Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional Learning of Fair Representations

We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. We show how these two components contribute to ensuring accuracy parity and equalized false-positive and false-negative rates across groups without impacting demographic parity. Furthermore, we also demonstrate both in theory and on two real-world experiments that the proposed algorithm leads to a better utility-fairness trade-off on balanced datasets compared with existing algorithms on learning fair representations.
UvA-DARE (Digital Academic Repository) Measuring the atomic composition of planetary building blocks

Context. Volatile molecules are critical to terrestrial planetary habitability, yet they are di ﬃ cult to observe directly where planets form at the midplanes of protoplanetary disks. It is unclear whether the inner ∼ 1AU of disks are volatile-poor or if this region is resupplied with ice-rich dust from colder disk regions. Dust traps at radial pressure maxima bounding disk gaps can cut o ﬀ the inner disk from these types of volatile reservoirs. However, the trap retention e ﬃ ciency and atomic composition of trapped dust have not been measured. Aims. We present a new technique to measure the absolute atomic abundances in the gas accreting onto T Tauri stars and infer the bulk atomic composition and distribution of midplane solids that have been retained in the disk around the young star TW Hya. Methods. We identify near-infrared atomic line emission from gas-phase material inside the dust sublimation rim of TW Hya. Gaus- sian decomposition of the strongest H Paschen lines isolates the inner disk hydrogen emission. We measure several key elemental abundances, relative to hydrogen, using a chemical photoionization model and infer dust retention in the disk. With a 1D transport model, we determine approximate radial locations and retention e ﬃ ciencies of dust traps for di ﬀ erent elements. Results. Volatile and refractory elements are depleted from TW Hya’s hot gas by factors of ∼ 10 2 and up to 10 5 , respectively. The abundances of the trapped solids are consistent with a combination of primitive Solar System bodies. Dust traps beyond the CO and N 2 snowline cumulatively sequester 96% of the total dust ﬂux, while the trap at 2AU, near the H 2 O snowline, retains 3%. The high depletions of Si, Mg, and Ca are explained by a third trap at 0.3AU with > 95% dust retention. Conclusion. TW Hya sports a signiﬁcant volatile reservoir rich in C- and N-ices in its outer submillimeter ring structure. However, unless the inner disk was enhanced in C by earlier radial transport, typical C destruction mechanisms and the lack of a C resupply should leave the terrestrial planet-forming region of TW Hya “dry” and carbon-poor. Any planets that form within the silicate dust trap at 0.3AU could resemble Earth in terms of the degree of their volatile depletion.
Occurence of A Cyber Security Eco-System: A Nature Oriented Project and Evaluation of An Indirect Social Experiment

Because of todays technological developments and the influence of digital systems into every aspect of our lives, importance of cyber security improves more and more day-by-day. Projects, educational processes and seminars realized for this aim create and improve awareness among individuals and provide useful tools for growing equipped generations. The aim of this study is to focus on a cyber security eco-system, which was self-occurred within the interactive educational environment designed under the scope of TUBITAK 4004 Nature Education and Science Schools Projects (with the name of A Cyber Security Adventure) with the use of important technologies such as virtual reality, augmented reality, and artificial intelligence. The eco-system occurred within the interactive educational process where high school students took place caused both students and the project team to experience an indirect social experiment environment. In this sense, it is thought that the findings and comments presented in the study will give important ideas to everyone involved in cyber security education, life-long learning processes, and the technology use in software oriented educational tools.
NORMAL FUNCTIONS FOR ALGEBRAICALLY TRIVIAL CYCLES ARE ALGEBRAIC FOR ARITHMETIC REASONS

For families of smooth complex projective varieties, we show that normal functions arising from algebraically trivial cycle classes are algebraic and defined over the field of definition of the family. In particular, the zero loci of those functions are algebraic and defined over such a field of definition. This proves a conjecture of Charles.
FSSG Scale System in Comparison with GERD Questionnaires in Predicting Endoscopic Findings with Reflux Esophagitis

