Miracle-dz/UniFAR-FTUs huggingface dataset:
Location of γ-ray emission and magnetic field strengths in OJ 287

Context. The γ-ray BL Lac object OJ 287 is known to exhibit inner-parsec "jet-wobbling", high degrees of variability at all wavelengths and quasi-stationary features, including an apparent (≈100°) position-angle change in projection on the sky plane. Aims. Sub-50 micro-arcsecond resolution 86 GHz observations with the global mm-VLBI array (GMVA) supplement ongoing multifrequency VLBI blazar monitoring at lower frequencies. Using these maps, together with cm/mm total intensity and γ-ray observations from Fermi-LAT from 2008.2014, we aim to determine the location of γ-ray emission and to explain the inner-mas structural changes. Methods. Observations with the GMVA oer approximately double the angular resolution compared with 43 GHz VLBA observations and enable us to observe above the synchrotron self-absorption peak frequency. Fermi-LAT γ-ray data were reduced and analysed. The jet was spectrally decomposed at multiple locations along the jet. From this, we could derive estimates of the magnetic field using equipartition and synchrotron self-absorption arguments. How the field decreases down the jet provided an estimate of the distance to the jet apex and an estimate of the magnetic field strength at the jet apex and in the broad line region. Combined with accurate kinematics, we attempt to locate the site of γ-ray activity, radio flares, and spectral changes. Results. Strong γ-ray flares appeared to originate from either the so-called core region, a downstream stationary feature, or both, with γ-ray activity significantly correlated with radio flaring in the downstream quasi-stationary feature. Magnetic field estimates were determined at multiple locations along the jet, with the magnetic field found to be ≥1.6G in the core and ≤0.4G in the downstream quasi-stationary feature.We therefore found upper limits on the location of the VLBI core as ≤6.0 pc from the jet apex and determined an upper limit on the magnetic field near the jet base of the order of thousands of Gauss.
Curriculum Learning for Self-supervised Speaker Verification

The goal of this paper is to train effective self-supervised speaker representations without identity labels. We propose two curriculum learning strategies within a self-supervised learning framework. The first strategy aims to gradually increase the number of speakers in the training phase by enlarging the used portion of the train dataset. The second strategy applies various data augmentations to more utterances within a mini-batch as the training proceeds. A range of experiments conducted using the DINO self-supervised framework on the VoxCeleb1 evaluation protocol demonstrates the effectiveness of our proposed curriculum learning strategies. We report a competitive equal error rate of 4.47% with a single-phase training, and we also demonstrate that the performance further improves to 1.84% by fine-tuning on a small labelled dataset.
Heterogeneous recurrent spiking neural network for spatio-temporal classification

Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This paper presents a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets (DVS128 Gesture). We observed an accuracy of 94.32% for the KTH dataset, 79.58% and 77.53% for the UCF11 and UCF101 datasets, respectively, and an accuracy of 96.54% on the event-based DVS Gesture dataset using the novel unsupervised HRSNN model. The key novelty of the HRSNN is that the recurrent layer in HRSNN consists of heterogeneous neurons with varying firing/relaxation dynamics, and they are trained via heterogeneous spike-time-dependent-plasticity (STDP) with varying learning dynamics for each synapse. We show that this novel combination of heterogeneity in architecture and learning method outperforms current homogeneous spiking neural networks. We further show that HRSNN can achieve similar performance to state-of-the-art backpropagation trained supervised SNN, but with less computation (fewer neurons and sparse connection) and less training data.
An Observed-Data-Consistent Approach to the Assignment of Bit Values in a Quantum Random Number Generator

The majority of Quantum Random Number Generators (QRNG) are designed as converters of a continuous quantum random variable into a discrete classical random bit value. For the resulting random bit sequence to be minimally biased, the conversion process demands an experimenter to fully characterize the underlying quantum system and implement parameter estimation routines. Here we show that conventional approaches to parameter estimation (such as e.g. {\it Maximum Likelihood Estimation}) used on a finite QRNG data sample without caution may introduce binning bias and lead to overestimation of the randomness of the QRNG output. To bypass these complications, we develop an alternative conversion approach based on the Bayesian statistical inference method. We illustrate our approach using experimental data from a time-of-arrival QRNG and numerically simulated data from a vacuum homodyning QRNG. Side-by-side comparison with the conventional conversion technique shows that our method provides an automatic on-line bias control and naturally bounds the best achievable QRNG bit rate for a given measurement record.
Designing an Adaptive Application-Level Checkpoint Management System for Malleable MPI Applications

