Miracle-dz/UniFAR-FTUs huggingface dataset:
HETDEX Pilot Survey. VI. Emitters and Expectations for a Local Sample of Star-forming Galaxies in HETDEX

We assemble an unbiased sample of 29 galaxies with [O ii] λ3727 and/or [O iii] λ5007 detections at z < 0.15 from the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) Pilot Survey (HPS). The HPS finds galaxies without preselection based on their detected emission lines via integral field spectroscopy. Sixteen of these objects were followed up with the second-generation, low-resolution spectrograph on the upgraded HET. Oxygen abundances were then derived via strong emission lines using a Bayesian approach. We find that most of the galaxies fall along the mass–metallicity relation derived from photometrically selected star-forming galaxies in the Sloan Digital Sky Survey (SDSS). However, two of these galaxies have low metallicity (similar to the very rare green pea galaxies in mass–metallicity space). The star formation rates (SFRs) of this sample fall in an intermediate space between the SDSS star-forming main sequence and the extreme green pea galaxies. We conclude that spectroscopic selection fills a part of the mass–metallicity–SFR phase space that is missed in photometric surveys with preselection like SDSS; i.e., we find galaxies that are actively forming stars but are faint in continuum. We use the results of this pilot investigation to make predictions for the upcoming unbiased, large spectroscopic sample of local line emitters from HETDEX. With the larger HETDEX survey, we will determine if galaxies selected spectroscopically without continuum brightness preselection have metallicities that fall on a continuum that bridges typical star-forming and rarer, more extreme systems like green peas.
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.
Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility

We study the Convex Set Disjointness (CSD) problem, where two players have input sets taken from an arbitrary fixed domain~$U\subseteq \mathbb{R}^d$ of size $\lvert U\rvert = n$. Their mutual goal is to decide using minimum communication whether the convex hulls of their sets intersect (equivalently, whether their sets can be separated by a hyperplane). Different forms of this problem naturally arise in distributed learning and optimization: it is equivalent to {\em Distributed Linear Program (LP) Feasibility} -- a basic task in distributed optimization, and it is tightly linked to {\it Distributed Learning of Halfdpaces in $\mathbb{R}^d$}. In {communication complexity theory}, CSD can be viewed as a geometric interpolation between the classical problems of {Set Disjointness} (when~$d\geq n-1$) and {Greater-Than} (when $d=1$). We establish a nearly tight bound of $\tilde \Theta(d\log n)$ on the communication complexity of learning halfspaces in $\mathbb{R}^d$. For Convex Set Disjointness (and the equivalent task of distributed LP feasibility) we derive upper and lower bounds of $\tilde O(d^2\log n)$ and~$\Omega(d\log n)$. These results improve upon several previous works in distributed learning and optimization. Unlike typical works in communication complexity, the main technical contribution of this work lies in the upper bounds. In particular, our protocols are based on a {\it Container Lemma for Halfspaces} and on two variants of {\it Caratheodory's Theorem}, which may be of independent interest. These geometric statements are used by our protocols to provide a compressed summary of the players' input.
Self-Attention with Structural Position Representations

Although self-attention networks (SANs) have advanced the state-of-the-art on various NLP tasks, one criticism of SANs is their ability of encoding positions of input words (Shaw et al., 2018). In this work, we propose to augment SANs with structural position representations to model the latent structure of the input sentence, which is complementary to the standard sequential positional representations. Specifically, we use dependency tree to represent the grammatical structure of a sentence, and propose two strategies to encode the positional relationships among words in the dependency tree. Experimental results on NIST Chinese-to-English and WMT14 English-to-German translation tasks show that the proposed approach consistently boosts performance over both the absolute and relative sequential position representations.
Response of a uniformly accelerated Unruh-DeWitt detector in polymer quantization

If an Unruh-DeWitt detector moves with a uniform acceleration in Fock-space vacuum, then the transition rate of the detector is proportional to the thermal spectrum. It is well known that the transition rate of the detector crucially depends on the two-point function along the detectors trajectory and in order to compute it the standard "$i \epsilon$" regularization is used for Fock space. Numerically, we show here that the regulator $\epsilon$ is generic in polymer quantization, the quantization method used in \emph{loop quantum gravity} with a finite value $\epsilon \approx 2.16$, which leads to non-thermal spectrum for the uniformly accelerated detector. We also discuss the response of a spatially smeared detector.
Tunable Signal Velocity in the Integer Quantum Hall Effect of Tailored Graphene

