Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotic analysis of the form-factors of quantum spin chains

Since a long-time, the quantum integrable systems have remained an area where modern mathematical methods have given an access to interesting results in the study of physical systems. The exact computations, both numerical and asymptotic, of the correlation function is one of the most important subject of the theory of the quantum integrable models. In this context an approach based on the calculation of form factors has been proved to be a more effective one. In this thesis, we develop a new method based on the algebraic Bethe ansatz for the computation of the form-factors in thermodynamic limit. It is both applied to and described in the context of isotropic XXX Heisenberg chain, which is one of the examples of an interesting case of critical models where the Fermi-zone is non-compact. In a particular case of two-spinon form-factors, we obtain an exact result in a closed-form which matches the previous result obtained from an approach based on $q$-vertex operator algebra. This method is then generalised to form-factors in higher spinon sectors where we find a reduced determinant representation for the form-factors, in which a higher-level structure for the form-factors is revealed.
Analytic approach to non-slow-roll inflation

Brief periods of non-slow-roll evolution during inflation can produce interesting observable consequences, as primordial black holes, or an inflationary gravitational wave spectrum enhanced at small scales. We develop a model independent, analytic approach for studying the predictions of single-field scenarios which include short phases of slow-roll violation. Our method is based on Taylor expanding the equations for cosmological fluctuations in a small quantity, which parameterizes the duration of the non-slow-roll eras. The super-horizon spectrum of perturbations is described by few effective parameters, and is characterized by a pronounced dip followed by a rapid growth in its amplitude, as typically found in numerical and analytical studies. The dip position $k_{\rm dip}/k_*$ and the maximal enhancement $\Pi_{\rm max}$ of the spectrum towards small scales are found to be related by the law $k_{\rm dip}/k_*\propto \Pi_{\rm max}^{-1/4}$, and we determine the proportionality constant. For a single epoch of slow-roll violation we confirm previous studies, finding that the steepest slope of the spectrum well after the dip has spectral index $n-1\,=\,4$. On the other hand, with multiple phases of slow-roll violation, the slope of the spectrum is generally enhanced. For example, when two epochs of slow-roll violation take place, separated by a phase of quasi-de Sitter expansion, we find that the spectral index can reach the value $n-1\,=\,8$. This phenomenon indicates that the slope of the spectrum keeps memory of the history of non-slow-roll phases occurred during inflation.
Photometric redshifts in the North Ecliptic Pole Wide field based on a deep optical survey with Hyper Suprime-Cam

The AKARI space infrared telescope has performed near-infrared to mid-infrared (MIR) observations on the North Ecliptic Pole Wide (NEPW) field (5.4 deg2) for about 1 yr. AKARI took advantage of its continuous nine photometric bands, compared with NASA's Spitzer and Wide-field Infrared Survey Explorer(WISE) space telescopes, which had only four filters with a wide gap in the MIR. The AKARI NEPW field lacked deep and homogeneous optical data, limiting the use of nearly half of the IR sources for extragalactic studies, because of the absence of photometric redshift (photo-z). To remedy this, we have recently obtained deep optical imaging over the NEPW field with five bands (g, r, i, z and Y) of the Hyper Suprime-Camera (HSC) on the Subaru 8-m telescope. We optically identify AKARI-IR sources along with supplementary Spitzer and WISE data as well as pre-existing optical data. In this work, we derive new photo-z using a χ2 template-fitting method code, PHotometric Analysis for Redshift Estimate (Le Phare) and reliable photometry from 26 selected filters including HSC, AKARI, Canada–France–Hawaii Telescope, Maidanak, Kitt Peak National Observatory, Spitzer and WISE data. We take 2026 spectroscopic redshifts (spec-z) from all available spectroscopic surveys over the NEPW field to calibrate and assess the accuracy of the photo-z. At z < 1.5, we achieve a weighted photo-z dispersion of σΔz/(1+z) = 0.053 with η = 11.3 per cent catastrophic errors.
Optimising Design Verification Using Machine Learning: An Open Source Solution

With the complexity of Integrated Circuits increasing, design verification has become the most time consuming part of the ASIC design flow. Nearly 70% of the SoC design cycle is consumed by verification. The most commonly used approach to test all corner cases is through the use of Constrained Random Verification. Random stimulus is given in order to hit all possible combinations and test the design thoroughly. However, this approach often requires significant human expertise to reach all corner cases. This paper presents an alternative using Machine Learning to generate the input stimulus. This will allow for faster thorough verification of the design with less human intervention. Furthermore, it is proposed to use the open source verification environment 'Cocotb'. Based on Python, it is simple, intuitive and has a vast library of functions for machine learning applications. This makes it more convenient to use than the bulkier approach using traditional Hardware Verification Languages such as System Verilog or Specman E.
Ten simple rules for making a vocabulary FAIR

