Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinguishing Dirac and Majorana Heavy Neutrinos at Lepton Colliders

We discuss the potential to observe lepton number violation (LNV) in displaced vertex searches for heavy neutral leptons (HNLs) at future lepton colliders. Even though a direct detection of LNV is impossible for the dominant production channel because lepton number is carried away by an unobservable neutrino, there are several signatures of LNV that can be searched for. They include the angular distribution and spectrum of decay products as well as the HNL lifetime. We comment on the perspectives to observe LNV in realistic neutrino mass models and argue that the dichotomy of Dirac vs Majorana HNLs is in general not suﬃcient to eﬀectively capture their phenomenology, but these extreme cases nevertheless represent well-deﬁned benchmarks for experimental searches. Finally, we present accurate analytic estimates for the number of events and sensitivity regions during the Z -pole run for both Majorana and Dirac HNLs.
Parametrizations of Collinear and kT-Dependent Parton Densities in Proton

A new type of parametrization for parton distribution functions in the proton, based on their Q2-evolution at large and small x values, is constructed. In our analysis, the valence and nonsinglet parts obey the Gross–Llewellyn–Smith and Gottfried sum rules, respectively. For the singlet quark and gluon densities, momentum conservation is taken into account. Then, using the Kimber–Martin–Ryskin prescription, we extend the consideration to Transverse Momentum Dependent (TMD, or unintegrated) gluon and quark distributions in the proton, which currently plays an important role in a the number of phenomenological applications. The analytical expressions for the latter, valid for both low and large x, are derived for the first time.
Iterative Teaching by Data Hallucination

We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of finite samples), which greatly limits the teacher's capability. To address this issue, we study iterative teaching under a continuous input space where the input example (i.e., image) can be either generated by solving an optimization problem or drawn directly from a continuous distribution. Specifically, we propose data hallucination teaching (DHT) where the teacher can generate input data intelligently based on labels, the learner's status and the target concept. We study a number of challenging teaching setups (e.g., linear/neural learners in omniscient and black-box settings). Extensive empirical results verify the effectiveness of DHT.
Understanding performance problems in deep learning systems

Deep learning (DL) has been widely applied to many domains. Unique challenges in engineering DL systems are posed by the programming paradigm shift from traditional systems to DL systems, and performance is one of the challenges. Performance problems (PPs) in DL systems can cause severe consequences such as excessive resource consumption and financial loss. While bugs in DL systems have been extensively investigated, PPs in DL systems have hardly been explored. To bridge this gap, we present the first comprehensive study to i) characterize symptoms, root causes, and introducing and exposing stages of PPs in DL systems developed in TensorFLow and Keras, with 224 PPs collected from 210 StackOverflow posts, and to ii) assess the capability of existing performance analysis approaches in tackling PPs, with a constructed benchmark of 58 PPs in DL systems. Our findings shed light on the implications on developing high-performance DL systems, and detecting and localizing PPs in DL systems. To demonstrate the usefulness of our findings, we develop a static checker DeepPerf to detect three types of PPs. It has detected 488 new PPs in 130 GitHub projects. 105 and 27 PPs have been confirmed and fixed.
Heterogeneous Position Effects and the Power of Rankings

Many online retailers and search intermediaries present products on ranked product lists. Because changing the ordering of products influences their revenues and consumer welfare, these platforms can design rankings to maximize either of these two metrics. In this paper, I study how rankings serving these two objectives differ and what determines this difference. First, I highlight that when position effects are heterogeneous, rankings can increase both revenues and consumer welfare by increasing the overall conversion rate. Second, I provide empirical evidence for this heterogeneity, highlighting that cheaper alternatives have stronger position effects. Finally, I quantify revenue and consumer welfare effects across the different rankings. To this end, I develop an estimation procedure for the search and discovery model of Greminger (2022) that yields a smooth likelihood function by construction. The results from the model show that rankings designed to maximize revenues can also increase consumer welfare. Moreover, these revenue-based rankings reduce consumer welfare only to a limited extent relative to a consumer-welfare-maximizing ranking.
Optimal phase measurements with bright- and vacuum-seeded SU(1,1) interferometers

