Miracle-dz/UniFAR-FTUs huggingface dataset:
Unpaired Learning of Dense Visual Depth Estimators for Urban Environments

This paper addresses the classical problem of learning-based monocular depth estimation in urban environments, in which a model is trained to directly map a single input image to its corresponding depth values. All currently available techniques treat monocular depth estimation as a regression problem, and thus require some sort of data pairing, either explicitly as input-output ground-truth pairs, using information from range sensors (i.e. laser), or as binocular stereo footage. We introduce a novel methodology that completely eliminates the need for data pairing, only requiring two unrelated datasets containing samples of input images and output depth values. A cycle-consistent generative adversarial network is used to learn a mapping between these two domains, based on a custom adversarial loss function specifically designed to improve performance on the task of monocular depth estimation, including local depth smoothness and boundary equilibrium. A wide range of experiments were conducted using a variety of well-known indoor and outdoor datasets, with depth estimates obtained from laser sensors, RGBD cameras and SLAM pointclouds. In all of them, the proposed CycleDepth framework reaches competitive results even under a more restricted training scenario.
NodeMerge: Template Based Efficient Data Reduction For Big-Data Causality Analysis

Today's enterprises are exposed to sophisticated attacks, such as Advanced Persistent Threats~(APT) attacks, which usually consist of stealthy multiple steps. To counter these attacks, enterprises often rely on causality analysis on the system activity data collected from a ubiquitous system monitoring to discover the initial penetration point, and from there identify previously unknown attack steps. However, one major challenge for causality analysis is that the ubiquitous system monitoring generates a colossal amount of data and hosting such a huge amount of data is prohibitively expensive. Thus, there is a strong demand for techniques that reduce the storage of data for causality analysis and yet preserve the quality of the causality analysis. To address this problem, in this paper, we propose NodeMerge, a template based data reduction system for online system event storage. Specifically, our approach can directly work on the stream of system dependency data and achieve data reduction on the read-only file events based on their access patterns. It can either reduce the storage cost or improve the performance of causality analysis under the same budget. Only with a reasonable amount of resource for online data reduction, it nearly completely preserves the accuracy for causality analysis. The reduced form of data can be used directly with little overhead. To evaluate our approach, we conducted a set of comprehensive evaluations, which show that for different categories of workloads, our system can reduce the storage capacity of raw system dependency data by as high as 75.7 times, and the storage capacity of the state-of-the-art approach by as high as 32.6 times. Furthermore, the results also demonstrate that our approach keeps all the causality analysis information and has a reasonably small overhead in memory and hard disk.
Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences

Standard evaluations of deep learning models for semantics using naturalistic corpora are limited in what they can tell us about the fidelity of the learned representations, because the corpora rarely come with good measures of semantic complexity. To overcome this limitation, we present a method for generating data sets of multiply-quantified natural language inference (NLI) examples in which semantic complexity can be precisely characterized, and we use this method to show that a variety of common architectures for NLI inevitably fail to encode crucial information; only a model with forced lexical alignments avoids this damaging information loss.
Research on Objective Evaluation of Recording Audio Restoration Based on Deep Learning Network

There are serious distortion problems in the history audio and video data. In view of the characteristics of audio data repair, the intelligent technology of audio evaluation is explored. As the traditional audio subjective evaluation method requires a large number of personal to audition and evaluation, the tester’s subjective sense of hearing deviation and sample space data limited the impact of the accuracy of the experiment. Based on the deep learning network, this paper designs an objective quality evaluation system for historical audio and video data and evaluates the performance of the system and the audio signal quality from the perspective of feature extraction and network parameter selection. Experiments show that the system has good performance in this experiment; the predictive results and subjective evaluation of the correlation and dispersion indicators are good, up to 0.91 and 0.19.
Step‐Wise Chondrogenesis of Human Induced Pluripotent Stem Cells and Purification Via a Reporter Allele Generated by CRISPR‐Cas9 Genome Editing

The differentiation of human induced pluripotent stem cells (hiPSCs) to prescribed cell fates enables the engineering of patient‐specific tissue types, such as hyaline cartilage, for applications in regenerative medicine, disease modeling, and drug screening. In many cases, however, these differentiation approaches are poorly controlled and generate heterogeneous cell populations. Here, we demonstrate cartilaginous matrix production in three unique hiPSC lines using a robust and reproducible differentiation protocol. To purify chondroprogenitors (CPs) produced by this protocol, we engineered a COL2A1‐GFP knock‐in reporter hiPSC line by CRISPR‐Cas9 genome editing. Purified CPs demonstrated an improved chondrogenic capacity compared with unselected populations. The ability to enrich for CPs and generate homogenous matrix without contaminating cell types will be essential for regenerative and disease modeling applications. Stem Cells 2019;37:65–76
Binocular temporal visual processing in myopia

