Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Person Pose Estimation With Enhanced Channel-Wise and Spatial Information

Multi-person pose estimation is an important but challenging problem in computer vision. Although current approaches have achieved significant progress by fusing the multi-scale feature maps, they pay little attention to enhancing the channel-wise and spatial information of the feature maps. In this paper, we propose two novel modules to perform the enhancement of the information for the multi-person pose estimation. First, a Channel Shuffle Module (CSM) is proposed to adopt the channel shuffle operation on the feature maps with different levels, promoting cross-channel information communication among the pyramid feature maps. Second, a Spatial, Channel-wise Attention Residual Bottleneck (SCARB) is designed to boost the original residual unit with attention mechanism, adaptively highlighting the information of the feature maps both in the spatial and channel-wise context. The effectiveness of our proposed modules is evaluated on the COCO keypoint benchmark, and experimental results show that our approach achieves the state-of-the-art results.
Ieee Transactions on Visualization and Computer Graphics 1 Interactive Visualization and Analysis of Transitional Flow

— A visualization application has been developed with optimizations allowing for the interactive exploration of datasets on the order of 1TB in size. These optimizations were made in data management, caching computations and visualization. Using this application, the user is able to interactively visualize and analyze all regions of a transitional flow volume, including the laminar, transitional and fully turbulent regions. The underlying goal of the visualizations produced from these transitional flow simulations is to localize turbulent spots in the laminar region of the boundary layer, determine under which conditions they form, and follow their evolution. The initiation of turbulent spots, which ultimately lead to full turbulence, was located via a proposed feature detection condition and verified by experimental results. The conditions under which these turbulent spots form and coalesce are validated and presented.
PPGNet: Learning Point-Pair Graph for Line Segment Detection

In this paper, we present a novel framework to detect line segments in man-made environments. Specifically, we propose to describe junctions, line segments and relationships between them with a simple graph, which is more structured and informative than end-point representation used in existing line segment detection methods. In order to extract a line segment graph from an image, we further introduce the PPGNet, a convolutional neural network that directly infers a graph from an image. We evaluate our method on published benchmarks including York Urban and Wireframe datasets. The results demonstrate that our method achieves satisfactory performance and generalizes well on all the benchmarks. The source code of our work is available at https://github.com/svip-lab/PPGNet.
Effect of Surface Layers in Sliding Contact of Viscoelastic Solids (3-D Model of Material)

Sliding contact of a smooth indenter and a two-layered half-space is considered taking into account rheological properties of materials. The case of a viscoelastic layer bonded with a rigid half-space is analyzed as well as an opposite one, which is viscoelastic half-space covered by rigid layer. The problem is formulated as quasi-static. Numerical-analytical method of solution is based on boundary element method and iteration procedure. New analytical solution is used to calculate influence coefficients for the computation procedure. Contact pressure, energy dissipation and internal stresses are analyzed in dependence on sliding velocity, layer thickness and Poisson's ratio.
On the Brain Response to Syntactic Anomalies: Manipulations of Word Position and Word Class Reveal Individual Differences

In two experiments, event-related brain potentials (ERPs) were recorded from 13 scalp locations while subjects read sentences containing a syntactically or a semantically anomalous word. The position (sentence-embedded vs sentence-final) and word class (open vs closed) of the syntactic anomalies were manipulated. In both experiments, semantically anomalous words elicited an enhanced N400 component. Syntactically anomalous closed class words elicited a widely distributed late positive wave (P600) regardless of the word's position and a smaller negative-going effect that was largest over anterior sites when the anomaly occurred in sentence-final position. The response to syntactically anomalous open class words revealed striking qualitative individual differences: These words elicited a P600 response in the majority of subjects and an N400 response in others. The proportion of subjects exhibiting the N400 response was greater when the anomaly occurred in sentence-final position. These results are interpreted in the context of prior findings, and implications for the hypothesis that syntactic and semantic anomalies elicit distinct brain potentials are discussed.
The Optimization of Parallel Frequent Pattern Growth Algorithm Based on Mahout in Cloud Manufacturing Environment

In cloud manufacturing environment, many manufacturing enterprises will produce massive data of a variety of forms. We do research of optimization parallel frequent pattern mining algorithm based on Mahout in this paper. We first analyze the implement and defects of PFP-Growth in Mahout. Then we propose two optimization strategies. One is parallel sequence optimization, and another is optimization the storage of counting information. Datasets from real manufacturing and Webdocs show the effectiveness of the strategy in time and space of the optimization.
Majorana dimers and holographic quantum error-correcting codes