The SU(1,1) interferometer can be thought of as a Mach-Zehnder interferometer with its linear beamsplitters replaced with parametric nonlinear optical processes. We consider the cases of bright and vacuum-seeded SU(1,1) interferometers using intensity or homodyne detectors. A simplified, truncated scheme with only one nonlinear interaction is introduced, which not only beats conventional intensity detection with a bright seed, but can saturate the phase sensitivity bound set by the quantum Fisher information. We also show that the truncated scheme achieves a sub-shot-noise phase sensitivity in the vacuum-seeded case, despite the phase-sensing optical beams having no well-defined phase.
Einstein-Vlasov system with equal-angular momenta in 
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We investigate solutions of the $5$--dimensional rotating Einstein-Vlasov system with an $R \times SU(2) \times U(1)$ isometry group. In a five-dimensional spacetime, there are two independent planes of rotation, thus, considering $U(1)$ symmetry on each rotation plane, we may impose an $R\times U(1) \times U(1)$ isometry to a stationary spacetime. Furthermore, when the values of the two angular momenta are equal to each other, the spatial symmetry gets enhanced to $R\times SU(2)\times U(1)$ symmetry, and the spacetime has a cohomogeneity-1 structure. Imposing the same symmetry to the distribution function of the particles of which the Vlasov system consists, the distribution function can be dependent on three mutually independent and commutative conserved charges for particle motion (energy, total angular momentum on $SU(2)$ and $U(1)$ angular momentum). We consider the distribution function which exponentially depends on the $U(1)$ angular momentum and reduces to the thermal equilibrium state in spherical symmetry. Then, in this paper, we numerically construct solutions of the asymptotically AdS Einstein--Vlasov system.
Synchrotron Firehose Instability

We demonstrate using linear theory and particle-in-cell (PIC) simulations that a synchrotron-cooling collisionless plasma acquires pressure anisotropy and, if the plasma beta is sufficiently high, becomes unstable to the firehose instability, in a process that we dub the synchrotron firehose instability (SFHI). The SFHI channels free energy from the pressure anisotropy of the radiating, relativistic electrons (and/or positrons) into small-amplitude, kinetic-scale, magnetic-field fluctuations, which pitch-angle scatter the particles and bring the plasma to a near-thermal state of marginal instability. The PIC simulations reveal a nonlinear cyclic evolution of firehose bursts interspersed by periods of stable cooling. We compare the SFHI for electron–positron and electron–ion plasmas. As a byproduct of the growing electron-firehose magnetic-field fluctuations, magnetized ions gain a pressure anisotropy opposite to that of the electrons. If these ions are relativistically hot, we find that they also experience cooling due to collisionless thermal coupling with the electrons, which we argue is mediated by a secondary ion-cyclotron instability. We suggest that the SFHI may be activated in a number of astrophysical scenarios, such as within ejecta from black hole accretion flows and relativistic jets, where the redistribution of energetic electrons from low to high pitch angles may cause transient bursts of radiation.
The Chandra Deep Field North Survey. XV. Optically Bright, X-Ray-Faint Sources

We have analyzed optically bright, X-ray–faint [OBXF; i.e., log (fX/fR)≲-2] sources identified in an 178.9 arcmin2 area having high exposure (greater than 1500 ks) within the Chandra Deep Field North 2 Ms survey. We find 43 OBXF sources in this area, making up ≈15% of the X-ray sources above a 0.5–2 keV flux of ≈2.3 × 10-17 ergs cm-2 s-1. We present spectroscopic identifications for 42 of the OBXF sources and optical spectra for 25, including five previously unpublished redshifts. Deep optical imaging data (either Hubble Space Telescope [HST] or ground-based) are presented for all the OBXF sources; we measure the optical morphologies of the 20 galaxies having HST imaging data. The OBXF population consists mainly of normal and starburst galaxies detected out to cosmologically significant distances (i.e., to a median redshift of z = 0.297 and a full redshift range z = 0.06–0.845). This is notable since these distances equate to look-back times of up to ≈8 Gyr; we are thus provided with a window on the X-ray emission from galaxies at redshifts much closer to the cosmic star formation peak than was possible prior to the Chandra X-Ray Observatory. The X-ray luminosity distribution of OBXF sources extends to higher luminosity than does that of "normal" galaxies, indicating that a significant fraction are likely dominated by low-luminosity active galactic nuclei or vigorous star formation. The lowest redshift galaxies (z ≈ 0.06–0.2) have very low X-ray–to–optical flux ratios [i.e., log (fX/fR) ≲ -3], which are consistent with those of normal galaxies in the local universe. By combining the detected X-ray counts, we find the average OBXF X-ray spectrum to be consistent with a Γ ≈ 2.0 power law. The 0.5–2 keV log N–log S for the OBXF galaxies is much steeper (α ≈ -1.7) than for the general X-ray source population. Indeed, the number of OBXF sources has doubled between the 1 and 2 Ms surveys, rising sharply in numbers at faint fluxes. The extragalactic OBXF sources are found to contribute ≈1%–2% of the soft extragalactic X-ray background. We report on the discovery of five candidate off-nuclear ultraluminous X-ray sources (LX ≳ 1039 ergs s-1) with z ≈ 0.1–0.2 within the OBXF population. These sources are "ultraluminous" in that they are typically more X-ray luminous than, e.g., Eddington-limited accretion onto stellar-mass black holes; these sources are found to dominate the X-ray emission of their host galaxies.
Khovanov homology and the Fukaya category of the traceless character variety for the twice-punctured torus

