Miracle-dz/UniFAR-FTUs huggingface dataset:
SURVIVAL OF INTERSTELLAR MOLECULES TO PRESTELLAR DENSE CORE COLLAPSE AND EARLY PHASES OF DISK FORMATION

An outstanding question of astrobiology is the link between the chemical composition of planets, comets, and other solar system bodies and the molecules formed in the interstellar medium. Understanding the chemical and physical evolution of the matter leading to the formation of protoplanetary disks is an important step for this. We provide some new clues to this long-standing problem using three-dimensional chemical simulations of the early phases of disk formation: we interfaced the full gas-grain chemical model Nautilus with the radiation-magnetohydrodynamic model RAMSES, for different configurations and intensities of the magnetic field. Our results show that the chemical content (gas and ices) is globally conserved during the collapsing process, from the parent molecular cloud to the young disk surrounding the first Larson core. A qualitative comparison with cometary composition suggests that comets are constituted of different phases, some molecules being direct tracers of interstellar chemistry, while others, including complex molecules, seem to have been formed in disks, where higher densities and temperatures allow for an active grain surface chemistry. The latter phase, and its connection with the formation of the first Larson core, remains to be modeled.
Driven particles at fluid interfaces acting as capillary dipoles

The dynamics of spherical particles driven along an interface between two immiscible fluids is investigated asymptotically. Under the assumptions of a pinned three-phase contact line (TCL) and very different viscosities of the two fluids, a particle assumes a tilted orientation. As it moves, it causes a deformation of the fluid interface which is also computed. The case of two interacting driven particles is studied via the linear superposition approximation. It is shown that the capillary interaction force resulting from the particle motion is dipolar in terms of the azimuthal angle and decays with the fifth power of the inter-particle separation, similar to a capillary quadrupole originating from undulations of the TCL. The dipolar interaction is demonstrated to exceed the quadrupolar interaction at moderate particle velocities.
Last Subcode Neural Network Assisted Polar Decoding

A fast successive-cancellation decoding scheme based on neural network (NNFSCD) called last subcode neural network assisted decoding (LSNNACD) scheme for polar codes is proposed to improve decoding performance. First, a neural network node (NNN) is proposed and a neutral network model is trained to decode it. Then, the node recognizer is proposed to recognize a polar code with a NNN. Simulations with polar codes and binary phase shift keying (BPSK) shows good performance and low latency in the additive white Gaussian noise (AWGN) channel. Moreover, the proposed scheme has a wide application range.
EXPLORING THE DUST CONTENT OF GALACTIC WINDS WITH HERSCHEL. I. NGC 4631

We present a detailed analysis of deep far-infrared observations of the nearby edge-on star-forming galaxy NGC 4631 obtained with the Herschel Space Observatory. Our Photodetector Array Camera and Spectrometer (PACS) images at 70 and 160 μm show a rich complex of filaments and chimney-like features that extends up to a projected distance of 6 kpc above the plane of the galaxy. The PACS features often match extraplanar Hα, radio-continuum, and soft X-ray features observed in this galaxy, pointing to a tight disk–halo connection regulated by star formation. On the other hand, the morphology of the colder dust component detected on a larger scale in the Spectral and Photometric Imaging Receiver 250, 350, and 500 μm data matches the extraplanar H I streams previously reported in NGC 4631 and suggests a tidal origin. The PACS 70/160 μm ratios are elevated in the central ∼3.0 kpc region above the nucleus of this galaxy (the “superbubble”). A pixel-by-pixel analysis shows that dust in this region has a higher temperature and/or an emissivity with a steeper spectral index ( β > 2 ? > ) than the dust in the disk, possibly the result of the harsher environment in the superbubble. Star formation in the disk seems energetically insufficient to lift the material out of the disk, unless it was more active in the past or the dust-to-gas ratio in the superbubble region is higher than the Galactic value. Some of the dust in the halo may also have been tidally stripped from nearby companions or lifted from the disk by galaxy interactions.
Demystifying Hidden Sensitive Operations in Android Apps

