Miracle-dz/UniFAR-FTUs huggingface dataset:
A Vector Monotonicity Assumption for Multiple Instruments

When a researcher wishes to use multiple instrumental variables for a single binary treatment, the familiar LATE monotonicity assumption can become restrictive: it requires that all units share a common direction of response even when different instruments are shifted in opposing directions. What I call vector monotonicity, by contrast, simply restricts treatment status to be monotonic in each instrument separately. This is a natural assumption in many contexts, capturing the intuitive notion of "no defiers" for each instrument. I show that in a setting with a binary treatment and multiple discrete instruments, a class of causal parameters is point identified under vector monotonicity, including the average treatment effect among units that are responsive to any particular subset of the instruments. I propose a simple "2SLS-like" estimator for the family of identified treatment effect parameters. An empirical application revisits the labor market returns to college education.
Dynamically formed black hole binaries: In-cluster versus ejected mergers

Abstract The growing number of black hole binary (BHB) mergers detected by the Laser Interferometer Gravitational-Wave Observatory have the potential to enable an unprecedented characterisation of the physical processes and astrophysical conditions that govern the formation of compact binaries. In this paper, we focus on investigating the dynamical formation of BHBs in dense star clusters through a state-of-the-art set of 58 direct N-body simulations with N $\leqslant200\,000$ particles which include stellar evolution, gravitational braking, orbital decay through gravitational radiation, and galactic tidal interactions. The simulations encompass a range of initial conditions representing typical young globular clusters, including the presence of primordial binaries. The systems are simulated for $\sim 12$ Gyr. The dataset yields 117 BHB gravitational wave (GW) events, with 97 binaries merging within their host cluster and 20 merging after having been ejected. Only 8% of all ejected BHBs merge within the age of the Universe. Systems in this merging subset tend to have smaller separations and larger eccentricities, as this combination of parameters results in greater emission of gravitational radiation. We confirm known trends from Monte Carlo simulations, such as the anti-correlation between the mass of the binary and age of the cluster. In addition, we highlight for the first time a difference at low values of the mass ratio distribution between in-cluster and ejected mergers. However, the results depend on assumptions on the strength of GW recoils, thus in-cluster mergers cannot be ruled out at a significant level of confidence. A more substantial catalogue of BHB mergers and a more extensive library of N-body simulations are needed to constrain the origin of the observed events.
Learning explanations that are hard to vary

In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning. We show that averaging gradients across examples -- akin to a logical OR of patterns -- can favor memorization and `patchwork' solutions that sew together different strategies, instead of identifying invariances. To inspect this, we first formalize a notion of consistency for minima of the loss surface, which measures to what extent a minimum appears only when examples are pooled. We then propose and experimentally validate a simple alternative algorithm based on a logical AND, that focuses on invariances and prevents memorization in a set of real-world tasks. Finally, using a synthetic dataset with a clear distinction between invariant and spurious mechanisms, we dissect learning signals and compare this approach to well-established regularizers.
Inverse problems on low-dimensional manifolds

We consider abstract inverse problems between infinite-dimensional Banach spaces. These inverse problems are typically nonlinear and ill-posed, making the inversion with limited and noisy measurements a delicate process. In this work, we assume that the unknown belongs to a finite-dimensional manifold: this assumption arises in many real-world scenarios where natural objects have a low intrinsic dimension and belong to a certain submanifold of a much larger ambient space. We prove uniqueness and Hölder and Lipschitz stability results in this general setting, also in the case when only a finite discretization of the measurements is available. Then, a Landweber-type reconstruction algorithm from a finite number of measurements is proposed, for which we prove global convergence, thanks to a new criterion for finding a suitable initial guess. These general results are then applied to several examples, including two classical nonlinear ill-posed inverse boundary value problems. The first is Calderón’s inverse conductivity problem, for which we prove a Lipschitz stability estimate from a finite number of measurements for piece-wise constant conductivities with discontinuities on an unknown triangle. A similar stability result is then obtained for Gel’fand-Calderón’s problem for the Schrödinger equation, in the case of piece-wise constant potentials with discontinuities on a finite number of non-intersecting balls.
Prime Values of Quadratic Polynomials

