when such attacks are carried out. Also, it uses object-oriented methodology to develop the SQLIV Scanner so that false +ve and -ve results can be reduced. The algorithm’s performance can be compared with existing algorithms and an experimental analysis can be conducted to test and validate its results. A new algorithm is expected to provide more accurate results.