Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Deep Network Embedding for Cross-network Node Classification

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.
Mapping the Interplanetary Filesystem

The Interplanetary Filesystem (IPFS) is a distributed data storage service frequently used by blockchain applications and for sharing content in a censorship-resistant manner. Data is hosted by an open set of peers, pointers to which are distributed using a Kademlia-based distributed hash table (DHT). In this paper, we study the structure of the resulting overlay network, as it significantly influences the robustness and performance of IPFS. We monitor and systematically crawl IPFS’ DHT towards mapping the IPFS overlay network. Our measurements found an average of 44474 nodes at every given time. At least 52. 19% of these reside behind a NAT and are not reachable from the outside, suggesting that a large share of the network is operated by private individuals on an as-needed basis. Based on our measurements and our analysis of the IPFS code, we observe that IPFS bears similarities to unstructured overlay networks in terms of content retrieval mechanics and overlay topology. While such a structure has benefits for robustness and the resistance against Sybil attacks, it leaves room for improvement in terms of performance and query privacy.
Constraining the intergalactic medium at z ≈ 9.1 using LOFAR Epoch of Reionization observations

We derive constraints on the thermal and ionization states of the intergalactic medium (IGM) at redshift ≈ 9.1 using new upper limits on the 21-cm power spectrum measured by the LOFAR radio telescope and a prior on the ionized fraction at that redshift estimated from recent cosmic microwave background (CMB) observations. We have used results from the reionization simulation code grizzly and a Bayesian inference framework to constrain the parameters which describe the physical state of the IGM. We find that, if the gas heating remains negligible, an IGM with ionized fraction ≳0.13 and a distribution of the ionized regions with a characteristic size ≳ 8 h−1 comoving megaparsec (Mpc) and a full width at half-maximum (FWHM) ≳16 h−1 Mpc is ruled out. For an IGM with a uniform spin temperature TS ≳ 3 K, no constraints on the ionized component can be computed. If the large-scale fluctuations of the signal are driven by spin temperature fluctuations, an IGM with a volume fraction ≲0.34 of heated regions with a temperature larger than CMB, average gas temperature 7–160 K, and a distribution of the heated regions with characteristic size 3.5–70 h−1 Mpc and FWHM of ≲110 h−1 Mpc is ruled out. These constraints are within the 95 per cent credible intervals. With more stringent future upper limits from LOFAR at multiple redshifts, the constraints will become tighter and will exclude an increasingly large region of the parameter space.
Spatial concept-based navigation with human speech instructions via probabilistic inference on Bayesian generative model

Robots are required to not only learn spatial concepts autonomously but also utilize such knowledge for various tasks in a domestic environment. Spatial concept represents a multimodal place category acquired from the robot's spatial experience including vision, speech-language, and self-position. The aim of this study is to enable a mobile robot to perform navigational tasks with human speech instructions, such as ‘Go to the kitchen’, via probabilistic inference on a Bayesian generative model using spatial concepts. Specifically, path planning was formalized as the maximization of probabilistic distribution on the path-trajectory under speech instruction, based on a control-as-inference framework. Furthermore, we described the relationship between probabilistic inference based on the Bayesian generative model and control problem including reinforcement learning. We demonstrated path planning based on human instruction using acquired spatial concepts to verify the usefulness of the proposed approach in the simulator and in real environments. Experimentally, places instructed by the user's speech commands showed high probability values, and the trajectory toward the target place was correctly estimated. Our approach, based on probabilistic inference concerning decision-making, can lead to further improvement in robot autonomy. GRAPHICAL ABSTRACT
Extending iLQR method with control delay

Iterative linear quadradic regulator(iLQR) has become a benchmark method to deal with nonlinear stochastic optimal control problem. However, it does not apply to delay system. In this paper, we extend the iLQR theory and prove new theorem in case of input signal with fixed delay. Which could be beneficial for machine learning or optimal control application to real time robot or human assistive device.
Deep Learning in Medical Ultrasound Image Segmentation: a Review

Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. It can be a key step to provide a reliable basis for clinical diagnosis, such as 3D reconstruction of human tissues, image-guided interventions, image analyzing and visualization. In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first. Secondly, for each group, several current representative algorithms are selected, introduced, analyzed and summarized in detail. In addition, common evaluation methods for image segmentation and ultrasound image segmentation datasets are summarized. Further, the performance of the current methods and their evaluations are reviewed. In the end, the challenges and potential research directions for medical ultrasound image segmentation are discussed.
Investigation of the Electrical Conduction Mechanisms in P-type Amorphous Germanium (a-Ge) Used as a-Ge Contacts for Ge Detectors

Electrical conduction mechanisms in the disordered material system is experimentally studied for p-type amorphous germanium (a-Ge) used for high-purity Ge detector contacts. The localization length and the hopping parameters in a-Ge are determined using the surface leakage current measured from three high-purity planar Ge detectors. The temperature-dependent hopping distance and hopping energy are obtained for a-Ge fabricated as the electrical contact materials for high-purity Ge planar detectors. As a result, we find that the hopping energy in a-Ge increases as temperature increases while the hopping distance in a-Ge decreases as temperature increases. The localization length of a-Ge is on the order of $2.13^{-0.05}_{+0.07} A^\circ$ to $5.07^{-0.83}_{+2.58}A^\circ$, depending on the density of states near the Fermi energy level within bandgap. Using these parameters, we predict that the surface leakage current from a Ge detector with a-Ge contacts can be much smaller than one yocto amp (yA) at helium temperature, suitable for rare-event physics searches.
Underlying magnetization responses of magnetic nanoparticles in assemblies

Magnetic nanoparticles (MNPs) have been proposed as an ultimate solution for diverse applications including nanomedicine and logic devices over decades. However, none has emerged revolutionary because realizing their magnetization response in an assembly is, surprisingly, still elusive. We employ our fast and universal magnetic characterization method, called the projection method, to underlie the reversible (irreversible) magnetization response of several assemblies. We then illustrate how the reversible (irreversible) magnetization response is correlated to the intrinsic properties (the coercivity and interaction fields) of the MNPs in an assembly. Our experimental observations indicate that the reversible magnetization does not solely depend on the interaction field, but it is indeed a function of the interaction field to coercivity ratio. Furthermore, for large porosities, the interaction field linearly changes with the porosity highlighting the predominant effects of dipole-dipole interactions on the interaction fields. However, at low porosities, the interaction field shows a nonlinear relationship with the porosity indicating the dipole fluctuations effects dominantly determine the interaction fields.
Skymaps of observables of three-dimensional magnetohydrodynamic astrosphere models

Context. Three-dimensional models of astrospheres have recently become of interest. However, comparisons between these models and observations are non-trivial because of the two-dimensional nature of observations. Aims. By projecting selected physical values of three-dimensional models of astrospheres onto the surface of a sphere that is centred on a virtual all-sky observer, these models can be compared to observational data in different observables: the column density, bremsstrahlung flux, rotation measure, Hα flux, and synchrotron or cyclotron flux. Methods. Projections were calculated by rotating and moving the astrosphere model to the desired position and orientation and by then computing the value of a given patch on the sphere by a modified line-of-sight integration. Contributions to the selected observable made by all model cells that are connected to the patch by the line of sight in question were taken into account. Results. When the model produces a bow shock, a distinct parabolic structure produced by the outer astrosheath can be seen in every observable of the projection, the exact shape depending on the orientations of the line of sight and the stellar motion. Of all four examined astrosphere models, only that of λ Cephei shows fluxes that are higher than current observational thresholds. This is due to the strong stellar wind and interstellar inflow of the λ Cephei model.
A Bayesian Time-to-event Pharmacokinetic Model for Sequential Phase I Dose-escalation Trials with Multiple Schedules

Background: Phase I dose-escalation trials constitute the first step in investigating the safety of potentially promising drugs in humans. Conventional methods for phase I dose-escalation trials are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.Methods: Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing dose-escalation trial to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model. Results: In a simulation study, the developed appraoch is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example is publicly available (https://github.com/gunhanb/TITEPK sequential). Conclusion: In sequential phase I dose-escalation trials, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.
Understanding the Effects of Sublethal Pesticide Exposure on Honey Bees: A Role for Probiotics as Mediators of Environmental Stress

