Miracle-dz/UniFAR-FTUs huggingface dataset:
NeurInt : Learning to Interpolate through Neural ODEs

A wide range of applications require learning image generation models whose latent space effectively captures the high-level factors of variation present in the data distribution. The extent to which a model represents such variations through its latent space can be judged by its ability to interpolate between images smoothly. However, most generative models mapping a fixed prior to the generated images lead to interpolation trajectories lacking smoothness and containing images of reduced quality. In this work, we propose a novel generative model that learns a flexible non-parametric prior over interpolation trajectories, conditioned on a pair of source and target images. Instead of relying on deterministic interpolation methods (such as linear or spherical interpolation in latent space), we devise a framework that learns a distribution of trajectories between two given images using Latent Second-Order Neural Ordinary Differential Equations. Through a hybrid combination of reconstruction and adversarial losses, the generator is trained to map the sampled points from these trajectories to sequences of realistic images that smoothly transition from the source to the target image. Through comprehensive qualitative and quantitative experiments, we demonstrate our approach's effectiveness in generating images of improved quality as well as its ability to learn a diverse distribution over smooth interpolation trajectories for any pair of real source and target images.
Identifying Circumgalactic Medium Absorption in QSO Spectra: A Bayesian Approach

We present a study of candidate galaxy–absorber pairs for 43 low-redshift QSO sightlines (0.06 < z < 0.85) observed with the Hubble Space Telescope/Cosmic Origins Spectrograph that lie within the footprint of the Sloan Digital Sky Survey with a statistical approach to match absorbers with galaxies near the QSO lines of sight using only the SDSS Data Release 12 photometric data for the galaxies, including estimates of their redshifts. Our Bayesian methods combine the SDSS photometric information with measured properties of the circumgalactic medium to find the most probable galaxy match, if any, for each absorber in the line-of-sight QSO spectrum. We find ∼630 candidate galaxy–absorber pairs using two different statistics. The methods are able to reproduce pairs reported in the targeted spectroscopic studies upon which we base the statistics at a rate of 72%. The properties of the galaxies comprising the candidate pairs have median redshift, luminosity, and stellar mass, all estimated from the photometric data, z = 0.13, L = 0.1L *, and log(M*/M⊙)=9.7 . The median impact parameter of the candidate pairs is ∼430 kpc, or ∼3.5 times the galaxy virial radius. The results are broadly consistent with the high Lyα covering fraction out to this radius found in previous studies. This method of matching absorbers and galaxies can be used to prioritize targets for spectroscopic studies, and we present specific examples of promising systems for such follow-up.
Increasing Data Diversity with Iterative Sampling to Improve Performance

As a part of the Data-Centric AI Competition, we propose a data-centric approach to improve the diversity of the training samples by iterative sampling. The method itself relies strongly on the fidelity of augmented samples and the diversity of the augmentation methods. Moreover, we improve the performance further by introducing more samples for the difficult classes especially providing closer samples to edge cases potentially those the model at hand misclassifies.
Uncertainty Quantification in Neural Differential Equations

Uncertainty quantification (UQ) helps to make trustworthy predictions based on collected observations and uncertain domain knowledge. With increased usage of deep learning in various applications, the need for efficient UQ methods that can make deep models more reliable has increased as well. Among applications that can benefit from effective handling of uncertainty are the deep learning based differential equation (DE) solvers. We adapt several state-of-the-art UQ methods to get the predictive uncertainty for DE solutions and show the results on four different DE types.
Open-Set Crowdsourcing using Multiple-Source Transfer Learning

We raise and define a new crowdsourcing scenario, open set crowdsourcing, where we only know the general theme of an unfamiliar crowdsourcing project, and we don't know its label space, that is, the set of possible labels. This is still a task annotating problem, but the unfamiliarity with the tasks and the label space hampers the modelling of the task and of workers, and also the truth inference. We propose an intuitive solution, OSCrowd. First, OSCrowd integrates crowd theme related datasets into a large source domain to facilitate partial transfer learning to approximate the label space inference of these tasks. Next, it assigns weights to each source domain based on category correlation. After this, it uses multiple-source open set transfer learning to model crowd tasks and assign possible annotations. The label space and annotations given by transfer learning will be used to guide and standardize crowd workers' annotations. We validate OSCrowd in an online scenario, and prove that OSCrowd solves the open set crowdsourcing problem, works better than related crowdsourcing solutions.
A Peculiar Type~II QSO Identified via Broad-band Detection of Extreme Nebular Line Emission