We describe a strategy for constructing reduced Khovanov homology for links in lens spaces by generalizing a symplectic interpretation of reduced Khovanov homology for links in S3 due to Hedden, Herald, Hogancamp, and Kirk. The strategy relies on a partly conjectural description of the Fukaya category of the traceless SU(2) character variety of the 2-torus with two punctures. From a diagram of a 1-tangle in a solid torus, we construct a corresponding object (X, δ) in the A∞ category of twisted complexes over this Fukaya category. The homotopy type of (X, δ) is an isotopy invariant of the tangle diagram. We use (X, δ) to construct cochain complexes for links in S3 and some links in S2 × S1. For links in S3, the cohomology of our cochain complex reproduces reduced Khovanov homology, though the cochain complex itself is not the usual one. For links in S2 × S1, we present results that suggest the cohomology of our cochain complex may be a link invariant.
Tree Detection and Diameter Estimation Based on Deep Learning

Tree perception is an essential building block toward autonomous forestry operations. Current developments generally consider input data from lidar sensors to solve forest navigation, tree detection and diameter estimation problems, whereas cameras paired with deep learning algorithms usually address species classification or forest anomaly detection. In either of these cases, data unavailability and forest diversity restrain deep learning developments for autonomous systems. Therefore, we propose two densely annotated image datasets—43 k synthetic, 100 real—for bounding box, segmentation mask and keypoint detections to assess the potential of vision-based methods. Deep neural network models trained on our datasets achieve a precision of 90.4 % for tree detection, 87.2 % for tree segmentation and centimeter accurate keypoint estimations. We measure our models’ generalizability when testing it on other forest datasets, and their scalability with different dataset sizes and architectural improvements. Overall, the experimental results offer promising avenues toward autonomous tree felling operations and other applied forestry problems. The datasets and pre-trained models in this article are publicly available on GitHub (https://github.com/norlab-ulaval/PercepTreeV1).
Laboratory and Astronomical Discovery of Magnesium Dicarbide, MgC2

We report the detection of magnesium dicarbide, MgC2, in the laboratory at centimeter wavelengths and assign 24MgC2, 25MgC2, and 26MgC2 to 14 unidentified lines in the radio spectrum of the circumstellar envelope of the evolved carbon star IRC+10216. The structure of MgC2 is found to be T-shaped with a highly ionic bond between the metal atom and the C2 unit, analogous to other dicarbides containing electropositive elements. A two-temperature excitation model of the MgC2 emission lines observed in IRC+10216 yields a very low rotational temperature of 6 ± 1 K, a kinetic temperature of 22 ± 13 K, and a column density of (1.0 ± 0.3) × 1012 cm−2. The abundance of MgC2 relative to the magnesium–carbon chains MgCCH, MgC4H, and MgC6H is 1:2:22:20 and provides a new constraint on the sequential radiative association–dissociative recombination mechanisms implicated in the production of metal-bearing molecules in circumstellar environments.
Unexpected bending behavior of 2D lattice materials

Architected 2D lattice materials are appealing for shape-shifting applications due to the tunable sign of Poisson’s ratio. It is commonly believed that the positive and negative Poisson’s ratios lead to anticlastic and synclastic curvatures respectively when the material is bent in one direction. Here, taking 2D beam lattices with star-shaped unit cells as examples, we show theoretically and demonstrate experimentally that this is not always true. At a fixed Poisson’s ratio, we find a transition between anticlastic and synclastic bending curvatures controlled by the beam’s cross-sectional aspect ratio. Such an unexpected behavior roots in the competition between torsion and bending of the beams, and can be well captured by a Cosserat continuum model.
Balanced truncation with relative/multiplicative error bounds in L/sub /spl infin// norm