Topological properties in condensed matter physics are often claimed to be a fruitful resource for technical applications, but so far they only play a minor role in applications. Here we propose to put topological edge states to use in tailored graphene for Fermi velocity engineering. By tuning external control parameters such as gate voltages, the dispersions of the edge states regime are modified in a controllable way. This enables the realizations of devices such as tunable delay lines and interferometers with switchable delays.
Clustering of count data through a mixture of multinomial PCA

Count data is becoming more and more ubiquitous in a wide range of applications, with datasets growing both in size and in dimension. In this context, an increasing amount of work is dedicated to the construction of statistical models directly accounting for the discrete nature of the data. Moreover, it has been shown that integrating dimension reduction to clustering can drastically improve performance and stability. In this paper, we introduce the mixture of multinomial PCA, a new mixture model for the clustering of count data. Related to the latent Dirichlet allocation model, it offers the flexibility of topic modeling while being able to assign each observation to a unique cluster. We propose an inference procedure, where inference and clustering are jointly done by mixing a classification variational expectation maximization algorithm, with a branch & bound like strategy on a variational lower bound. An integrated classification likelihood criterion is derived for model selection, and a thorough study with numerical experiments is proposed to assess both the performance and robustness of the method. Finally, we illustrate the qualitative interest of the latter in a real-world application, for the clustering of anatomopathological medical reports, in partnership with expert practitioners from the Institut Curie hospital.
Gaze-Driven Adaptive Interventions for Magazine-Style Narrative Visualizations

In this article, we investigate the value of gaze-driven adaptive interventions to support the processing of textual documents with embedded visualizations, i.e., Magazine Style Narrative Visualizations (MSNVs). These interventions are provided dynamically by highlighting relevant data points in the visualization when the user reads related sentences in the MSNV text, as detected by an eye-tracker. We conducted a user study during which participants read a set of MSNVs with our interventions, and compared their performance and experience with participants who received no interventions. Our work extends previous findings by showing that dynamic, gaze-driven interventions can be delivered based on reading behaviors in MSNVs, a widespread form of documents that have never been considered for gaze-driven adaptation so far. Next, we found that the interventions significantly improved the performance of users with low levels of visualization literacy, i.e., those users who need help the most due to their lower ability to process and understand data visualizations. However, high literacy users were not impacted by the interventions, providing initial evidence that gaze-driven interventions can be further improved by personalizing them to the levels of visualization literacy of their users.
A Unified Statistical Model for Atmospheric Turbulence-Induced Fading in Orbital Angular Momentum Multiplexed FSO Systems

This paper proposes a unified statistical channel model to characterize the atmospheric turbulence induced distortions faced by orbital angular momentum (OAM) in free space optical (FSO) communication systems. In this channel model, the self-channel irradiance of OAM modes as well as crosstalk irradiances between different OAM modes are characterized by a Generalized Gamma distribution (GGD). The latter distribution is shown to provide an excellent match with simulated data for all regimes of atmospheric turbulence. Therefore, it can be used to overcome the computationally complex numerical simulations to model the propagation of OAM modes through atmospheric turbulent FSO channels. The GGD allows obtaining very simple tractable closed-form expressions for a variety of performance metrics. Indeed, the average capacity, the bit-error rate, and the outage probability are derived for FSO systems using single OAM mode transmission with direct detection. Furthermore, we extend our study to FSO systems using OAM mode diversity to improve the performance. By using a maximum ratio combining (MRC) at the receiver, the GGD is also shown to fit the simulated combined received optical powers. Finally, space-time (ST) coding is proposed to provide diversity and multiplexing gains, and the error probability is theoretically derived under the newly proposed generic model.
The Chemical Structure of Young High-mass Star-forming Clumps. I. Deuteration

