Miracle-dz/UniFAR-FTUs huggingface dataset:
Fallback Supernova Assembly of Heavy Binary Neutron Stars and Light Black Hole–Neutron Star Pairs and the Common Stellar Ancestry of GW190425 and GW200115

The detection of the unusually heavy binary neutron star merger GW190425 marked a stark contrast to the mass distribution from known Galactic pulsars in double neutron star binaries and gravitational-wave source GW170817. We suggest here a formation channel for heavy binary neutron stars and light black hole–neutron star binaries in which massive helium stars, which had their hydrogen envelope removed during a common-envelope phase, remain compact and avoid mass transfer onto the neutron star companion, possibly avoiding pulsar recycling. We present three-dimensional simulations of the supernova explosion of the massive stripped helium star and follow the mass fallback evolution and the subsequent accretion onto the neutron star companion. We find that fallback leads to significant mass growth in the newly formed neutron star. This can explain the formation of heavy binary neutron star systems such as GW190425, as well as predict the assembly of light black hole–neutron star systems such as GW200115. This formation avenue is consistent with the observed mass–eccentricity correlation of binary neutron stars in the Milky Way. Finally, avoiding mass transfer suggests an unusually long spin-period population of pulsar binaries in our Galaxy.
Cooperativity-Dependent Folding of Single-Stranded DNA

The folding of biological macromolecules is a fundamental process of which we lack a full comprehension. Mostly studied in proteins and RNA, single-stranded DNA (ssDNA) also folds, at physiological salt conditions, by forming non-specific secondary structures that are difficult to characterize with biophysical techniques. Here we present a helix-coil model for secondary structure formation, where ssDNA bases are organized in two different types of domains (compact and free). The model contains two parameters: the energy gain per base in a compact domain, ǫ, and the cooperativity related to the interfacial energy between different domains, γ. We tested the ability of the model to quantify the formation of secondary structure in ssDNA molecules mechanically stretched with optical tweezers. The model 1 reproduces the experimental force-extension curves in ssDNA of different molecular lengths and varying sodium and magnesium concentrations. Salt-correction effects for the energy of compact domains and the interfacial energy are found to be compatible with those of DNA hybridization. The model also predicts the folding free energy and the average size of domains at zero force, finding good agreement with secondary structure predictions by Mfold. We envision the model could be further extended to investigate native folding in RNA and proteins.
Discussing anthropogenic global warming from an econometric perspective: a change scenario based on the Arima paleoclimate time series model

Global warming has divided the scientific community worldwide with predominance for anthropogenic alarmism. This article aims to project a climate change scenario using a stochastic model of paleotemperature time series and compare it with the dominant thesis. The ARIMA model – an integrated autoregressive process of moving averages, popularly known as Box-Jenkins was used for this purpose. The results showed that the estimates of the model parameters were below 1°C for a scenario of 100 years which suggests a period of temperature reduction and a probable cooling, contrary to the prediction of the IPCC and the anthropogenic current of an increase in 1.50° C to 2.0° C by the end of this century. Thus, we hope with this study to contribute to the discussion by adding a statistical element of paleoclimate in counterpoint to the current consensus and to placing the debate in a long-term historical dimension, in line with other research already present in the scientific literature.
Autonomous Cooperative Transportation System involving Multi-Aerial Robots with Variable Attachment Mechanism

Cooperative transportation by multi-aerial robots has the potential to support various payloads and improve fail- safe against dropping. Furthermore, changing the attachment positions of robots according payload characteristics increases the stability of transportation. However, there are almost no transportation systems capable of scaling to the payload weight and size and changing the optimal attachment positions. To address this issue, we propose a cooperative transportation sys- tem comprising autonomously executable software and suitable hardware, covering the entire process, from pre-takeoff setting to controlled flight. The proposed system decides the formation of the attachment positions by prioritizing controllability based on the center of gravity obtained from Bayesian estimations with robot pairs. We investigated the cooperative transportation of an unknown payload larger than that of whole carrier robots through numerical simulations. Furthermore, we performed cooperative transportation of an unknown payload (with a weight of about 3.2 kg and maximum length of 1.76 m) using eight robots. The proposed system was found to be versatile with regard to handling unknown payloads with different shapes and center-of-gravity positions.
Adaptive Contrast Test for Dose-Response Studies and Modeling

