Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Differentiation of Some First-Order Methods in Parametric Optimization

We aim at computing the derivative of the solution to a parametric optimization problem with respect to the involved parameters. For a class broader than that of strongly convex functions, this can be achieved by automatic differentiation of iterative minimization algorithms. If the iterative algorithm converges pointwise, then we prove that the derivative sequence also converges pointwise to the derivative of the minimizer with respect to the parameters. Moreover, we provide convergence rates for both sequences. In particular, we prove that the accelerated convergence rate of the Heavy-ball method compared to Gradient Descent also accelerates the derivative computation. An experiment with L2-Regularized Logistic Regression validates the theoretical results.
A hidden integral structure endows Absolute Concentration Robust systems with resilience to dynamical concentration disturbances

Biochemical systems that express certain chemical species of interest at the same level at any positive equilibrium are called “absolute concentration robust” (ACR). These species behave in a stable, predictable way, in the sense that their expression is robust with respect to sudden changes in the species concentration, regardless the new positive equilibrium reached by the system. Such a property has been proven to be fundamentally important in certain gene regulatory networks and signaling systems. In the present paper, we mathematically prove that a well-known class of ACR systems studied by Shinar and Feinberg in 2010 hides an internal integral structure. This structure confers these systems with a higher degree of robustness that what was previously unknown. In particular, disturbances much more general than sudden changes in the species concentrations can be rejected, and robust perfect adaptation is achieved. Significantly, we show that these properties are maintained when the system is interconnected with other chemical reaction networks. This key feature enables design of insulator devices that are able to buffer the loading effect from downstream systems - a crucial requirement for modular circuit design in synthetic biology.
One-Shot Neural Architecture Search via Self-Evaluated Template Network

Neural architecture search (NAS) aims to automate the search procedure of architecture instead of manual design. Even if recent NAS approaches finish the search within days, lengthy training is still required for a specific architecture candidate to get the parameters for its accurate evaluation. Recently one-shot NAS methods are proposed to largely squeeze the tedious training process by sharing parameters across candidates. In this way, the parameters for each candidate can be directly extracted from the shared parameters instead of training them from scratch. However, they have no sense of which candidate will perform better until evaluation so that the candidates to evaluate are randomly sampled and the top-1 candidate is considered the best. In this paper, we propose a Self-Evaluated Template Network (SETN) to improve the quality of the architecture candidates for evaluation so that it is more likely to cover competitive candidates. SETN consists of two components: (1) an evaluator, which learns to indicate the probability of each individual architecture being likely to have a lower validation loss. The candidates for evaluation can thus be selectively sampled according to this evaluator. (2) a template network, which shares parameters among all candidates to amortize the training cost of generated candidates. In experiments, the architecture found by SETN achieves the state-of-the-art performance on CIFAR and ImageNet benchmarks within comparable computation costs.
Interventional Experiment Design for Causal Structure Learning

It is known that from purely observational data, a causal DAG is identifiable only up to its Markov equivalence class, and for many ground truth DAGs, the direction of a large portion of the edges will be remained unidentified. The golden standard for learning the causal DAG beyond Markov equivalence is to perform a sequence of interventions in the system and use the data gathered from the interventional distributions. We consider a setup in which given a budget $k$, we design $k$ interventions non-adaptively. We cast the problem of finding the best intervention target set as an optimization problem which aims to maximize the number of edges whose directions are identified due to the performed interventions. First, we consider the case that the underlying causal structure is a tree. For this case, we propose an efficient exact algorithm for the worst-case gain setup, as well as an approximate algorithm for the average gain setup. We then show that the proposed approach for the average gain setup can be extended to the case of general causal structures. In this case, besides the design of interventions, calculating the objective function is also challenging. We propose an efficient exact calculator as well as two estimators for this task. We evaluate the proposed methods using synthetic as well as real data.
A-type antiferromagnetic order in 
MnBi4Te7
 and 
MnBi6Te10
 single crystals