The chemical structure of high-mass star nurseries is important for a general understanding of star formation. Deuteration is a key chemical process in the earliest stages of star formation because its efficiency is sensitive to the environment. Using the IRAM-30 m telescope at 1.3–4.3 mm wavelengths, we have imaged two parsec-scale high-mass protostellar clumps (P1 and S) that show different evolutionary stages but are located in the same giant filamentary infrared dark cloud G28.34+0.06. Deep spectral images at subparsec resolution reveal the dust and gas physical structures of both clumps. We find that (1) the low-J lines of N2H+, HCN, HNC, and HCO+ isotopologues are subthermally excited; and (2) the deuteration of N2H+ is more efficient than that of HCO+, HCN, and HNC by an order of magnitude. The deuterations of these species are enriched toward the chemically younger clump S compared with P1, indicating that this process favors the colder and denser environment (Tkin ∼ 14 K, N(NH3) ∼ 9 × 1015 cm−2). In contrast, single deuteration of NH3 is insensitive to the environmental difference between P1 and S; and (3) single deuteration of CH3OH (>10%) is detected toward the location where CO shows a depletion of ∼10. This comparative chemical study between P1 and S links the chemical variations to the environmental differences and shows chemical similarities between the early phases of high- and low-mass star-forming regions.
Simulation and computational analysis of multiscale graph agent-based tumor model

This paper deals with the cellular biological network analysis of the tumor-growth model, consisting of multiple spaces and time scales. In this paper, we present a model in graph simulation using ABM for tumor growth. In particular, we propose a graph agent-based modeling and simulation system in the format of tumor growth scenario for evolving analysis. To manage cellular biological network analysis, we developed a workflow that allows us to estimate the tumor model and the complexity of the evolving behavior in a principled manner. By developing the model using Python, which has enabled us to run the model multiple times (more than what is possible by conventional means) to generate a large amount of data, we have succeeded in getting deep in to the micro-environment of the tumor, employing network analysis.Combining agent-based modeling with graph-based modeling to simulate the structure, dynamics, and functions of complex networks is exclusively important for biological systems with a large number of open parameters, e.g., epidemic models of disease spreading or cancer. Extracting data from evolutionary directed graphs and a set of centrality algorithms helps us to tackle the problems of pathway analysis and to develop the ability to predict, control, and design the function of metabolisms. Reproducing and performing complex parametric simulations a known phenomenon at a sufficient level of detail for computational biology could be an impressive achievement for fast analysis purposes in clinics, both on the predictive diagnostic and therapeutic side.
Adversarial Bootstrapping for Dialogue Model Training

Open domain neural dialogue models, despite their successes, are known to produce responses that lack relevance, diversity, and in many cases coherence. These shortcomings stem from the limited ability of common training objectives to directly express these properties as well as their interplay with training datasets and model architectures. Toward addressing these problems, this paper proposes bootstrapping a dialogue response generator with an adversarially trained discriminator. The method involves training a neural generator in both autoregressive and traditional teacher-forcing modes, with the maximum likelihood loss of the auto-regressive outputs weighted by the score from a metric-based discriminator model. The discriminator input is a mixture of ground truth labels, the teacher-forcing outputs of the generator, and distractors sampled from the dataset, thereby allowing for richer feedback on the autoregressive outputs of the generator. To improve the calibration of the discriminator output, we also bootstrap the discriminator with the matching of the intermediate features of the ground truth and the generator's autoregressive output. We explore different sampling and adversarial policy optimization strategies during training in order to understand how to encourage response diversity without sacrificing relevance. Our experiments shows that adversarial bootstrapping is effective at addressing exposure bias, leading to improvement in response relevance and coherence. The improvement is demonstrated with the state-of-the-art results on the Movie and Ubuntu dialogue datasets with respect to human evaluations and BLUE, ROGUE, and distinct n-gram scores.
Cooperative Multi-spacecraft Observation of Incoming Space Threats.

Earth is constantly being bombarded with material from space. Most of the natural material end up being dust grains that litter the surface of Earth, but larger bodies are known to impact every few decades. The most recent large impact was Chelyabinsk which set off a 500-kiloton explosion which was 40 times that of the Hiroshima nuclear explosion. Apart from meteors, there is a growing threat of space assets deorbiting. With these impending space threats, it is critical to have a constellation of satellites to autonomously lookout for meteors and reentering space debris. By using multiple spacecraft, it is possible to perform multipoint observation of the event. Through multipoint observation, it is possible to triangulate the location of the observed event. The detection, tracking, and analysis of these objects all need to be performed autonomously. Our previous work focused on developing several vision algorithms including blob-detection, feature detection, and neural network-based image segment classification. For this multipoint observation to occur, it requires multiple spacecraft to coordinate their actions particularly fixating on the space observation target. Furthermore, communication and coordination are needed for bringing new satellites into observation view and removing other satellites that have lost their view. In this paper, we analyze state-of-the-art observation technology for small satellites and perform detailed design of its implementation. Through this study, we estimate the error estimates on position, velocity, and acceleration. We presume use of low to mid-tier cameras for the spacecraft.
Story-oriented Image Selection and Placement