Security of Android devices is now paramount, given their wide adoption among consumers. As researchers develop tools for statically or dynamically detecting suspicious apps, malware writers regularly update their attack mechanisms to hide malicious behavior implementation. This poses two problems to current research techniques: static analysis approaches, given their over-approximations, can report an overwhelming number of false alarms, while dynamic approaches will miss those behaviors that are hidden through evasion techniques. We propose in this work a static approach specifically targeted at highlighting hidden sensitive operations (HSOs), mainly sensitive data flows. The prototype version of HiSenDroid has been evaluated on a large-scale dataset of thousands of malware and goodware samples on which it successfully revealed anti-analysis code snippets aiming at evading detection by dynamic analysis. We further experimentally show that, with FlowDroid, some of the hidden sensitive behaviors would eventually lead to private data leaks. Those leaks would have been hard to spot either manually among the large number of false positives reported by the state-of-the-art static analyzers, or by dynamic tools. Overall, by putting the light on hidden sensitive operations, HiSenDroid helps security analysts in validating potentially sensitive data operations, which would be previously unnoticed.
SS-VAERR: Self-Supervised Apparent Emotional Reaction Recognition from Video

This work focuses on the apparent emotional reaction recognition (AERR) from the video-only input, conducted in a self-supervised fashion. The network is first pre-trained on different self-supervised pretext tasks and later fine-tuned on the downstream target task. Self-supervised learning facilitates the use of pre-trained architectures and larger datasets that might be deemed unfit for the target task and yet might be useful to learn informative representations and hence provide useful initializations for further fine-tuning on smaller more suitable data. Our presented contribution is two-fold: (1) an analysis of different state-of-the-art (SOTA) pretext tasks for the video-only apparent emotional reaction recognition architecture, and (2) an analysis of various combinations of the regression and classification losses that are likely to improve the performance further. Together these two contributions result in the current state-of-the-art performance for the video-only spontaneous apparent emotional reaction recognition with continuous annotations.
Hypothesis Testing using Causal and Causal Variational Generative Models

Hypothesis testing and the usage of expert knowledge, or causal priors, has not 1 been well explored in the context of generative models. We propose a novel set 2 of generative architectures, Causal Gen and Causal Variational Gen, that can uti- 3 lize nonparametric structural causal knowledge combined with a deep learning 4 functional approximation. We show how, using a deliberate (non-random) split 5 of training and testing data, these models can generalize better to similar, but 6 out-of-distribution data points, than non-causal generative models and prediction 7 models such as Variational autoencoders and Fully Connected Neural Networks. 8 We explore using this generalization error as a proxy for causal model hypothesis 9 testing. We further show how dropout can be used to learn functional relation- 10 ships of structural models that are difficult to learn with traditional methods. We 11 validate our methods on a synthetic pendulum dataset, as well as a trauma surgery 12 ground level fall dataset. 13
Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity

Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC. In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task. We propose SCOPE which builds upon a shared encoder two parallel decoders, one for the primary CSC task and the other for a fine-grained auxiliary CPP task, with a novel adaptive weighting scheme to balance the two tasks. In addition, we design a delicate iterative correction strategy for further improvements during inference. Empirical evaluation shows that SCOPE achieves new state-of-the-art on three CSC benchmarks, demonstrating the effectiveness and superiority of the auxiliary CPP task. Comprehensive ablation studies further verify the positive effects of adaptivity and granularity of the task.
Holbert: Reading, Writing, Proving and Learning in the Browser

This paper presents Holbert: a work-in-progress pedagogical proof assistant and online textbook platform, aimed at the educational use-case, specifically for the teaching of programming language theory. Holbert allows proof exercises and rule definitions to be embedded directly in an online textbook, where proofs and rules can be manipulated using a graphical interface. We give an overview of the logical foundations of Holbert, examples of its use, and give an update as to its current implementation status.
A survey on Self Supervised learning approaches for improving Multimodal representation learning