We present ten simple rules that support converting a legacy vocabulary—a list of terms available in a print-based glossary or in a table not accessible using web standards—into a FAIR vocabulary. Various pathways may be followed to publish the FAIR vocabulary, but we emphasise particularly the goal of providing a globally unique resolvable identifier for each term or concept. A standard representation of the concept should be returned when the individual web identifier is resolved, using SKOS or OWL serialised in an RDF-based representation for machine-interchange and in a web-page for human consumption. Guidelines for vocabulary and term metadata are provided, as well as development and maintenance considerations. The rules are arranged as a stepwise recipe for creating a FAIR vocabulary based on the legacy vocabulary. By following these rules you can achieve the outcome of converting a legacy vocabulary into a standalone FAIR vocabulary, which can be used for unambiguous data annotation. In turn, this increases data interoperability and enables data integration.
Ultrasound Scatterer Density Classification Using Convolutional Neural Networks by Exploiting Patch Statistics

Quantitative ultrasound (QUS) can reveal crucial information on tissue properties such as scatterer density. If the scatterer density per resolution cell is above or below 10, the tissue is considered as fully developed speckle (FDS) or low-density scatterers (LDS), respectively. Conventionally, the scatterer density has been classified using estimated statistical parameters of the amplitude of backscattered echoes. However, if the patch size is small, the estimation is not accurate. These parameters are also highly dependent on imaging settings. In this paper, we propose a convolutional neural network (CNN) architecture for QUS, and train it using simulation data. We further improve the network performance by utilizing patch statistics as additional input channels. We evaluate the network using simulation data, experimental phantoms and in vivo data. We also compare our proposed network with different classic and deep learning models, and demonstrate its superior performance in classification of tissues with different scatterer density values. The results also show that the proposed network is able to work with different imaging parameters with no need for a reference phantom. This work demonstrates the potential of CNNs in classifying scatterer density in ultrasound images.
On the Iterate Convergence and Manifold Identification of Inexact Proximal-Newton-Type Methods Under a Sharpness Condition

We consider minimizing the sum of a smooth function and a regularization term that promotes structured solution using an inexact proximal variable-metric approach, which encompasses widely-used algorithms including proximal Newton and proximal quasi-Newton. Convergence of the iterates is proven when both terms are convex and the sum satisfies a sharpness condition locally, of which the quadratic growth condition and the weak sharp minima are special cases. When the regularization is partly smooth, a condition satisfied by many popular subproblem solvers for obtaining approximate update steps is then presented to be sufficient for identification of the optimal manifold around a local solution. Through the theoretical results, IPVM+, an algorithm with improved time efficiency, is then presented in detail with numerical results on real-world problems.
Model Reduction Methods for Complex Network Systems

Network systems consist of subsystems and their interconnections and provide a powerful framework for the analysis, modeling, and control of complex systems. However, subsystems may have high-dimensional dynamics and a large number of complex interconnections, and it is therefore relevant to study reduction methods for network systems. Here, we provide an overview of reduction methods for both the topological (interconnection) structure of a network and the dynamics of the nodes while preserving structural properties of the network. We first review topological complexity reduction methods based on graph clustering and aggregation, producing a reduced-order network model. Next, we consider reduction of the nodal dynamics using extensions of classical methods while preserving the stability and synchronization properties. Finally, we present a structure-preserving generalized balancing method for simultaneously simplifying the topological structure and the order of the nodal dynamics.
Concept-based model explanations for electronic health records

Recurrent Neural Networks (RNNs) are often used for sequential modeling of adverse outcomes in electronic health records (EHRs) due to their ability to encode past clinical states. These deep, recurrent architectures have displayed increased performance compared to other modeling approaches in a number of tasks, fueling the interest in deploying deep models in clinical settings. One of the key elements in ensuring safe model deployment and building user trust is model explainability. Testing with Concept Activation Vectors (TCAV) has recently been introduced as a way of providing human-understandable explanations by comparing high-level concepts to the network's gradients. While the technique has shown promising results in real-world imaging applications, it has not been applied to structured temporal inputs. To enable an application of TCAV to sequential predictions in the EHR, we propose an extension of the method to time series data. We evaluate the proposed approach on an open EHR benchmark from the intensive care unit, as well as synthetic data where we are able to better isolate individual effects.
Quantum internet under random breakdowns and intentional attacks