${\mathrm{MnBi}}_{4}{\mathrm{Te}}_{7}$ and ${\mathrm{MnBi}}_{6}{\mathrm{Te}}_{10}$ are two members with $n=2$ and 3 in the family of ${\mathrm{MnBi}}_{2n}{\mathrm{Te}}_{3n+1}$ where the $n=1$ member, ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$, has been intensively investigated as the first intrinsic antiferromagnetic topological insulator. Here we report the A-type antiferromagnetic order in these two compounds by measuring magnetic properties, electrical and thermal transport, specific heat, and single-crystal neutron diffraction. Both compounds order into an A-type antiferromagnetic structure as does ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$ with ferromagnetic planes coupled antiferromagnetically along the crystallographic $c$ axis. While no evidence for any in-plane ordered moment is found for ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$ or ${\mathrm{MnBi}}_{6}{\mathrm{Te}}_{10}$, weak reflections at half-$L$ positions along the [0 0 $L]$ direction are observed for ${\mathrm{MnBi}}_{4}{\mathrm{Te}}_{7}$ suggesting an in-plane ordered moment around $0.15{\ensuremath{\mu}}_{B}/\mathrm{Mn}$. The ordering temperature, ${T}_{N}$, is 13 K for ${\mathrm{MnBi}}_{4}{\mathrm{Te}}_{7}$ and 11 K for ${\mathrm{MnBi}}_{6}{\mathrm{Te}}_{10}$. The magnetic order is also manifested in the anisotropic magnetic properties. For both compounds, the interlayer coupling is weak and a spin-flip transition occurs when a magnetic field of around 1.6 kOe is applied along the $c$ axis at 2 K. As observed in ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$, when cooling across ${T}_{N}$, no anomaly was observed in the temperature dependence of thermopower. On the other hand, critical scattering effects are observed in thermal conductivity although the effect is less pronounced than that in ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$.
Adapting a solid accretion scenario for migrating planets in fargo3d

In this work, we adapt a module for planetary formation within the hydrodynamic code fargo3d. Planetary formation is modelled by a solid core accretion scenario, with the core growing in oligarchic regime. The initial superficial density of planetesimals is proportional to the initial superficial density of gas in the disc. We include a numerical approach to describe the evolution of the eccentricity and the inclination of planetesimals during the formation. This approach impacts directly on the accretion rate of solids. When the core reaches a critical mass, gas accretion begins, following the original fargo scheme adapted to the fargo3d code. To exemplify how the module for planetary formation can be used, we investigate the migration of a planet in a 2D, locally isothermal gas disc with a prescribed accretion rate, analysing the time-scale involved in the planetary migration process along with the time-scale for planetary formation. The analysis reveals that the mass of the nucleus must be close to its critical value when crossing the ice line to avoid the planet’s fall into the stellar envelope. This will allow enough time for the planet to initiate runaway gas accretion, leading to a rapid mass increase and entering type II planetary migration.
EFFECT OF AGGREGATION MORPHOLOGY ON THERMAL CONDUCTIVITY AND VISCOSITY OF Al2O3-CO2 NANOFLUID: A MOLECULAR DYNAMICS APPROACH

CO2 cooling systems are the wave of the future for industrial refrigeration. CO2 refrigeration systems are gaining traction in recent years which involves heat transfer between CO2 and the base fluid. The high viscosity of CO2 is of interest to the oil and gas industry in enhanced oil recovery and well-fracturing applications. A need arises to improve the thermal conductivity and viscosity of CO2 to increase the efficiency of these significant applications. Aggregation of nanoparticles is one of the crucial mechanisms to improve the thermal conductivity and viscosity of nanofluids. Since the aggregation morphology of nanoparticles is unclear so far, we have evaluated the stable configurations of the aggregation of nanoparticles by determining the potential energy of the different configurations system. In this paper, Green-Kubo formalism is used to calculate the mentioned thermo-physical properties of the different aggregated nanofluids. The nanofluid in this study consists of alumina (Al2O3) nanoparticles and CO2 as a base fluid. Results indicate that the enhancement in the thermal conductivity and viscosity of nanofluid is inversely proportional to the potential energy of the system. The results also mark that various morphologies of the aggregated nanoparticles have different enhancements of thermo-physical properties of the nanofluid. This study is conducive for the researchers to perceive the importance and influence of aggregation morphology of nanoparticles and their stability on the thermal conductivity and viscosity of nanofluid.
DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud Services