We propose a powerful adaptive contrast test with ordinal constraint contrast coefficients determined by observed responses. The adaptive contrast test can perform using easily calculated contrast coefficients and existing statistical software. We provide the sample SAS program codes of analysis and calculation of power for the adaptive contrast test. After the adaptive contrast test shows the statistically significant dose-response, we consider to select the best dose-response model from multiple dose-response models. Based on the best model, we identify a recommended dose. We demonstrate the adaptive contrast test for sample data. In addition, we show the calculation of coefficient, test statistic, and recommended dose for the actual study. We perform the simulation study with eleven scenarios to evaluate the performance of the adaptive contrast test. We confirmed the statistically significant dose-response for the sample data and the actual study. In the simulation study, we confirmed that the adaptive contrast test has higher power in most scenarios compared to the conventional method. In addition, we confirmed that the type 1 error rate of the adaptive contrast test was maintained at a significance level when there was no difference between the treatment groups. We conclude that the adaptive contrast test can be applied unproblematically to the dose-response study.
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning

The development of autonomous driving has attracted extensive attention in recent years, and it is essential to evaluate the performance of autonomous driving. However, testing on the road is expensive and inefficient. Virtual testing is the primary way to validate and verify self-driving cars, and the basis of virtual testing is to build simulation scenarios. In this paper, we propose a training, testing, and evaluation pipeline for the lane-changing task from the perspective of deep reinforcement learning. First, we design lane change scenarios for training and testing, where the test scenarios include stochastic and deterministic parts. Then, we deploy a set of benchmarks consisting of learning and non-learning approaches. We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios. The designed lane-changing scenarios and benchmarks are both opened to provide a consistent experimental environment for the lane-changing task1 .
A Method for Adding Motion-Blur on Arbitrary Objects by Using Auto-Segmentation and Color Compensation Techniques

When dynamic objects are captured by a camera, motion blur inevitably occurs. Such a blur is sometimes considered as just a noise, however, it sometimes gives an important effect to add dynamism in the scene for photographs or videos. Unlike the similar effects, such as defocus blur, which is now easily controlled even by smartphones, motion blur is still uncontrollable and makes undesired effects on photographs. In this paper, an unified framework to add motion blur on per-object basis is proposed. In the method, multiple frames are captured without motion blur and they are accumulated to create motion blur on target objects. To capture images without motion blur, shutter speed must be short, however, it makes captured images dark, and thus, a sensor gain should be increased to compensate it. Since a sensor gain causes a severe noise on image, we propose a color compensation algorithm based on non-linear filtering technique for solution. Another contribution is that our technique can be used to make HDR images for fast moving objects by using multi-exposure images. In the experiments, effectiveness of the method is confirmed by ablation study using several data sets.
Evaluation of dynamic cointegration-based pairs trading strategy in the cryptocurrency market

Purpose This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios. Design/methodology/approach This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. Findings By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. Originality/value This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.
Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques

The use of learning-based methods for vehicle behavior prediction is a promising research topic. However, many publicly available data sets suffer from class distribution skews which limits learning performance if not addressed. This paper proposes an interaction-aware prediction model consisting of an LSTM autoencoder and SVM classifier. Additionally, an imbalanced learning technique, the multiclass balancing ensemble is proposed. Evaluations show that the method enhances model performance, resulting in improved classification accuracy. Good generalization properties of learned models are important and therefore a generalization study is done where models are evaluated on unseen traffic data with dissimilar traffic behavior stemming from different road configurations. This is realized by using two distinct highway traffic recordings, the publicly available NGSIM US-101 and I80 data sets. Moreover, methods for encoding structural and static features into the learning process for improved generalization are evaluated. The resulting methods show substantial improvements in classification as well as generalization performance.
Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data

