Miracle-dz/UniFAR-FTUs huggingface dataset:
Esophageal Perforation by a Bone: À Case Report

Case Report The ingestion of esophageal foreign bodies is a particularly frequent situation in clinical practice. It interests both the pediatric population (the majority of foreign bodies ingested are between 6 months and 6 years old) as the adult population as in our clinical case. The esophageal entrapment site is the main site for ingesting foreign bodies. The majority of them pass spontaneously. The interrogation makes it possible, in most cases, to make the diagnosis of foreign bodies of the esophagus and the difficult diagnostic situations are represented by the situations where the interrogation is impossible (child, adult mentally retarded, etc.). The clinic, more or less completed with imaging, nevertheless makes it possible to make the diagnosis in the majority of cases. The therapeutic urgency depends essentially on the patient's tolerance of the foreign body but also on the nature of this body (button cell, sharp foreign bodies, etc.). The therapeutic methods are multiple (abstention, drug treatment, flexible or rigid endoscopy, surgical treatment) and depend on local management skills but also on the nature of the foreign body. The presence but also the extraction of the foreign body is a source of complications whose existence must be known for optimal care.
The metal-rich atmosphere of the exo-Neptune HAT-P-26b

Transmission spectroscopy is enabling precise measurements of atmospheric H2O abundances for numerous giant exoplanets. For hot Jupiters, relating H2O abundances to metallicities provides a powerful probe of their formation conditions. However, metallicity measurements for Neptune-mass exoplanets are only now becoming viable. Exo-Neptunes are expected to possess super-solar metallicities from accretion of H2O-rich and solid-rich planetesimals. However, initial investigations into the exo-Neptune HAT-P-26b suggested a significantly lower metallicity than predicted by the core-accretion theory of planetary formation and solar system expectations from Uranus and Neptune. Here, we report an extensive atmospheric retrieval analysis of HAT-P-26b, combining all available observations, to reveal its composition, temperature structure, and cloud properties. Our analysis reveals an atmosphere containing 1.5(+2.1)(-0.9)% H2O, an O/H of 18.1(+25.9)(-11.3)x solar, and C/O 5$\sigma$ confidence.
The ALMA Spectroscopic Survey in the Hubble Ultra Deep Field: Evolution of the Molecular Gas in CO-selected Galaxies

We analyze the interstellar medium properties of a sample of 16 bright CO line emitting galaxies identified in the ALMA Spectroscopic Survey in the Hubble Ultra Deep Field (ASPECS) Large Program. This CO−selected galaxy sample is complemented by two additional CO line emitters in the UDF that are identified based on their Multi-Unit Spectroscopic Explorer (MUSE) optical spectroscopic redshifts. The ASPECS CO−selected galaxies cover a larger range of star formation rates (SFRs) and stellar masses compared to literature CO emitting galaxies at z > 1 for which scaling relations have been established previously. Most of ASPECS CO-selected galaxies follow these established relations in terms of gas depletion timescales and gas fractions as a function of redshift, as well as the SFR–stellar mass relation (“galaxy main sequence”). However, we find that ∼30% of the galaxies (5 out of 16) are offset from the galaxy main sequence at their respective redshift, with ∼12% (2 out of 16) falling below this relationship. Some CO-rich galaxies exhibit low SFRs, and yet show substantial molecular gas reservoirs, yielding long gas depletion timescales. Capitalizing on the well-defined cosmic volume probed by our observations, we measure the contribution of galaxies above, below, and on the galaxy main sequence to the total cosmic molecular gas density at different lookback times. We conclude that main-sequence galaxies are the largest contributors to the molecular gas density at any redshift probed by our observations (z ∼ 1−3). The respective contribution by starburst galaxies above the main sequence decreases from z ∼ 2.5 to z ∼ 1, whereas we find tentative evidence for an increased contribution to the cosmic molecular gas density from the passive galaxies below the main sequence.
Measuring the inflaton coupling in the CMB

We study the perspectives to extract information about the microphysical parameters that governed the reheating process after cosmic inflation from CMB data. We identify conditions under which the inflaton coupling to other fields can be constrained for a given model of inflation without having to specify the details of the particle physics theory within which this model is realised. This is possible when the effective potential during reheating is approximately parabolic, and when the coupling constants are smaller than an upper bound that is determined by the ratios between the inflaton mass and the Planck mass or the scale of inflation. We consider scalar, Yukawa, and axion-like interactions and estimate that these conditions can be fulfilled if the inflaton coupling is comparable to the electron Yukawa coupling or smaller, and if the inflaton mass is larger than 105 GeV. Constraining the order of magnitude of the coupling constant requires measuring the scalar-to-tensor ratio at the level of 10-3, which is possible with future CMB observatories. Such a measurement would provide an important clue to understand how a given model of inflation may be embedded into a more fundamental theory of nature.
Repairing mappings under policy views

