Miracle-dz/UniFAR-FTUs huggingface dataset:
Influences of oceanic ozone deposition on tropospheric photochemistry

Abstract. The deposition of ozone to seawater is an important ozone sink. Despite constituting as much as a third of the total ozone deposition, it receives significantly less attention than the deposition to terrestrial ecosystems. Models have typically calculated the deposition rate based on a resistance-in-series model with a uniform waterside resistance. This leads to models having an essentially uniform deposition velocity of approximately 0.05 cm s−1 to seawater, which is significantly higher than the limited observational dataset. Following from Luhar et al. (2018) we include a representation of the oceanic deposition of ozone in the GEOS-Chem model of atmospheric chemistry and transport based on its reaction with sea-surface iodide. The updated scheme halves the calculated annual area-weighted mean deposition velocity to water from 0.0464 cm s−1 (25th and 75th percentiles of 0.0461 cm s−1 and 0.0471 cm s−1 respectively) to 0.0231 cm s−1 (25th and 75th percentiles of 0.0121 cm s−1 and 0.0303 cm s−1 respectively). The calculated ozone deposition velocity varies from 0.009 cm s−1 in polar waters to 0.040 cm s−1 at the tropics. This improves comparisons to observations. The variability is driven mainly by the temperature-dependent rate constant for the reaction between iodide and ozone, the temperature dependence of the solubility, and variations in the ocean iodide concentration. The calculated annual deposition flux of ozone to the ocean is reduced from 222 to 122 Tg yr−1, and overall deposition of ozone to all surface types reduces from 862 to 758 Tg yr−1. Tropospheric ozone burdens and global mean OH increase from 324 to 328 Tg, and from 1.17×106 to 1.18×106 molec.cm-3, respectively. A total of 34 % of surface grid boxes experience a 10 % or greater increase in ozone concentration. Comparisons between observations of surface ozone and the model are improved with the new parameterization notably around the Southern Ocean. Process-level representation of oceanic deposition of ozone thus appears essential for representing the concentration of surface ozone over the planet.
Source Model Selection for Deep Learning in the Time Series Domain

Transfer Learning aims to transfer knowledge from a source task to a target task. We focus on a situation when there is a large number of available source models, and we are interested in choosing a single source model that can maximize the predictive performance in the target domain. Existing methods compute some form of “similarity” between the source task data and the target task data. They then select the most similar source task and use the model trained on it for transfer learning. Previous methods do not account for the fact that it is the model parameters that are transferred rather than the data. Therefore, the “similarity” of the source data does not directly influence transfer learning performance. In addition, we would like the possibility of confidently selecting a source model even when the data it was trained on is not available, for example, due to privacy or copyright constraints. We propose to use the truncated source models as encoders for the target data. We then select a source model based on how well it clusters the target data in the latent encoding space, which we calculate using the Mean Silhouette Coefficient. We prove that if the encodings achieve a Mean Silhouette Coefficient of 1, optimal classification can be achieved using just the final layer of the target network. We evaluate our method using the University of California, Riverside (UCR) time series archive and show that the proposed method achieves comparable results to previous work, without using the source data.
Simplified Inception Unit based Filter for HEVC

The HEVC standard has shown great effect in video coding, however, the block-level compression scheme introduces block artifacts into reconstructed videos. Meanwhile, the quantization also brings ringing effect and blur to the compressed sequence. In order to solve this problem, we propose a simplified inception unit based convolutional neural network to reduce the compression distortion in HEVC. In the proposed method, a simplified inception unit is designed to analyze and extract image features. By stacking multiple inception units, the internal and border information of each frame can be fully extracted and the images are able to achieve higher quality. Meanwhile, the proposed network is incorporated into the HEVC reference software, which can be directly used in coding process and improve coding performance. The experimental results demonstrate that the proposed method, compared with HEVC, reduces 5.5% BDrate at average in ‘Al’ mode. In ‘RA’ mode, the proposed method still achieves 6.02% BD-rate reduction at most.
Volumetric Representation of Human Body Parts Using Superquadrics

