Miracle-dz/UniFAR-FTUs huggingface dataset:
ARKGraph: All-Range Approximate K-Nearest-Neighbor Graph

Given a collection of vectors, the approximate K-nearest-neighbor graph (KGraph for short) connects every vector to its approximate K-nearest-neighbors (KNN for short). KGraph plays an important role in high dimensional data visualization, semantic search, manifold learning, and machine learning. The vectors are typically vector representations of real-world objects (e.g., images and documents), which often come with a few structured attributes, such as times-tamps and locations. In this paper, we study the all-range approximate K-nearest-neighbor graph (ARKGraph) problem. Specifically, given a collection of vectors, each associated with a numerical search key (e.g., a timestamp), we aim to build an index that takes a search key range as the query and returns the KGraph of vectors whose search keys are within the query range. ARKGraph can facilitate interactive high dimensional data visualization, data mining, etc. A key challenge of this problem is the huge index size. This is because, given n vectors, a brute-force index stores a KGraph for every search key range, which results in O (K n 3 ) index size as there are O ( n 2 ) search key ranges and each KGraph takes O (K n ) space. We observe that the KNN of a vector in nearby ranges are often the same, which can be grouped together to save space. Based on this observation, we propose a series of novel techniques that reduce the index size significantly to just O (K n log n ) in the average case. Furthermore, we develop an efficient indexing algorithm that constructs the optimized ARKGraph index directly without exhaustively calculating the distance between every pair of vectors. To process a query, for each vector in the query range, we only need O (log log n + K log K) to restore its KNN in the query range from the optimized ARKGraph index. We conducted extensive experiments on real-world datasets. Experimental results show that our optimized ARKGraph index achieved a small index size, low query latency, and good scalability. Specifically, our approach was 1000x faster than the baseline method that builds a KGraph for all the vectors in the query range on-the-fly.
Strong bi-metric regularity in affine optimal control problems

The paper presents new suﬃcient conditions for the property of strong bi-metric regularity of the optimality map associated with an optimal control problem which is aﬃne with respect to the control variable ( aﬃne problem ). The optimality map represents the system of ﬁrst order optimality conditions (Pontryagin principle), and its regularity is of key importance for the qualitative and numerical analysis of optimal control problems. The case of aﬃne problems is especially challenging due to the typical discontinuity of the optimal control functions. A remarkable feature of the obtained suﬃcient conditions is that they do not require convexity of the objective functional. As an application, the result is used for proving uniform convergence of the Euler discretization method for a family of aﬃne optimal control problems
On the Quasi-2-Day Planetary Waves in the Middle 1 Atmosphere During Different QBO Phases 2

We found that the interannual difference of the W3 and W4 10 Q2DW is significantly correlated with the Quasi-Biennial Oscillation 11 westerly (QBOW) and easterly (QBOE) phase, identified from the analysis 12 of the 2003 to 2020 MERRA-2 and SABER atmospheric data. The 13 amplitude of the zonal wind in the QBOE phase is approximately ∼ 10 m/s 14 stronger than that in the QBOW phase. Mean zonal easterly winds are 15 stronger in the QBOE phase than in the QBOW phase, while westerly 16 winds are stronger in the QBOW phase. The Q2DW is present in the 17 summer, and the background wind is easterly in both hemispheres. The 18 mean temperature amplitudes of W3 and W4 in the QBOW phase are 19 stronger than those in the QBOE phase, and the difference is ~2 K and ~3 20 K (in the Southern Hemisphere); ~2 K and ~3 K (in the Northern 21 Hemisphere), respectively. The mean wave period of W4 in the QBOW 22 phase in the Northern Hemisphere is shorter than that in the QBOE phase. 23 The W3 mode is modulated by atmospheric eigenmodes in both 24 hemispheres and shows slight differences in the QBOW and QBOE phases, 25 while the W4 mode is more likely to show significant differences in the 26 different QBO phases. Our diagnostic analysis suggests that the 27 amplification of the QBOW phases W3 and W4 may be due to stronger 28 mean-flow instabilities and background winds in the mesosphere. In 29 addition, planetary waves gain stronger source activity during the QBOW 30 phase to provide sufficient energy for propagation and amplification. 31
Credit Risk Models for Financial Fraud Detection: A New Outlier Feature Analysis Method of XGBoost With SMOTE

