Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling relations of z ~ 0:25-1:5 galaxies in various environments from the morpho-kinematics analysis of the MAGIC sample

The evolution of galaxies is influenced by many physical processes which may vary depending on their environment. We combine Hubble Space Telescope (HST) and Multi-Unit Spectroscopic Explorer (MUSE) data of galaxies at 0.25<z<1.5 to probe the impact of environment on the size-mass relation, the Main Sequence (MS) and the Tully-Fisher relation (TFR). We perform a morpho-kinematic modelling of 593 [Oii] emitters in various environments in the COSMOS area from the MUSE-gAlaxy Groups In Cosmos (MAGIC) survey. The HST F814W images are modelled with a bulge-disk decomposition to estimate their bulge-disk ratio, effective radius and disk inclination. We use the [Oii]{\lambda}{\lambda}3727, 3729 doublet to extract the ionised gas kinematic maps from the MUSE cubes, and we model them for a sample of 146 [Oii] emitters, with bulge and disk components constrained from morphology and a dark matter halo. We find an offset of 0.03 dex on the size-mass relation zero point between the field and the large structure subsamples, with a richness threshold of N=10 to separate between small and large structures, and of 0.06 dex with N=20. Similarly, we find a 0.1 dex difference on the MS with N=10 and 0.15 dex with N=20. These results suggest that galaxies in massive structures are smaller by 14% and have star formation rates reduced by a factor of 1.3-1.5 with respect to field galaxies at z=0.7. Finally, we do not find any impact of the environment on the TFR, except when using N=20 with an offset of 0.04 dex. We discard the effect of quenching for the largest structures that would lead to an offset in the opposite direction. We find that, at z=0.7, if quenching impacts the mass budget of galaxies in structures, these galaxies would have been affected quite recently, for roughly 0.7-1.5 Gyr. This result holds when including the gas mass, but vanishes once we include the asymmetric drift correction.
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.
Bots, Disinformation, and the First Trump Impeachment

We study the behavior of bots and supporters of the Qanon conspiracy theory on Twitter during the ﬁrst impeachment of U.S. President Donald Trump. During a 103 day period covering the impeachment, we collect over 67.7 million tweets from 3.6 million users, along with their 53.6 million edge follower network. We ﬁnd that bots represent 1% of all users, but have a prevalence near 10% among Qanon supporters. Despite their small numbers, bots generate over 31% of all impeachment related tweets. We also ﬁnd that bots share more disinformation, but use less toxic language than other users. The follower network of Qanon supporters exhibits a hierarchical structure, with bots acting as central hubs surrounded by isolated humans. We quantify the daily impact of bots on the impeachment discussion using the generalized harmonic inﬂuence centrality measure. Overall, we ﬁnd that due to their greater numbers, pro-Trump bots have more inﬂuence on most days. On a per bot basis, anti-Trump and pro-Trump bots have similar impact, while Qanon bots have less impact than non-Qanon bots.
Depinning and flow of a vortex line in a uniaxial random medium

We study numerically and analytically the dynamics of a single directed elastic string driven through a 3-dimensional disordered medium. In the quasistatic limit the string is super-rough in the driving direction, with roughness exponent $\zeta_{\parallel} = 1.25\pm 0.01$, dynamic exponent $z_{\parallel}= 1.43 \pm 0.01$, correlation-length exponent $\nu= 1.33 \pm 0.02$, depinning exponent $\beta = 0.24\pm 0.01$, and avalanche-size exponent $\tau_{\parallel} = 1.09 \pm 0.03$. In the transverse direction we find $\zeta_{\perp} = 0.5 \pm 0.01$, $z_{\perp} = 2.27 \pm 0.05$, and $\tau_{\perp} =1.17\pm 0.06$. We show that transverse fluctuations do not alter the critical exponents in the driving direction, as predicted by the planar approximation (PA) proposed in 1996 by Ertas and Kardar (EK). We check the PA for the force-force correlator, comparing to the functional renormalization group and simulations. Both Random-Bond (RB) and Random-Field (RF) disorder yield a single universality class, indistinguishable from the one of an elastic string in a 2-dimensional random medium. While $z_{\perp}=z_{\parallel}+1/\nu$ and $\nu=1/(2-\zeta_{\parallel})$ of EK are satisfied, the transversal movement is that of a Brownian, with a clock set locally by the forward movement. This implies $\zeta_\perp = (2-d)/2$, distinct from EK. At small driving velocities the distribution of local parallel displacements has a negative skewness, while in the transverse direction it is a Gaussian. For large scales, the system can be described by anisotropic effective temperatures. In the fast-flow regime the local displacement distributions become Gaussian in both directions and the effective temperatures vanish as $T^{\perp}_{\tt eff}\sim 1/v$ and $T^{\parallel}_{\tt eff}\sim 1/v^3$ for RB and as $T^{\perp}_{\tt eff} \approx T^{\parallel}_{\tt eff} \sim 1/v$ for RF.
RNNCTPs: A Neural Symbolic Reasoning Method Using Dynamic Knowledge Partitioning Technology