Large scale cloud services use Key Performance Indicators (KPIs) for tracking and monitoring performance. They usually have Service Level Objectives (SLOs) baked into the customer agreements which are tied to these KPIs. Dependency failures, code bugs, infrastructure failures, and other problems can cause performance regressions. It is critical to minimize the time and manual effort in diagnosing and triaging such issues to reduce customer impact. Large volume of logs and mixed type of attributes (categorical, continuous) in the logs makes diagnosis of regressions non-trivial.In this paper, we present the design, implementation and experience from building and deploying DeCaf, a system for automated diagnosis and triaging of KPI issues using service logs. It uses machine learning along with pattern mining to help service owners automatically root cause and triage performance issues. We present the learnings and results from case studies on two large scale cloud services in Microsoft where DeCaf successfully diagnosed 10 known and 31 unknown issues. DeCaf also automatically triages the identified issues by leveraging historical data. Our key insights are that for any such diagnosis tool to be effective in practice, it should a) scale to large volumes of service logs and attributes, b) support different types of KPIs and ranking functions, c) be integrated into the DevOps processes.
Bayesian Optimization using Pseudo-Points

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, and robotics. BO usually models the objective function by a Gaussian process (GP), and iteratively samples the next data point by maximizing an acquisition function. In this paper, we propose a new general framework for BO by generating pseudo-points (i.e., data points whose objective values are not evaluated) to improve the GP model. With the classic acquisition function, i.e., upper confidence bound (UCB), we prove that the cumulative regret can be generally upper bounded. Experiments using UCB and other acquisition functions, i.e., probability of improvement (PI) and expectation of improvement (EI), on synthetic as well as real-world problems clearly show the advantage of generating pseudo-points.
Astro- and Quantum Physical Tests of Screened Scalar Fields

In general, modified gravity theories are modifications or extensions of Einstein’s general relativity. Some of them give rise to additional scalar degrees of freedom in Nature. If these scalar fields exist and are light enough, they should cause a gravity-like fifth force that could, in principle, exceed gravity in its strength. However, there are tight constraints on fifth forces from Solar System-based tests. Screening mechanisms are popular means for avoiding these constraints by suppressing a fifth force in regions of high environmental mass density but allowing for phenomenologically interesting effects in environments of lower densities. In this thesis, scalar field models with screening mechanisms will be discussed and some astroand quantum physical tests for their existence presented. At first, the impact of disformally coupled symmetrons on gravitational lensing by galaxies will be evaluated. Secondly, it will be shown how fluctuations of a chameleon scalar field induce the open dynamics of a quantum test particle. For this, tools from non-equilibrium quantum field theory will be introduced, developed and applied, and a quantum master equation derived. Zusammenfassung In der Regel sind modifizierte Gravitationstheorien Modifikationen oder Erweiterungen von Einsteins allgemeiner Relativitätstheorie. Einige davon bedingen zusätzliche skalare Freiheitsgrade in der Natur. Falls solche Skalarfelder existieren und sie leicht genug sind, sollten sie eine gravitationsähnliche fünfte Kraft verursachen, welche sogar stärker als die Schwerkraft sein könnte. Jedoch schränken im Sonnensystem gemachte Beobachtungen die Existenz einer solchen Kraft stark ein. Abschirmmechanismen sind beliebte Methoden, um solche Einschränkungen zu umgehen, indem sie eine fünfte Kraft in Regionen hoher Massendichte unterdrücken, aber in Umgebungen niedrigerer Dichte phänomenologisch interessante Effekte zulassen. In dieser Arbeit werden Skalarfeldmodelle mit Abschirmmechanismen diskutiert und ein paar astround quantenphysikalische Tests ihrer Existenz präsentiert. Zunächst wird der Einfluss von disform gekoppelten Symmetronen auf den Gravitationslinseneffekt von Galaxien untersucht. Anschließend wird gezeigt, wie Fluktuationen eines Chamäleonskalarfeldes die offene Dynamik eines Quantentestteilchens induzieren. Dafür werden Werkzeuge aus der Nichtgleichgewichtsquantenfeldtheorie eingeführt, entwickelt und angewandt, sowie damit eine Quantenmastergleichung hergeleitet.
Pathological Spectra of the Fisher Information Metric and Its Variants in Deep Neural Networks