The problem of data exchange involves a source schema, a target schema and a set of mappings from transforming the data between the two schemas. We study the problem of data exchange in the presence of privacy restrictions on the source. The privacy restrictions are expressed as a set of policy views representing the information that is safe to expose over all instances of the source. We propose a protocol that provides formal privacy guarantees and is data-independent, i.e., if certain criteria are met, then the protocol guarantees that the mappings leak no sensitive information independently of the data that lies in the source. We also propose an algorithm for repairing an input mapping w.r. t. a set of policy views, in cases where the input mapping leaks sensitive information. The empirical evaluation of our work shows that the proposed algorithm is quite efficient, repairing sets of 300 s-t tgds in an average time of 5s on a commodity machine. To the best of our knowledge, our work is the first one that studies the problems of exchanging data and repairing mappings under such privacy restrictions. Furthermore, our work is the first to provide practical algorithms for a logical privacy-preservation paradigm, described as an open research challenge in previous work on this area.
Numerical simulations of neutron star-black hole binaries in the near-equal-mass regime

Simulations of neutron star-black hole (NSBH) binaries generally consider black holes with masses in the range $(5-10)M_\odot$, where we expect to find most stellar mass black holes. The existence of lower mass black holes, however, cannot be theoretically ruled out. Low-mass black holes in binary systems with a neutron star companion could mimic neutron star-neutron (NSNS) binaries, as they power similar gravitational wave (GW) and electromagnetic (EM) signals. To understand the differences and similarities between NSNS mergers and low-mass NSBH mergers, numerical simulations are required. Here, we perform a set of simulations of low-mass NSBH mergers, including systems compatible with GW170817. Our simulations use a composition and temperature dependent equation of state (DD2) and approximate neutrino transport, but no magnetic fields. We find that low-mass NSBH mergers produce remnant disks significantly less massive than previously expected, and consistent with the post-merger outflow mass inferred from GW170817 for moderately asymmetric mass ratio. The dynamical ejecta produced by systems compatible with GW170817 is negligible except if the mass ratio and black hole spin are at the edge of the allowed parameter space. That dynamical ejecta is cold, neutron-rich, and surprisingly slow for ejecta produced during the tidal disruption of a neutron star : $v\sim (0.1-0.15)c$. We also find that the final mass of the remnant black hole is consistent with existing analytical predictions, while the final spin of that black hole is noticeably larger than expected -- up to $\chi_{\rm BH}=0.84$ for our equal mass case.
Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases

Abstract Metadata—the machine-readable descriptions of the data—are increasingly seen as crucial for describing the vast array of biomedical datasets that are currently being deposited in public repositories. While most public repositories have firm requirements that metadata must accompany submitted datasets, the quality of those metadata is generally very poor. A key problem is that the typical metadata acquisition process is onerous and time consuming, with little interactive guidance or assistance provided to users. Secondary problems include the lack of validation and sparse use of standardized terms or ontologies when authoring metadata. There is a pressing need for improvements to the metadata acquisition process that will help users to enter metadata quickly and accurately. In this paper, we outline a recommendation system for metadata that aims to address this challenge. Our approach uses association rule mining to uncover hidden associations among metadata values and to represent them in the form of association rules. These rules are then used to present users with real-time recommendations when authoring metadata. The novelties of our method are that it is able to combine analyses of metadata from multiple repositories when generating recommendations and can enhance those recommendations by aligning them with ontology terms. We implemented our approach as a service integrated into the CEDAR Workbench metadata authoring platform, and evaluated it using metadata from two public biomedical repositories: US-based National Center for Biotechnology Information BioSample and European Bioinformatics Institute BioSamples. The results show that our approach is able to use analyses of previously entered metadata coupled with ontology-based mappings to present users with accurate recommendations when authoring metadata.
The ALMA Spectroscopic Survey in the HUDF: Nature and Physical Properties of Gas-mass Selected Galaxies Using MUSE Spectroscopy