Holographic quantum error-correcting codes have been proposed as toy models that describe key aspects of the AdS/CFT correspondence. In this work, we introduce a versatile framework of Majorana dimers capturing the intersection of stabilizer and Gaussian Majorana states. This picture allows for an efficient contraction with a simple diagrammatic interpretation and is amenable to analytical study of holographic quantum error-correcting codes. Equipped with this framework, we revisit the recently proposed hyperbolic pentagon code (HyPeC). Relating its logical code basis to Majorana dimers, we efficiently compute boundary state properties even for the non-Gaussian case of generic logical input. The dimers characterizing these boundary states coincide with discrete bulk geodesics, leading to a geometric picture from which properties of entanglement, quantum error correction, and bulk/boundary operator mapping immediately follow. We also elaborate upon the emergence of the Ryu-Takayanagi formula from our model, which realizes many of the properties of the recent bit thread proposal. Our work thus elucidates the connection between bulk geometry, entanglement, and quantum error correction in AdS/CFT, and lays the foundation for new models of holography.
Block-online multi-channel speech enhancement using deep neural network-supported relative transfer function estimates

This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when short utterances are processed, e.g. in voice assistant applications. We consider several variants of a system that performs beamforming supported by deep neural network-based voice activity detection followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently to make the method applicable in highly dynamic environments. Due to short processed blocks, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to batch processing regime, when recordings are treated as one block. The experimental evaluation is performed on large datasets of CHiME-4 and another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria. Moreover, word error rate (WER) of a speech recognition system is evaluated, for which the method serves as a front-end. The results indicate that the proposed method is robust for short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms.
FI3D: Direct-Touch Interaction for the Exploration of 3D Scientific Visualization Spaces

We present the design and evaluation of FI3D, a direct-touch data exploration technique for 3D visualization spaces. The exploration of three-dimensional data is core to many tasks and domains involving scientific visualizations. Thus, effective data navigation techniques are essential to enable comprehension, understanding, and analysis of the information space. While evidence exists that touch can provide higher-bandwidth input, somesthetic information that is valuable when interacting with virtual worlds, and awareness when working in collaboration, scientific data exploration in 3D poses unique challenges to the development of effective data manipulations. We present a technique that provides touch interaction with 3D scientific data spaces in 7 DOF. This interaction does not require the presence of dedicated objects to constrain the mapping, a design decision important for many scientific datasets such as particle simulations in astronomy or physics. We report on an evaluation that compares the technique to conventional mouse-based interaction. Our results show that touch interaction is competitive in interaction speed for translation and integrated interaction, is easy to learn and use, and is preferred for exploration and wayfinding tasks. To further explore the applicability of our basic technique for other types of scientific visualizations we present a second case study, adjusting the interaction to the illustrative visualization of fiber tracts of the brain and the manipulation of cutting planes in this context.
A novel compression tool for efficient storage of genome resequencing data

With the advent of DNA sequencing technologies, more and more reference genome sequences are available for many organisms. Analyzing sequence variation and understanding its biological importance are becoming a major research aim. However, how to store and process the huge amount of eukaryotic genome data, such as those of the human, mouse and rice, has become a challenge to biologists. Currently available bioinformatics tools used to compress genome sequence data have some limitations, such as the requirement of the reference single nucleotide polymorphisms (SNPs) map and information on deletions and insertions. Here, we present a novel compression tool for storing and analyzing Genome ReSequencing data, named GRS. GRS is able to process the genome sequence data without the use of the reference SNPs and other sequence variation information and automatically rebuild the individual genome sequence data using the reference genome sequence. When its performance was tested on the first Korean personal genome sequence data set, GRS was able to achieve ∼159-fold compression, reducing the size of the data from 2986.8 to 18.8 MB. While being tested against the sequencing data from rice and Arabidopsis thaliana, GRS compressed the 361.0 MB rice genome data to 4.4 MB, and the A. thaliana genome data from 115.1 MB to 6.5 KB. This de novo compression tool is available at http://gmdd.shgmo. org/Computational-Biology/GRS.
Metabolomic Strategies in Biomarker Research–New Approach for Indirect Identification of Drug Consumption and Sample Manipulation in Clinical and Forensic Toxicology?