Quantum networks (QNs) will play a key role in distributed quantum information processing. As the network size increases, network-level errors like random breakdown and intentional attack are inevitable; therefore, it is important to understand the robustness of large-scale QNs, similar to what has been done for the classical counterpart—the internet. For exponential networks such as Waxman networks, errors simply re-parameterize the network and lead to a linear decrease of the quantum capacity with the probability of error. The same linear decay happens for scale-free QNs under random breakdowns, despite the previously discovered robustness in terms of the connectivity. In presence of attack, however, the capacity of scale-free QNs shows a sharp exponential decay with the increasing attack fraction. Our results apply to quantum internet based on fibers for all kinds of quantum communications and provide implications for the future construction of QNs with regard to its robustness.
Decaying Orbit of the Hot Jupiter WASP-12b: Confirmation with TESS Observations

Theory suggests that the orbits of some close-in giant planets should decay due to tidal interactions with their host stars. To date, WASP-12b is the only hot Jupiter reported to have a decaying orbit, at a rate of 29 ± 2 ms yr−1. We analyzed data from NASA’s Transiting Exoplanet Survey Satellite (TESS) to verify that WASP-12b’s orbit is indeed changing. We find that the TESS transit and occultation data are consistent with a decaying orbit with an updated period of 1.091420090 ± 0.000000041 days and a decay rate of 32.53 ± 1.62 ms yr−1. We find an orbital decay timescale of . If the observed decay results from tidal dissipation, the modified tidal quality factor is , which falls at the lower end of values derived for binary star systems and hot Jupiters. Our result highlights the power of space-based photometry for investigating the orbital evolution of short-period exoplanets.
Learning in two-player games between transparent opponents

We consider a scenario in which two reinforcement learning agents repeatedly play a matrix game against each other and update their parameters after each round. The agents' decision-making is transparent to each other, which allows each agent to predict how their opponent will play against them. To prevent an infinite regress of both agents recursively predicting each other indefinitely, each agent is required to give an opponent-independent response with some probability at least epsilon. Transparency also allows each agent to anticipate and shape the other agent's gradient step, i.e. to move to regions of parameter space in which the opponent's gradient points in a direction favourable to them. We study the resulting dynamics experimentally, using two algorithms from previous literature (LOLA and SOS) for opponent-aware learning. We find that the combination of mutually transparent decision-making and opponent-aware learning robustly leads to mutual cooperation in a single-shot prisoner's dilemma. In a game of chicken, in which both agents try to manoeuvre their opponent towards their preferred equilibrium, converging to a mutually beneficial outcome turns out to be much harder, and opponent-aware learning can even lead to worst-case outcomes for both agents. This highlights the need to develop opponent-aware learning algorithms that achieve acceptable outcomes in social dilemmas involving an equilibrium selection problem.
Active Learning from Crowd in Document Screening

In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of machine-learning filters. Specifically, we focus on building a set of machine learning classifiers that evaluate documents, and then screen them efficiently. It is a challenging task since the budget is limited and there are countless number of ways to spend the given budget on the problem. We propose a multi-label active learning screening specific sampling technique -- objective-aware sampling -- for querying unlabelled documents for annotating. Our algorithm takes a decision on which machine filter need more training data and how to choose unlabeled items to annotate in order to minimize the risk of overall classification errors rather than minimizing a single filter error. We demonstrate that objective-aware sampling significantly outperforms the state of the art active learning sampling strategies.
ReMix: Calibrated Resampling for Class Imbalance in Deep learning

Class imbalance is a problem of significant importance in applied deep learning where trained models are exploited for decision support and automated decisions in critical areas such as health and medicine, transportation, and finance. The challenge of learning deep models from imbalanced training data remains high, and the state-of-the-art solutions are typically data dependent and primarily focused on image data. Real-world imbalanced classification problems, however, are much more diverse thus necessitating a general solution that can be applied to tabular, image and text data. In this paper, we propose ReMix, a training technique that leverages batch resampling, instance mixing and soft-labels to enable the induction of robust deep models for imbalanced learning. Our results show that dense nets and CNNs trained with ReMix generally outperform the alternatives according to the g-mean and are better calibrated according to the balanced Brier score.
Interactive Tools for Reproducible Science - Understanding, Supporting, and Motivating Reproducible Science Practices

Reproducibility should be a cornerstone of science as it enables validation and reuse. In recent years, the scientific community and the general public became increasingly aware of the reproducibility crisis, i.e. the wide-spread inability of researchers to reproduce published work, including their own. Scientific research is increasingly focused on the creation, observation, processing, and analysis of large data volumes. On the one hand, this transition towards computational and data-intensive science poses new challenges for research reproducibility and reuse. On the other hand, increased availability and advances in computation and web technologies offer new opportunities to address the reproducibility crisis. This thesis reports on user-centered design research conducted at CERN, a key laboratory in data-intensive particle physics. In this thesis, we build a wider understanding of researchers' interactions with tools that support research documentation, preservation, and sharing. From a Human-Computer Interaction (HCI) perspective the following aspects are fundamental: (1) Characterize and map requirements and practices around research preservation and reuse. (2) Understand the wider role and impact of research data management (RDM) tools in scientific workflows. (3) Design tools and interactions that promote, motivate, and acknowledge reproducible research practices. Research reported in this thesis represents the first systematic application of HCI methods in the study and design of interactive tools for reproducible science. We advocate the unique role of HCI in supporting, motivating, and transforming reproducible research practices through the design of tools that enable effective RDM. This thesis paves new ways for interaction with RDM tools that support and motivate reproducible science.
Data Processing and Annotation Schemes for FinCausal Shared Task

