Miracle-dz/UniFAR-FTUs huggingface dataset:
Registration for Incomplete Non-Gaussian Functional Data

Accounting for phase variability is a critical challenge in functional data analysis. To separate it from amplitude variation, functional data are registered, i.e., their observed domains are deformed elastically so that the resulting functions are aligned with template functions. At present, most available registration approaches are limited to datasets of complete and densely measured curves with Gaussian noise. However, many real-world functional data sets are not Gaussian and contain incomplete curves, in which the underlying process is not recorded over its entire domain. In this work, we extend and refine a framework for joint likelihood-based registration and latent Gaussian process-based generalized functional principal component analysis that is able to handle incomplete curves. Our approach is accompanied by sophisticated open-source software, allowing for its application in diverse non-Gaussian data settings and a public code repository to reproduce all results. We register data from a seismological application comprising spatially indexed, incomplete ground velocity time series with a highly volatile Gamma structure. We describe, implement and evaluate the approach for such incomplete non-Gaussian functional data and compare it to existing routines. ∗This work was supported by the German Research Foundation (DFG) under Grant KU 1359/4-1; and the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IS18036A. 1 ar X iv :2 10 8. 05 63 4v 1 [ st at . M E ] 1 2 A ug 2 02 1
Hierarchical automatic lane-changing motion planning: from self-optimum to local-optimum

In order to minimize the impact of lane change (LC) maneuver on surrounding traffic environment, a hierarchical automatic LC algorithm that could realize local optimum has been proposed. This algorithm consists of a tactical layer planner and an operational layer controller. The former generates a local-optimum trajectory. The comfort, efficiency, and safety of the LC vehicle and its surrounding vehicles are simultaneously satisfied in the optimization objective function. The later is designed based on vehicle kinematics model and the Model Predictive Control (MPC), which aims to minimize the tracking error and control increment. Combining macro-level and micro-level analysis, we verify the effectiveness of the proposed algorithm. Our results demonstrate that our proposed algorithm could greatly reduce the impact of LC maneuver on traffic flow. This is reflected in the decrease of total loss for nearby vehicles (such as discomfort and speed reduction), and the increase of traffic speed and throughput within the LC area. In addition, in order to guide the practical application of our algorithm, we employ the HighD dataset to validate the algorithm. This research could also be regarded as a preliminary foundational work to develop locally-optimal automatic LC algorithm. We anticipate that this research could provide valuable insights into autonomous driving technology.
Powder diffraction study of Pd2HgSe3

The Pd2HgSe3 phase was synthetized from individual elements by the silica glass tube technique and its crystal structure has been refined by the Rietveld method. The Pd2HgSe3 phase crystalizes in P $\bar 3$ m1 space group with the unit-cell parameters a = 7.3096(2) Å, c = 5.2829(1) Å, V = 244.45(1) Å3, D c = 8.84 g/cm3, and Z = 2. In its layered crystal structure, the [PdSe6] octahedra share opposing Se–Se edges with adjacent [PdSe4] squares forming layers parallel with the (001) plane. The layers show AA type stacking along the c-axis. Hg atoms occupy the anti-cubooctahedral voids between two consecutive layers. Pd2HgSe3 is isostructural with Pt2HgSe3 and Pt4Tl2 X 6 (X = S, Se, or Te) phases. The structure can be viewed as a 2a.2a. c superstructure of PtSe2.
Multimodal analysis of the predictability of hand-gesture properties

Embodied conversational agents benefit from being able to accompany their speech with gestures. Although many data-driven approaches to gesture generation have been proposed in recent years, it is still unclear whether such systems can consistently generate gestures that convey meaning. We investigate which gesture properties (phase, category, and semantics) can be predicted from speech text and/or audio using contemporary deep learning. In extensive experiments, we show that gesture properties related to gesture meaning (semantics and category) are predictable from text features (time-aligned FastText embeddings) alone, but not from prosodic audio features, while rhythm-related gesture properties (phase) on the other hand can be predicted from audio features better than from text. These results are encouraging as they indicate that it is possible to equip an embodied agent with content-wise meaningful co-speech gestures using a machine-learning model.
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) for semantic segmentation has been actively studied to mitigate the domain gap between label-rich source data and unlabeled target data. Despite these efforts, UDA still has a long way to go to reach the fully supervised performance. To this end, we propose a Labeling Only if Required strategy, LabOR, where we introduce a human-in-the-loop approach to adaptively give scarce labels to points that a UDA model is uncertain about. In order to find the uncertain points, we generate an inconsistency mask using the proposed adaptive pixel selector and we label these segment-based regions to achieve near supervised performance with only a small fraction (about 2.2%) ground truth points, which we call "Segment based Pixel-Labeling (SPL). " To further reduce the efforts of the human annotator, we also propose "Point based Pixel-Labeling (PPL)," which finds the most representative points for labeling within the generated inconsistency mask. This reduces efforts from 2.2% segment label → 40 points label while minimizing performance degradation. Through extensive experimentation, we show the advantages of this new framework for domain adaptive semantic segmentation while minimizing human labor costs.
Message in a Bottle: Unveiling the Magneto-Ionic Complexity of AGNs through the Stokes QU-Fitting Technique