Recently self supervised learning has seen explosive growth and use in variety of machine learning tasks because of its ability to avoid the cost of annotating large-scale datasets. This paper gives an overview for best self supervised learning approaches for multimodal learning. The presented approaches have been aggregated by extensive study of the literature and tackle the application of self supervised learning in different ways. The approaches discussed are cross modal generation, cross modal pretraining, cyclic translation, and generating unimodal labels in self supervised fashion.
On the Origin of the Dichotomy of Stellar Activity Cycles

The presence of possible correlations between stellar rotation rate Ω and the frequency of the activity cycle ω cyc is still much debated. We implement a new Bayesian classification algorithm based on a simultaneous regression analysis of multiple scaling laws and we demonstrate the existence of two different scalings in the log10ωcyc – log10Ω plane for an extended Mt. Wilson sample of 67 stars. Thanks to metallicity measurements obtained from both ESA Gaia and high-resolution spectroscopy, we argue that the origin of this dichotomy is likely related to the chemical composition: stars whose magnetic cycle frequency increases with rotation rate are less metallic than stars whose magnetic cycle frequency decreases with stellar rotation rates. On the contrary, no clear difference in chromospheric magnetic activity indicators characterizes the two branches.
Putting Einstein to Test - Astrometric Experiments from Space, Fundamental Physics and Local Cosmology

First experimental evidences of General Relativity (GR) came from Astrometry. Its measurements not only had put to evidence the existence of the effect of an excess of perihelion precession in the motion of the Solar System objects which couldn’t be explained in terms of the NewtonianMechanics (Le Verrier, 1859) but also provided a striking confirmation of Einstein’s theory through the experiment conducted at Sobral and Principe by Dyson, Eddington & Davidson (1920) during the Solar Eclipse of 1919, which measured the light deflection foreseen by GR with an accuracy of ∼ 10%.
Optimality Conditions for a Class of Relaxed Quasiconvex Minimax Problems

A class of minimax problems is considered. We approach it with the techniques of quasiconvex optimization, which includes most important nonsmooth and relaxed convex problems and has been intensively developed. Observing that there have been many contributions to various themes of minimax problems, but surprisingly very few on optimality conditions, the most traditional and developed topic in optimization, we establish both necessary and sufficient conditions for solutions and unique solutions. A main feature of this work is that the involved functions are relaxed quasiconvex in the sense that the sublevel sets need to be convex only at the considered point. We use star subdifferentials, which are slightly bigger than other subdifferentials applied in many existing results for minimization problems, but may be empty or too small in various situations. Hence, when applied to the special case of minimization problems, our results may be more suitable. Many examples are provided to illustrate the applications of the results and also to discuss the imposed assumptions.
Effect of a Moving Mirror on the Free Fall of a Quantum Particle in a Homogeneous Gravitational Field

We investigate the effect of time-dependent boundary conditions on the dynamics of a quantum bouncer—a particle falling in a homogeneous gravitational field on a moving mirror. We examine more particularly the way a moving mirror modifies the properties of the entire wavefunction of a falling particle. We find that some effects, such as the fact that a quantum particle hitting a moving mirror may bounce significantly higher than when the mirror is fixed, are in line with classical intuition. Other effects, such as the change in relative phases or in the current density in spatial regions arbitrarily far from the mirror are specifically quantum. We further discuss how the effects produced by a moving mirror could be observed in link with current experiments, in particular with cold neutrons.
MMRNet: Improving Reliability for Multimodal Computer Vision for Bin Picking via Multimodal Redundancy

