Miracle-dz/UniFAR-FTUs huggingface dataset:
Approach to risk assessment for air pollutants using PRTR data and atmospheric dispersion model

In order to utilize the emission data of PRTR in risk assessment for human health, the environmental simulation models for estimating the environmental concentrations using emission data are required. In Japan, the environmental concentrations of the target substances, such as SOx and NOx, of Total Pollutant Load Control have been estimated to a high accuracy, using simulation models (Total Control Model). However the Total Control Model is considered to be unsuitable for risk assessment and communication because the program source code and calculation conditions are closed to the public. In this study, the NOx concentration was calculated by simulation using the ISC (Industrial Source Complex Model), which is used in health risk assessment and whose source code is opened to the public. The results were compared with the results obtained using the Total Control Model. It was shown that the error between the value calculated using the Total Control Model and our modeling approach using ISC was 10% or less on average in each region, and that the approach using ISC was adaptable for roughly estimating NOx concentration. As a result of the calculation using the emission data of benzene reported by the PRTR Pilot Project of Kawasaki city in fiscal 1998, the ratio of the calculated concentration to the measured concentration of benzene was under 1/4 of that of NOx. Reasons for underestimating benzene concentration compared to NOx concentration were discussed.
Modeling and parameter tuning for templated directed self-assembly

In this paper, we study the impact of topographic guide or template properties on pattern formation in a directed self-assembly (DSA) process. In particular, we investigate the relationship between free energy and defect generation or process robustness, and analyze the influence of guide affinity. The good correlation between experimental and simulation results confirms the role of certain setup parameters and process conditions on the DSA patterning.
Identifying microlensing events using neural networks

Current gravitational microlensing surveys are observing hundreds of millions of stars in the Galactic bulge - which makes finding rare microlensing events a challenging tasks. In almost all previous works, microlensing events have been detected either by applying very strict selection cuts or manually inspecting tens of thousands of light curves. However, the number of microlensing events expected in the future space-based microlensing experiments forces us to consider fully-automated approaches. They are especially important for selecting binary-lens events that often exhibit complex light curve morphologies and are otherwise difficult to find. There are no dedicated selection algorithms for binary-lens events in the literature, which hampers their statistical studies. Here, we present two simple neural-network-based classifiers for detecting single and binary microlensing events. We demonstrate their robustness using OGLE-III and OGLE-IV data sets and show they perform well on microlensing events detected in data from the Zwicky Transient Facility (ZTF). Classifiers are able to correctly recognize ~98% of single-lens events and 80-85% of binary-lens events.
Mixed Noise Removal with Pareto Prior

Denoising images contaminated by the mixture of additive white Gaussian noise (AWGN) and impulse noise (IN) is an essential but challenging problem. The presence of impulsive disturbances inevitably affects the distribution of noises and thus largely degrades the performance of traditional AWGN denoisers. Existing methods target to compensate the effects of IN by introducing a weighting matrix, which, however, is lack of proper priori and thus hard to be accurately estimated. To address this problem, we exploit the Pareto distribution as the priori of the weighting matrix, based on which an accurate and robust weight estimator is proposed for mixed noise removal. Particularly, a relatively small portion of pixels are assumed to be contaminated with IN, which should have weights with small values and then be penalized out. This phenomenon can be properly described by the Pareto distribution of type 1. Therefore, armed with the Pareto distribution, we formulate the problem of mixed noise removal in the Bayesian framework, where nonlocal self-similarity priori is further exploited by adopting nonlocal low rank approximation. Compared to existing methods, the proposed method can estimate the weighting matrix adaptively, accurately, and robust for different level of noises, thus can boost the denoising performance. Experimental results on widely used image datasets demonstrate the superiority of our proposed method to the state-of-the-arts.
Privacy Intelligence: A Survey on Image Sharing on Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharing images on OSNs. However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing. Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. The framework is divided into three main stages: local management, online management and social experience. At each stage, we identify typical sharing-related user behaviors, the privacy issues generated by those behaviors, and review representative intelligent solutions. The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets.
Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification

We present a novel adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. Our method is based on probabilistic regression and classification models, which act as a surrogate for the optimization goals and allow us to suggest multiple design points at once in each iteration. The proposed acquisition function is intuitively understandable and can be tuned to the demands of the problems at hand. We also present a novel ellipsoid truncation method to speed up the expected hypervolume calculation in a straightforward way for regression models with a normal probability density. We benchmark our approach with an evolutionary algorithm on multiple test problems.
Random quench predicts universal properties of amorphous solids

