Miracle-dz/UniFAR-FTUs huggingface dataset:
A Regret Analysis of Bilateral Trade

Bilateral trade, a fundamental topic in economics, models the problem of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold private valuations. Despite the simplicity of this problem, a classical result by Myerson and Satterthwaite (1983) affirms the impossibility of designing a mechanism that is simultaneously efficient, incentive compatible, individually rational, and budget balanced. This impossibility result fostered an intense investigation of meaningful trade-offs between these desired properties. Much work has focused on approximately efficient fixed-price mechanisms, e.g., Blumrosen and Dobzinski (2014, 2016), Colini-Baldeschi et al. (2016), which have been shown to fully characterize strong budget balanced and ex-post individually rational direct revelation mechanisms. All these results, however, either assume some knowledge on the priors of the seller/buyer valuations, or black-box access to some samples of the distributions, as in Dütting et al. (2021). In this paper, we cast for the first time the bilateral trade problem in a regret minimization framework over T rounds of seller/buyer interactions, with no prior knowledge on their private valuations. Our main contribution is a complete characterization of the regret regimes for fixed-price mechanisms with different feedback models and private valuations, using as a benchmark the best fixed-price in hindsight. More precisely, we prove the following bounds on the regret ~Θ (√T) for full-feedback (i.e., direct revelation mechanisms); ~Θ(T2/3) for realistic feedback (i.e., posted-price mechanisms) and independent seller/buyer valuations with bounded densities; Θ(T) for realistic feedback and seller/buyer valuations with bounded densities; Θ(T) for realistic feedback and independent seller/buyer valuations; Θ(T) for the adversarial setting.
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.
Radio and optical observations of the possible AE Aqr twin, LAMOST J024048.51+195226.9

Thorstensen (2020) recently argued that the cataclysmic variable (CV) LAMOST J024048.51+195226.9 may be a twin to the unique magnetic propeller system AE Aqr. If this is the case, two predictions are that it should display a short period white dwarf spin modulation, and that it should be a bright radio source. We obtained follow-up optical and radio observations of this CV, in order to see if this holds true. Our optical high-speed photometry does not reveal a white dwarf spin signal, but lacks the sensitivity to detect a modulation similar to the 33-s spin signal seen in AE Aqr. We detect the source in the radio, and measure a radio luminosity similar to that of AE Aqr and close to the highest so far reported for a CV. We also find good evidence for radio variability on a time scale of tens of minutes. Optical polarimetric observations produce no detection of linear or circular polarization. While we are not able to provide compelling evidence, our observations are all consistent with this object being a propeller system.
A Context-Enhanced De-identification System

Many modern entity recognition systems, including the current state-of-the-art de-identification systems, are based on bidirectional long short-term memory (biLSTM) units augmented by a conditional random field (CRF) sequence optimizer. These systems process the input sentence by sentence. This approach prevents the systems from capturing dependencies over sentence boundaries and makes accurate sentence boundary detection a prerequisite. Since sentence boundary detection can be problematic especially in clinical reports, where dependencies and co-references across sentence boundaries are abundant, these systems have clear limitations. In this study, we built a new system on the framework of one of the current state-of-the-art de-identification systems, NeuroNER, to overcome these limitations. This new system incorporates context embeddings through forward and backward n-grams without using sentence boundaries. Our context-enhanced de-identification (CEDI) system captures dependencies over sentence boundaries and bypasses the sentence boundary detection problem altogether. We enhanced this system with deep affix features and an attention mechanism to capture the pertinent parts of the input. The CEDI system outperforms NeuroNER on the 2006 i2b2 de-identification challenge dataset, the 2014 i2b2 shared task de-identification dataset, and the 2016 CEGS N-GRID de-identification dataset (p < 0.01). All datasets comprise narrative clinical reports in English but contain different note types varying from discharge summaries to psychiatric notes. Enhancing CEDI with deep affix features and the attention mechanism further increased performance.
A higher-multipole gravitational waveform model for an eccentric binary black holes based on the effective-one-body-numerical-relativity formalism