The Fisher information matrix (FIM) plays an essential role in statistics and machine learning as a Riemannian metric tensor or a component of the Hessian matrix of loss functions. Focusing on the FIM and its variants in deep neural networks (DNNs), we reveal their characteristic scale dependence on the network width, depth, and sample size when the network has random weights and is sufficiently wide. This study covers two widely used FIMs for regression with linear output and for classification with softmax output. Both FIMs asymptotically show pathological eigenvalue spectra in the sense that a small number of eigenvalues become large outliers depending on the width or sample size, while the others are much smaller. It implies that the local shape of the parameter space or loss landscape is very sharp in a few specific directions while almost flat in the other directions. In particular, the softmax output disperses the outliers and makes a tail of the eigenvalue density spread from the bulk. We also show that pathological spectra appear in other variants of FIMs: one is the neural tangent kernel; another is a metric for the input signal and feature space that arises from feedforward signal propagation. Thus, we provide a unified perspective on the FIM and its variants that will lead to more quantitative understanding of learning in large-scale DNNs.
More Powerful Selective Kernel Tests for Feature Selection

Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical analysis. An approach for addressing this is via conditioning on the selection procedure to account for how we have used the data to generate our hypotheses, and prevent information to be used again after selection. Many selective inference (a.k.a. post-selection inference) algorithms typically take this approach but will "over-condition" for sake of tractability. While this practice yields well calibrated statistic tests with controlled false positive rates (FPR), it can incur a major loss in power. In our work, we extend two recent proposals for selecting features using the Maximum Mean Discrepancy and Hilbert Schmidt Independence Criterion to condition on the minimal conditioning event. We show how recent advances in multiscale bootstrap makes conditioning on the minimal selection event possible and demonstrate our proposal over a range of synthetic and real world experiments. Our results show that our proposed test is indeed more powerful in most scenarios.
X-ray signatures of the polar dusty gas in AGN

Recent mid-infrared interferometry observations of nearby active galactic nuclei (AGN) revealed that a significant part of the dust emission extends in the polar direction, rather than the equatorial torus/disc direction as expected by the traditional unification model. We study the X-ray signatures of this polar dusty gas with ray-tracing simulations. Different from those from the ionized gas, the scattered emission from the polar dusty gas produces self-absorption and neutral-like fluorescence lines, which are potentially a unique probe of the kinematics of the polar dusty gas. The anomalously small Fe Kα/Si Kα ratios of type 2 AGN observed previously can be naturally explained by the polar dusty gas, because the polar emission does not suffer from heavy absorption by the dense equatorial gas. The observed Si Kα lines of the Circinus galaxy and NGC 1068 show blueshifts with respect to the systemic velocities of the host galaxies, consistent with an outflowing scenario of the Si Kα-emitting gas. The 2.5–3 keV image of the Circinus galaxy is elongated along the polar direction, consistent with an origin of the polar gas. These results show that the polar-gas-scattered X-ray emission of type 2 AGN is an ideal objective for future X-ray missions, such as Athena.
Unveiling the nuclear matter EoS from neutron star properties: a supervised machine learning approach