Dynamic resource management opens up numerous opportunities in High Performance Computing. It improves the system-level services as well as application performance. Checkpointing can also be deemed as a system-level service and can reap the benefits offered by dynamism. A checkpointing system can have better resource availability by integrating with a malleable resource management system. In addition to fault tolerance, the checkpointing system can cater to the data redistribution demand of malleable applications during resource change. Therefore, we propose iCheck, an adaptive application-level checkpoint management system that can efficiently utilize the system and application level dynamism to provide better checkpointing and data redistribution services to applications.
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.
The ERA Theorem for Safe Memory Reclamation

Safe memory reclamation (SMR) schemes for concurrent data structures offer trade-offs between three desirable properties: ease of integration, robustness, and applicability. In this paper we rigorously define SMR and these three properties, and we present the ERA theorem, asserting that any SMR scheme can only provide at most two of the three properties.
Corner contribution to percolation cluster numbers in three dimensions

In three-dimensional critical percolation we study numerically the number of clusters, $N_{\Gamma}$, which intersect a given subset of bonds, $\Gamma$. If $\Gamma$ represents the interface between a subsystem and the environment, then $N_{\Gamma}$ is related to the entanglement entropy of the critical diluted quantum Ising model. Due to corners in $\Gamma$ there are singular corrections to $N_{\Gamma}$, which scale as $b_{\Gamma} \ln L_{\Gamma}$, $L_{\Gamma}$ being the linear size of $\Gamma$ and the prefactor, $b_{\Gamma}$, is found to be universal. This result indicates that logarithmic finite-size corrections exist in the free-energy of three-dimensional critical systems.
Giraud's Theorem and Categories of Representations

We present an alternate proof of Giraud's Theorem based on the fact that given the conditions on a category E for being a topos, its objects are sheaves by construction. Generalizing sets to R-modules for R a commutative ring, we prove that a category with small hom-sets and finite limits is equivalent to a category of sheaves of R-modules on a site if and only if it satisfies Giraud's axioms and in addition is enriched in a certain symmetric monoidal category parametrized by an R-module.
THE COMPACT RADIO STRUCTURE OF RADIO-LOUD NARROW LINE SEYFERT 1 GALAXIES

We present the compact radio structure of three radio-loud narrow line Seyfert 1 galaxies from the Very Long Baseline Array archive data at 2.3, 5, and 8.4 GHz. In RXS J16290+4007, the radio structure is mostly unresolved. The combination of compact radio structure, high brightness temperature, and inverted spectrum between simultaneous 2.3 and 8.4 GHz strongly favors jet relativistic beaming. Combined with the very long baseline interferometry data at 1.6 and 8.4 GHz from the literature, we argue that RXS J16333+4718 also may harbor a relativistic jet, with resolved core-jet structure in 5 GHz. B3 1702+457 is clearly resolved with a well-defined jet component. The overall radio steep spectrum indicates that B3 1702+457 is likely a source optically defined as NLS1 with radio definition of compact steep spectrum sources. From these three sources, we found that radio loud NLS1s can be either intrinsically radio loud (e.g., B3 1702+457) or apparently radio loud due to jet beaming effects (e.g., RXS J16290+4007 and RXS J16333+4718).
Measuring the lensing potential with tomographic galaxy number counts

We investigate how the lensing potential can be measured tomographically with future galaxy surveys using their number counts. Such a measurement is an independent test of the standard ΛCDM framework and can be used to discern modified theories of gravity. We perform a Fisher matrix forecast based on galaxy angular-redshift power spectra, assuming specifications consistent with future photometric Euclid-like surveys and spectroscopic SKA-like surveys. For the Euclid-like survey we derive a fitting formula for the magnification bias. Our analysis suggests that the cross correlation between different redshift bins is very sensitive to the lensing potential such that the survey can measure the amplitude of the lensing potential at the same level of precision as other standard ΛCDM cosmological parameters.
Relating quantum character varieties and skein modules

We relate the Kauffman bracket stated skein modules to two independent constructions of quantum representation spaces of Habiro and Van der Veen with the second author. We deduce from this relation a description of the classical limit of stated skein modules, a quantum Van Kampen theorem and a quantum HNN extension theorem for stated skein modules and obtain a new description of the skein modules of mapping tori and links exteriors.
THE DEMOGRAPHICS OF BROAD-LINE QUASARS IN THE MASS–LUMINOSITY PLANE. II. BLACK HOLE MASS AND EDDINGTON RATIO FUNCTIONS