Recently, there has been tremendous interest in industry 4.0 infrastructure to address labor shortages in global supply chains. Deploying artiﬁcial intelligence-enabled robotic bin picking systems in real world has become particularly important for reducing labor demands and costs while increasing efﬁciency. To this end, artiﬁcial intelligence-enabled robotic bin picking systems may be used to automate order picking, but may also cause expensive damage during an abnormal event such as a sensor failure. As such, reliability becomes a critical factor for translating artiﬁcial intelligence research to real world applications and products. In this paper, we propose a reliable vision system with M ulti M odal R edundancy (MMRNet) for tackling object detection and segmentation for robotic bin picking using data from different modalities. This is the ﬁrst system that introduces the concept of multimodal redundancy to combat sensor failure issues during deployment. In particular, we realize the multimodal redundancy framework with a gate fusion module and dynamic ensemble learning. Finally, we present a new label-free multimodal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty. Through experiments, we demonstrate that in an event of missing modality, our system provides a much more reliable performance compared to baseline models. We also demonstrate that our MC score is a more powerful reliability indicator for outputs during inference time when model generated conﬁdence scores are often over-conﬁdent.
High-resolution isotope-shift spectroscopy of Cd i

We present absolute frequency measurements of the ${}^{1}\text{P}_1 \leftarrow {}^{1}\text{S}_0$ ($229$nm) and ${}^{3}\text{P}_1 \leftarrow {}^{1}\text{S}_0$ ($326$nm) transitions for all naturally occurring isotopes of cadmium. The isotope shifts and hyperfine intervals of the fermionic isotopes are determined with an accuracy of 3.3MHz. We find that quantum interference in the laser-induced fluorescence spectra of the ${}^{1}\text{P}_1 \leftarrow {}^{1}\text{S}_0$ transition causes an error of up to 29(5)MHz in determining the hyperfine splitting, when not accounted for with an appropriate model. Using a King-plot analysis, we extract the field- and mass-shift parameters and determine nuclear charge radius differences for the fermions. The lifetime of the $^1\text{P}_1$ state is determined to be 1.60(5)ns by measuring the natural linewidth of the ${}^{1}\text{P}_1 \leftarrow {}^{1}\text{S}_0$ transition. These results resolve significant discrepancies among previous measurements.
Block subsampled randomized Hadamard transform for low-rank approximation on distributed architectures

This article introduces a novel structured random matrix composed blockwise from subsampled randomized Hadamard transforms (SRHTs). The block SRHT is expected to outperform well-known dimension reduction maps, including SRHT and Gaussian matrices, on distributed architectures with not too many cores compared to the dimension. We prove that a block SRHT with enough rows is an oblivious subspace embedding, i.e., an approximate isometry for an arbitrary low-dimensional subspace with high probability. Our estimate of the required number of rows is similar to that of the standard SRHT. This suggests that the two transforms should provide the same accuracy of approximation in the algorithms. The block SRHT can be readily incorporated into randomized methods, for instance to compute a low-rank approximation of a large-scale matrix. For completeness, we revisit some common randomized approaches for this problem such as Randomized Singular Value Decomposition and Nystr\"{o}m approximation, with a discussion of their accuracy and implementation on distributed architectures.
Detection of exclusive reactions in the HERMES Recoil Fiber Tracker