Here, I overview one of the available techniques for the analysis of broad-band spectropolarimetric data, the Stokes QU-fitting. Since broad-band receivers have been installed at most radio facilities, the collection of radio data, both the total intensity and the linear polarization, is revealing interesting features in their spectra. The polarized light, and therefore its properties, i.e. the fractional polarization p and the polarized angle χ, are now finally well sampled in wide wavelength ranges. The new complex behaviors revealed by the data can be studied using the Stokes QU-fitting, which consists of modeling the Stokes parameters Q and U using wavelength-dependent analytical models, available in the literature. This technique provides a very good diagnostic of the nature and structure of the magnetized plasma, with the possibility to identify complex structures, internal or external, of the source of study. A summary of the available and most used models describing the polarization behavior, is presented. Moreover, some of the most significant observational works which use this technique are also summarized.
The K2 Galactic Archaeology Program Data Release 3: Age-abundance Patterns in C1–C8 and C10–C18

We present the third and final data release of the K2 Galactic Archaeology Program (K2 GAP) for Campaigns C1–C8 and C10–C18. We provide asteroseismic radius and mass coefficients, κ R and κ M , for ∼19,000 red giant stars, which translate directly to radius and mass given a temperature. As such, K2 GAP DR3 represents the largest asteroseismic sample in the literature to date. K2 GAP DR3 stellar parameters are calibrated to be on an absolute parallactic scale based on Gaia DR2, with red giant branch and red clump evolutionary state classifications provided via a machine-learning approach. Combining these stellar parameters with GALAH DR3 spectroscopy, we determine asteroseismic ages with precisions of ∼20%–30% and compare age-abundance relations to Galactic chemical evolution models among both low- and high-α populations for α, light, iron-peak, and neutron-capture elements. We confirm recent indications in the literature of both increased Ba production at late Galactic times as well as significant contributions to r-process enrichment from prompt sources associated with, e.g., core-collapse supernovae. With an eye toward other Galactic archeology applications, we characterize K2 GAP DR3 uncertainties and completeness using injection tests, suggesting that K2 GAP DR3 is largely unbiased in mass/age, with uncertainties of 2.9% (stat.) ± 0.1% (syst.) and 6.7% (stat.) ± 0.3% (syst.) in κ R and κ M for red giant branch stars and 4.7% (stat.) ± 0.3% (syst.) and 11% (stat.) ± 0.9% (syst.) for red clump stars. We also identify percent-level asteroseismic systematics, which are likely related to the time baseline of the underlying data, and which therefore should be considered in TESS asteroseismic analysis.
Gravitational waves and primordial black holes from supersymmetric hybrid inflation

We study the effect of supergravity corrections due to a linear and a bilinear term in the K\"ahler potential, in the context of a supersymmetric hybrid inflation model. By appropriate choice of the parameters associated to these terms, we are able to satisfy the main cosmological constraints for the spectral index $n_s$ and the tensor-to-scalar ratio $r$. In addition, this model predicts primordial black hole abundance enough to account for the whole dark matter of the Universe and gravitational wave spectra within the reach of future detection experiments. The predictions of the model can be made compatible to the NANOGrav reported signal, at the cost of significantly lower primordial black hole abundance.
Progressive Coordinate Transforms for Monocular 3D Object Detection

Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment. While significant progress has been achieved with expensive LiDAR point clouds, it poses a great challenge for 3D object detection given only a monocular image. While there exist different alternatives for tackling this problem, it is found that they are either equipped with heavy networks to fuse RGB and depth information or empirically ineffective to process millions of pseudo-LiDAR points. With in-depth examination, we realize that these limitations are rooted in inaccurate object localization. In this paper, we propose a novel and lightweight approach, dubbed {\em Progressive Coordinate Transforms} (PCT) to facilitate learning coordinate representations. Specifically, a localization boosting mechanism with confidence-aware loss is introduced to progressively refine the localization prediction. In addition, semantic image representation is also exploited to compensate for the usage of patch proposals. Despite being lightweight and simple, our strategy leads to superior improvements on the KITTI and Waymo Open Dataset monocular 3D detection benchmarks. At the same time, our proposed PCT shows great generalization to most coordinate-based 3D detection frameworks. The code is available at: https://github.com/amazon-research/progressive-coordinate-transforms .
Deep Learning-Based Codebook Design for Code-Domain Non-Orthogonal Multiple Access: Approaching Single-User Bit-Error Rate Performance