Amorphous solids display numerous universal features in their mechanics, structure, and response. Current models assume heterogeneity in mesoscale elastic properties, but require fine-tuning in order to quantitatively explain vibrational properties. A complete model should derive the magnitude and character of elastic heterogeneity from an initially structureless medium, through a model of the quenching process during which the temperature is rapidly lowered and the solid is formed. Here we propose a field-theoretic model of a quench, and compute structural, mechanical, and vibrational observables in arbitrary dimension d. This allows us to relate the properties of the amorphous solid to those of the initial medium, and to those of the quench. We show that previous mean-field results are subsumed by our analysis and unify spatial fluctuations of elastic moduli, long-range correlations of inherent state stress, universal vibrational anomalies, and localized modes into one picture.
Type Size Code for Compressing Erdos-Rényi Graphs

We consider universal source coding of unlabeled graphs which are commonly referred to as graphical structures. We adopt an Erdös-Rényi model to generate the random graphical structures. We propose a variant of the previously introduced Type Size code, where type classes are characterized based on the number of edges of the graphical structures. The proposed scheme sorts the graphical structures based on the size of their type classes and assigns binary sequences to them in this order. The $\epsilon$-coding rate of the Type Size code (up to the third-order term) for compressing graphical structures is derived.
Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces

Recent research has proposed neural architectures for solving combinatorial problems in structured output spaces. In many such problems, there may exist multiple solutions for a given input, e.g. a partially filled Sudoku puzzle may have many completions satisfying all constraints. Further, we are often interested in finding {\em any one} of the possible solutions, without any preference between them. Existing approaches completely ignore this solution multiplicity. In this paper, we argue that being oblivious to the presence of multiple solutions can severely hamper their training ability. Our contribution is two fold. First, we formally define the task of learning one-of-many solutions for combinatorial problems in structured output spaces, which is applicable for solving several problems of interest such as N-Queens, and Sudoku. Second, we present a generic learning framework that adapts an existing prediction network for a combinatorial problem to handle solution multiplicity. Our framework uses a selection module, whose goal is to dynamically determine, for every input, the solution that is most effective for training the network parameters in any given learning iteration. We propose an RL based approach to jointly train the selection module with the prediction network. Experiments on three different domains, and using two different prediction networks, demonstrate that our framework significantly improves the accuracy in our setting, obtaining up to $21$ pt gain over the baselines.
Large Scale Photometric Bundle Adjustment

Direct methods have shown promise on visual odometry and SLAM, leading to greater accuracy and robustness over feature-based methods. However, offline 3-d reconstruction from internet images has not yet benefited from a joint, photometric optimization over dense geometry and camera parameters. Issues such as the lack of brightness constancy, and the sheer volume of data, make this a more challenging task. This work presents a framework for jointly optimizing millions of scene points and hundreds of camera poses and intrinsics, using a photometric cost that is invariant to local lighting changes. The improvement in metric reconstruction accuracy that it confers over feature-based bundle adjustment is demonstrated on the large-scale Tanks & Temples benchmark. We further demonstrate qualitative reconstruction improvements on an internet photo collection, with challenging diversity in lighting and camera intrinsics.
Near-infrared Census of RR Lyrae Variables in the Messier 3 Globular Cluster and the Period–Luminosity Relations

We present new near-infrared (NIR), JHKs, time-series observations of RR Lyrae variables in the Messier 3 (NGC 5272) globular cluster using the WIRCam instrument at the 3.6 m Canada–France–Hawaii Telescope. Our observations cover a sky area of ∼21′ × 21′ around the cluster center and provide an average of 20 epochs of homogeneous JHKs-band photometry. New homogeneous photometry is used to estimate robust mean magnitudes for 175 fundamental-mode (RRab), 47 overtone-mode (RRc), and 11 mixed-mode (RRd) variables. Our sample of 233 RR Lyrae variables is the largest thus far obtained in a single cluster with time-resolved, multiband NIR photometry. NIR-to-optical amplitude ratios for RR Lyrae in Messier 3 exhibit a systematic increase moving from RRc to short-period (P < 0.6 day) and long-period (P ≳ 0.6 day) RRab variables. We derive JHKs-band period–luminosity relations for RRab, RRc, and the combined sample of variables. Absolute calibrations based on the theoretically predicted period–luminosity–metallicity relations for RR Lyrae stars yield a distance modulus, mag, to Messier 3. When anchored to trigonometric parallaxes for nearby RR Lyrae stars from the Hubble Space Telescope and the Gaia mission, our distance estimates are consistent with those resulting from the theoretical calibrations, albeit with relatively larger systematic uncertainties.
$k$-ported vs. $k$-lane Broadcast, Scatter, and Alltoall Algorithms