We employ a flexible Bayesian technique to estimate the black hole (BH) mass and Eddington ratio functions for Type 1 (i.e., broad line) quasars from a uniformly selected data set of ∼58, 000 quasars from the Sloan Digital Sky Survey (SDSS) DR7. We find that the SDSS becomes significantly incomplete at MBH ≲ 3 × 108 M☉ or L/LEdd ≲ 0.07, and that the number densities of Type 1 quasars continue to increase down to these limits. Both the mass and Eddington ratio functions show evidence of downsizing, with the most massive and highest Eddington ratio BHs experiencing Type 1 quasar phases first, although the Eddington ratio number densities are flat at z < 2. We estimate the maximum Eddington ratio of Type 1 quasars in the observable universe to be L/LEdd ∼ 3. Consistent with our results in Shen & Kelly, we do not find statistical evidence for a so-called sub-Eddington boundary in the mass–luminosity plane of broad-line quasars, and demonstrate that such an apparent boundary in the observed distribution can be caused by selection effect and errors in virial BH mass estimates. Based on the typical Eddington ratio in a given mass bin, we estimate growth times for the BHs in Type 1 quasars and find that they are comparable to or longer than the age of the universe, implying an earlier phase of accelerated (i.e., with higher Eddington ratios) and possibly obscured growth. The large masses probed by our sample imply that most of our BHs reside in what are locally early-type galaxies, and we interpret our results within the context of models of self-regulated BH growth.
Towards the Assignments for \(1^{1}D_{2}\) and \(1^{3}D_{2}\) Meson Nonets

In this work, we investigate the mass spectrum of $1^{1}D_{2}$ and $1^{3}D_{2}$ meson nonets in the framework of the meson mass matrix and Regge phenomenology. The results are compared with the values from different phenomenological models and may be useful for the assignment of the $1^{1}D_{2}$ and $1^{3}D_{2}$ meson nonets in the future.
Graph Summarization via Node Grouping: A Spectral Algorithm

Graph summarization via node grouping is a popular method to build concise graph representations by grouping nodes from the original graph into supernodes and encoding edges into superedges such that the loss of adjacency information is minimized. Such summaries have immense applications in large-scale graph analytics due to their small size and high query processing efficiency. In this paper, we reformulate the loss minimization problem for summarization into an equivalent integer maximization problem. By initially allowing relaxed (fractional) solutions for integer maximization, we analytically expose the underlying connections to the spectral properties of the adjacency matrix. Consequently, we design an algorithm called SpecSumm that consists of two phases. In the first phase, motivated by spectral graph theory, we apply k-means clustering on the k largest (in magnitude) eigenvectors of the adjacency matrix to assign nodes to supernodes. In the second phase, we propose a greedy heuristic that updates the initial assignment to further improve summary quality. Finally, via extensive experiments on 11 datasets, we show that SpecSumm efficiently produces high-quality summaries compared to state-of-the-art summarization algorithms and scales to graphs with millions of nodes.
The Nielsen Realization Problem for Non-Orientable Surfaces

We show the Teichm\"uller space of a non-orientable surface with marked points (considered as a Klein surface) can be identified with a subspace of the Teichm\"uller space of its orientable double cover. Also, it is well known that the mapping class group $\text{Mod} (N_g; k)$ of a non-orientable surface can be identified with a subgroup of $\text{Mod} (S_{g-1}; 2k)$, the mapping class group of its orientable double cover. These facts together with the classical Nielsen realization theorem are used to prove that every finite subgroup of $\text{Mod}(N_g; k)$ can be lifted isomorphically to a subgroup of the group of diffeomorphisms $\text{Diff}(N_g; k)$. In contrast, we show the projection $\text{Diff}(N_g) \to \text{Mod}(N_g)$ does not admit a section for large $g$.
Detecting Hidden Communities by Power Iterations with Connections to Vanilla Spectral Algorithms

Community detection in the stochastic block model is one of the central problems of graph clustering. Since its introduction, many subsequent papers have made great strides in solving and understanding this model. In this setup, spectral algorithms have been one of the most widely used frameworks. However, despite the long history of study, there are still unsolved challenges. One of the main open problems is the design and analysis of"simple"(vanilla) spectral algorithms, especially when the number of communities is large. In this paper, we provide two algorithms. The first one is based on the power-iteration method. It is a simple algorithm which only compares the rows of the powered adjacency matrix. Our algorithm performs optimally (up to logarithmic factors) compared to the best known bounds in the dense graph regime by Van Vu (Combinatorics Probability and Computing, 2018). Furthermore, our algorithm is also robust to the"small cluster barrier", recovering large clusters in the presence of an arbitrary number of small clusters. Then based on a connection between the powered adjacency matrix and eigenvectors, we provide a vanilla spectral algorithm for large number of communities in the balanced case. This answers an open question by Van Vu (Combinatorics Probability and Computing, 2018) in the balanced case. Our methods also partially solve technical barriers discussed by Abbe, Fan, Wang and Zhong (Annals of Statistics, 2020). In the technical side, we introduce a random partition method to analyze each entry of a powered random matrix. This method can be viewed as an eigenvector version of Wigner's trace method. Recall that Wigner's trace method links the trace of powered matrix to eigenvalues. Our method links the whole powered matrix to the span of eigenvectors. We expect our method to have more applications in random matrix theory.
Thermoelectromechanical effects in relaxed-shape graphene and band structures of graphene quantum dots