Managed populations of the European honey bee (Apis mellifera) support the production of a global food supply. This important role in modern agriculture has rendered honey bees vulnerable to the noxious effects of anthropogenic stressors such as pesticides. Although the deleterious outcomes of lethal pesticide exposure on honey bee health and performance are apparent, the ominous role of sublethal pesticide exposure is an emerging concern as well. Here, we use a data harvesting approach to better understand the toxicological effects of pesticide exposure across the honey bee life cycle. Through compiling adult- and larval-specific median lethal dose (LD50) values from 93 published data sources, LD50 estimates for insecticides, herbicides, acaricides, and fungicides are highly variable across studies, especially for herbicides and fungicides, which are underrepresented in the meta-data set. Alongside major discrepancies in these reported values, further examination of the compiled data suggested that LD50 may not be an ideal metric for honey bee risk assessment. We also discuss how sublethal effects of pesticide exposure, which are not typically measured in LD50 studies, can diminish honey bee reproduction, immunity, cognition, and overall physiological functioning, leading to suboptimal honey bee performance and population reduction. In consideration of actionable solutions to mitigate the effects of sublethal pesticide exposure, we have identified the potential for probiotic supplementation as a promising strategy that can be easily incorporated alongside current agricultural infrastructure and apicultural management practices. Probiotic supplementation is regularly employed in apiculture but the potential for evidence-based targeted approaches has not yet been fully explored within a formal toxicological context. We discuss the benefits, practical considerations, and limitations for the use and delivery of probiotics to hives. Ultimately, by subverting the sublethal effects of pesticides we can help improve the long-term survival of these critical pollinators.
Optimizing the Pharmacological Properties of Discoidal Polymeric Nanoconstructs Against Triple-Negative Breast Cancer Cells

Fine-tuning loading and release of therapeutic and imaging agents associated with polymeric matrices is a fundamental step in the preclinical development of novel nanomedicines. Here, 1,000 × 400 nm Discoidal Polymeric Nanoconstructs (DPNs) were realized via a top-down, template-based fabrication approach, mixing together poly(lactic-co-glycolic acid) (PLGA) and poly(ethylene glycol)-diacrylate (PEG-DA) chains in a single polymer paste. Two different loading strategies were tested, namely the “direct loading” and the “absorption loading. ” In the first case, the agent was directly mixed with the polymeric paste to realize DPNs whereas, in the second case, DPNs were first lyophilized and then rehydrated upon exposure to a concentrated aqueous solution of the agent. Under these two loading conditions, the encapsulation efficiencies and release profiles of different agents were systematically assessed. Specifically, six agents were realized by conjugating lipid chains (DSPE) or polymeric chains (PEG) to the near-infrared imaging molecule Cy5 (DSPE-Cy5 A and DSPE-Cy5 B); the chemotherapeutic molecules methotrexate (DSPE-MTX and PEG-MTX) and doxorubicin (LA-DOX and DSPE-DOX). Moderately hydrophobic compounds with low molecular weights (MW) returned encapsulation efficiencies as high as 80% for the absorption loading. In general, direct loading was associated with encapsulation efficiencies lower than 1%. The agent hydrophobicity and MW were shown to be critical also in tailoring the release profiles from DPNs. On triple-negative breast cancer cells (MDA-MB-231), absorption loaded DOX-DPNs showed cytotoxic activities comparable to free DOX but slightly delayed in time. Preliminary in vivo studies demonstrated the high stability of Cy5-DPNs. Collectively, these results demonstrate that the pharmacological properties of DPNs can be finely optimized by changing the loading strategies (direct vs. absorption) and compound attributes (hydrophobicity and molecular weight).
Screening of Secreted Proteins of Sporisorium reilianum f. sp. zeae for Cell Death Suppression in Nicotiana benthamiana