ABSTRACT Background: Gastroesophageal reflux diseases ( xe "GERD" GERD) frequently manifests in varied symptoms other than its classics (heartburn and regurgitation), this variation might hinder its diagnostic effort. Several questionnaires based on symptoms filled by the patients themselves may help in diagnosing xe "GERD" GERD without previous endoscopic examination. This study objects to compare endoscopic findings in patients which previously asked to fill the questionnaires ( xe "frequency scale for the symptoms of GERD (FSSG)" frequency scale for the symptoms of xe "GERD" GERD (FSSG)) and xe "GERD" xe "GERD questionnaire (GerdQ)" GERD questionnaire (GerdQ) in pursuit of reliable and valid instrument to detect xe "GERD" GERD before endoscopic approaches. Methods: This study was conducted in cross-sectional design involving 72 patients in Adam Malik Hospital, Medan with symptomps of upper abdominal pain or discomfort with or without heartburn/regurgitation. Subjects were asked to fill both FSSG and GerdQ and underwent endoscopy. Diagnoses made were classified as reflux esophagitis, functional dyspepsia, or other diagnoses. Subsequently we conduct a comparison analysis of both questionnaires’ specificity and accuracy using receiver operator curve (ROC) by analyzing the area below the curve. Results: According to endoscopic findings from 72 subjects, we ascertained the following results: 52.8% gastritis, antral ulcer, and esophageal hiatal hernia, 37.5% functional dyspepsia, and reflux esophagitis in 9.7% cases. GerdQ is concluded to be superior in terms of specificity and accuracy compared with FSSG with the following percentages in terms of sensitivity, specificity, accuracy, and p value, respectively: 100%, 23.1%, 61.5%, 0.318 vs. 100%, 73.8%, 86.9%, 0.001. Conclusion: GerdQ is superior compared to FSSG in diagnosing xe "GERD" GERD based on clinical symptoms in daily practice. Keywords: reflux esophagitis, xe "GERD" GERD, FSSG, GerdQ, endoscopy, Los Angeles classification, heartburn.
Orthogonal Gradient Descent for Continual Learning

Neural networks are achieving state of the art and sometimes super-human performance on learning tasks across a variety of domains. Whenever these problems require learning in a continual or sequential manner, however, neural networks suffer from the problem of catastrophic forgetting; they forget how to solve previous tasks after being trained on a new task, despite having the essential capacity to solve both tasks if they were trained on both simultaneously. In this paper, we propose to address this issue from a parameter space perspective and study an approach to restrict the direction of the gradient updates to avoid forgetting previously-learned data. We present the Orthogonal Gradient Descent (OGD) method, which accomplishes this goal by projecting the gradients from new tasks onto a subspace in which the neural network output on previous task does not change and the projected gradient is still in a useful direction for learning the new task. Our approach utilizes the high capacity of a neural network more efficiently and does not require storing the previously learned data that might raise privacy concerns. Experiments on common benchmarks reveal the effectiveness of the proposed OGD method.
Electro-Magnetic Side-Channel Attack Through Learned Denoising and Classification

This paper proposes an upgraded Electro Magnetic (EM) sidechannel attack that automatically reconstructs the intercepted data. A novel system is introduced, running in parallel with leakage signal interception and catching compromising data on the fly. Leveraging on deep learning and Character Recognition (CR) the proposed system retrieves more than 57% of characters present in intercepted signals regardless of signal type: analog or digital. The building of the learning database is detailed and the resulting data made publicly available. The solution is based on Software-Defined Radio (SDR) and Graphics Processing Unit (GPU) architectures. It can be easily deployed onto existing information systems to detect compromising data leakage that should be kept secret.
Unsupervised Domain Adaptation Meets Offline Recommender Learning

It is critical to eliminate selection biases of the rating feedback to construct a well-performing recommender offline. Currently, a promising solution to the challenge is the propensity weighting approach that models the missing mechanism of rating feedback. However, the performance of existing propensity-based algorithms can be significantly affected by the propensity estimation bias. To alleviate the problem, we formulate the missing-not-at-random recommendation as the unsupervised domain adaptation problem and drive the propensity-agnostic generalization error bound. We further propose a corresponding algorithm that minimizes the bound via adversarial learning. Our proposed theoretical framework and algorithm do not depend on the propensity score and can obtain a well-performing rating predictor without the true propensity information. Empirical evaluation using benchmark real-world datasets demonstrates the effectiveness and real-world applicability of the proposed approach.
Active uniaxially ordered suspensions on disordered substrates.