We present S82-20, an unusual redshift ≈3 object identified in SDSS-Stripe 82 broad-band images. The rest-frame ultraviolet spectrum of S82-20 shows emission lines from highly ionized species, including He ii λ1640, and the C iv λλ1548, 1550 and Ovi λλ1032, 1038 doublets. The high Lyα luminosity (3.5×1044 erg s−1), the high emission line equivalent widths (> 200Å for Lyα ), the FWHM of the emission lines (< 800km s−1), and the high ionization Ovi line strongly support the interpretation that S82-20 is a Type II QSO. However, photoionization models using Type II QSO do not fully explain the measured C iv/He ii line ratio, which requires either some contribution from star-formation or high velocity shocks. Additionally, S82-20 is not detected at wavelengths longer than 2μm, in tension with the expectation of isotropically IR emission of a luminous QSO. We consider the possibility that S82-20 is a rare example of a changing-look QSO, observed in a temporarily low state, where the broad line region has faded, while the narrow line region still emits emission line. Otherwise, it may be a rare case of the short phase of the life of a massive galaxy, in which active star formation and accretion onto a supermassive black hole coexist.
Can viewer proximity be a behavioural marker for Autism Spectrum Disorder?

Screening for any of the Autism Spectrum Disorders is a complicated process often involving a hybrid of behavioural observations and questionnaire based tests. Typically carried out in a controlled setting, this process requires trained clinicians or psychiatrists for such assessments. Riding on the wave of technical advancement in mobile platforms, several attempts have been made at incorporating such assessments on mobile and tablet devices. In this paper we analyse videos generated using one such screening test. This paper reports the first use of the efficacy of using the observer's distance from the display screen while administering a sensory sensitivity test as a behavioural marker for autism for children aged 2-7 years The potential for using a test such as this in casual home settings is promising.
The main sequence of quasars: The taming of the extremes

1Padua Astronomical Observatory, National Institute for Astrophysics (INAF), Padua, Country name 2 Astronomical Observatory, Belgrade, Serbia 3Department of Physics and Astronomy “Galileo Galilei”, University of Padua, Padua, Italy 4ICRA, Physics Department, University La Sapienza, Rome, Italy 5Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, Warsaw, Poland 6Center for Theoretical Physics, Polish Academy of Sciences, Warsaw, Poland 7Instituto de Astrofísica de Andalucía, CSIC, Granada, Spain 8Instituto de Astronomía, UNAM, Mexico, Mexico
Entropy production and correlation spreading in Gaussian quantum mechanics

Entropy production is the key quantity behind the second law of thermodynamics, and it is well defined by considering a joint unitary evolution of a system S and a thermal environment E. However, due to the diversity of initial state and Hamiltonian of S and E, it is hard to evaluate the characterization of the entropy production. In this Letter we propose that the evolution of S and E can be solved non-perturbatively in the framework of Gaussian quantum mechanics (GQM) for Gaussian states and quadratic Hamiltonian. We study the entropy production and correlation spreading in two models of E: one-dimensional free massless scalar quantum field theory in a cavity and an oscillator chain with adjacent interactions. The result is not what one might naively expect, S being thermalized and keeps stable, but is all the physical quantities, such as entropy production, mutual information, and the effective temperature of S, are fluctuating. In general the interaction between S and E creates correlation, and this correlation could spread in E and cause the physical quantities to fluctuate. The detailed information depends on the coupling strength, number of modes, boundary condition and other parameters. This analysis can be extended to any other models in the framework of QGM.
Probing the Gardner transition in an active quasi-thermal pressure-controlled granular system

To experimentally search for signals of the Gardner transition, an active quasi-thermal granular glass is constructed using a monolayer of air-fluidized star-shaped granular particles. The pressure of the system is controlled by adjusting the tension exerted on an enclosing boundary. Velocity distributions of the internal particles and the scaling of the pressure, density, effective-temperature, and relaxation time are examined, demonstrating that the system has important features of a thermal system. Using a pressure-based quenching protocol that brings the system into deeper glassy states, signals of the Gardner transition are detected via cage size and separation order parameters for both particle positions and orientations, offering experimental evidence of the Gardner transition for a system with in low spatial dimensions that is quasi-thermal.
A Non-Markovian Model to Assess Contact Tracing for the Containment of COVID-19