Studies a class of balanced truncation algorithms applicable to relative/multiplicative model reduction. These algorithms seek to balance the controllability gramian of a given transfer function and the observability gramian of its right inverse. For this reason, the algorithms are referred to as inverse-weighted balanced truncation (IWBT) algorithms. It is shown that by using IWBT algorithms one can derive relative and multiplicative L/sub /spl infin// error bounds that are known to hold for other reduction algorithms. It is also shown that the balanced stochastic truncation (BST) method is actually one special, but an "optimal" version of the IWBT algorithms. As such, the authors' result also serves to establish the fact that the available error bounds pertaining to BST algorithms actually hold for IWBT algorithms.
Artificial intelligence in government: Concepts, standards, and a unified framework

Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI.
PARC: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials

The thermo-mechanical response of shock-initiated energetic materials (EMs) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructures in a “materials-by-design” framework. However, the current design practice is limited, as a large ensemble of simulations is required to construct the complex EM structure-property-performance linkages. We present the physics-aware recurrent convolutional (PARC) neural network, a deep learning algorithm capable of learning the mesoscale thermo-mechanics of EM from a modest number of high-resolution direct numerical simulations (DNS). Validation results demonstrated that PARC could predict the themo-mechanical response of shocked EMs with comparable accuracy to DNS but with notably less computation time. The physics-awareness of PARC enhances its modeling capabilities and generalizability, especially when challenged in unseen prediction scenarios. We also demonstrate that visualizing the artificial neurons at PARC can shed light on important aspects of EM thermos-mechanics and provide an additional lens for conceptualizing EM.
Fusion: Efficient and Secure Inference Resilient to Malicious Servers

In secure machine learning inference, most of the schemes assume that the server is semi-honest (honestly following the protocol but attempting to infer additional information). However, the server may be malicious (e.g., using a low-quality model or deviating from the protocol) in the real world. Although a few studies have considered a malicious server that deviates from the protocol, they ignore the verification of model accuracy (where the malicious server uses a low-quality model) meanwhile preserving the privacy of both the server's model and the client's inputs. To address these issues, we propose \textit{Fusion}, where the client mixes the public samples (which have known query results) with their own samples to be queried as the inputs of multi-party computation to jointly perform the secure inference. Since a server that uses a low-quality model or deviates from the protocol can only produce results that can be easily identified by the client, \textit{Fusion} forces the server to behave honestly, thereby addressing all those aforementioned issues without leveraging expensive cryptographic techniques. Our evaluation indicates that \textit{Fusion} is 48.06$\times$ faster and uses 30.90$\times$ less communication than the existing maliciously secure inference protocol (which currently does not support the verification of the model accuracy). In addition, to show the scalability, we conduct ImageNet-scale inference on the practical ResNet50 model and it costs 8.678 minutes and 10.117 GiB of communication in a WAN setting, which is 1.18$\times$ faster and has 2.64$\times$ less communication than those of the semi-honest protocol.
Galaxy Populations in Groups and Clusters: Evidence for a Characteristic Stellar Mass Scale at M ∗ ∼ 109.5 M ⊙

We use the DR9 of the DESI legacy imaging survey and SDSS galaxy groups to measure the conditional luminosity function (CLF) for groups with halo mass M h ≥ 1012 M ⊙ and redshift 0.01 ≤ z ≤ 0.08, down to a limiting r-band magnitude of M r = −10 to −12. For given halo masses we measure the CLF for the total populations and for the red and blue populations classified using the (g − z) color. We find a clear faint-end upturn in the CLF of red satellites, with a slope α ≈ −1.8, which is almost independent of halo mass. This faint-end upturn is not seen for the blue and total populations. Our stellar population synthesis modeling shows that (g − z) provides a clean red/blue division and that red group galaxies defined by (g − z) are all dominated by old stellar populations. The fraction of old galaxies as a function of galaxy luminosity shows a minimum at M r ∼ −18, corresponding to M * ∼ 109.5 M ⊙. This scale is independent of halo mass and is comparable to the characteristic luminosity at which galaxies show a dichotomy in surface brightness and size, suggesting that the dichotomy in the old fraction and in galaxy structure may have a common origin. The rising of the old fraction at the faint end for Milky Way (MW)−sized halos is in good agreement with the quenched fraction measured for the MW/M31 system and from the ELVES survey. We discuss the implications of our results for the formation and evolution of low-mass galaxies and for the stellar mass functions of low-mass galaxies to be observed at high redshift.