Superquadrics are one of the ideal shape representations for adapting various kinds of primitive shapes with a single equation. This paper revisits the task of representing a 3D human body with multiple superquadrics. As a single superquadric surface can only represent symmetric primitive shapes, we present a method that segments the human body into body parts to estimate their superquadric parameters. Moreover, we propose a novel initial parameter estimation method by using 3D skeleton joints. The results show that superquadric parameters are estimated, which represent human body parts volumetrically.
MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos

Image-based sports analytics enable automatic retrieval of key events in a game to speed up the analytics process for human experts. However, most existing methods focus on structured television broadcast video datasets with a straight and fixed camera having minimum variability in the capturing pose. In this paper, we study the case of event detection in sports videos for unstructured environments with arbitrary camera angles. The transition from structured to unstructured video analysis produces multiple challenges that we address in our paper. Specifically, we identify and solve two major problems: unsupervised identification of players in an unstructured setting and generalization of the trained models to pose variations due to arbitrary shooting angles. For the first problem, we propose a temporal feature aggregation algorithm using person re-identification features to obtain high player retrieval precision by boosting a weak heuristic scoring method. Additionally, we propose a data augmentation technique, based on multi-modal image translation model, to reduce bias in the appearance of training samples. Experimental evaluations show that our proposed method improves precision for player retrieval from 0.78 to 0.86 for obliquely angled videos. Additionally, we obtain an improvement in F1 score for rally detection in table tennis videos from 0.79 in case of global frame-level features to 0.89 using our proposed player-level features. Please see the supplementary video submission at https://ibm.biz/BdzeZA.
A Natural Language Process-Based Framework for Automatic Association Word Extraction

Word association, revealing mental representations and connections of human, has been widely studied in psychology. However, the scale of available associative cue-response words is severely restricted due to the traditional manually collecting methodology. Meanwhile, with the tremendous success in Natural Language Process (NLP) tasks, an extremely large amount of plain texts can be easily acquired. This suggests an insight about the potential to find association words automatically from the text corpus instead of manually collection. As an original attempt, this paper takes a small step toward proposing a deep learning based framework for automatic association word extraction. The framework mainly consists of two stages of association word detection and machine association network construction. In particular, attention mechanism based Reading Comprehension (RC) algorithm is explored to find valuable association words automatically. To validate the value of the extracted association words, the correlation coefficient between semantic similarities of machine and human association words is introduced as an effective measurement for evaluating association consistence. The experiments are conducted on two text datasets from which together about $20k$ association words, more than the existing largest human association word dataset, are finally derived. The experiment further verifies that the machine association words are generally consistent with human association words with respect to semantic similarity, which highlights the promising utilization of the machine association words in the future researches of both psychology and NLP.
Size and Accuracy in Model Inference

Many works infer finite-state models from execution logs. Large models are more accurate but also more difficult to present and understand. Small models are easier to present and understand but are less accurate. In this work we investigate the tradeoff between model size and accuracy in the context of the classic k-Tails model inference algorithm. First, we define mk-Tails, a generalization of k-Tails from one to many parameters, which enables fine-grained control over the tradeoff. Second, we extend mk-Tails with a reduction based on past-equivalence, which effectively reduces the size of the model without decreasing its accuracy. We implemented our work and evaluated its performance and effectiveness on real-world logs as well as on models and generated logs from the literature.
Metallic fractures assessments: OCT versus SEM

Metals can break either in a ductile or brittle manner if a static or dynamic load is applied to the same material. This depends on a variety of factors, such as the manner in which the load is applied, the shape of the mechanical part, the operating conditions, the nature and structure of the metallic material, and the working temperature. If subjected to variable loads, metallic materials break due to what is called fatigue. The microscopic analysis of fracture surfaces is currently carried out by using scanning electron microscopy (SEM). We have proposed, for the first time to our knowledge, a new method to analyze fracture surfaces, using a low coherence interferometry technique, Optical Coherence Tomography (OCT) [Gh. Hutiu, V. -F. Duma, et al., Surface imaging of metallic material fractures using optical coherence tomography, Appl. Opt. 53, 5912-5916 (2014); Gh. Hutiu, V. -F. Duma, et al., Assessment of ductile, brittle, and fatigue fractures of metals using optical coherence tomography, Metals 8, 117 (2018)]. The present paper presents the way we have demonstrated that OCT can replace the gold standard in such assessments, i.e. SEM, despite the fact that OCT has a resolution of 20 to 4 μm (in our investigations), while the SEM we employed has a 4 to 2 nm resolution. A few examples are given in this respect–for different types of fractures. The advantages of OCT versus SEM are discussed. This development opens the way for in situ investigations, for example in forensic sciences, where OCT can be applied (including with handheld scanning probes. as we have developed). In contrast, SEM, TEM, and AFM are lab-based techniques, more expensive, and they require trained operators.
End-to-end models for self-driving cars on UPB campus roads