This note investigates the prime values of the polynomial $f(t)=qt^2+a$ for any fixed pair of relatively prime integers $ a\geq 1$ and $ q\geq 1$ of opposite parity. For a large number $x\geq1$, an asymptotic result of the form $\sum_{n\leq x^{1/2},\, n \text{ odd}}\Lambda(qn^2+a)\gg qx^{1/2}/2\varphi(q)$ is achieved for $q\ll (\log x)^b$, where $ b\geq 0 $ is a constant.
Treatment of flux shape uncertainties in unfolded, flux-averaged neutrino cross-section measurements

The exact way of treating flux shape uncertainties in unfolded, flux-averaged neutrino cross-section measurements can lead to subtle issues when comparing the results to model predictions. There is a difference between reporting a cross section in the (unknown) real flux, and reporting a cross section that was extrapolated from the (unknown) real flux to a fixed reference flux. A lot of (most? ) current analyses do the former, while the results are compared to model predictions as if they were the latter. This leads to (part of) the flux shape uncertainty being ignored, potentially leading to wrong physics conclusions. The size of the effect is estimated to be sub-dominant, but non-negligible in two example measurements from T2K and MINERvA. This paper describes how the issue arises and provides instructions for possible ways how to treat the flux shape uncertainties correctly.
A Survey of Deep Active Learning

Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions.
Instability of mixing in the Kuramoto model: From bifurcations to patterns

We study patterns observed right after the loss of stability of mixing in the Kuramoto model of coupled phase oscillators with random intrinsic frequencies on large graphs, which can also be random. We show that the emergent patterns are formed via two independent mechanisms determined by the shape of the frequency distribution and the limiting structure of the underlying graph sequence. Specifically, we identify two nested eigenvalue problems whose eigenvectors (unstable modes) determine the structure of the nascent patterns. The analysis is illustrated with the results of the numerical experiments with the Kuramoto model with unimodal and bimodal frequency distributions on certain graphs.
Observing the Stellar Halo of Andromeda in Cosmological Simulations: The AURIGA2PANDAS Pipeline

We present a direct comparison of the Pan-Andromeda Archaeological Survey (PAndAS) observations of the stellar halo of M31 with the stellar halos of six galaxies from the Auriga simulations. We process the simulated halos through the Auriga2PAndAS pipeline and create PAndAS-like mocks that fold in all observational limitations of the survey data (foreground contamination from the Milky Way stars, incompleteness of the stellar catalogs, photometric uncertainties, etc.). This allows us to study the survey data and the mocks in the same way and generate directly comparable density maps and radial density profiles. We show that the simulations are overall compatible with the observations. Nevertheless, some systematic differences exist, such as a preponderance for metal-rich stars in the mocks. While these differences could suggest that M31 had a different accretion history or has a different mass compared with the simulated systems, it is more likely a consequence of an underquenching of the star formation history of galaxies, related to the resolution of the Auriga simulations. The direct comparison enabled by our approach offers avenues to improve our understanding of galaxy formation as they can help pinpoint the observable differences between observations and simulations. Ideally, this approach will be further developed through an application to other stellar halo simulations. To facilitate this step, we release the pipeline to generate the mocks, along with the six mocks presented and used in this contribution.
A QUBO Formulation for Qubit Allocation

To run an algorithm on a quantum computer, one must choose an assignment from logical qubits in a circuit to physical qubits on quantum hardware. This task of initial qubit placement, or qubit allocation, is especially important on present-day quantum computers which have a limited number of qubits, connectivity constraints, and varying gate fidelities. In this work we formulate and implement the qubit placement problem as a quadratic, unconstrained binary optimization (QUBO) problem and solve it using simulated annealing to obtain a spectrum of initial placements. Compared to contemporary allocation methods available in t|ket$\rangle $ and Qiskit, the QUBO method yields allocations with improved circuit depth for $>$50% of a large set of benchmark circuits, with many also requiring fewer CX gates.
Neural Architecture Search For Keyword Spotting

Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice. In this paper, we apply neural architecture search to search for convolutional neural network models that can help boost the performance of keyword spotting based on features extracted from acoustic signals while maintaining an acceptable memory footprint. Specifically, we use differentiable architecture search techniques to search for operators and their connections in a predefined cell search space. The found cells are then scaled up in both depth and width to achieve competitive performance. We evaluated the proposed method on Google's Speech Commands Dataset and achieved a state-of-the-art accuracy of over 97% on the setting of 12-class utterance classification commonly reported in the literature.
On the Detection Potential of Blazar Flares for Current Neutrino Telescopes