Drug of abuse (DOA) consumption is a growing problem worldwide, particularly with increasing numbers of new psychoactive substances (NPS) entering the drug market. Generally, little information on their adverse effects and toxicity are available. The direct detection and identification of NPS is an analytical challenge due to their ephemerality on the drug scene. An approach that does not directly focus on the structural detection of an analyte or its metabolites, would be beneficial for this complex analytical scenario and the development of alternative screening methods could help to provide fast response on suspected NPS consumption. A metabolomics approach might represent such an alternative strategy for the identification of biomarkers for different questions in DOA testing. Metabolomics is the monitoring of changes in small (endogenous) molecules (<1,000 Da) in response to a certain stimulus, e.g., DOA consumption. For this review, a literature search targeting “metabolomics” and different DOAs or NPS was conducted. Thereby, different applications of metabolomic strategies in biomarker research for DOA identification were identified: (a) as an additional tool for metabolism studies bearing the major advantage that particularly a priori unknown or unexpected metabolites can be identified; and (b) for identification of endogenous biomarker or metabolite patterns, e.g., for synthetic cannabinoids or also to indirectly detect urine manipulation attempts by chemical adulteration or replacement with artificial urine samples. The majority of the currently available literature in that field, however, deals with metabolomic studies for DOAs to better assess their acute or chronic effects or to find biomarkers for drug addiction and tolerance. Certain changes in endogenous compounds are detected for all studied DOAs, but often similar compounds/pathways are influenced. When evaluating these studies with regard to possible biomarkers for drug consumption, the observed changes appear, albeit statistically significant, too small to reliably work as biomarker for drug consumption. Further, different drugs were shown to affect the same pathways. In conclusion, metabolomic approaches possess potential for detection of biomarkers indicating drug consumption. More studies, including more sensitive targeted analyses, multi-variant statistical models or deep-learning approaches are needed to fully explore the potential of omics science in DOA testing.
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks

Deep neural networks have yielded superior performance in many applications; however, the gradient computation in a deep model with millions of instances lead to a lengthy training process even with modern GPU/TPU hardware acceleration. In this paper, we propose AutoAssist, a simple framework to accelerate training of a deep neural network. Typically, as the training procedure evolves, the amount of improvement in the current model by a stochastic gradient update on each instance varies dynamically. In AutoAssist, we utilize this fact and design a simple instance shrinking operation, which is used to filter out instances with relatively low marginal improvement to the current model; thus the computationally intensive gradient computations are performed on informative instances as much as possible. We prove that the proposed technique outperforms vanilla SGD with existing importance sampling approaches for linear SVM problems, and establish an O(1/k) convergence for strongly convex problems. In order to apply the proposed techniques to accelerate training of deep models, we propose to jointly train a very lightweight Assistant network in addition to the original deep network referred to as Boss. The Assistant network is designed to gauge the importance of a given instance with respect to the current Boss such that a shrinking operation can be applied in the batch generator. With careful design, we train the Boss and Assistant in a nonblocking and asynchronous fashion such that overhead is minimal. We demonstrate that AutoAssist reduces the number of epochs by 40% for training a ResNet to reach the same test accuracy on an image classification data set and saves 30% training time needed for a transformer model to yield the same BLEU scores on a translation dataset.
An Update of Recent Use of Aegilops Species in Wheat Breeding

Aegilops species have significantly contributed to wheat breeding despite the difficulties involved in the handling of wild species, such as crossability and incompatibility. A number of biotic resistance genes have been identified and incorporated into wheat varieties from Aegilops species, and this genus is also contributing toward improvement of complex traits such as yield and abiotic tolerance for drought and heat. The D genome diploid species of Aegilops tauschii has been utilized most often in wheat breeding programs. Other Aegilops species are more difficult to utilize in the breeding because of lower meiotic recombination frequencies; generally they can be utilized only after extensive and time-consuming procedures in the form of translocation/introgression lines. After the emergence of Ug99 stem rust and wheat blast threats, Aegilops species gathered more attention as a form of new resistance sources. This article aims to update recent progress on Aegilops species, as well as to cover new topics around their use in wheat breeding.
Morphological classification of radio galaxies: capsule networks versus convolutional neural networks

Next-generation radio surveys will yield an unprecedented amount of data, warranting analysis by use of machine learning techniques. Convolutional neural networks are the deep learning technique that has proven to be the most successful in classifying image data. Capsule networks are a more recently developed technique that use capsules comprised of groups of neurons, that describe properties of an image including the relative spatial locations of features. The current work explores the performance of different capsule network architectures against simpler convolutional neural network architectures, in reproducing the classifications into the classes of unresolved, FRI and FRII morphologies. We utilise images from a LOFAR survey which is the deepest, wide-area radio survey to date, revealing more complex radio-source structures compared to previous surveys, presenting further challenges for machine learning algorithms. The 4- and 8-layer convolutional networks attain an average precision of 93.3% and 94.3% respectively, compared to 89.7% obtained with the capsule network, when training on original and augmented images. Implementing transfer learning achieves a precision of 94.4%, that is within the confidence interval of the 8-layer convolutional network. The convolutional networks always outperform any variation of the capsule network, as they prove to be more robust to the presence of noise in images. The use of pooling appears to allow more freedom for the intra-class variability of radio galaxy morphologies, as well as reducing the impact of noise.
Distributed Dantzig-Wolfe Decomposition