A general form of codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered equivalent to an autoencoder (AE)-based constellation design for multi-user multidimensional modulation (MU-MDM). Due to a constrained design space for optimal constellation, e.g., fixed resource mapping and equal power allocation to all codebooks, however, existing AE architectures produce constellations with suboptimal bit-error-rate (BER) performance. Accordingly, we propose a new architecture for MU-MDM AE and underlying training methodology for joint optimization of resource mapping and a constellation design with bit-to-symbol mapping, aiming at approaching the BER performance of a single-user MDM (SU-MDM) AE model with the same spectral efficiency. The core design of the proposed AE architecture is dense resource mapping combined with the novel power allocation layer that normalizes the sum of user codebook power across the entire resources. This globalizes the domain of the constellation design by enabling flexible resource mapping and power allocation. Furthermore, it allows the AE-based training to approach a global optimal MU-MDM constellations for CD-NOMA. Extensive BER simulation results demonstrate that the proposed design outperforms the existing CD-NOMA designs while approaching the single-user BER performance achieved by the equivalent SU-MDM AE within 0.3dB over the additive white Gaussian noise channel.
Defense Against Mobile Phishing Attack

We developed a web-based phishing attack targeting the iOS mobile platform, speciﬁcally exploiting the full-screen touch-based user interface. Our demonstration attack requires no special access or modiﬁcation to the device and mimics the native application GUI to pass casual observation. We then developed a proof of concept defense against this and other similar spooﬁng phishing attacks. Our system logs user keystrokes and warns the user when potentially sensitive information is about to be entered while running an untrusted application. Our defense system works for malicious website in a mobile web browser as well as all third-party applications we tested.
Anti-malarial drug dihydroartemisinin downregulates the expression levels of CDK1 and CCNB1 in liver cancer

Liver cancer is the third leading cause of cancer-associated mortality worldwide. By the time liver cancer is diagnosed, it is already in the advanced stage. Therefore, novel therapeutic strategies need to be identified to improve the prognosis of patients with liver cancer. In the present study, the profiles of GSE84402, GSE19665 and GSE121248 were used to screen differentially expressed genes (DEGs). Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for DEGs were conducted using the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction network was established to screen the hub genes associated with liver cancer. Additionally, the expression levels of hub genes were validated using the Gene Expression Profiling Interactive Analysis and Oncomine databases. In addition, the prognostic value of hub genes in patients with liver cancer was analyzed using Kaplan-Meier Plotter. It was demonstrated that 132 and 246 genes were upregulated and downregulated, respectively, in patients with liver cancer. Among these DEGs, 10 hub genes with high connected node values were identified, which were AURKA, BIRC5, BUB1B, CCNA2, CCNB1, CCNB2, CDC20, CDK1, DLGAP5 and MAD2L1. CDK1 and CCNB1 had the most connection nodes and the highest score and were therefore, the most significantly expressed. In addition, it was demonstrated that high expression levels of CDK1 and CCNB1 were associated with poor overall survival time of patients with liver cancer. Dihydroartemisinin (DHA) is a Food and Drug Administration-approved drug, which is derived from the traditional Chinese medicine Artemisia annua Linn. DHA inhibits cell proliferation in numerous cancer types, including liver cancer. In our previous study, it was revealed that DHA inhibited the proliferation of HepG2215 cells. In the present study, it was further demonstrated that DHA reduced the expression levels of CDK1 and CCNB1 in liver cancer. Overall, CDK1 and CCNB1 were the potential therapeutic targets of liver cancer, and DHA reduced the expression levels of CDK1 and CCNB1, and inhibited the proliferation of liver cancer cells.
Mepolizumab decreased the levels of serum galectin-10 and eosinophil cationic protein in asthma