In autonomous driving, making the vehicle trajectory planning a learnable process (so called end-to-end driving), given visual input and car motion measurements alone is an idea with longstanding tradition. In a recent project we investigate the potential to incorporate the steering predictions of an end-to-end model operating under constraints of a single monocular camera input, into the planning pipeline of existing open-source frameworks for autonomous driving. We describe a process to design and train such a model given a specific geographical area with its own particularities in terms of road network, traffic and driving conditions. We investigate data augmentation techniques which improve the performance of the model. Quality and performance analysis of the model is performed based on a collected dataset in the UPB campus. The evaluation is carried out using a simulation pipeline which applies transformations to the frames in test videos such that they reflect decisions made by the model in earlier steps. This brings the system evaluation closer to out of distribution testing. Results indicate that the proposed models offers a good performance for the collected dataset.
Derivations and Relations of Various Cost Functions for Allpass Phase-Equalizing Filter Design

This paper derives a new error function for the design of an allpass phase-equalizing filter (PEF). The new error function is nonlinear with respect to the unknown PEF coefficients, and minimizing the error function can obtain the unknown coefficients. Then, the relationship between the existing phase-error function in the literature that is derived by using the Taylor series expansion and this new error function is revealed. Furthermore, the relationships among the new error function and two existing frequency-response error functions are derived. Therefore, this paper has two objectives. One is to develop a new error function for designing an allpass PEF, and the other is to reveal the relationships among the newly developed error function and various existing error functions, including a phase-error function and two frequency-response error functions. Those error functions can be used as cost functions to be minimized in developing new optimization techniques for the design of an allpass PEF.
University of Groningen Testing elementary and secondary school students’ ability to perform historical perspective taking: the constructing of valid and reliable measure instruments Huijgen,

Historical reasoning competencies play an important role in history education. However, valid and reliable large-scale measurement instruments to assess these competencies are scarce. This study considers two instruments for measuring students’ ability to perform historical perspective taking (HPT) as a historical reasoning competency. The instruments have been tested for validity and reliability among 1,270 Dutch upper elementary and secondary school students, ranging in age from 10 to 17 years. One instrument offers effective validity and reliability and can map HPT performance among a large and heterogeneous student population. The results show that even upper elementary school students are capable of performing HPT. However, as students age, their ability to perform HPT increases. Differences regarding the ability to perform HPT were also found between educational levels. Pre-university students performed HPT more successfully compared to students at lower educational levels. The results of this study can be used to gain insight into the construct of HPT and into how historical reasoning competencies such as HPT can be measured. Furthermore, the results provide insight into how differences between students, such as age and educational levels, influence the performance of HPT.
Seasonal Peak Demand Classification with Machine Learning Techniques

Peak load management allows utilities to reduce demand for electricity and optimal utilization of available electricity during peak usage time. Accurate peak load forecasting is crucial for such task. In this paper, we used data mining scheme to detect both winter and summer peak load demand in the Polish electricity system based on seasonal historical data for both electricity and weather conditions. Deliberately, we undertook the approach different from time series forecasting and represented it as a classical pattern recognition problem. We compared three classification techniques to detect the power peaks. The results show that the algorithms can accurately, up to 24 hours ahead, detect 97.5% of the electricity peaks during winter period and 100% of the peaks in summer time.
Compass: Quantum Coherence and Entanglement