We discuss the nature and physical properties of gas-mass selected galaxies in the ALMA spectroscopic survey (ASPECS) of the Hubble Ultra Deep Field (HUDF). We capitalize on the deep optical integral-field spectroscopy from the Multi Unit Spectroscopic Explorer (MUSE) HUDF Survey and multiwavelength data to uniquely associate all 16 line emitters, detected in the ALMA data without preselection, with rotational transitions of carbon monoxide (CO). We identify 10 as CO(2–1) at 1 < z < 2, 5 as CO(3–2) at 2 < z < 3, and 1 as CO(4–3) at z = 3.6. Using the MUSE data as a prior, we identify two additional CO(2–1) emitters, increasing the total sample size to 18. We infer metallicities consistent with (super-)solar for the CO-detected galaxies at z ≤ 1.5, motivating our choice of a Galactic conversion factor between CO luminosity and molecular gas mass for these galaxies. Using deep Chandra imaging of the HUDF, we determine an X-ray AGN fraction of 20% and 60% among the CO emitters at z ∼ 1.4 and z ∼ 2.6, respectively. Being a CO-flux-limited survey, ASPECS-LP detects molecular gas in galaxies on, above, and below the main sequence (MS) at z ∼ 1.4. For stellar masses ≥1010 (1010.5) , we detect about 40% (50%) of all galaxies in the HUDF at 1 < z < 2 (2 < z < 3). The combination of ALMA and MUSE integral-field spectroscopy thus enables an unprecedented view of MS galaxies during the peak of galaxy formation.
Towards adversarial learning of speaker-invariant representation for speech emotion recognition

Speech emotion recognition (SER) has attracted great attention in recent years due to the high demand for emotionally intelligent speech interfaces. Deriving speaker-invariant representations for speech emotion recognition is crucial. In this paper, we propose to apply adversarial training to SER to learn speaker-invariant representations. Our model consists of three parts: a representation learning sub-network with time-delay neural network (TDNN) and LSTM with statistical pooling, an emotion classification network and a speaker classification network. Both the emotion and speaker classification network take the output of the representation learning network as input. Two training strategies are employed: one based on domain adversarial training (DAT) and the other one based on cross-gradient training (CGT). Besides the conventional data set, we also evaluate our proposed models on a much larger publicly available emotion data set with 250 speakers. Evaluation results show that on IEMOCAP, DAT and CGT provides 5.6% and 7.4% improvement respectively, over a baseline system without speaker-invariant representation learning on 5-fold cross validation. On the larger emotion data set, while CGT fails to yield better results than baseline, DAT can still provide 9.8% relative improvement on a standalone test set.
A Simplified Dynamical Model for Tuned Wireless Power Transfer Systems

Dynamical models of wireless power transfer (WPT) systems are of primary importance for the dynamical behavior studies and controller design. However, the existing dynamical models usually suffer from high orders and complicated forms due to the complex nature of the coupled resonances and switched-mode power converters in WPT systems. This letter finds that a well-tuned WPT system can be accurately described by a much simpler dynamical model. Specifically, at the tuned condition, the existing dynamical model can be decomposed into two parts. One is controllable and the other one is uncontrollable. The former should be considered in the modeling while the latter can be ignored because it always exponentially converges to zero. For illustration, the recently proposed zero-voltage-switching full-bridge pulse-density modulation WPT system is modeled as an example since such a system can efficiently operate at the tuned condition with soft switching and control capabilities. The derived model was verified in experiments by time-domain and frequency-domain responses.
Gradient-only line searches: An Alternative to Probabilistic Line Searches

Step sizes in neural network training are largely determined using predetermined rules such as fixed learning rates and learning rate schedules, which require user input to determine their functional form and associated hyperparameters. Global optimization strategies to resolve these hyperparameters are computationally expensive. Line searches are capable of adaptively resolving learning rate schedules. However, due to discontinuities induced by mini-batch sampling, they have largely fallen out of favor. Notwithstanding, probabilistic line searches have recently demonstrated viability in resolving learning rates for stochastic loss functions. This method creates surrogates with confidence intervals, where restrictions are placed on the rate at which the search domain can grow along a search direction. This paper introduces an alternative paradigm, Gradient-Only Line Searches that are inexact (GOLS-I), as an alternative strategy to automatically resolve learning rates in stochastic cost functions over a range of 15 orders of magnitude without the use of surrogates. We show that GOLS-I is a competitive strategy to reliably resolve step sizes, adding high value in terms of performance, while being easy to implement. Considering mini-batch sampling, we open the discussion on how to split the effort to resolve quality search directions from quality step size estimates along a search direction.
Quantum Computer Architecture Toward Full-Stack Quantum Accelerators