We have previously constructed a waveform model, SEOBNRE , for spinning binary black hole (BBH) moving along eccentric orbit based on effective-one-body (EOB) formalism. In the current paper, we update SEOBNRE waveform model in the following three respects. Firstly, we update the EOB dynamics from SEOBNRv1 to SEOBNRv4 . Secondly we properly treat the Schott term which has been ignored in previous SEOBNRE . Thirdly, we construct a new factorized waveform including (l, |m|) = (2, 2), (2, 1), (3, 3), (4, 4) modes based on EOB formalism, which is valid for spinning BBHs in general equatorial orbit. Following our previous SEOBNRE waveform model, we call our new waveform model SEOBNREHM . The (l, |m|) = (2, 2) mode waveform of SEOBNREHM can fit the original SEOBNRv4 waveform very well in the case of a quasi-circular orbit. We have validated SEOBNREHM waveform model through comparing the waveform against the Simulating eXtreme Spacetimes (SXS) catalog. The comparison is done for BBH with total mass in (20, 200)M ⊙ using Advanced LIGO designed sensitivity. For the quasi-circular cases we have compared our (2, 2) mode waveforms to the 281 numerical relativity (NR) simulations of BBH along quasi-circular orbits. All of the matching factors are bigger than 98%. For the elliptical cases, 24 NR simulations of BBH along an elliptic orbit are used. For each elliptical BBH system, we compare our modeled gravitational polarizations against the NR results for different combinations of the inclination angle, the initial orbit phase and the source localization in the sky. We use the minimal matching factor respect to the inclination angle, the initial orbit phase and the source localization to quantify the performance of the higher modes waveform. We found that after introducing the higher modes, the minimum of the minimal matching factor among the 24 tested elliptical BBHs increases from 90% to 98%. Our SEOBNREHM waveform model can match all tested 305 SXS waveforms better than 98% including highly spinning (χ = 0.99) BBH, highly eccentric (e ≈ 0.6 at reference frequency Mf 0 = 0.002) BBH and large mass ratio (q = 10) BBH.
PageRank asymptotics on directed preferential attachment networks

We characterize the tail behavior of the distribution of the PageRank of a uniformly chosen vertex in a directed preferential attachment graph and show that it decays as a power law with an explicit exponent that is described in terms of the model parameters. Interestingly, this power law is heavier than the tail of the limiting in-degree distribution, which goes against the commonly accepted {\em power law hypothesis}. This deviation from the power law hypothesis points at the structural differences between the inbound neighborhoods of typical vertices in a preferential attachment graph versus those in static random graph models where the power law hypothesis has been proven to hold (e.g., directed configuration models and inhomogeneous random digraphs). In addition to characterizing the PageRank distribution of a typical vertex, we also characterize the explicit growth rate of the PageRank of the oldest vertex as the network size grows.
Application of X-ray radiography to study the segregation process of iron from silicate under high pressure and high temperature

X-ray radiography was applied to observe the segregation process of iron from silicate at high pressure and high temperature in mixtures containing light elements. As the temperature of the hydrogen-containing sample increases, the molten iron becomes coherent. Small droplets of iron sink to the bottom of the chamber, where they merge into a single, large droplet. The small iron droplets exhibit complex motion, moving in random directions. Markedly different behavior is found in the sulfur-containing sample, where no clear motion of the molten iron is observed. Instead, as the sample temperature is increased, the concentration of iron near the wall of the sample chamber gradually increases. These observations demonstrate that the behavior of molten iron changes according to the dissolved elements. This X-ray radiography experiment represents a powerful technique to study the segregation process of molten iron from solid or partially molten silicate, particularly when combined with high-resolution tomography techniques.
Spectral Signatures of Population iii and Envelope-stripped Stars in Galaxies at the Epoch of Reionization

While most simulations of the epoch of reionization have focused on single-stellar populations in star-forming dwarf galaxies, products of binary evolution are expected to significantly contribute to emissions of hydrogen-ionizing photons. Among these products are stripped stars (or helium stars), which have their envelopes stripped from interactions with binary companions, leaving an exposed helium core. Previous work has suggested these stripped stars can dominate the Lyman Continuum (LyC) photon output of high-redshift, low-luminosity galaxies post-starburst. Other sources of hard radiation in the early universe include zero-metallicity Population iii stars, which may have similar spectral energy distribution (SED) properties to galaxies with radiation dominated by stripped-star emissions. Here, we use four metrics (the power-law exponent over wavelength intervals 240–500 Å, 600–900 Å, and 1200–2000 Å, and the ratio of total luminosity in FUV wavelengths to LyC wavelengths) to compare the SEDs of simulated galaxies with only single-stellar evolution, galaxies containing stripped stars, and galaxies containing Population iii stars, with four different initial mass functions (IMFs). We find that stripped stars significantly alter SEDs in the LyC range of galaxies at the epoch of reionization. SEDs in galaxies with stripped stars have lower power-law indices in the LyC range and lower FUV to LyC luminosity ratios. These differences in SEDs are present at all considered luminosities ( MUV>−15 , AB system), and are most pronounced for lower-luminosity galaxies. Intrinsic SEDs as well as those with interstellar medium absorption of galaxies with stripped stars and Population iii stars are found to be distinct for all tested Population iii IMFs.
ACTA: A Mobile-Health Solution for Integrated Nudge-Neurofeedback Training for Senior Citizens