Although traditional symbolic reasoning methods are highly interpretable, their application in knowledge graph link prediction is limited due to their low computational efficiency. In this paper, we propose a new neural symbolic reasoning method: RNNCTPs, which improves computational efficiency by re-filtering the knowledge selection of Conditional Theorem Provers (CTPs), and is less sensitive to the embedding size parameter. RNNCTPs are divided into relation selectors and predictors. The relation selectors are trained efficiently and interpretably, so that the whole model can dynamically generate knowledge for the inference of the predictor. In all four datasets, the method shows competitive performance against traditional methods on the link prediction task, and can have higher applicability to the selection of datasets relative to CTPs.
Deep learning based closed-loop optimization of geothermal reservoir production

To maximize the economic benefits of geothermal energy production, it is essential to optimize geothermal reservoir management strategies, in which geologic uncertainty should be considered. In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs. In this framework, we construct a hybrid convolution-recurrent neural network surrogate, which combines the convolution neural network (CNN) and long short-term memory (LSTM) recurrent network. The convolution structure can extract spatial information of geologic parameter fields and the recurrent structure can approximate sequence-to-sequence mapping. The trained model can predict time-varying production responses (rate, temperature, etc.) for cases with different permeability fields and well control sequences. In the closed-loop optimization framework, production optimization based on the differential evolution (DE) algorithm, and data assimilation based on the iterative ensemble smoother (IES), are performed alternately to achieve real-time well control optimization and geologic parameter estimation as the production proceeds. In addition, the averaged objective function over the ensemble of geologic parameter estimations is adopted to consider geologic uncertainty in the optimization process. Several geothermal reservoir development cases are designed to test the performance of the proposed production optimization framework. The results show that the proposed framework can achieve efficient and effective real-time optimization and data assimilation in the geothermal reservoir production process.
The Electromagnetic Characteristics of the Tianlai Cylindrical Pathfinder Array

A great challenge for 21 cm intensity mapping experiments is the strong foreground radiation which is orders of magnitude brighter than the 21 cm signal. Removal of the foreground takes advantage of the fact that its frequency spectrum is smooth while the redshifted 21 cm signal spectrum is stochastic. However, a complication is the non-smoothness of the instrument response. This paper describes the electromagnetic simulation of the Tianlai cylinder array, a pathfinder for 21 cm intensity mapping experiments. Due to the vast scales involved, a direct simulation requires a large amount of computing resources. We have made the simulation practical by using a combination of methods: first simulate a single feed, then an array of feed units, finally with the feed array and a cylindrical reflector together, obtain the response for a single cylinder. We studied its radiation pattern, bandpass response and the effects of mutual coupling between feed units, and compared the results with observation. Many features seen in the measurement result are reproduced well in the simulation, especially the oscillatory features which are associated with the standing waves on the reflector. The mutual coupling between feed units is quantified with S-parameters, which decrease as the distance between the two feeds increases. Based on the simulated S-parameters, we estimate the correlated noise which has been seen in the visibility data, and the results show very good agreement with the data in both magnitude and frequency structures. These results provide useful insights on the problem of 21 cm signal extraction for real instruments.
Machine Learning-Based Automated Thermal Comfort Prediction: Integration of Low-Cost Thermal and Visual Cameras for Higher Accuracy

: Recent research is trying to leverage occupants' demand in the building's control loop to consider individuals' well-being and the buildings' energy savings. To that end, a real-time feedback system is needed to provide data about occupants' comfort conditions that can be used to control the building's heating, cooling, and air conditioning (HVAC) system. The emergence of thermal imaging techniques provides an excellent opportunity for contactless data gathering with no interruption in occupant conditions and activities. There is increasing attention to infrared thermal camera usage in public buildings because of their non-invasive quality in reading the human skin temperature. However, the state-of-the-art methods need additional modifications to become more reliable. To capitalize potentials and address some existing limitations, new solutions are required to bring a more holistic view toward non-intrusive thermal scanning by leveraging the benefit of machine learning and image processing. This research implements an automated approach to collect and register simultaneous thermal and visual images and read the facial temperature in different regions. This paper also presents two additional investigations. First, through utilizing IButton wearable thermal sensors on the forehead area, we investigate the reliability of an in-expensive thermal camera (FLIR Lepton) in reading the skin temperature. Second, by studying the false-color version of thermal images, we look into the possibility of non-radiometric thermal images for predicting personalized thermal comfort. The results shows the strong performance of Random Forest and K-Nearest Neighbor prediction algorithms in predicting personalized thermal comfort. In addition, we have found that non-radiometric images can also indicate thermal comfort when the algorithm is trained with larger amounts of data.
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models