This article presents the definition and implementation of a quantum computer architecture to enable creating a new computational device—a quantum computer as an accelerator. A key question addressed is what such a quantum computer is and how it relates to the classical processor that controls the entire execution process. In this article, we present explicitly the idea of a quantum accelerator that contains the full stack of the layers of an accelerator. Such a stack starts at the highest level describing the target application of the accelerator. The next layer abstracts the quantum logic outlining the algorithm that is to be executed on the quantum accelerator. In our case, the logic is expressed in the universal quantum-classical hybrid computation language developed in the group, called OpenQL, which visualized the quantum processor as a computational accelerator. The OpenQL compiler translates the program to a common assembly language, called cQASM, which can be executed on a quantum simulator. The cQASM represents the instruction set that can be executed by the microarchitecture implemented in the quantum accelerator. In a subsequent step, the compiler can convert the cQASM to generate the eQASM, which is executable on a particular experimental device incorporating the platform-specific parameters. This way, we are able to distinguish clearly the experimental research toward better qubits, and the industrial and societal applications that need to be developed and executed on a quantum device. The first case offers experimental physicists with a full-stack experimental platform using realistic qubits with decoherence and error-rates, whereas the second case offers perfect qubits to the quantum application developer, where there is neither decoherence nor error-rates. We conclude the article by explicitly presenting three examples of full-stack quantum accelerators, for an experimental superconducting processor, for quantum accelerated genome sequencing and for near-term generic optimization problems based on quantum heuristic approaches. The two later full-stack models are currently being actively researched in our group.
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.
LINSPECTOR: Multilingual Probing Tasks for Word Representations

Despite an ever-growing number of word representation models introduced for a large number of languages, there is a lack of a standardized technique to provide insights into what is captured by these models. Such insights would help the community to get an estimate of the downstream task performance, as well as to design more informed neural architectures, while avoiding extensive experimentation that requires substantial computational resources not all researchers have access to. A recent development in NLP is to use simple classification tasks, also called probing tasks, that test for a single linguistic feature such as part-of-speech. Existing studies mostly focus on exploring the linguistic information encoded by the continuous representations of English text. However, from a typological perspective the morphologically poor English is rather an outlier: The information encoded by the word order and function words in English is often stored on a subword, morphological level in other languages. To address this, we introduce 15 type-level probing tasks such as case marking, possession, word length, morphological tag count, and pseudoword identification for 24 languages. We present a reusable methodology for creation and evaluation of such tests in a multilingual setting, which is challenging because of a lack of resources, lower quality of tools, and differences among languages. We then present experiments on several diverse multilingual word embedding models, in which we relate the probing task performance for a diverse set of languages to a range of five classic NLP tasks: POS-tagging, dependency parsing, semantic role labeling, named entity recognition, and natural language inference. We find that a number of probing tests have significantly high positive correlation to the downstream tasks, especially for morphologically rich languages. We show that our tests can be used to explore word embeddings or black-box neural models for linguistic cues in a multilingual setting. We release the probing data sets and the evaluation suite LINSPECTOR with https://github.com/UKPLab/linspector.
Precise radial velocities of giant stars

Context. Using the Hamilton Échelle Spectrograph at Lick Observatory, we have obtained precise radial velocities (RVs) of a sample of 373 G- and K-giant stars over more than 12 yr, leading to the discovery of several single and multiple planetary systems. The RVs of the long-period (~53 yr) spectroscopic binary ε Cyg (HIP 102488) are found to exhibit additional regular variations with a much shorter period (~291 days). Aims. We intend to improve the orbital solution of the ε Cyg system and attempt to identify the cause of the nearly periodic shorter period variations, which might be due to an additional substellar companion. Methods. We used precise RV measurements of the K-giant star ε Cyg from Lick Observatory, in combination with a large set of RVs collected more recently with the SONG telescope, as well as archival data sets. We fit Keplerian and fully dynamical N-body models to the RVs in order to explore the properties of a previously known spectroscopic stellar companion and to investigate whether there is an additional planetary companion in the system. To search for long-term stable regions in the parameter space around the orbit of this putative planet, we ran a stability analysis using an N-body code. Furthermore, we explored the possibility of co-orbital bodies to the planet with a demodulation technique. We tested the hypothesis of ε Cyg being a hierarchical stellar triple by using a modified version of the N-body code. Alternative causes for the observed RV variations, such as stellar spots and oscillations, were examined by analyzing photometric data of the system and by comparing its properties to known variable stars with long secondary periods and heartbeat stars from the literature. Results. Our Keplerian model characterizes the orbit of the spectroscopic binary to higher precision than achieved previously, resulting in a semi-major axis of a = 15.8 AU, an eccentricity of e = 0.93, and a minimum mass of the secondary of msini = 0.265 M⊙. Additional short-period RV variations closely resemble the signal of a Jupiter-mass planet orbiting the evolved primary component with a period of 291 d, but the period and amplitude of the putative orbit change strongly over time. Furthermore, in our stability analysis of the system, no stable orbits could be found in a large region around the best fit. Both of these findings deem a planetary cause of the RV variations unlikely. Most of the investigated alternative scenarios also fail to explain the observed variability convincingly. Due to its very eccentric binary orbit, it seems possible, however, that ε Cyg could be an extreme example of a heartbeat system.