Sporisorium reilianum f. sp. zeae (SRZ) is a biotrophic fungus causing head smut in maize. Maize infection with SRZ leads to very little cell death suggesting the presence of cell-death suppressinpg effectors. Several hundred effector proteins have been predicted based on genome annotation, genome comparison, and bioinformatic analysis. For only very few of these effectors, an involvement in virulence has been shown. In this work, we started to test a considerable subset of these predicted effector proteins for a possible function in suppressing cell death. We generated an expression library of 62 proteins of SRZ under the control of a strong constitutive plant promoter for delivery into plant cells via Agrobacterium tumefaciens-mediated transient transformation. Potential apoplastic effectors with high cysteine content were cloned with signal peptide while potential intracellular effectors were also cloned without signal peptide to ensure proper localization after expression in plant cells. After infiltration of Nicotiana benthamiana leaves, infiltration sites were evaluated for apparent signs of hypersensitive cell death in absence or presence of the elicitin INF1 of Phytophthora infestans. None of the tested candidates was able to induce cell death, and most were unable to suppress INF1-induced cell death. However, the screen revealed one predicted cytoplasmic effector (sr16441) of SRZ that was able to reliably suppress INF1-induced cell death when transiently expressed in N. benthamiana lacking its predicted secretion signal peptide. This way, we discovered a putative function for one new effector of SRZ.
Geographic Authentication of Eucommia ulmoides Leaves Using Multivariate Analysis and Preliminary Study on the Compositional Response to Environment

To explore the influences of different cultivated areas on the chemical profiles of Eucommia ulmoides leaves (EUL) and rapidly authenticate its geographical origins, 187 samples from 13 provinces in China were systematically investigated using three data fusion strategies (low, mid, and high level) combined with two discrimination model algorithms (partial least squares discrimination analysis; random forest, RF). RF models constructed by high-level data fusion with different modes of different spectral data (Fourier transform near-infrared spectrum and attenuated total reflection Fourier transform mid-infrared spectrum) were most suitable for identifying EULs from different geographical origins. The accuracy rates of calibration and validation set were 92.86% and 93.44%, respectively. In addition, climate parameters were systematically investigated the cluster difference in our study. Some interesting and novel information could be found from the clustering tree diagram of hierarchical cluster analysis. The Xinjiang Autonomous Region (Region 5) located in the high latitude area was the only region in the middle temperate zone of all sample collection areas in which the samples belonged to an individual class no matter their distance in the tree diagram. The samples were from a relatively high elevation in the Shennongjia Forest District in Hubei Province (>1200 m), which is the main difference from the samples from Xiangyang City (78 m). Thus, the sample clusters from region 9 are different from the sample clusters from other regions. The results would provide a reference for further research to those samples from the special cluster.
Proposal for Lines of Research Into Consumer Behavior: Examples in the Tourism Industry

Departing from the high relevance that loyalty plays in the promotion of tourist destinations, the study of the variables that result in loyalty is key. For this reason, there are myriads of studies devoted to the analysis of the variables that result in loyalty, especially considering either or both attitudinal and behavioral loyalty; in turn, relatively less frequent are the studies that also/instead consider active and passive loyalty.Within this research line, we go one step further and propose the importance of focusing on the study of the variables that are conducive to attitudinal and behavioral loyalty; moreover, within attitudinal loyalty, we also acknowledge the distinction of two further types of loyalty, that is, active attitudinal and passive attitudinal loyalty. This opinion paper aims at adding knowledge to the field of consumer behavior in tourism, proposing the importance of studying the variables that are able to drive loyalty in a very differentiated way (active attitudinal loyalty, passive attitudinal loyalty, and behavioral loyalty).
Managing Distress Over Time in Psychotherapy: Guiding the Client in and Through Intense Emotional Work

Clients who seek psychotherapeutic treatment have had personal experiences involving some form of distress. Although research has shown that the client's ability to experience and express painful emotions during therapy can have a therapeutic benefit, it has also been argued that displaying distress may convey a form of helplessness and vulnerability, and thus, clients may be reluctant to cast themselves in this light. Using the methods of conversation analysis, this paper explores how a client's upsetting experience is managed over the course of a single session of client-centered therapy. The main analytic focus will be on (1) the different therapist practices used to orient to the client's distress, (2) the varying forms of client opposition to the therapist's attempts to work with the distress, and (3) the context sensitivity of orienting to distress and how certain practices may be uniquely shaped by what had occurred in prior talk. It was found that, whereas certain types of therapist responses tended to be endorsed by the client, others were forcefully rejected as inappropriate displays of understanding or empathy. By focusing on repeated sequential episodes over time in which a client conveys distress, followed by the therapist's response, this paper sheds light on the interactional trajectory through which a client and therapist are able to resolve impasses to emotional exploration and to successfully secure extended and intense emotional work.