This document explains the annotation schemes used to label the data for the FinCausal Shared Task (Mariko et al., 2020). This task is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), to be held at The 28th International Conference on Computational Linguistics (COLING'2020), on December 12, 2020.
Constraining $M_\nu$ with the Bispectrum II: The Total Information Content of the Galaxy Bispectrum

Massive neutrinos suppress the growth of structure on small scales and leave an imprint on large-scale structure that can be measured to constrain their total mass, $M_\nu$. With standard analyses of two-point clustering statistics, $M_\nu$ constraints are severely limited by parameter degeneracies. Hahn et al.(2020) demonstrated that the bispectrum, the next higher-order statistic, can break these degeneracies and dramatically improve constraints on $M_\nu$ and other cosmological parameters. In this paper, we present the constraining power of the redshift-space galaxy bispectrum, $B_0^g$. We construct the Molino suite of 75,000 mock galaxy catalogs from the Quijote $N$-body simulations using the halo occupation distribution (HOD) model, which provides a galaxy bias framework well-suited for simulation-based approaches. Using these mocks, we present Fisher matrix forecasts for $\{\Omega_m,\Omega_b,h,n_s,\sigma_8, M_\nu\}$ and quantify, for the first time, the total information content of $B_0^g$ down to nonlinear scales. For $k_{\rm max}=0.5h/Mpc$, $B_0^g$ improves constraints on $\Omega_m,\Omega_b,h,n_s,\sigma_8$, and $M_\nu$ by 2.8, 3.1, 3.8, 4.2, 4.2, and $4.6\times$ over the power spectrum, after marginalizing over HOD parameters. Even with priors from $Planck$, $B_0^g$ improves all of the cosmological constraints by $\gtrsim2\times$. In fact, for $P_\ell^g$ and $B_0^g$ out to $k_{\rm max}=0.5h/Mpc$ with $Planck$ priors, we achieve a $1\sigma$ $M_\nu$ constraint of 0.048 eV, which is tighter than the current best cosmological constraint. While effects such as survey geometry and assembly bias will have an impact, these constraints are derived for $(1h^{-1}{\rm Gpc})^3$, a substantially smaller volume than upcoming surveys. Therefore, we conclude that the galaxy bispectrum will significantly improve cosmological constraints for upcoming galaxy surveys -- especially for $M_\nu$.
Shrinkage under Random Projections, and Cubic Formula Lower Bounds for $\mathbf{AC}^0$.

H\r{a}stad showed that any De Morgan formula (composed of AND, OR and NOT gates) shrinks by a factor of $O(p^{2})$ under a random restriction that leaves each variable alive independently with probability $p$ [SICOMP, 1998]. Using this result, he gave an $\widetilde{\Omega}(n^{3})$ formula size lower bound for the Andreev function, which, up to lower order improvements, remains the state-of-the-art lower bound for any explicit function. In this work, we extend the shrinkage result of H\r{a}stad to hold under a far wider family of random restrictions and their generalization -- random projections. Based on our shrinkage results, we obtain an $\widetilde{\Omega}(n^{3})$ formula size lower bound for an explicit function computed in $\mathbf{AC}^0$. This improves upon the best known formula size lower bounds for $\mathbf{AC}^0$, that were only quadratic prior to our work. In addition, we prove that the KRW conjecture [Karchmer et al., Computational Complexity 5(3/4), 1995] holds for inner functions for which the unweighted quantum adversary bound is tight. In particular, this holds for inner functions with a tight Khrapchenko bound. Our random projections are tailor-made to the function's structure so that the function maintains structure even under projection -- using such projections is necessary, as standard random restrictions simplify $\mathbf{AC}^0$ circuits. In contrast, we show that any De Morgan formula shrinks by a quadratic factor under our random projections, allowing us to prove the cubic lower bound. Our proof techniques build on the proof of H\r{a}stad for the simpler case of balanced formulas. This allows for a significantly simpler proof at the cost of slightly worse parameters. As such, when specialized to the case of $p$-random restrictions, our proof can be used as an exposition of H\r{a}stad's result.