Vision-based deep learning (DL) methods have made great progress in learning autonomous driving models from large-scale crowdsourced video datasets. They are trained to predict instantaneous driving behaviors from video data captured by on-vehicle cameras. In this paper, we develop a geo-context aware visualization system for the study of Autonomous Driving Model (ADM) predictions together with large-scale ADM video data. The visual study is seamlessly integrated with the geographical environment by combining DL model performance with geospatial visualization techniques. Model performance measures can be studied together with a set of geospatial attributes over map views. Users can also discover and compare prediction behaviors of multiple DL models in both city-wide and street-level analysis, together with road images and video contents. Therefore, the system provides a new visual exploration platform for DL model designers in autonomous driving. Use cases and domain expert evaluation show the utility and effectiveness of the visualization system.
Third-party Evaluation of Robotic Hand Designs Using a Mechanical Glove

A robotic hand design suitable for dexterity should be examined using functional tests. To achieve this, we designed a mechanical glove, which is a rigid wearable glove that enables us to develop the corresponding isomorphic robotic hand and evaluate its hardware properties. Subsequently, the effectiveness of multiple degrees-of-freedom (DOFs) was evaluated by human participants. Several fine motor skills were evaluated using the mechanical glove under two conditions: one- and three-DOF conditions. To the best of our knowledge, this is the first extensive evaluation method for robotic hand designs suitable for dexterity. (This paper was peer-reviewed and is the full translation from the Journal of the Robotics Society of Japan, Vol.39, No.6, pp.557-560, 2021.)
Deep Learning Model for Demodulation Reference Signal based Channel Estimation

In this paper, we propose a deep learning model for Demodulation Reference Signal (DMRS) based channel estimation task. Specifically, a novel Denoise, Linear interpolation and Refine (DLR) pipeline is proposed to mitigate the noise propagation problem during channel information interpolation and to restore the nonlinear variation of wireless channel over time. At the same time, the Small-norm Sample Cost-sensitive (SSC) learning method is proposed to equalize the qualities of channel estimation under different kinds of wireless environments and improve the channel estimation reliability. The effectiveness of the propose DLR-SSC model is verified on WAIC Dataset. Compared with the well know ChannelNet channel estimation model, our DLR-SSC model reduced normalized mean square error (NMSE) by 27.2dB, 22.4dB and 16.8dB respectively at 0dB, 10dB, and 20dB SNR. The proposed model has won the second place in the 2nd Wireless Communication Artificial Intelligence Competition (WAIC). The code is about to open source.
On the Transferability of Adversarial Attacks against Neural Text Classifier

Deep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction. In many cases, malicious inputs intentionally crafted for one model can fool another model. In this paper, we present the first study to systematically investigate the transferability of adversarial examples for text classification models and explore how various factors, including network architecture, tokenization scheme, word embedding, and model capacity, affect the transferability of adversarial examples. Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models. Such adversarial examples reflect the defects of the learning process and the data bias in the training set. Finally, we derive word replacement rules that can be used for model diagnostics from these adversarial examples.
AGN and star formation at cosmic noon: comparison of data to theoretical models

In theoretical models of galaxy evolution, active galactic nucleus (AGN) and star formation (SF) activity are closely linked and AGN feedback is routinely invoked to regulate galaxy growth. In order to constrain such models, we compare the hydrodynamical simulations IllustrisTNG and SIMBA, and the semi-analytical model SAG to the empirical results on AGN and SF at cosmic noon (0.75 < z < 2.25) reported in Florez et al. (2020). The empirical results are based on a large mass-complete sample drawn from 93 307 galaxies with and without high X-ray luminosity AGN (LX ≳ 1044 erg s−1), selected from a 11.8 deg2 area (∼0.18 Gpc3 comoving volume at z = 0.75–2.25). The main results of our comparisons are: (i) SAG and IllustrisTNG both qualitatively reproduce the empirical result that galaxies with high X-ray luminosity AGN have higher mean SFR, at a given stellar mass, than galaxies without such AGN. SAG, however, strongly overproduces the number density of high X-ray luminosity AGN by a factor of 10–100, while IllustrisTNG shows a lack of high X-ray luminosity AGN at high stellar mass ($M* \gt 10^{11} \, \mathrm{M}_{\odot }$) at z ∼ 2. (ii) In SIMBA, the mean SFR of galaxies with high X-ray luminosity AGN is lower than the SFR of galaxies without such AGN. Contrary to the data, many high X-ray luminosity AGN in SIMBA have quenched SF, suggesting that AGN feedback, or other feedback modes in galaxies with such AGN, might be too efficient in SIMBA.
PVT: Point-Voxel Transformer for 3D Deep Learning