Blazar jets are extreme environments, in which relativistic proton interactions with an ultraviolet photon field could give rise to photopion production. High-confidence associations of individual high-energy neutrinos with blazar flares could be achieved via spatially and temporally coincident detections. In 2017, the track-like, extremely high-energy neutrino event IC 170922A was found to coincide with increased γ-ray emission from the blazar TXS 0506+056, leading to the identification of the most promising neutrino point-source candidate so far. We calculate the expected number of neutrino events that can be detected with IceCube, based on a broadband parameterization of bright short-term blazar flares that were observed in the first 6.5 yr of Fermi/Large Area Telescope observations. We find that the integrated keV-to-GeV fluence of most individual blazar flares is far too small to yield a substantial Poisson probability for the detection of one or more neutrinos with IceCube. We show that the sample of potentially detectable high-energy neutrinos from individual blazar flares is rather small. We further show that the blazars 3C 279 and PKS 1510−089 dominate the all-sky neutrino prediction from bright and short-term blazar flares. In the end, we discuss strategies to search for more significant associations in future data unblindings of IceCube and KM3NeT.
Great enhancement of Curie temperature and magnetic anisotropy in two-dimensional van der Waals magnetic semiconductor heterostructures

In two-dimensional (2D) magnetic systems, large magnetic anisotropy is needed to stabilize the magnetic order according to Mermin-Wagner theorem. Based on density-functional theory (DFT) calculations, we propose that the magnetic anisotropic energy (MAE) of 2D ferromagnetic (FM) semiconductors can be strongly enhanced in van der Waals heterostructures by attaching a nonmagnetic semiconductor monolayer with large spin-orbit coupling. We studied ${\mathrm{Cr}}_{2}{\mathrm{Ge}}_{2}{\mathrm{Te}}_{6}/{\mathrm{PtSe}}_{2}$ bilayer heterostructures, where each layer has been realized in recent experiments. The DFT calculations show that the MAE of ${\mathrm{Cr}}_{2}{\mathrm{Ge}}_{2}{\mathrm{Te}}_{6}/{\mathrm{PtSe}}_{2}$ is enhanced by 70%, and the Curie temperature ${T}_{C}$ is increased far beyond room temperature. A model Hamiltonian is suggested to analyze the DFT results, showing that both the Dzyaloshinskii-Moriya interaction and single-ion anisotropy contribute to the enhancement of the MAE. Based on the superexchange picture, we find that the decreased energy difference between $3d$ orbitals of Cr and $5p$ orbitals of Te contributes partially to the increase of ${T}_{C}$. Our present work indicates a promising way to enhance the MAE and ${T}_{C}$ by constructing van der Waals semiconductor heterostructures, which will inspire further studies on the 2D magnetic semiconductor systems.
Safety of Connected Vehicle Platoons

Conventionally, a frequency domain condition on the spacing error transfer function is employed to assure string stability in a vehicle platoon. While this criteria guarantees that the power of spacing error signals diminish downstream, in order to avoid a collision it is more relevant to study the maximum spacing errors across the platoon. In this paper, we first re-examine the notion of string stability as it relates to safety by providing an upper bound on the maximum spacing error of any vehicle in a homogeneous platoon in terms of the lead vehicle's input. We also extend our previous work by providing a sufficient condition for minimum string stable headway for platoons experiencing burst-noise packet losses. Finally, we utilize throttle and brake maps to develop a longitudinal vehicle model and validate it against a Lincoln MKZ which is then used for numerical corroboration of the proposed lossy vehicle following algorithms.
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection

Vision-based dynamic pedestrian intrusion detection (PID), judging whether pedestrians intrude an area-of-interest (AoI) by a moving camera, is an important task in mobile surveillance. The dynamically changing AoIs and a number of pedestrians in video frames increase the difficulty and computational complexity of determining whether pedestrians intrude the AoI, which makes previous algorithms incapable of this task. In this paper, we propose a novel and efficient multi-task deep neural network, PIDNet, to solve this problem. PIDNet is mainly designed by considering two factors: accurately segmenting the dynamically changing AoIs from a video frame captured by the moving camera and quickly detecting pedestrians from the generated AoI-contained areas. Three efficient network designs are proposed and incorporated into PIDNet to reduce the computational complexity: 1) a special PID task backbone for feature sharing, 2) a feature cropping module for feature cropping, and 3) a lighter detection branch network for feature compression. In addition, considering there are no public datasets and benchmarks in this field, we establish a benchmark dataset to evaluate the proposed network and give the corresponding evaluation metrics for the first time. Experimental results show that PIDNet can achieve 67.1% PID accuracy and 9.6 fps inference speed on the proposed dataset, which serves as a good baseline for the future vision-based dynamic PID study.
Defending SVMs against poisoning attacks: the hardness and DBSCAN approach