Outlier detection is currently applied in many fields, where existing research focuses on improving imbalanced data or enhancing classification accuracy. In the financial area, financial fraud detection puts higher demands on real-time and interpretability. This paper attempts to develop a credit risk model for financial fraud detection based on an extreme gradient boosting tree (XGBoost). SMOTE is adopted to deal with imbalanced data. AUC is the assessment indicator, and the running time is taken as the reference to compare with other frequently used classification algorithms. The results indicate that the method proposed by this paper performs better than others. At the same time, XGBoost can obtain a ranking of important features that impact the classification results when performing classification tasks, making the evaluation results of the model interpretable. The above shows that the model proposed in the paper is more practical in solving credit risk assessment problems. It has faster response times, reduced costs, and better interpretability.
Measuring Linearity of Connected Conﬁgurations of a Finite Number of 2 D and 3 D Curves

We deﬁne a new linearity measure for a wide class of objects consisting of a set of of curves, in both 2 D and 3 D . After initially observing closed curves, which can be represented in a parametric form, we extended the method to connected compound curves – i.e. to connected conﬁgurations of a number of curves representable in a parametric form. In all cases, the measured linearities range over the interval (0 , 1] , and do not change under translation, rotation and scaling transformations of the considered curve. We prove that the linearity is equal to 1 if and only if the measured curve consists of two straight line overlapping segments. The new linearity measure is theoretically well founded and all related statements are supported with rigorous mathematical proofs. The behavior and applicability of the new linearity measure are explained and illustrated by a number of experiments.
Measurement Report: The Palau Atmospheric Observatory and its Ozonesonde Record - Continuous Monitoring of Tropospheric Composition and Dynamics in the Tropical West Paciﬁc

The Tropical West Paciﬁc is recognized as an important region for stratosphere-troposphere exchange, but has been a measurement gap in the global ozone sounding network. The Palau Atmospheric Observatory (PAO) was established to study the atmospheric composition above the remote Tropical West Paciﬁc with a comprehensive instrumental setup. Since 2016, two laboratory containers in Palau host an Fourier-transform infrared spectrometer, a lidar (micro lidar until 2016, cloud and aerosol lidar from 2018), a Pandora 2S photometer and laboratory space for weather balloon soundings with ozone, 5 water-vapor-, aerosol-and radiosondes. In this analysis, we focus on the continuous, fortnightly ozone sounding program with Electrochemical Concentration Cell (ECC) ozone sondes. The aim of this study is to introduce the PAO and its research potential, present the ﬁrst observation of the typical seasonal cycle of tropospheric ozone in the Tropical West Paciﬁc based on a multiannual record of in situ observations, and investigate major drivers of variability and seasonal variation from 01/2016 until 12/2021 related to the large scale atmospheric circulation. 10 We present the PAO ozone ( O 3 ) volume mixing ratios (VMR) and relative humidity (RH) time series complemented by other observations. The site is exposed to year-round high convective activity reﬂected in dominating low O 3 VMR and high RH. In 2016, the impact of the strong El Niño is evident as a particularly dry, ozone-rich
M-FUSE: Multi-frame

In-between convolutions. As can be seen in Figure 1, in every depth level for the contracting as well as the expanding part one additional in-between convolutional layer is used to process information. Thus, we performed an ablation over several options: completely omitting this layer (none), having one (1 conv) or two (2 convs) convolutions, or using a residual block [1] (resblock). The results for none, one or two convolutional layers are inconclusive, with no significant best option. As a compromise, we chose one convolutional layer for our method since it is most similar to other U-Nets in the literature. Finally, despite being most closely related to the two convolutions, the residual block slightly decreases the quality compared to all other cases.
Analyzing Gender Bias in Multilingual Machine Translation

Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In recent years, several debiasing approaches have been proposed, including debiasing the data before model training, the models during training, or post-processing their outputs; however, it has yet to be explored how limited availability of large amounts of data affects gender bias in multilingual NMT, specifically for zero-shot directions. In this thesis, we compare pivotbased and zero-shot translation and study the influence of the bridge language—the language participating in all language pairs during training—and the effect of language-agnostic hidden representations—proven to achieve zero-shot performance gains—on models’ ability to preserve the feminine and masculine gender equally well in their outputs. We test three bridge languages with differently pronounced gender-inflectional systems—English, German, and Spanish— as well as three different model modifications that encourage language-agnostic representations: i) removing a residual connection in a middle Transformer encoder to lift positional correspondences to the input tokens; ii) promoting similar source and target language representations through an auxiliary loss; and iii) joint adversarial training penalizing successful recovery of source language signals from the hidden representations. We find that language-agnostic representations reduce the gap between better masculine and worse feminine results (i.e., masculine bias) found throughout all models’ outputs; they improve zero-shot models’ performances to the point where they can outperform pivoting when bridging via English, a low gender-inflected language. With increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation when gender inflection depends on the speaker’s gender; otherwise zero-shot translation performs slightly better regarding balanced gender preservation for feminine and masculine words.
Supplementary Material for Detecting Human-Object Contact in Images

In Sec. 1, we introduce the detailed human-body part labels for human-object contact. In Sec. 2, we describe more details for the annotation protocol for “HOT-Annotated” and how we generate pseudo ground truth for “HOT-Generated”. In Sec. 3, we report more implementation details. Sec. 4 shows more experimental results in the contact detection task, including failure cases, evaluation under different settings and attention maps, etc. In Sec. 5, we provide more details of the part-specific contact detector that we compare with HOT. In Sec. 6, we report more experiment details and results to illustrate the use of our HOT contact detection for 3D human pose estimation. Sec. 7 includes more details on how the HOT dataset can facilitate 3D contact estimation. Section 8 discusses more potential downstream applications for contact detection and qualitative results on self-contact and human-human contact. The use of existing assets is listed in Sec. 9.
P HENOLIC P ROFILE , C HEMICAL R ELATIONSHIP AND A NTIOXIDANT A CTIVITY OF I RANIAN V ERBASCUM S PECIES

Hydro-ethanolic extracts of flowers from four Verbascum species were evaluated for the phenolic content, composition, and antioxidant activity using Folin-Ciocalteu assay, HPLC-DAD analysis, and DPPH assay, respectively. The highest flavonoid content was detected in V. sinuatum extract from Khoramabad (19.91 mg RE/g DW). The extract of V. pseudo-digitalis from Maymand yielded the highest amount of total phenols, together with the highest apigenin and luteolin levels (55.62 mg GAE/g DW, 12.18 and 88.13 µ g/mg DW, respectively), while that of V. songaricum from Ardekan showed the highest naringin content (12.44 µ g/mg DW). The extract of V. songaricum from Shirmard exhibited the highest quercetin and rutin levels (1.0 and 24.24 µ g/mg DW, respectively), whereas that of V. sinuatum from Ardekan had the highest caffeic acid content (7.78 µ g/mg DW). The antioxidant activity of Verbascum samples showed IC 50 values from 45.12 to 226.62 µ g/mL.
Atmospheric composition and climate impacts of a future hydrogen economy