Our ability to utilize binocular visual information depends on the visibility of the retinal images in each eye, which varies with both their spatial and temporal frequency content. Although the effects of spatial information on binocular function have been established, the effects of temporal frequency on binocularity are less well understood. These factors may also vary with refractive error if spatiotemporal sensitivity is affected by structural changes during the emmetropization process that may differentially affect distinct ganglion cells. In a cross-sectional study, we evaluated the potential effects of temporal and spatial frequency on binocularity in young individuals with emmetropia or myopia. Stereopsis and binocular balance were measured as a function of temporal (0–12 Hz) and spatial (1–8 c/deg) frequency. Stereopsis thresholds were measured by determining the minimum disparity at which subjects accurately identified the depth of bandpass-filtered rings. Binocular balance was measured by determining the relative contrast at which subjects reported dichoptic bandpass-filtered letters with equal frequency. Stereopsis thresholds were temporal but not spatial frequency dependent whereas binocular balance was spatial and temporal frequency dependent. There were no differences in monocular spatiotemporal contrast sensitivity between refractive groups in our sample. However, individuals with myopia showed reduced stereopsis with flickering stimuli and greater binocular imbalance at higher spatial and lower temporal frequencies compared to emmetropes. Differences in binocular vision between emmetropia and corrected myopia depend on temporal as well as spatial frequency and may be the cause or consequence of abnormal emmetropization during visual development.
Three-dimensional position estimation method via AM pulse light modulation and an application to control multiple UAVs

ABSTRACT In this study, a three-dimensional position estimation is proposed to estimate self-position via amplitude-modulated pulse infrared light to solve the problem of position estimation from an existing device used in the cooperation of unmanned air vehicles (UAVs) that can be used in various environments. The proposed method estimates self-position as the distance between the receiver and the transmitter and the angle of direction by attenuation of amplitude-modulated pulse infrared light. By attaching a transmitter to a leader UAV and a receiver to follower UAVs, the cooperation flight can be achieved. The following three experiments are conducted: (1) evaluation of the accuracy of self-position estimation, (2) implementation of the proposed method on an UAV, and (3) evaluation of the robustness of the proposed method in an outdoor environment. The findings confirm the effectiveness of the proposed method. The proposed approach allows estimation of position with high speed, precision in various environments, and low-cost devices. GRAPHICAL ABSTRACT
Performance Evaluation of DPDK Open vSwitch with Parallelization Feature on Multi-Core Platform

—In recent years, SDN (Software Defined Network) and NFV (Network Function Virtualization) play important roles in the cloud computing datacenter. Open vSwitch is one of the popular open source software of Openflow switch. However, the performance degradation of Open vSwitch caused by processing smaller packets has been widely criticized. Open vSwitch implements OVS-DPDK version based on DPDK (Data Plane Development Kit) and takes full advantage of the efficient characteristics provided by DPDK to improve the performance of packet processing. In this paper, we analyze the parallel performance of OVS-DPDK on the multi-core platform from two aspects of throughput and delay. We analyzed the impact of configuration parameters on the performance of ovsdpdk, such as the length of the receiving queue, the number of active streams, and the number of cores. At the same time, the advantages and disadvantages of the load balancing strategy of OVS-DPDK parallel system are also analyzed. Finally, the reasonable configuration interval of the receiving queue and the optimal selection of the number of cores are obtained under the different number of active flows.
Dermal mycobacteriosis and warming sea surface temperatures are associated with elevated mortality of striped bass in Chesapeake Bay