In k-ported message-passing systems, a processor can simultaneously receive k different messages from k other processors, and send k different messages to k other processors that may or may not be different from the processors from which messages are received. Modern clustered systems may not have such capabilities. Instead, compute nodes consisting of n processors can simultaneously send and receive k messages from other nodes, by letting k processors on the nodes concurrently send and receive at most one message. We pose the question of how to design good algorithms for this k-lane model, possibly by adapting algorithms devised for the traditional k-ported model. We discuss and compare a number of (non-optimal) k-lane algorithms for the broadcast, scatter and alltoall collective operations (as found in, e.g., MPI), and experimentally evaluate these on a small 36× 32-node cluster with a dual OmniPath network (corresponding to k = 2). Results are preliminary.
Dynamic Noise Embedding: Noise Aware Training and Adaptation for Speech Enhancement

Estimating noise information exactly is crucial for noise aware training in speech applications including speech enhancement (SE) which is our focus in this paper. To estimate noise-only frames, we employ voice activity detection (VAD) to detect non-speech frames by applying optimal threshold on speech posterior. Here, the non-speech frames can be regarded as noise-only frames in noisy signal. These estimated frames are used to extract noise embedding, named dynamic noise embedding (DNE), which is useful for an SE module to capture the characteristic of background noise. The DNE is extracted by a simple neural network, and the SE module with the DNE can be jointly trained to be adaptive to the environment. Experiments are conducted on TIMIT dataset for single-channel denoising task and U-Net is used as a backbone SE module. Experimental results show that the DNE plays an important role in the SE module by increasing the quality and the intelligibility of corrupted signal even if the noise is non-stationary and unseen in training. In addition, we demonstrate that the DNE can be flexibly applied to other neural network-based SE modules.
Second-order perturbations of Kerr black holes: Formalism and reconstruction of the first-order metric

Motivated by gravitational wave observations of binary black hole mergers, we present a procedure to compute the leading order nonlinear gravitational wave interactions around a Kerr black hole. We describe the formalism used to derive the equations for second order perturbations. We develop a procedure that allows us to reconstruct the first order metric perturbation solely from knowledge of the solution to the first order Teukolsky equation, without the need of Hertz potentials. Finally, we illustrate this metric reconstruction procedure in the asymptotic limit for the first order quasi-normal modes of Kerr. In a companion paper, we present a numerical implementation of these ideas.
A Hydrophobic‐Interaction‐Based Mechanism Triggers Docking between the SARS‐CoV‐2 Spike and Angiotensin‐Converting Enzyme 2

A recent experimental study found that the binding affinity between the cellular receptor human angiotensin‐converting enzyme 2 (ACE2) and receptor‐binding domain (RBD) in the spike (S) protein of novel severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is more than tenfold higher than that of the original severe acute respiratory syndrome coronavirus (SARS‐CoV). However, main chain structures of the SARS‐CoV‐2 RBD are almost the same with that of the SARS‐CoV RBD. Understanding the physical mechanism responsible for the outstanding affinity between the SARS‐CoV‐2 S and ACE2 is an “urgent challenge” for developing blockers, vaccines, and therapeutic antibodies against the coronavirus disease 2019 (COVID‐19) pandemic. Taking into account the mechanisms of hydrophobic interaction, hydration shell, surface tension, and the shielding effect of water molecules, this study reveals a hydrophobic‐interaction‐based mechanism by means of which SARS‐CoV‐2 S and ACE2 bind together in an aqueous environment. The hydrophobic interaction between the SARS‐CoV‐2 S and ACE2 protein is found to be significantly greater than that between SARS‐CoV S and ACE2. At the docking site, the hydrophobic portions of the hydrophilic side chains of SARS‐CoV‐2 S are found to be involved in the hydrophobic interaction between SARS‐CoV‐2 S and ACE2.
Creating optimal conditions for reproducible data analysis in 
 R with ‘fertile’