We investigate the in-plane oscillations of the relaxed shape graphene due to externally applied tensile edge stress along both the armchair and zigzag directions. Thermo-electromechanical effects are treated via pseudomorphic vector potentials to analyze the influence of these coupled effects on the bandstructures of bilayer graphene quantum dots (QDs). We show that the total elastic energy density is enhanced with temperature for the case of applied tensile edge stress along the zigzag direction. We report that the level crossing between ground and first excited states in the localized edge states can be achieved with the accessible values of temperature. In particular, the level crossing point extends to higher temperatures with decreasing values of externally applied tensile edge stress along the armchair direction. Such kind of level crossing is absent in the states formed at the center of the graphene sheet due to the presence of three fold symmetry.
Iris: Automatic Generation of Efficient Data Layouts for High Bandwidth Utilization

Optimizing data movements is becoming one of the biggest challenges in heterogeneous computing to cope with data deluge and, consequently, big data applications. When creating specialized accelerators, modern high-level synthesis (HLS) tools are increasingly efficient in optimizing the computational aspects, but data transfers have not been adequately improved. To combat this, novel architectures such as High-Bandwidth Memory with wider data busses have been developed so that more data can be transferred in parallel. Designers must tailor their hardware/software interfaces to fully exploit the available bandwidth. HLS tools can automate this process, but the designer must follow strict coding-style rules. If the bus width is not evenly divisible by the data width (e.g., when using custom-precision data types) or if the arrays are not power-of-two length, the HLS-generated accelerator will likely not fully utilize the available bandwidth, demanding even more manual effort from the designer. We propose a methodology to automatically find and implement a data layout that, when streamed between memory and an accelerator, uses a higher percentage of the available bandwidth than a naive or HLS-optimized design. We borrow concepts from multiprocessor scheduling to achieve such high efficiency.
Reconstruct from BEV: A 3D Lane Detection Approach based on Geometry Structure Prior

In this paper, we propose an advanced approach in targeting the problem of monocular 3D lane detection by leveraging geometry structure underneath the process of 2D to 3D lane reconstruction. Inspired by previous methods, we first analyze the geometry heuristic between the 3D lane and its 2D representation on the ground and propose to impose explicit supervision based on the structure prior, which makes it achievable to build inter-lane and intra-lane relationships to facilitate the reconstruction of 3D lanes from local to global. Second, to reduce the structure loss in 2D lane representation, we directly extract BEV lane information from front view images, which tremendously eases the confusion of distant lane features in previous methods. Furthermore, we propose a novel task-specific data augmentation method by synthesizing new training data for both segmentation and reconstruction tasks in our pipeline, to counter the imbalanced data distribution of camera pose and ground slope to improve generalization on unseen data. Our work marks the first attempt to employ the geometry prior information into DNN-based 3D lane detection and makes it achievable for detecting lanes in an extra-long distance, doubling the original detection range. The proposed method can be smoothly adopted by other frameworks without extra costs. Experimental results show that our work outperforms state-of-the-art approaches by 3.8% F-Score on Apollo 3D synthetic dataset at real-time speed of 82 FPS without introducing extra parameters.
Hyperbolic Centroid Calculations for Text Classification

A new development in NLP is the construction of hyperbolic word embeddings. As opposed to their Euclidean counterparts, hyperbolic embeddings are represented not by vectors, but by points in hyperbolic space. This makes the most common basic scheme for constructing document representations, namely the averaging of word vectors, meaningless in the hyperbolic setting. We reinterpret the vector mean as the centroid of the points represented by the vectors, and investigate various hyperbolic centroid schemes and their effectiveness at text classification.
Optimal shepherding and transport of a flock

We investigate how a shepherd should move in order to effectively herd and guide a flock of agents towards a target. Using a detailed agent-based model (ABM) for the members of the flock, we pose and solve an optimization problem for the shepherd that has to simultaneously work to keep the flock cohesive while coercing it towards a prescribed project. We find that three distinct strategies emerge as potential solutions as a function of just two parameters: the ratio of herd size to shepherd repulsion length and the ratio of herd speed to shepherd speed. We term these as: (i) mustering, in which the shepherd circles the herd to ensure compactness, (ii) droving, in which the shepherd chases the herd in a desired direction, and (iii) driving, a hitherto unreported strategy where the flock surrounds a shepherd that drives it from within. A minimal dynamical model for the size, shape and position of the herd captures the effective behavior of the ABM, and further allows us to characterize the different herding strategies in terms of the behavior of the shepherd that librates (mustering), oscillates (droving) or moves steadily (driving). All together, our study yields a simple and intuitive classification of herding strategies that ought to be of general interest in the context of controlling the collective behavior of active matter.