We review the spin radical pair mechanism which is a promising explanation of avian navigation. This mechanism is based on the dependence of product yields on 1) the ∗kais@purdue.edu 1 hyperfine interaction involving electron spins and neighboring nuclear spins and 2) the intensity and orientation of the geomagnetic field. This review describes the general scheme of chemical reactions involving radical pairs generated from singlet and triplet precursors; the spin dynamics of the radical pairs; and the magnetic field dependence of product yields caused by the radical pair mechanism. The main part of the review includes a description of the chemical compass in birds. We review: the general properties of the avian compass; the basic scheme of the radical pair mechanism; the reaction kinetics in cryptochrome; quantum coherence and entanglement in the avian compass; and the effects of noise. We believe that the quantum avian compass can play an important role in avian navigation and can also provide the foundation for a new generation of sensitive and selective magnetic-sensing nano-devices.
Non-Parametric Anthropometric Graph Convolutional Network for Virtual Mannequin Reconstruction

In this paper, we present a novel non-parametric method for precisely reconstructing a three dimensional (3D) virtual mannequin from anthropometric measurements and mask image(s) based on Graph Convolution Network (GCN). The proposed method avoids heavy dependence on a particular parametric body model such as SMPL or SCPAE and can predict mesh vertices directly, which is significantly more comfortable using a GCN than a typical Convolutional Neural Network (CNN). To further improve the accuracy of the reconstruction and make the reconstruction more controllable, we incorporate the anthropometric measurements into the developed GCN. Our non-parametric reconstruction results distinctly outperform the previous graph convolution method, both visually and in terms of anthropometric accuracy. We also demonstrate that the proposed network possesses the capability to reconstruct a plausible 3D mannequin from a single-view mask. The proposed method can be effortless extended to a parametric method by appending a Multilayer Perception (MLP) to regress the parametric space of the Principal Component Analysis (PCA) model to achieve 3D reconstruction as well. Extensive experimental results demonstrate that our anthropometric GCN itself is very useful in improving the reconstruction accuracy, and the proposed method is effective and robust for 3D mannequin reconstruction.
The Motion Capture as Behavior Analyzing Method of Spontaneous Motor Activity in Human Infants

Motion capture technology helps to automate the process of behavioral analysis in various monitoring systems. These may be supporting systems for everyday life at home, rehabilitation complexes, treatment progress or predictive analysis. The existing motion capture methods affect the input data. There is a dilemma between two sides in each of these tasks. One is the getting the most precise data and the second is minimization of input data. This issue is quite difficult to solve in advance. In such a case, it is necessary to collect a redundant dataset for a further narrowing of the dataflow. In this paper, motion capture is considered as an automating tool for estimating the spontaneous motor activity. The main focus is on a prior processing of incoming data, which is needed for the subsequent processing. The article shows two methods of motion capture: video and inertial microelectromechanical sensors captures. To sum up, the article is devoted to the data pre-processing for the behavioral analysis.
Inferring Program Transformations From Singular Examples via Big Code

Inferring program transformations from concrete program changes has many potential uses, such as applying systematic program edits, refactoring, and automated program repair. Existing work for inferring program transformations usually rely on statistical information over a potentially large set of program-change examples. However, in many practical scenarios we do not have such a large set of program-change examples. In this paper, we address the challenge of inferring a program transformation from one single example. Our core insight is that "big code" can provide effective guide for the generalization of a concrete change into a program transformation, i.e., code elements appearing in many files are general and should not be abstracted away. We first propose a framework for transformation inference, where programs are represented as hypergraphs to enable fine-grained generalization of transformations. We then design a transformation inference approach, GENPAT, that infers a program transformation based on code context and statistics from a big code corpus. We have evaluated GENPAT under two distinct application scenarios, systematic editing and program repair. The evaluation on systematic editing shows that GENPAT significantly outperforms a state-of-the-art approach, SYDIT, with up to 5.5x correctly transformed cases. The evaluation on program repair suggests that GENPAT has the potential to be integrated in advanced program repair tools-GENPAT successfully repaired 19 real-world bugs in the Defects4J benchmark by simply applying transformations inferred from existing patches, where 4 bugs have never been repaired by any existing technique. Overall, the evaluation results suggest that GENPAT is effective for transformation inference and can potentially be adopted for many different applications.