In this paper, we present an efﬁcient and high-performance neural architecture, termed Point-Voxel Transformer (PVT) for 3D deep learning, which deeply integrates both 3D voxel - based and point -based self-attention computation to learn more discriminative features from 3D data. Speciﬁcally, we conduct multi-head self-attention (MSA) computation in voxels to obtain efﬁcient learning pattern and the coarse-grained local features while performing self-attention in points to provide ﬁner-grained information about the global context. In addition, to reduce the cost of MSA computation but achieve high efﬁciency, we design a cyclic shifted boxing scheme by limiting the MSA computation to non-overlapping local box and also preserving cross-box connection. Evaluated on clas-siﬁcation benchmark, our PVT not only achieves state-of-the-art accuracy of 94.0% (no voting) but outperforms previous Transformer-based models with 7 × measured speedup on average. On part and semantic segmentation, our model also obtains strong performance (86.5% and 68.2% mIoU, respectively). For 3D object detection task, we replace the primitives in Frustrum PointNet with PVT layer and achieve an improvement of 8.6% AP.
Towards a Real-Time Facial Analysis System

Facial analysis is an active research area in computer vision, with many practical applications. Most of the existing studies focus on addressing one specific task and maximizing its performance. For a complete facial analysis system, one needs to solve these tasks efficiently to ensure a smooth experience. In this work, we present a system-level design of a real-time facial analysis system. With a collection of deep neural networks for object detection, classification, and regression, the system recognizes age, gender, facial expression, and facial similarity for each person that appears in the camera view. We investigate the parallelization and interplay of individual tasks. Results on common off-the-shelf architecture show that the system’s accuracy is comparable to the state-of-the-art methods, and the recognition speed satisfies real-time requirements. Moreover, we propose a multitask network for jointly predicting the first three attributes, i.e., age, gender, and facial expression. Source code and trained models are available at https://github.com/mahehu/TUT-live-age-estimator.
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks

In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks --- small modifications of the input that change the predictions. Besides rigorously studied $\ell_p$-bounded additive perturbations, semantic perturbations (e.g. rotation, translation) raise a serious concern on deploying ML systems in real-world. Therefore, it is important to provide provable guarantees for deep learning models against semantically meaningful input transformations. In this paper, we propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds that can be used in general attack settings. We estimate the probability of a model to fail if the attack is sampled from a certain distribution. Our theoretical findings are supported by experimental results on different datasets.
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios

In recent years, control under urban intersection scenarios has become an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehicle is supposed to avoid collisions while pursuing better efficiency. The existing work fails to provide a framework that emphasizes the integrity of the scenarios while deploying and testing reinforcement learning(RL) methods. Specifically, we propose a benchmark for training and testing RL-based autonomous driving agents in complex intersection scenarios, which is called RL-CIS. Then, a set of baselines consisting various algorithms are deployed. The test benchmark and baselines provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing RL-based methods for autonomous driving control. The code of our proposed framework can be found at https://github.com/liuyufi123/ComplexUrbanScenarios.
Extremism & Whataboutism: A Case Study on Bangalore Riots

A common diversionary tactic used to deflect attention from contested issues is whataboutery which, when used by majoritarian groups to justify their behaviour against marginalised communities, can quickly devolve into extremism. We explore the manifestations of extreme speech in the Indian context, through a case study of violent protests and policing in the city of Bangalore, provoked by a derogatory Facebook post. Analyses of the dominant narratives on Twitter surrounding the incident reveal that, most of them employ whataboutism to deflect attention from the triggering post and serve as breeding grounds for religion-based extreme speech. We conclude by discussing how our study proposes an alternative lens of viewing extremism in the Global South.