Background Mepolizumab, a humanized antibody targeting interleukin-5, decreases the number of blood eosinophils and the frequency of exacerbation of severe asthma. Galectin-10 is a protein within the cytoplasm of eosinophils and constitutes Charcot-Leyden crystals, which promotes key features of asthma. However, the relationship between time kinetics and clinical response of eosinophil-derived molecules such as galectin-10 or eosinophil cationic protein (ECP) has not been precisely investigated. Objective This study aimed to clarify the precise time course of the levels of serum galectin-10 and ECP after mepolizumab treatment and to analyze the relationship between the levels of eosinophil-derived molecules and the clinical background or response to mepolizumab treatment. Methods This multicenter, prospective open-label study recruited 20 patients with severe eosinophilic asthma. Mepolizumab was administered every 4 weeks for 32 weeks and the levels of various biomarkers were serially analyzed. Results The serum galectin-10 and ECP significantly and rapidly decreased 4 weeks after initial administration of mepolizumab. In contrast, basophil count, fractional exhaled nitric oxide, and the serum total IgE level were unchanged during treatment. Asthma Control Questionnaire-5, Asthma Health Questionnaire-33, and Lund-Mackay scores significantly improved after mepolizumab treatment. Both high ECP and eosinophil count related to better response in forced expiratory volume in 1 second (FEV1) and measurable ECP level at 4 weeks after administration of mepolizumab related to the further improvement in FEV1 toward week 32. No significant difference in improvement in FEV1 was observed in galectin-10 high group. The level of ECP at baseline was significantly related to the higher prevalence of nasal polyp and Lund-Mackay score. Conclusion This study was the first to show that the levels of serum galectin-10 decreases after initial administration of mepolizumab. The significant relationship between serum ECP and better response in FEV1 suggested the potential role of serum ECP as a predictive biomarker for the efficacy of mepolizumab (UMIN000030466).
Aspect ratio dependent air stability of PbSe nanorods and photovoltaic applications

Development of unique strategies to overcome Shockley–Queisser (SQ) limit in solar cells has gained a great deal of interest. Multiple exciton generation (MEG) process has been considered as one of the best approaches to the SQ limitation. In this respect, PbSe quantum dots (QDs) and nanorods (NRs) have been regarded as promising solar energy harvesting materials owing to their noticeable MEG yields. Although air stability has been regarded as one of the main disadvantage of PbSe QDs, no study has pointed out to the air sensitivity of PbSe NRs yet. Here, we reveal the effect of aspect ratio on air sensitivity and optical properties of PbSe NRs and discover that NRs with higher aspect ratios are more air stable, attributed to the reduced density of NR ends with air sensitive {100} facets. Furthermore, a band offset was created by utilization of tetrabutylammonium iodide and 1,2-ethanedithiol ligands in cell designs. We found that solar cells based on pristine PbSe NRs are limited by low open circuit voltages due to leakage current pathways. On the other hand, modified cells comprising light absorbing layers prepared by blending NRs and QDs and hole transporting QD layer exhibit a 10-fold improvement in solar cell efficiency.
Factors associated with unawareness of HIV-positive status in urban Ethiopia: Evidence from the Ethiopia population-based HIV impact assessment 2017-2018

Background The HIV epidemic in Ethiopia is concentrated in urban areas. Ethiopia conducted a Population-based HIV Impact Assessment (EPHIA) in urban areas between October 2017 and April 2018 to measure the status of the country’s response to the epidemic. Methods We conducted field data collection and HIV testing in randomly selected households using the national, rapid testing algorithm with laboratory confirmation of seropositive samples using a supplemental assay. In addition to self-report on HIV diagnosis and treatment, all HIV-positive participants were screened for a set of HIV antiretroviral (ARV) drugs indicative of the first- and second-line regimens. We calculated weighted frequencies and 95% confidence intervals to assess regional variation in participants’ level of unawareness of their HIV-positive status (adjusted for ARV status). Results We interviewed 20,170 survey participants 15–64 years of age, of which 19,136 (95%) were tested for HIV, 614 (3.2%) tested positive, and 119 (21%) of HIV-positive persons were unaware of their HIV status. Progress towards the UNAIDS first 90 target (90% of people living with HIV would be aware of their HIV status by 2020) substantially differed by administrative region of the country. In the bivariate analysis using log binomial regression, three regions (Oromia, Addis Ababa, and Harari), male gender, and young age (15–24 years) were significantly associated with awareness of HIV positive status. In multivariate analysis, the same variables were associated with awareness of HIV-positive status. Conclusion One-fifth of the HIV-positive urban population were unaware of their HIV-positive status. The number of unaware HIV-positive individuals has a different distribution than the HIV prevalence. National and regional planning and monitoring activities could address this potentially substantial source of undetected HIV infection by increasing HIV testing among young people, men and individuals who do not use condoms.