Multiple experiments on active systems consider oriented active suspensions on substrates or in chambers tightly confined along one direction. The theories of polar and apolar phases in such geometries were considered in A. Maitra et al. [Phys. Rev. Lett. 124, 028002 (2020)10.1103/PhysRevLett.124. 028002] and A. Maitra et al. [Proc. Natl. Acad. Sci. USA 115, 6934 (2018)10.1073/pnas.1720607115], respectively. However, the presence of quenched random disorder due to the substrate cannot be completely eliminated in many experimental contexts possibly masking the predictions from those theories. In this paper, I consider the effect of quenched orientational disorder on the phase behavior of both polar and apolar suspensions on substrates. I show that polar suspensions have long-range order in two dimensions with anomalous number fluctuations, while their apolar counterparts have only short-range order, albeit with a correlation length that can increase with activity, and even more violent number fluctuations than active nematics without quenched disorder. These results should be of value in interpreting experiments on active suspensions on substrates with random disorder.
Soft Actor-Critic for Discrete Action Settings

Soft Actor-Critic is a state-of-the-art reinforcement learning algorithm for continuous action settings that is not applicable to discrete action settings. Many important settings involve discrete actions, however, and so here we derive an alternative version of the Soft Actor-Critic algorithm that is applicable to discrete action settings. We then show that, even without any hyperparameter tuning, it is competitive with the tuned model-free state-of-the-art on a selection of games from the Atari suite.
Representation stability for sequences of 0-Hecke modules

We define a new category analogous to ${\bf FI}$ for the $0$-Hecke algebra $H_n(0)$ called the $0$-Hecke category, $\mathcal{H}$, indexing sequences of representations of $H_n(0)$ as $n$ varies under suitable compatibility conditions. We establish a new type of representation stability in this setting and prove it is implied by being a finitely generated $\mathcal{H}$-module. We then provide examples of $\mathcal{H}$-modules and discuss further desirable properties these modules possess.
Spectral Analysis of Fermi-LAT Gamma-Ray Bursts with Known Redshift and their Potential Use as Cosmological Standard Candles

Long duration gamma-ray bursts may serve as standard candles to constrain cosmological parameters by probing the Hubble diagram well beyond the range of redshift currently accessible using SNe Ia. The standardization of gamma-ray bursts (GRBs) is based on phenomenological relations between two or more parameters found from spectral modeling, one of which is strongly dependent on the cosmological model. The Amati relation links the source-frame energy at which the prompt gamma-ray spectral energy distribution νFν peaks, and the isotropic-equivalent bolometric energy emitted during the prompt phase. We performed spectral analysis of 26 GRBs with known redshift that have been detected by the Fermi-Large Area Telescope (LAT) during its nine years of operations from 2008 July to 2017 September, thus extending the computation of Eiso to the 100 MeV range. Multiple components are required to fit the spectra of a number of GRBs. We found that the Amati relation is satisfied by the 25 LGRBs, with best-fit parameters similar to previous studies that used data from different satellite experiments, while the only short GRB with known redshift is an outlier. Using the Amati relation, we extend the Hubble diagram to redshift 4.35 and constrain the Hubble constant and dark-energy density in the ΛCDM model, with Fermi-LAT GRBs alone and together with another sample of 94 GRBs and with the latest Supernovae type-Ia data. Our results are consistent with the currently acceptable ranges of those cosmological parameters within errors.
Newton vs the machine: solving the chaotic three-body problem using deep neural networks

Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved. Currently, the solution for a given initialization can only be found by performing laborious iterative calculations that have unpredictable and potentially infinite computational cost, due to the system’s chaotic nature. We show that an ensemble of converged solutions for the planar chaotic three-body problem obtained using an arbitrarily precise numerical integrator can be used to train a deep artificial neural network (ANN) that, over a bounded time interval, provides accurate solutions at a fixed computational cost and up to 100 million times faster than the numerical integrator. In addition, we demonstrate the importance of training an ANN using converged solutions from an arbitrary precise integrator, relative to solutions computed by a conventional fixed precision integrator, which can introduce errors in the training data, due to numerical round-off and time discretization, that are learned by the ANN. Our results provide evidence that, for computationally challenging regions of phase space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black hole binary systems or the origin of the core collapse in dense star clusters.
Unsupervised Question Answering for Fact-Checking

Recent Deep Learning (DL) models have succeeded in achieving human-level accuracy on various natural language tasks such as question-answering, natural language inference (NLI), and textual entailment. These tasks not only require the contextual knowledge but also the reasoning abilities to be solved efficiently. In this paper, we propose an unsupervised question-answering based approach for a similar task, fact-checking. We transform the FEVER dataset into a Cloze-task by masking named entities provided in the claims. To predict the answer token, we utilize pre-trained Bidirectional Encoder Representations from Transformers (BERT). The classifier computes label based on the correctly answered questions and a threshold. Currently, the classifier is able to classify the claims as “SUPPORTS” and “MANUAL_REVIEW”. This approach achieves a label accuracy of 80.2% on the development set and 80.25% on the test set of the transformed dataset.
The cosmic evolution of the stellar mass–velocity dispersion relation of early-type galaxies

We study the evolution of the observed correlation between central stellar velocity dispersion σe and stellar mass M* of massive ($M_*\gtrsim 3\times 10^{10}\, \mathrm{M_\odot}$) early-type galaxies (ETGs) out to redshift z ≈ 2.5, taking advantage of a Bayesian hierarchical inference formalism. Collecting ETGs from state-of-the-art literature samples, we build a fiducial sample (0 ≲ z ≲ 1), which is obtained with homogeneous selection criteria, but also a less homogeneous extended sample (0 ≲ z ≲ 2.5). Based on the fiducial sample, we find that at z ≲ 1 the M*–σe relation is well represented by $\sigma _{\mathrm{e}}\propto M_*^{\beta }(1+z)^{\zeta}$, with β ≃ 0.18 independent of redshift and ζ ≃ 0.4 (at a given M*, σe decreases for decreasing z, for instance by a factor of ≈1.3 from z = 1 to z = 0). When the slope β is allowed to evolve, we find it increasing with redshift: β(z) ≃ 0.16 + 0.26log (1 + z) describes the data as well as constant β ≃ 0.18. The intrinsic scatter of the M*–σe relation is ≃0.08 dex in σe at given M*, independent of redshift. Our results suggest that, on average, the velocity dispersion of individual massive (M* ≳ 3 × 1011M⊙) ETGs decreases with time while they evolve from z ≈ 1 to z ≈ 0. The analysis of the extended sample, over the wider redshift range 0 ≲ z ≲ 2.5, leads to results similar to that of the fiducial sample, with slightly stronger redshift dependence of the normalization (ζ ≃ 0.5) and weaker redshift dependence of the slope (dβ/dlog (1 + z) ≃ 0.18) when β varies with time. At z = 2 ETGs with $M_*\approx 10^{11}\, \mathrm{M_\odot}$ have, on average, ≈1.7 higher σe than ETGs of similar stellar mass at z = 0.
The irreducible weak modules for the fixed point subalgebra of the vertex algebra associated to a non-degenerate even lattice by an automorphism of order $2$