Adversarial machine learning has attracted a great amount of attention in recent years. In a poisoning attack, the adversary can inject a small number of specially crafted samples into the training data which make the decision boundary severely deviate and cause unexpected misclassification. Due to the great importance and popular use of support vector machines (SVM), we consider defending SVM against poisoning attacks in this paper. We study two commonly used strategies for defending: designing robust SVM algorithms and data sanitization. Though several robust SVM algorithms have been proposed before, most of them either are in lack of adversarial-resilience, or rely on strong assumptions about the data distribution or the attacker's behavior. Moreover, the research on their complexities is still quite limited. We are the first, to the best of our knowledge, to prove that even the simplest hard-margin one-class SVM with outliers problem is NP-complete, and has no fully PTAS unless P$=$NP (that means it is hard to achieve an even approximate algorithm). For the data sanitization defense, we link it to the intrinsic dimensionality of data; in particular, we provide a sampling theorem in doubling metrics for explaining the effectiveness of DBSCAN (as a density-based outlier removal method) for defending against poisoning attacks. In our empirical experiments, we compare several defenses including the DBSCAN and robust SVM methods, and investigate the influences from the intrinsic dimensionality and data density to their performances.
Mortality

Background: Multifractal analysis of human heartbeat dynamics has been demonstrated to provide promising markers of congestive heart failure (CHF). Yet, it crucially builds on the interpolation of RR interval series which has been generically performed with limited links to CHF pathophysiology. Objective: We devise a novel methodology estimating multifractal autonomic dynamics from heartbeat-derived series defined in the continuous time. We hypothesize that markers estimated from our novel framework are also effective for mortality prediction in severe CHF. Methods: We merge multifractal analysis within a methodological framework based on inhomogeneous point process models of heartbeat dynamics. Specifically, wavelet coefficients and wavelet leaders are computed over measures extracted from instantaneous statistics of probability density functions characterizing and predicting the time until the next heartbeat event occurs. The proposed approach is tested on data from 94 CHF patients aiming at predicting survivor and nonsurvivor individuals as determined after a four years follow up. Results and Discussion: Instantaneous markers of vagal and sympathovagal dynamics display power-law scaling for a large range of scales, from ≃0.5 to ≃100 s. Using standard support vector machine algorithms, the proposed inhomogeneous point-process representation-based multifractal analysis achieved the best CHF mortality prediction accuracy of 79.11% (sensitivity 90.48%, specificity 67.74%). Conclusion: Our results suggest that heartbeat scaling and multifractal properties in CHF patients are not generated at the sinusThis work was supported in part by Grant ANR-16-CE33-0020 MultiFracs and Grant CNRS PICS 7260 MATCHA. (Corresponding author: Gaetano Valenza.) G. Valenza is with the Computational Physiology and Biomedical Instruments group at the Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, University of Pisa, Pisa 56126, Italy (e-mail: g.valenza@ieee.org). H. Wendt is with IRIT, Universit. de Toulouse, CNRS. K. Kiyono is with the Osaka University. J. Hayano is with the Graduate School of Medical Science, Department of Medical Education, Nagoya City University. E. Watanabe is with the Department of Cardiology, Fujita Health University School of Medicine. Y. Yamamoto is with the Graduate School of Education, The University of Tokyo, Tokio, Japan. P. Abry is with the Physics Department, ENS Lyon, CNRS. R. Barbieri is with the Department of Electronics, Informatics and Bioengineering, Politecnico di Milano. Digital Object Identifier 10.1109/TBME.2018. 2797158 node level, but rather by the intrinsic action of vagal shortterm control and of sympatho-vagal fluctuations associated with circadian cardiovascular control especially within the very low frequency band. These markers might provide crit-ical information in devising a clinical tool for individualized prediction of survivor and nonsurvivor CHF patients.