Abstract Temperature is hypothesized to alter disease dynamics, particularly when species are living at or near their thermal limits. When disease occurs in marine systems, this can go undetected, particularly if the disease is chronic and progresses slowly. As a result, population‐level impacts of diseases can be grossly underestimated. Complex migratory patterns, stochasticity in recruitment, and data and knowledge gaps can hinder collection and analysis of data on marine diseases. New tools enabling quantification of disease impacts in marine environments include coupled biogeochemical hydrodynamic models (to hindcast key environmental data), and multievent, multistate mark–recapture (MMSMR) (to quantify the effects of environmental conditions on disease processes and assess population‐level impacts). We used MMSMR to quantify disease processes and population impacts in an estuarine population of striped bass (Morone saxatilis) in Chesapeake Bay from 2005 to 2013. Our results supported the hypothesis that mycobacteriosis is chronic, progressive, and, frequently, lethal. Yearly disease incidence in fish age three and above was 89%, suggesting that this disease impacts nearly every adult striped bass. Mortality of diseased fish was high, particularly in severe cases, where it approached 80% in typical years. Severely diseased fish also had a 10‐fold higher catchability than healthy fish, which could bias estimates of disease prevalence. For both healthy and diseased fish, mortality increased with the modeled average summer sea surface temperature (SST) at the mouth of the Rappahannock River; in warmer summers (average SST ≥ 29°C), a cohort is predicted to experience >90% mortality in 1 year. Regression of disease signs in mildly and moderately diseased fish was <2%. These results suggest that these fish are living at their maximum thermal tolerance and that this is driving increased disease and mortality. Management of this fishery should account for the effects of temperature and disease on impacted populations.
Scalable Laplacian K-modes

We advocate Laplacian K-modes for joint clustering and density mode finding, and propose a concave-convex relaxation of the problem, which yields a parallel algorithm that scales up to large datasets and high dimensions. We optimize a tight bound (auxiliary function) of our relaxation, which, at each iteration, amounts to computing an independent update for each cluster-assignment variable, with guaranteed convergence. Therefore, our bound optimizer can be trivially distributed for large-scale data sets. Furthermore, we show that the density modes can be obtained as byproducts of the assignment variables via simple maximum-value operations whose additional computational cost is linear in the number of data points. Our formulation does not need storing a full affinity matrix and computing its eigenvalue decomposition, neither does it perform expensive projection steps and Lagrangian-dual inner iterates for the simplex constraints of each point. Furthermore, unlike mean-shift, our density-mode estimation does not require inner-loop gradient-ascent iterates. It has a complexity independent of feature-space dimension, yields modes that are valid data points in the input set and is applicable to discrete domains as well as arbitrary kernels. We report comprehensive experiments over various data sets, which show that our algorithm yields very competitive performances in term of optimization quality (i.e., the value of the discrete-variable objective at convergence) and clustering accuracy.
Three, two, one! Revision of the long-bodied sphaerodorids (Sphaerodoridae, Annelida) and synonymization of Ephesiella, Ephesiopsis and Sphaerodorum

Background Long-bodied sphaerodorids (Annelida, Sphaerodoridae) is the common name for members of the three closely and morphologically homogenous currently accepted genera of benthic marine bristle worms: Ephesiella, Ephesiopsis and Sphaerodorum. Members of this group share the presence of two dorsal and longitudinal rows of macrotubercles with terminal papillae, and two longitudinal rows of microtubercles, features that are unique among sphaerodorids. Genera are distinguished by the chaetae morphology. Members of Ephesiella are characterised by having compound chaetae (except, sometimes, simple chaetae in the first chaetigers), Sphaerodorum bear only simple chaetae, and Ephesiopsis have both compound and simple chaetae in all parapodia. Methods Mitochondrial (partial COI and 16S rDNA) and nuclear (partial 18S rDNA and 28S rDNA) sequence data of long-bodied sphaerodorids with compound and simple chaetae, and an outgroup of additional seven sphaerodorid species were analysed separately and in combination using Bayesian inference (BA), and Maximum Likelihood (ML) methods. Long-bodied sphaerodorids from around the world (including type specimens) were examined under a range of optical equipment in order to evaluate putative generic and specific diagnostic features, in addition to intraspecific variability. Results Phylogenetic analyses of mitochondrial and nuclear DNA sequences of specimens identified as Ephesiella and Sphaerodorum, based on chaeta morphology, were performed. Sphaerodorum and Ephesiella were recovered as paraphyletic and nested within each other. Revision of current nominal species diagnostic features are performed and discussed. Discussion Results contradict current generic definitions. Recovery of paraphyletic compound and simple chaetae clades urge the synonymization of these two genera of long-bodied sphaerodorids. Morphological data also suggest the synonymization of Ephesiopsis.
The influence of Neanderthal alleles on cytotoxic response