We explore supervised machine learning methods in extracting the non-linear maps between neutron stars (NS) observables and the equation of state (EoS) of nuclear matter. Using a Taylor expansion around saturation density, we have generated a set of model independent EoS describing stellar matter constrained by nuclear matter parameters that are thermodynamically consistent, causal, and consistent with astrophysical observations. From this set, the full non-linear dependencies of the NS tidal deformability and radius on the nuclear matter parameters were learned using two distinct machine learning methods. Due to the high accuracy of the learned non-linear maps, we were able to analyze the impact of each nuclear matter parameter on the NS observables, identify dependencies on the EoS properties beyond linear correlations and predict which stars allow us to draw strong constraints.
Uniform convergence rates for the approximated halfspace and projection depth

The computational complexity of some depths that satisfy the projection property, such as the halfspace depth or the projection depth, is known to be high, especially for data of higher dimensionality. In such scenarios, the exact depth is frequently approximated using a randomized approach: The data are projected into a finite number of directions uniformly distributed on the unit sphere, and the minimal depth of these univariate projections is used to approximate the true depth. We provide a theoretical background for this approximation procedure. Several uniform consistency results are established, and the corresponding uniform convergence rates are provided. For elliptically symmetric distributions and the halfspace depth it is shown that the obtained uniform convergence rates are sharp. In particular, guidelines for the choice of the number of random projections in order to achieve a given precision of the depths are stated.
On the Weights of General MDS Codes

The weight spectra of MDS codes of length <italic>n</italic> and dimension <italic>k</italic> over arbitrary alphabets are studied. For all <italic>q</italic>-ary MDS codes of dimension <italic>k</italic> and length <inline-formula> <tex-math notation="LaTeX">${n}\ne {q}+{k}-1 $ </tex-math></inline-formula> containing the zero codeword, it is shown that all <italic>k</italic> weights from <italic>n</italic> to <inline-formula> <tex-math notation="LaTeX">${n}-{k}+1 $ </tex-math></inline-formula> are realized. The weight spectrum in the remaining case <inline-formula> <tex-math notation="LaTeX">${n}={q}+{k}-1 $ </tex-math></inline-formula> is also determined. Additionally, we prove that all binary MDS codes are equivalent to linear MDS codes. The proofs are combinatorial, and self contained.
An extreme-mass ratio, short-period eclipsing binary consisting of a B dwarf primary and a pre-main-sequence M star companion discovered by KELT

We present the discovery of KELT J072709 + 072007 (HD 58730), a very low mass ratio (q ≡ M2/M1 ≈ 0.07) eclipsing binary (EB) identified by the Kilodegree Extremely Little Telescope (KELT) survey. We present the discovery light curve and perform a global analysis of four high-precision ground-based light curves, the Transiting Exoplanets Survey Satellite (TESS) light curve, radial velocity (RV) measurements, Doppler Tomography (DT) measurements, and the broad-band spectral energy distribution. Results from the global analysis are consistent with a fully convective ($M_2 = 0.22 \pm 0.02\ \, \mathrm{M}_{\odot })$ M star transiting a late-B primary ($M_1 = 3.34^{+0.07}_{-0.09}\ \, \mathrm{M}_{\odot }\,\mathrm{ and}\,\ T_{\rm eff,1} = 11960^{+430}_{-520}\ {\rm K}$). We infer that the primary star is $183_{-30}^{+33}$ Myr old and that the companion star’s radius is inflated by $26 \pm 8{{\ \rm per\ cent}}$ relative to the predicted value from a low-mass isochrone of similar age. We separately and analytically fit for the variability in the out-of-eclipse TESS phase curve, finding good agreement between the resulting stellar parameters and those from the global fit. Such systems are valuable for testing theories of binary star formation and understanding how the environment of a star in a close-but-detached binary affects its physical properties. In particular, we examine how a star’s properties in such a binary might differ from the properties it would have in isolation.