Let $V_{L}$ be the vertex algebra associated to a non-degenerate even lattice $L$, $\theta$ the automorphism of $V_{L}$ induced from the $-1$ symmetry of $L$, and $V_{L}^{+}$ the fixed point subalgebra of $V_{L}$ under the action of $\theta$. We classify the irreducible weak $V_{L}^{+}$-modules and show that any irreducible weak $V_{L}^{+}$-module is isomorphic to a weak submodule of some irreducible weak $V_{L}$-module or to a submodule of some irreducible $\theta$-twisted $V_{L}$-module.
The Landscape of Academic Literature in Quantum Information Technologies

In this study, we have investigated the landscape of academic literature in quantum information technologies (QIT) using bibliometric tools of analysis. In the first part of the study, we have used an initial set of 50,822 articles obtained from the Web of Science using an extensive search query constructed through expert opinion. Analysis of this section revealed that QIT is deeply rooted in the field of physics and over 80% of the articles are published in journals mainly reserved for publication in physics. Additionally, it can be seen from this initial set that on a quantitative level based on the number of published scientific articles, the US, China, and Europe are almost on par. Furthermore, a keyword analysis on this set revealed that the main academic literature can be clustered into three distinct sets, which are (i) quantum communication/cryptography, (ii) quantum computation, and (iii) physical realizations of quantum systems. Finally, for the first section, a burst analysis on this set using different density parameters showed the emergence and fading away of certain key concepts in the literature. In the second part, we focused on 808 "highly cited" articles provided by the Web of Science. Using the co-citation analysis of these articles we have devised a set of core corpus of 34 publications for QIT literature and showed how this core corpus is clustered in itself. Finally for the second section, using bibliographic coupling we have mapped collaboration networks on the country and institutional levels. Search queries and access to enhanced views of figures are provided in appendix.
Half-mass Radii of Quiescent and Star-forming Galaxies Evolve Slowly from 0 ≲ z ≤ 2.5: Implications for Galaxy Assembly Histories

We use high-resolution, multiband imaging of ∼16,500 galaxies in the CANDELS fields at 0 ≲ z ≤ 2.5 to study the evolution of color gradients and half-mass radii over cosmic time. We find that galaxy color gradients at fixed mass evolve rapidly between z ∼ 2.5 and z ∼ 1, but remain roughly constant below z ∼ 1. This result implies that the sizes of both star-forming and quiescent galaxies increase much more slowly than previous studies found using half-light radii. The half-mass radius evolution of quiescent galaxies is fully consistent with a model that uses observed minor merger rates to predict the increase in sizes due to the accretion of small galaxies. Progenitor bias may still contribute to the growth of quiescent galaxies, particularly if we assume a slower timescale for the minor merger growth model. The slower half-mass radius evolution of star-forming galaxies is in tension with cosmological simulations and semianalytic galaxy models. Further detailed, consistent comparisons with simulations are required to place these results in context.
The nuclear disc of the Milky Way: Early formation, long quiescence, and starburst activity one billion years ago

The nuclear disc is a dense stellar structure at the centre of the Milky Way, with a radius of $\sim$150 pc. It has been a place of intense star formation in the past several tens of millions of years but its overall formation history has remained unknown up to now. Here we report the first detailed star formation history of this region. The bulk of its stars formed at least eight billion years ago. This initial activity was followed by a long period of quiescence that was ended by an outstanding event about 1 Gyr ago, during which roughly 5% of its mass formed in a time window $\sim$100 Myr, in what may arguably have been one of the most energetic events in the history of the Milky Way. Star formation continued subsequently on a lower level, creating a few percent of the stellar mass in the past $\sim$500 Myr, with an increased rate up to $\sim$30 Myr ago. Our findings contradict the previously accepted paradigm of quasi-continuous star formation at the centre of the Milky Way. The long quiescent phase agrees with the overall quiescent history of the Milky Way and suggests that our Galaxy's bar may not have existed until recently, or that gas transport through the bar was extremely inefficient during a long stretch of the Milky Way's life, and that the central black hole may have acquired most of its mass already in the early days of the Milky Way.