We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models. The framework, called GenRL, trains deep policies by introducing an action latent variable such that the feed-forward policy search can be divided into two parts: (i) training a sub-policy that outputs a distribution over the action latent variable given a state of the system, and (ii) unsupervised training of a generative model that outputs a sequence of motor actions conditioned on the latent action variable. GenRL enables safe exploration and alleviates the data-inefficiency problem as it exploits prior knowledge about valid sequences of motor actions. Moreover, we provide a set of measures for evaluation of generative models such that we are able to predict the performance of the RL policy training prior to the actual training on a physical robot. We experimentally determine the characteristics of generative models that have most influence on the performance of the final policy training on two robotics tasks: shooting a hockey puck and throwing a basketball. Furthermore, we empirically demonstrate that GenRL is the only method which can safely and efficiently solve the robotics tasks compared to two state-of-the-art RL methods.
Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks

Deep neural networks have become an integral part of our software infrastructure and are being deployed in many widely-used and safety-critical applications. However, their integration into many systems also brings with it the vulnerability to test time attacks in the form of Universal Adversarial Perturbations (UAPs). UAPs are a class of perturbations that when applied to any input causes model misclassification. Although there is an ongoing effort to defend models against these adversarial attacks, it is often difficult to reconcile the trade-offs in model accuracy and robustness to adversarial attacks. Jacobian regularization has been shown to improve the robustness of models against UAPs, whilst model ensembles have been widely adopted to improve both predictive performance and model robustness. In this work, we propose a novel approach, Jacobian Ensembles-a combination of Jacobian regularization and model ensembles to significantly increase the robustness against UAPs whilst maintaining or improving model accuracy. Our results show that Jacobian Ensembles achieves previously unseen levels of accuracy and robustness, greatly improving over previous methods that tend to skew towards only either accuracy or robustness.
Parâmetros tecnológicos e produtivos da cana-de-açúcar quando submetida à aplicação de maturadores químicos no início de safra

The use of chemical ripeners is a reality in the sugarcane sector. The objective of this work was to evaluate the technological and productive parameters of sugarcane when submitted to the application of chemical ripeners in the beginning of the harvest. The experimental design used was in randomized blocks arranged in split plots with four repetitions. The main plots consisted of the application of the following products with their respective dosages: control - without application; sulfometuron-methyl 0.02 kg ha -1 + glyphosate 0.15 L ha -1 ; ethephon 0.34 L ha -1 + glyphosate 0.15 L ha -1 ; glyphosate 0.35 L ha -1 ; compounds of organic carboxylic radicals – CC 1.0 L ha -1 + glyphosate 0.15 L ha -1 . The subplots were composed of many different evaluation times: 0; 15 and 30 days after the application of products. The samples were collected manually and sent to the laboratory to determination of the brix, pol; reducing sugars; total reducing sugars; fiber; purity; humidity; theoretical recoverable sugar. Moreover, the productive parameters were determined: tons of culms per hectare and tons of sugar per hectare. Lastly, it was made the analysis of contents of shikimic acid and salicylic acid. The application of the mixture of chemical ripening compounds of organic carboxylic radicals + glyphosate showed an increase for the technological parameters of sugarcane15 days after application (DAA), followed by the treatment with application of glyphosate at 30 DAA.
Antiferromagnetic skyrmion crystal in Janus monolayer CrSi2N2As2

Antiferromagnetic skyrmion crystal (AF-SkX), a regular array of antiferromagnetic skyrmions, is a fundamental phenomenon in the field of condensed-matter physics. So far, only very few proposals have been made to realize the AF-SkX, and most based on three-dimensional (3D) materials. Herein, using first-principles calculations and Monte-Carlo simulations, we report the identification of AF-SkX in two-dimensional lattice of Janus monolayer CrSi 2 N 2 As 2 . Arising from the broken inversion symmetry and strong spin-orbit coupling, large Dzyaloshinskii–Moriya interaction is obtained in Janus monolayer CrSi 2 N 2 As 2 . This, combined with the geometric frustration of its triangular lattice, gives rise to the skyrmion physics and long-sought AF-SkX in the presence of external magnetic field. More intriguingly, this system presents two different antiferromagnetic skyrmion phases, and such phenomenon is distinct from those reported in 3D systems. Furthermore, by contacting with Sc 2 CO 2 , the creation and annihilation of AF-SkX in Janus monolayer CrSi 2 N 2 As 2 can be achieved through ferroelectricity. These findings greatly enrich the research on antiferromagnetic skyrmions.
Disappeared Command: Spoofing Attack On Automatic Speech Recognition Systems with Sound Masking