As the worldwide population gets increasingly aged, in-home telemedicine and mobile-health solutions represent promising services to promote active and independent aging and to contribute to a paradigm shift towards patient-centric healthcare. In this work, we present ACTA (Advanced Cognitive Training for Aging), a prototype mobile-health solution to provide advanced cognitive training for senior citizens with mild cognitive impairments. We disclose here the conceptualization of ACTA as the integration of two promising rehabilitation strategies: the"Nudge theory", from the cognitive domain, and the neurofeedback, from the neuroscience domain. Moreover, in ACTA we exploit the most advanced machine learning techniques to deliver customized and fully adaptive support to the elderly, while training in an ecological environment. ACTA represents the next-step beyond SENIOR, an earlier mobile-health project for cognitive training based on Nudge theory, currently ongoing in Lombardy Region. Beyond SENIOR, ACTA represents a highly-usable, accessible, low-cost, new-generation mobile-health solution to promote independent aging and effective motor-cognitive training support, while empowering the elderly in their own aging.
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

We present a self-supervised learning framework, COCO-LM, that pretrains Language Models by COrrecting and COntrasting corrupted text sequences. Following ELECTRA-style pretraining, COCO-LM employs an auxiliary language model to corrupt text sequences, upon which it constructs two new tasks for pretraining the main model. The first token-level task, Corrective Language Modeling, is to detect and correct tokens replaced by the auxiliary model, in order to better capture token-level semantics. The second sequence-level task, Sequence Contrastive Learning, is to align text sequences originated from the same source input while ensuring uniformity in the representation space. Experiments on GLUE and SQuAD demonstrate that COCO-LM not only outperforms recent state-of-the-art pretrained models in accuracy, but also improves pretraining efficiency. It achieves the MNLI accuracy of ELECTRA with 50% of its pretraining GPU hours. With the same pretraining steps of standard base/large-sized models, COCO-LM outperforms the previous best models by 1+ GLUE average points.
Comparative high-resolution spectroscopy of M dwarfs: Exploring non-LTE effects

Context. M dwarfs are key targets for high-resolution spectroscopy and model atmosphere analyses because of the high incidence of these stars in the solar neighbourhood and their importance as exoplanetary hosts. Several methodological challenges make such analyses difficult, leading to significant discrepancies in the published results. Aims. The aim of our work is to compare M dwarf parameters derived by recent high-resolution near-infrared studies with each other and with fundamental stellar parameters. We also assess to what extent deviations from local thermodynamic equilibrium (LTE) for iron and potassium influence the outcome of these studies. Methods. We carry out line formation calculations based on a modern model atmosphere grid appropriate for M dwarfs along with a synthetic spectrum synthesis code that treats formation of atomic and molecular lines in cool-star atmospheres including departures from LTE. We use near-infrared spectra collected with the CRIRES instrument at the ESO VLT as reference observational data. Results. We find that the effective temperatures obtained with spectroscopic techniques in different studies mostly agree to better than 100 K and are mostly consistent with the fundamental temperatures derived from interferometric radii and bolometric fluxes. At the same time, much worse agreement in the surface gravities and metallicities is evident. Significant discrepancies in the latter parameters appear when results of the studies based on the optical and near-infrared observations are intercompared. We demonstrate that non-LTE effects are negligible for Fe I in M-dwarf atmospheres but are important for K I, which has a number of strong lines in the near-infrared spectra of these stars. These effects, leading to potassium abundance and metallicity corrections on the order of 0.2 dex, may be responsible for some of the discrepancies in the published analyses. Differences in the temperature–pressure structures of the atmospheric models may be another factor contributing to the deviations between the spectroscopic studies, in particular at low metallicities and high effective temperatures. Conclusions. High-resolution spectroscopic studies of M dwarfs are yet to reach the level of consistency and reproducibility typical of similar investigations of FGK stars. Attention should be given to details of the line formation physics as well as input atomic and molecular data. Collecting high-quality spectra with a wide wavelength coverage of M dwarfs with known fundamental parameters is an essential step in benchmarking spectroscopic parameter determination of low-mass stars.
Active Face Frontalization using Commodity Unmanned Aerial Vehicles

This paper describes a system by which Unmanned Aerial Vehicles (UAVs) can gather high-quality face images that can be used in biometric identification tasks. Success in face-based identification depends in large part on the image quality, and a major factor is how frontal the view is. Face recognition software pipelines can improve identification rates by synthesizing frontal views from non-frontal views by a process call {\em frontalization}. Here we exploit the high mobility of UAVs to actively gather frontal images using components of a synthetic frontalization pipeline. We define a frontalization error and show that it can be used to guide an UAVs to capture frontal views. Further, we show that the resulting image stream improves matching quality of a typical face recognition similarity metric. The system is implemented using an off-the-shelf hardware and software components and can be easily transfered to any ROS enabled UAVs.
Dissecting Supervised Constrastive Learning