Dantzig-Wolfe decomposition (DWD) is a classical algorithm for solving large-scale linear programs whose constraint matrix involves a set of independent blocks coupled with a set of linking rows. The algorithm decomposes such a problem into a master problem and a set of independent subproblems that can be solved in a distributed manner. In a typical implementation, the master problem is solved centrally. In certain settings, solving the master problem centrally is undesirable or infeasible. For example, in the case of decentralized storage of data, or when independent agents who are responsible for the subproblems desire privacy of information. In this paper, we propose a fully distributed DWD algorithm that relies on solving the master problem using a distributed consensus-based Alternating Direction Method of Multipliers (ADMM) algorithm. We derive error bounds on the optimality gap and feasibility violation of the proposed approach. We provide preliminary computational results for our algorithm using a Message Passing Interface (MPI) implementation on randomly generated instances where we obtain high quality solutions.
CDO Pricing: Copula Implied by Risk Neutral Dynamics

When dealing with multi-issuer credit derivatives such as CDO, it is customary to refer the reader to either of two approaches: “static models” which focus on the copula between the variables of interest, and “dynamic models” where the diffusion of the underlying variables is described directly. While the former is widely used due to its simplicity, it is not clear that there is a well behaved dynamic model consistent with a given static approach. For this reason, it is often argued that an understanding of the dynamics used in model for CDO is required to bring it to par with derivative models used for other asset classes, such as the risk neutral diffusion models used for equity, currency and commodity options derived from Black and Scholes, or the characterization of arbitrage free term structure of interest rates obtained by HJM. Clearly, a “dynamic model” implies a certain copula between the random variables of interest. The goal of this article is to develop a unified view compatible with both approaches, and reach a better understanding of the properties that a good “dynamic model” used for pricing and hedging would have when seen as a static model. We focus on credit models where large homogeneous pool portfolio are mathematically possible, a common assumption among practitioners. In a general credit term structure dynamics framework similar to HJM, we identify a “systemic loss” process linked to the survival dynamics that allows to identify the density of loss for large portfolios, and to explicit the default copula between the issuers. We then apply these results to different classes of CDO models that have been put forward for their tractability, to see what copula with implied by given a popular dynamic model, and what dynamic models could give rise to some popular copula model. The three classes we review are the one factor copula models, the markovian loss intensity models, and the systemic intensity jump diffusion models.
Parental depressive and anxiety symptoms during pregnancy and attention problems in children: a cross-cohort consistency study

Background Maternal depression and anxiety during pregnancy have been associated with offspring-attention deficit problems. Aim We explored possible intrauterine effects by comparing maternal and paternal symptoms during pregnancy, by investigating cross-cohort consistency, and by investigating whether parental symptoms in early childhood may explain any observed intrauterine effect. Methods This study was conducted in two cohorts (Generation R, n = 2,280 and ALSPAC, n = 3,442). Pregnant women and their partners completed questionnaires to assess symptoms of depression and anxiety. Child attention problems were measured in Generation R at age 3 with the Child Behavior Checklist, and in ALSPAC at age 4 with the Strengths and Difficulties Questionnaire. Results In both cohorts, antenatal maternal symptoms of depression (Generation R: OR 1.23, 95% CI 1.05–1.43; ALSPAC: OR 1.33, 95% CI 1.19–1.48) and anxiety (Generation R: OR 1.24, 95% CI 1.06–1.46; ALSPAC: OR 1.32, 95% CI 1.19–1.47) were associated with a higher risk of child attention problems. In ALSPAC, paternal depression was also associated with a higher risk of child attention problems (OR 1.11, 95% CI 1.00–1.24). After adjusting for maternal symptoms after giving birth, antenatal maternal depression and anxiety were no longer associated with child attention problems in Generation R. Moreover, there was little statistical evidence that antenatal maternal and paternal depression and anxiety had a substantially different effect on attention problems of the child. Conclusions The apparent intrauterine effect of maternal depression and anxiety on offspring-behavioural problems may be partly explained by residual confounding. There was little evidence of a difference between the strength of associations of maternal and paternal symptoms during pregnancy with offspring-attention problems. That maternal symptoms after childbirth were also associated with offspring-behavioural problems may indicate a contribution of genetic influences to the association.