Abstract. Hydrogen is expected to play a key role in the global energy transition to net zero emissions in many scenarios. However, fugitive emissions of hydrogen into the atmosphere during its production, storage, distribution and use could reduce the climate benefit and also have implications for air quality. Here, we explore the atmospheric composition and climate impacts of increases in atmospheric hydrogen abundance using the UK Earth System Model (UKESM1) chemistry–climate model. Increases in hydrogen result in increases in methane, tropospheric ozone and stratospheric water vapour, resulting in a positive radiative forcing. However, some of the impacts of hydrogen leakage are partially offset by potential reductions in emissions of methane, carbon monoxide, nitrogen oxides and volatile organic compounds from the consumption of fossil fuels. We derive a refined methodology for determining indirect global warming potentials (GWPs) from parameters derived from steady-state simulations, which is applicable to both shorter-lived species and those with intermediate and longer lifetimes, such as hydrogen. Using this methodology, we determine a 100-year global warming potential for hydrogen of 12 ± 6. Based on this GWP and hydrogen leakage rates of 1 % and 10 %, we find that hydrogen leakage offsets approximately 0.4 % and 4 % respectively of total equivalent CO2 emission reductions in our global hydrogen economy scenario. To maximise the benefit of hydrogen as an energy source, emissions associated with hydrogen leakage and emissions of the ozone precursor gases need to be minimised.
Analysis of Dual-Element Antenna Configurations

Dual-dipole antennas have been extensively researched previously for their bandwidth enhancing effect on Yagi type antennas. In this thesis, dual-dipole antennas are fabricated and measured. These experimental measurement results are verified with simulated values. First, a standard dual-dipole antenna is investigated and found that the high magnitude, opposite current directions on the dipole arms are the reasoning behind the creation of a high gain mode. This pattern is similar to a Yagi antenna. Next, a dual-band implementation of the dual-dipole antenna is shown, with two distinct resonances in a lower band and an upper band. Both bandwidths exhibit a dipole like mode, as well as a high gain mode. Finally, a dual-element cross-dipole antenna application is investigated. The antenna exhibits multiple dipole like modes within the bandwidth, high gain points, and CP generation at the center frequency. INDEX WORDS: Bandwidth enhancement, Dipole antenna, Dual-band antenna, Double dipole, Dualelement dipole. ANALYSIS OF DUAL-ELEMENT ANTENNA CONFIGURATIONS
The NOAA Aerosol Reanalysis version 1.0 (NARA v1.0): Description of the Modeling System and its Evaluation

In this manuscript, we describe the first ever global aerosol reanalysis at the National Oceanic and Atmospheric Administration (NOAA), the NOAA Aerosol ReAnalysis version 1.0 (NARA v1.0) that was produced for the year 2016. In NARA v1.0, the forecast model is an early version of the operational Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) model. The three-dimensional ensemble-variational (3D-EnVar) data assimilation (DA) system configuration is built using elements of the Joint Effort for Data assimilation Integration (JEDI) framework being developed at the Joint 15 Center for Satellite Data Assimilation (JCSDA). The Neural Network Retrievals (NNR) of Aerosol Optical Depth (AOD) at 550 nm from the MODerate resolution Imaging Spectroradiometer (MODIS) instruments are assimilated to provide reanalysis of aerosol mass mixing ratios. We evaluate NARA v1.0 against a wide variety of Aerosol Robotic NETwork (AERONET) observations, against National Aeronautics and Space Administration’s (NASA) Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2; Gelaro et al., 2017; Randles et al., 2017; Buchard et al., 2017) and 20 European Centre for Medium-Range Weather Forecasts’ (ECMWF) Copernicus Atmosphere Monitoring Service ReAnalysis
Valuations of EQ-5D health states: could United Kingdom results be used as informative priors for the United States

ABSTRACT Valuations of health state descriptors such as the generic EuroQol five-dimensional (EQ-5D) or the six-dimensional short form (SF-6D) have been conducted in different countries. There is a scope to make use of the results in one country as informative priors to help with the analysis of a study in another, for this to enable better estimation to be obtained in the new country than analysing its data separately. This article analyses data from 2 EQ-5D valuation studies where, using similar time trade-off protocols, values for 42 common health states were elicited from representative samples of the US and UK general adult populations. We apply a nonparametric Bayesian method to improve the accuracy of predictions of the US population utility function where the UK results were used as informative priors. The results suggest that drawing extra information from the UK data produces a better estimation of the US population utility than analysing its data separately. The implications of these results will be extremely crucial in countries where valuation studies are limited.
Sensorized Manipulation Challenge Kit for Benchmarking Robotic Manipulation