Minimizing cross-entropy over the softmax scores of a linear map composed with a high-capacity encoder is arguably the most popular choice for training neural networks on supervised learning tasks. However, recent works show that one can directly optimize the encoder instead, to obtain equally (or even more) discriminative representations via a supervised variant of a contrastive objective. In this work, we address the question whether there are fundamental differences in the sought-for representation geometry in the output space of the encoder at minimal loss. Specifically, we prove, under mild assumptions, that both losses attain their minimum once the representations of each class collapse to the vertices of a regular simplex, inscribed in a hypersphere. We provide empirical evidence that this configuration is attained in practice and that reaching a close-to-optimal state typically indicates good generalization performance. Yet, the two losses show remarkably different optimization behavior. The number of iterations required to perfectly fit to data scales superlinearly with the amount of randomly flipped labels for the supervised contrastive loss. This is in contrast to the approximately linear scaling previously reported for networks trained with cross-entropy.
The case for thermalization as a contributor to the [C ii] deficit

The [C ii] deficit, which describes the observed decrease in the ratio of [C ii] 158 μm emission to continuum infrared emission in galaxies with high star formation surface densities, places a significant challenge to the interpretation of [C ii] detections from across the observable universe. In an attempt to further decode the cause of the [C ii] deficit, the [C ii] and dust continuum emission from 18 Local Volume galaxies has been split based on conditions within the interstellar medium where it originated. This is completed using the Key Insights in Nearby Galaxies: a Far-Infrared Survey with Herschel (KINGFISH) and Beyond the Peak (BtP) surveys and the wide-range of wavelength information, from UV to far-infrared emission lines, available for a selection of star-forming regions within these samples. By comparing these subdivided [C ii] emissions to isolated infrared emission and other properties, we find that the thermalization (collisional de-excitation) of the [C ii] line in H ii regions plays a significant role in the deficit observed in our sample.
Metastable Helium Reveals an Extended Atmosphere for the Gas Giant HAT-P-18b

The metastable helium line at 1083 nm can be used to probe the extended upper atmospheres of close-in exoplanets and thus provide insight into their atmospheric mass loss, which is likely to be significant in sculpting their population. We used an ultra-narrow band filter centered on this line to observe two transits of the low-density gas giant HAT-P-18b, using the 200″ Hale Telescope at Palomar Observatory, and report the detection of its extended upper atmosphere. We constrain the excess absorption to be 0.46% ± 0.12% in our 0.635 nm bandpass, exceeding the transit depth from the Transiting Exoplanet Survey Satellite (TESS) by 3. 9σ. If we fit this signal with a 1D Parker wind model, we find that it corresponds to an atmospheric mass loss rate between 8.3−1.9+2.8×10−5MJ Gyr−1 and 2.63−0.64+0.46×10−3MJ Gyr−1 for thermosphere temperatures ranging from 4000 K to 13,000 K, respectively. With a J magnitude of 10.8, this is the faintest system for which such a measurement has been made to date, demonstrating the effectiveness of this approach for surveying mass loss on a diverse sample of close-in gas giant planets.
A Comparison of Circumgalactic Mg ii Absorption between the TNG50 Simulation and the MEGAFLOW Survey

The circumgalactic medium (CGM) contains information on gas flows around galaxies, such as accretion and supernova-driven winds, which are difficult to constrain from observations alone. Here, we use the high-resolution TNG50 cosmological magnetohydrodynamical simulation to study the properties and kinematics of the CGM around star-forming galaxies in 1011.5–1012 M ⊙ halos at z ≃ 1 using mock Mg ii absorption lines, which we generate by postprocessing halos to account for photoionization in the presence of a UV background. We find that the Mg ii gas is a very good tracer of the cold CGM, which is accreting inward at inflow velocities of up to 50 km s−1. For sight lines aligned with the galaxy’s major axis, we find that Mg ii absorption lines are kinematically shifted due to the cold CGM’s significant corotation at speeds up to 50% of the virial velocity for impact parameters up to 60 kpc. We compare mock Mg ii spectra to observations from the MusE GAs FLow and Wind (MEGAFLOW) survey of strong Mg ii absorbers (EW2796 Å 0 > 0.5 Å). After matching the equivalent-width (EW) selection, we find that the mock Mg ii spectra reflect the diversity of observed kinematics and EWs from MEGAFLOW, even though the sight lines probe a very small fraction of the CGM. Mg ii absorption in higher-mass halos is stronger and broader than in lower-mass halos but has qualitatively similar kinematics. The median-specific angular momentum of the Mg ii CGM gas in TNG50 is very similar to that of the entire CGM and only differs from non-CGM components of the halo by normalization factors of ≲1 dex.