The standard model of particle physics describes successfully the fundamental constituents and forces in our world; nevertheless, many details of the subatomic world are still beyond the scope of theoretical predictions. The internal structure of the nucleon has been investigated in detail and it was found that the nucleon spin budget, i.e. the composition of the nucleon spin by the spin and orbital angular momentum of quarks and gluons is not yet understood. It has been measured that the intrinsic quark spin contribution is only about 30% of the total spin of the nucleon. A recently developed formalism allows to describe the internal structure of the nucleon by so-called GPDs (Generalized Parton Distributions) in a more complete way than the previously used PDFs (Parton Density Functions). The GPDs are linked by the Ji sum rule to the angular momentum contributions of quarks and gluons. These GPDs can be accessed by the investigation of hard exclusive reactions. DVCS (deeply virtual Compton scattering) is the cleanest exclusive reaction to determine some of these distributions, using lepton beams with different helicity states and charges. HERMES (HERA measurements of spin) is one of the experiments which were carried out to complete the information about the nucleon spin budget. It is located at HERA which is an e-p-collider at DESY but uses only the polarized electronand positron-beam, which is scattered off a gaseous internal target. The HERMES forward spectrometer consists of a set of detectors that are used for tracking, while another set of detectors provides information on particle identification and triggering. In the first phase of HERMES, only forward going particles were detected. Exclusive reactions have been measured using a missing invariant mass technique. In order to improve exclusivity and to enhance the resolution of kinematic variables the HERMES collaboration decided to remove the equipment for the polarized target and to install the RD (Recoil Detector) with an unpolarized target at this position. This detector consists of the Silicon Strip Detector, the SFT (Scintillating Fiber Tracker), the Photon Detector and is surrounded by a 1T superconducting magnet. It provides several space points for tracking and thus momentum reconstruction. The energy deposition in the various detectors is used to achieve particle identification. The main part of the thesis work was the implementation of the SFT and the RD readout system. Before the installation of the RD a series of test runs were carried out to proof the concept of the detector, to measure the internal alignment and to prepare the installation. These test runs for the SFT are described and major results are shown. Furthermore a preliminary analysis of the latest data 06d/06d0 was carried out to show the performance of the installed Recoil Detector in combination with the HERMES forward spectrometer.
A resolution to the mystery of an etalon that is optically expanding and contracting at the same time

A white light illuminated etalon is a valuable resource for spectrograph calibration in radial velocity exoplanet detection, and other astronomical applications. These etalons benefit from low drift (${\sim} 10^{-11}$/day) and well-characterized stability of their mode structure. However, measurements of several etalon systems across bandwidths greater than $500$ nm indicate that the modes exhibit complex, wavelength-dependent drift. Surprisingly, modes in different regions of the spectrum were found to drift in different directions, implying that the optical length of the etalon is getting both longer and shorter at the same time. In this paper, we provide a solution to this puzzling observation. With Fresnel analysis and the transfer matrix method, we model the reflective phase of the multi-layer dielectric mirrors in the etalon used as a calibrator for the Habitable Zone Planet Finder (HPF). We use this phase to calculate the etalon mode positions and are able to reproduce the observed oscillatory chromatic drift of the etalon's mode spectrum across 800-1300 nm. Despite the complexity of the mirror structure, our modeling indicates that the gradual relaxation of the outermost layers of the etalon mirrors is the dominant source of the observed behavior. We also model the effect of temperature, incident angle alignment variations, and manufacturing tolerances, and show that they are likely not causes of the chromatic mode frequency shifts. Our work highlights techniques that can be employed in the design of broad bandwidth mirrors for future etalons to make them more useful for the highest precision astronomical spectroscopy.
Overlapping Community Detection using Dynamic Dilated Aggregation in Deep Residual GCN

Overlapping community detection is a key problem in graph mining. Some research has considered applying graph convolutional networks (GCN) to tackle the problem. However, it is still challenging to incorporate deep graph convolutional networks in the case of general irregular graphs. In this study, we design a deep dynamic residual graph convolutional network (DynaResGCN) based on our novel dynamic dilated aggregation mechanisms and a unified end-to-end encoder-decoder-based framework to detect overlapping communities in networks. The deep DynaResGCN model is used as the encoder, whereas we incorporate the Bernoulli-Poisson (BP) model as the decoder. Consequently, we apply our overlapping community detection framework in a research topics dataset without having ground truth, a set of networks from Facebook having a reliable (hand-labeled) ground truth, and in a set of very large co-authorship networks having empirical (not hand-labeled) ground truth. Our experimentation on these datasets shows significantly superior performance over many state-of-the-art methods for the detection of overlapping communities in networks.