Multimodal contents have become commonplace on the Internet today, manifested as news articles, social media posts, and personal or business blog posts. Among the various kinds of media (images, videos, graphics, icons, audio) used in such multimodal stories, images are the most popular. The selection of images from a collection - either author's personal photo album, or web repositories - and their meticulous placement within a text, builds a succinct multimodal commentary for digital consumption. In this paper we present a system that automates the process of selecting relevant images for a story and placing them at contextual paragraphs within the story for a multimodal narration. We leverage automatic object recognition, user-provided tags, and commonsense knowledge, and use an unsupervised combinatorial optimization to solve the selection and placement problems seamlessly as a single unit.
Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources

In this paper we propose a data acquisition methodology, and a Machine Learning solution for the partially automated evaluation of real estate properties. The novelty and importance of the approach lies in two aspects: (1) when compared to Automated Valuation Models (AVMs) as available to real estate operators, it is highly adaptive and non-parametric, and integrates diverse data sources; (2) when compared to Machine Learning literature that has addressed real estate applications, it is more directly linked to the actual business processes of appraisal companies: in this context prices that are advertised online are normally not the most relevant source of information, while an appraisal document must be proposed by an expert and approved by a validator, possibly with the help of technological tools. We describe a case study using a set of 7988 appraisal documents for residential properties in Turin, Italy. Open data were also used, including location, nearby points of interest, comparable property prices, and the Italian revenue service area code. The observed mean error as measured on an independent test set was around 21 K€, for an average property value of about 190 K€. The AVM described here can help the stakeholders in this process (experts, appraisal company) to provide a reference price to be used by the expert, to allow the appraisal company to validate their evaluations in a faster and cheaper way, to help the expert in listing a set of comparable properties, that need to be included in the appraisal document.
Büchi automata for distributed temporal logic

The distributed temporal logic DTL is a logic for reasoning about temporal properties of distributed systems from the local point of view of the system's agents, which are assumed to execute sequentially and to interact by means of synchronous event sharing. Different versions of DTL have been provided over the years for a number of different applications, reflecting different perspectives on how non-local information can be accessed by each agent. In this paper, we propose a novel notion of distributed Buchi automaton envisaged to encompass DTL with a model-checking mechanism.
Empirical Study of Diachronic Word Embeddings for Scarce Data

Word meaning change can be inferred from drifts of time-varying word embeddings. However, temporal data may be too sparse to build robust word embeddings and to discriminate significant drifts from noise. In this paper, we compare three models to learn diachronic word embeddings on scarce data: incremental updating of a Skip-Gram from Kim et al. (2014), dynamic filtering from Bamler & Mandt (2017), and dynamic Bernoulli embeddings from Rudolph & Blei (2018). In particular, we study the performance of different initialisation schemes and emphasise what characteristics of each model are more suitable to data scarcity, relying on the distribution of detected drifts. Finally, we regularise the loss of these models to better adapt to scarce data.
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking

We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-oriented dialogue systems and the use of explicit semantics in the form of task-specific ontologies. The PolyResponse engine is trained on hundreds of millions of examples extracted from real conversations: it learns what responses are appropriate in different conversational contexts. It then ranks a large index of text and visual responses according to their similarity to the given context, and narrows down the list of relevant entities during the multi-turn conversation. We introduce a restaurant search and booking system powered by the PolyResponse engine, currently available in 8 different languages.
Self-Supervised Deep Depth Denoising

Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligible noise. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned non-uniform noise, while preserving geometric details. Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth. In this paper, we propose a fully convolutional deep autoencoder that learns to denoise depth maps, surpassing the lack of ground truth data. Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference. To enforce self-supervision, we leverage a differentiable rendering technique to exploit photometric supervision, which is further regularized using geometric and surface priors. As the proposed approach relies on raw data acquisition, a large RGB-D corpus is collected using Intel RealSense sensors. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed self-supervised denoising approach on established 3D reconstruction applications. Code is avalable at https://github.com/VCL3D/DeepDepthDenoising