—We introduce the Sensorized Manipulation Challenge Kit ( SMaCK ) for benchmarking robotic manipulation capabilities. Existing sets of physical objects for benchmarking robotic manipulation focus on physical dexterity (e.g. grasping, in-hand manipulation) but do not assess planning ability (e.g. unlocking a box before the handle can be lifted), or goal-directed exploration, critical features of embodied intelligence. SMaCK is a collection of puzzle boxes that systematically test manipulation and reasoning capabilities and which are cheap and easy to fabricate. The boxes include sensors for measuring object state information (e.g: inertial data, pose, articulated joint state) that can be recorded and used to either evaluate or train a manipulation policy. We also include simulated MuJoCo domains using the CAD descriptions of the boxes, and evaluate a deep reinforcement learning agent on the simplest benchmark. Instructions for building the SMaCK and our codebase can be found at https://github.com/babbatem/smack/ .
Evaluating Interpretability Techniques with Human Attention in Visual Question Answering

In this paper, we explore interpetability in Visual Question Answering. Our model is a baseline CNN+LSTM VQA model, and we implement 4 gradient based pixel at-tribution algorithms on top of this model (Vanilla Saliency, Guided Backprop, DeconvNet, Guided GradCam). We run the model and attribution algorithms on a subset of the MS COCO VQA dataset, generating a collection of saliency maps, model answers, and model confidences. We then pro-pose and implement a scoring pipeline that computes an ensemble of metrics for how similar an attribution heatmap is to the ground truth human attention from the VQA-Hat dataset [3]. Using these metrics, we analyze the dataset and investigate novel interactions between models predictions, model confidence (pre-softmax score), and model attention as quantified by saliency maps. Namely, a model’s attention is most similar to human attention when it produces correct but low-confidence answers, and least similar to human attention when it produces wrong-and-unconfident or correct-and-confident answers. We implement some statistical tests to determine the significance of our results. Finally, we investigate qualitatively some motivating examples from the dataset.
Fast Ion Dynamics in ASDEX Upgrade and TEXTOR Measured by Collective Thomson Scattering

Fast ions are an essential ingredient in burning nuclear fusion plasmas: they are responsible for heating the bulk plasma, carry a significant amount of plasma current and moreover interact with various magnetohydrodynamic (MHD) instabilities. The collective Thomson scattering (CTS) diagnostic is sensitive to the projection of fast ion velocity distribution function. This thesis is mainly devoted to investigations of fast ion physics in tokamak plasmas by means of CTS. ISSN 0106-2840 ISBN 978-87-550-3958-2
The Influence of Awe on Prosociality: Considering the Role of Empathic Concern and Emotion Regulation

This study aimed to understand how the relationship between awe and prosociality is impacted by empathic concern and emotion regulation. Witnessing others in need and feeling a sense of connectedness towards them may elicit empathic concern and lead to prosocial behavior. However, emotion regulation impacts the capacity to focus on others. Greater emotion regulation difficulty has been associated with greater self-focus and may interfere with considering the perspective of others. Experiences of awe have been shown to not only decrease self-focus but may also increase connectedness to others, promoting empathic concern, and subsequently prosocial outcomes. We predicted that eliciting awe would increase empathic concern and prosocial behavior. We also predicted that the relationship between awe and prosocial behavior would depend on the level of emotion regulation difficulty, such that participants with greater emotion regulation difficulty would experience a greater magnitude of the effect of awe on prosocial behavior than those with less emotion regulation difficulty. Participants completed a measure of emotion regulation, watched an awe inducing or neutral video, read a story depicting human suffering and rated the degree of empathic concern felt before making a prosocial decision. We aimed to investigate empathic concern as a mediator between awe and prosociality and emotion regulation as a potential moderator. Neither of our hypotheses were supported in the current study. Possible explanations, limitations, and future directions are discussed.