The advancement of scientific knowledge increasingly depends on ensuring that data‐driven research is reproducible: that two people with the same data obtain the same results. However, while the necessity of reproducibility is clear, there are significant behavioral and technical challenges that impede its widespread implementation and no clear consensus on standards of what constitutes reproducibility in published research. We present fertile, an R package that focuses on a series of common mistakes programmers make while conducting data science projects in R, primarily through the RStudio integrated development environment. fertile operates in two modes: proactively, to prevent reproducibility mistakes from happening in the first place, and retroactively, analyzing code that is already written for potential problems. Furthermore, fertile is designed to educate users on why their mistakes are problematic and how to fix them.
Indirect Object-to-Robot Pose Estimation from an External Monocular RGB Camera

We present a robotic grasping system that uses a single external monocular RGB camera as input. The object-to-robot pose is computed indirectly by combining the output of two neural networks: one that estimates the object-to-camera pose, and another that estimates the robot-to-camera pose. Both networks are trained entirely on synthetic data, relying on domain randomization to bridge the sim-to-real gap. Because the latter network performs online camera calibration, the camera can be moved freely during execution without affecting the quality of the grasp. Experimental results analyze the effect of camera placement, image resolution, and pose refinement in the context of grasping several household objects. We also present results on a new set of 28 textured household toy grocery objects, which have been selected to be accessible to other researchers. To aid reproducibility of the research, we offer 3D scanned textured models, along with pre-trained weights for pose estimation.1
DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks

Synthetic creation of drum sounds (e.g., in drum machines) is commonly performed using analog or digital synthesis, allowing a musician to sculpt the desired timbre modifying various parameters. Typically, such parameters control low-level features of the sound and often have no musical meaning or perceptual correspondence. With the rise of Deep Learning, data-driven processing of audio emerges as an alternative to traditional signal processing. This new paradigm allows controlling the synthesis process through learned high-level features or by conditioning a model on musically relevant information. In this paper, we apply a Generative Adversarial Network to the task of audio synthesis of drum sounds. By conditioning the model on perceptual features computed with a publicly available feature-extractor, intuitive control is gained over the generation process. The experiments are carried out on a large collection of kick, snare, and cymbal sounds. We show that, compared to a specific prior work based on a U-Net architecture, our approach considerably improves the quality of the generated drum samples, and that the conditional input indeed shapes the perceptual characteristics of the sounds. Also, we provide audio examples and release the code used in our experiments.
Spectral Planting and the Hardness of Refuting Cuts, Colorability, and Communities in Random Graphs

We study the problem of efficiently refuting the k-colorability of a graph, or equivalently certifying a lower bound on its chromatic number. We give formal evidence of average-case computational hardness for this problem in sparse random regular graphs, showing optimality of a simple spectral certificate. This evidence takes the form of a computationally-quiet planting: we construct a distribution of d-regular graphs that has significantly smaller chromatic number than a typical regular graph drawn uniformly at random, while providing evidence that these two distributions are indistinguishable by a large class of algorithms. We generalize our results to the more general problem of certifying an upper bound on the maximum k-cut. This quiet planting is achieved by minimizing the effect of the planted structure (e.g. colorings or cuts) on the graph spectrum. Specifically, the planted structure corresponds exactly to eigenvectors of the adjacency matrix. This avoids the pushout effect of random matrix theory, and delays the point at which the planting becomes visible in the spectrum or local statistics. To illustrate this further, we give similar results for a Gaussian analogue of this problem: a quiet version of the spiked model, where we plant an eigenspace rather than adding a generic low-rank perturbation. Our evidence for computational hardness of distinguishing two distributions is based on three different heuristics: stability of belief propagation, the local statistics hierarchy, and the low-degree likelihood ratio. Of independent interest, our results include general-purpose bounds on the low-degree likelihood ratio for multi-spiked matrix models, and an improved low-degree analysis of the stochastic block model.
A magnetic velocity Verlet method

We discuss an extension of the velocity Verlet method that accurately approximates the kinetic-energy-conserving charged particle motion that comes from magnetic forcing. For a uniform magnetic field, the method is shown to conserve both particle kinetic energy and magnetic dipole moment better than midpoint Runge-Kutta. We then use the magnetic velocity Verlet method to generate trapped particle trajectories, both in a cylindrical magnetic mirror machine setup, and for dipolar fields like the earth's magnetic field. Finally, the method is used to compute an example of (single) mirror motion in the presence of a magnetic monopole field, where the trajectory can be described in closed form.