The development of deep learning technology has greatly promoted the performance improvement of automatic speech recognition (ASR) technology, which has demonstrated an ability comparable to human hearing in many tasks. Voice interfaces are becoming more and more widely used as input for many applications and smart devices. However, existing research has shown that DNN is easily disturbed by slight disturbances and makes false recognition, which is extremely dangerous for intelligent voice applications controlled by voice.
GestureLens: Visual Analysis of Gestures in Presentation Videos

Appropriate gestures can enhance message delivery and audience engagement in both daily communication and public presentations. In this paper, we contribute a visual analytic approach that assists professional public speaking coaches in improving their practice of gesture training through analyzing presentation videos. Manually checking and exploring gesture usage in the presentation videos is often tedious and time-consuming. There lacks an efficient method to help users conduct gesture exploration, which is challenging due to the intrinsically temporal evolution of gestures and their complex correlation to speech content. In this paper, we propose GestureLens, a visual analytics system to facilitate gesture-based and content-based exploration of gesture usage in presentation videos. Specifically, the exploration view enables users to obtain a quick overview of the spatial and temporal distributions of gestures. The dynamic hand movements are firstly aggregated through a heatmap in the gesture space for uncovering spatial patterns, and then decomposed into two mutually perpendicular timelines for revealing temporal patterns. The relation view allows users to explicitly explore the correlation between speech content and gestures by enabling linked analysis and intuitive glyph designs. The video view and dynamic view show the context and overall dynamic movement of the selected gestures, respectively. Two usage scenarios and expert interviews with professional presentation coaches demonstrate the effectiveness and usefulness of GestureLens in facilitating gesture exploration and analysis of presentation videos.
An improved bound for regular decompositions of $3$-uniform hypergraphs of bounded $VC_2$-dimension

A regular partition $\mathcal{P}$ for a $3$-uniform hypergraph $H=(V,E)$ consists of a partition $V=V_1\cup \ldots \cup V_t$ and for each $ij\in {[t]\choose 2}$, a partition $K_2[V_i,V_j]=P_{ij}^1\cup \ldots \cup P_{ij}^{\ell}$, such that certain quasirandomness properties hold. The complexity of $\mathcal{P}$ is the pair $(t,\ell)$. In this paper we show that if a $3$-uniform hypergraph $H$ has $VC_2$-dimension at most $k$, then there is a regular partition $\mathcal{P}$ for $H$ of complexity $(t,\ell)$, where $\ell$ is bounded by a polynomial in the degree of regularity. This is a vast improvement on the bound arising from the proof of this regularity lemma in general, in which the bound generated for $\ell$ is of Wowzer type. This can be seen as a higher arity analogue of the efficient regularity lemmas for graphs and hypergraphs of bounded VC-dimension due to Alon-Fischer-Newman, Lov\'{a}sz-Szegedy, and Fox-Pach-Suk.
Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing

We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image. Our pixel-aligned method estimates detailed 3D geometry and, for the first time, the unshaded surface color together with the scene illumination. Observing that 3D supervision alone is not sufficient for high fidelity color reconstruction, we introduce patch-based rendering losses that enable reliable color reconstruction on visible parts of the human, and detailed and plausible color estimation for the non-visible parts. Moreover, our method specifically addresses methodological and practical limitations of prior work in terms of representing geometry, albedo, and illumination effects, in an end-to-end model where factors can be effectively disentangled. In extensive experiments, we demonstrate the versatility and robustness of our approach. Our state-of-the-art results validate the method qualitatively and for different metrics, for both geometric and color reconstruction.
CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex

CodexDB is an SQL processing engine whose internals can be customized via natural language instructions. CodexDB is based on OpenAI's GPT-3 Codex model which translates text into code. It is a framework on top of GPT-3 Codex that decomposes complex SQL queries into a series of simple processing steps, described in natural language. Processing steps are enriched with user-provided instructions and descriptions of database properties. Codex translates the resulting text into query processing code. An early prototype of CodexDB is able to generate correct code for a majority of queries of the WikiSQL benchmark and can be customized in various ways.