Various studies have shown that people of Eurasian origin contain traces of DNA inherited from interbreeding with Neanderthals. Recent studies have demonstrated that these Neanderthal variants influence a range of clinically important traits and diseases. Thus, understanding the genetic factors responsible for the variability in individual response to drug or chemical exposure is a key goal of pharmacogenomics and toxicogenomics, as dose responses are clinically and epidemiologically important traits. It is well established that ethnic and racial differences are important in dose response traits, but to our knowledge the influence of Neanderthal ancestry on response to xenobiotics is unknown. Towards this aim, we examined if Neanderthal ancestry plays a role in cytotoxic response to anti-cancer drugs and toxic environmental chemicals. We identified common Neanderthal variants in lymphoblastoid cell lines (LCLs) derived from the globally diverse 1000 Genomes Project and Caucasian cell lines from the Children’s Hospital of Oakland Research Institute. We analyzed the effects of these Neanderthal alleles on cytotoxic response to 29 anti-cancer drugs and 179 environmental chemicals at varying concentrations using genome-wide data. We identified and replicated single nucleotide polymorphisms (SNPs) from these association results, including a SNP in the SNORD-113 cluster. Our results also show that the Neanderthal alleles cumulatively lead to increased sensitivity to both the anti-cancer drugs and the environmental chemicals. Our results demonstrate the influence of Neanderthal ancestry-informative markers on cytotoxic response. These results could be important in identifying biomarkers for personalized medicine or in dissecting the underlying etiology of dose response traits.
Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation

Transcranial direct current stimulation (TDCS) has been widely used to improve cognitive function. However, current deficiencies in mechanistic understanding hinders wider applicability. To clarify its physiological effects, we acquired fMRI whilst simultaneously acquiring TDCS to the right inferior frontal gyrus (rIFG) of healthy human participants, a region involved in coordinating activity within brain networks. TDCS caused widespread modulation of network activity depending on brain state (‘rest’ or choice reaction time task) and polarity (anodal or cathodal). During task, TDCS increased salience network activation and default mode network deactivation, but had the opposite effect during ‘rest’. Furthermore, there was an interaction between brain state and TDCS polarity, with cathodal effects more pronounced during task performance and anodal effects more pronounced during ‘rest’. Overall, we show that rIFG TDCS produces brain state and polarity dependent effects within large-scale cognitive networks, in a manner that goes beyond predictions from the current literature.
Data Analysis of Wireless Networks Using Computational Intelligence

In the last decade a great technological advance in mobile technologies infrastructure was seen. The increase in the use of wireless local networks and the use of services from satellites is also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to monitor wireless networks to ensure the integrity and confidentiality of the information transmitted. Therefore, it is necessary to quickly and efficiently identify the normal and abnormal traffic of these networks, so that the administrators can take action. This work aims, from a database of wireless networks, to classify this data in some classes of pre-established anomalies according to some defined criteria of the MAC layer, using supervised and unsupervised intelligent algorithms Multilayer Perceptron (MLP), K-Means and Self-Organizing Maps (SOM). For the analysis of the mentioned algorithms, the WEKA Data Mining software (Waikato Environment for Knowledge Analysis) is used. The algorithms have high success rate in the classification of the data, being indicated in the use of Intrusion Detection Systems for Wireless Networks.
SplineNets: Continuous Neural Decision Graphs

We present SplineNets, a practical and novel approach for using conditioning in convolutional neural networks (CNNs). SplineNets are continuous generalizations of neural decision graphs, and they can dramatically reduce runtime complexity and computation costs of CNNs, while maintaining or even increasing accuracy. Functions of SplineNets are both dynamic (i.e., conditioned on the input) and hierarchical (i.e.,conditioned on the computational path). SplineNets employ a unified loss function with a desired level of smoothness over both the network and decision parameters, while allowing for sparse activation of a subset of nodes for individual samples. In particular, we embed infinitely many function weights (e.g. filters) on smooth, low dimensional manifolds parameterized by compact B-splines, which are indexed by a position parameter. Instead of sampling from a categorical distribution to pick a branch, samples choose a continuous position to pick a function weight. We further show that by maximizing the mutual information between spline positions and class labels, the network can be optimally utilized and specialized for classification tasks. Experiments show that our approach can significantly increase the accuracy of ResNets with negligible cost in speed, matching the precision of a 110 level ResNet with a 32 level SplineNet.
Adaptable replanning with compressed linear action models for learning from demonstrations

We propose an adaptable and efficient model-based reinforcement learning approach well suited for continuous domains with sparse samples, a setting often encountered when learning from demonstrations. The flexibility of our method originates from the approximate transition models, estimated from data, and the online replanning approach proposed. Together, these components allow for immediate adaptation to a new task, given in the form of a reward function. The efficiency of our method comes from two approximations. First, rather than representing a complete distribution over the results of taking an action, which is difficult in continuous state spaces, it learns a linear model of the expected transition for each action. Second, it uses a novel strategy for compressing these linear action models, which significantly reduces space and time for learning models, and supports efficient online generation of open-loop plans. The effectiveness of these methods is demonstrated in a simulated driving domain with a 20-dimensional continuous input space.