Non-pharmaceutical interventions, such as contact tracing has been an important tool in controlling epidemic outbreaks. In this paper, we propose a non-Markovian, network-based mathematical model to assess the effectiveness of contact tracing in COVID-19 containment. To improve the reliability of the model, empirically determined distributions were incorporated for the sojourn times of the model's states. The first-order closure approximation was used to derive an expression for the epidemic threshold. Using survey contact data collected during the 2020 fall academic semester from a university population, we determined that even four to five close contacts were sufficient to maintain the viral transmission. Additionally, our model reveals that contact tracing can be an effective outbreak mitigation measure by reducing the epidemic size by more than three-fold. Furthermore, we show that our proposed model, which accounts for the underlying complexities of the COVID-19 spreading process, performs better in short-term forecasts of case counts.
OMD: Orthogonal Malware Detection using Audio, Image, and Static Features

With the growing number of malware and cyber attacks, there is a need for “orthogonal” cyber defense approaches, which are complementary to existing methods by detecting unique malware samples that are not predicted by other methods. In this paper, we propose a novel and orthogonal malware detection (OMD) approach to identify malware using a combination of audio descriptors, image similarity descriptors and other static/statistical features. First, we show how audio descriptors are effective in classifying malware families when the malware binaries are represented as audio signals. Then, we show that the predictions made on the audio descriptors are orthogonal to the predictions made on image similarity descriptors and other static features. Further, we develop a framework for error analysis and a metric to quantify how orthogonal a new feature set (or type) is with respect to other feature sets. This allows us to add new features and detection methods to our overall framework. Experimental results on malware datasets show that our approach provides a robust framework for orthogonal malware detection.
Dzyaloshinskii-Moriya interaction induced magnetoelectric coupling in a tetrahedral molecular spin-frustrated system

We have investigated magnetoelectric coupling in the single-molecule magnet $\mathrm{Mn}_{4}\mathrm{Te}_{4}(\mathrm{P}\mathrm{Et}_{3})_{4}$ with tetrahedral spin frustration. Our density functional studies found that an electric dipole moment can emerge with various non-collinear spin orderings. The forms of spin-dependent dipole are determined and consistent with that in non-centrosymmetric magnets driven by the Dzyaloshinskii-Moriya interaction. Writing a parameterized spin Hamiltonian, after solving for eigenvalues and eigenstates we quantified the magnetoelectric coupling by calculating the thermal average of the electric and magnetic susceptibilities, which can be influenced by external magnetic and electric fields, respectively. The quadratic relations are expected to be observable in experiments.
Receiver-device-independent quantum key distribution protocols

We discuss quantum key distribution protocols and their security analysis, considering a receiver-device-independent (RDI) model. The sender’s (Alice’s) device is partially characterized, in the sense that we assume bounds on the overlaps of the prepared quantum states. The receiver’s (Bob’s) device requires no characterisation and can be represented as a black-box. Our protocols are therefore robust to any attack on Bob, such as blinding attacks. In particular, we show that a secret key can be established even when the quantum channel has arbitrarily low transmission by considering RDI protocols exploiting sufficiently many states. Finally, we discuss how the hypothesis of bounded overlaps can be naturally applied to practical devices.
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for learning visual representations by using large-scale contrastive image-text pairs. It shows impressive performance on zero-shot knowledge transfer to downstream tasks. To further enhance CLIP's few-shot capability, CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and significantly improves the performance for few-shot classification. However, such a process still needs extra training and computational resources. In this paper, we propose \textbf{T}raining-Free CL\textbf{IP}-\textbf{Adapter} (\textbf{Tip-Adapter}), which not only inherits CLIP's training-free advantage but also performs comparably or even better than CLIP-Adapter. Tip-Adapter does not require any back propagation for training the adapter, but creates the weights by a key-value cache model constructed from the few-shot training set. In this non-parametric manner, Tip-Adapter acquires well-performed adapter weights without any training, which is both efficient and effective. Moreover, the performance of Tip-Adapter can be further boosted by fine-tuning such properly initialized adapter for only a few epochs with super-fast convergence speed. We conduct extensive experiments of few-shot classification on ImageNet and other 10 datasets to demonstrate the superiority of proposed Tip-Adapter. The code will be released at \url{https://github.com/gaopengcuhk/Tip-Adapter}.
Parallel Nearest Neighbors in Low Dimensions with Batch Updates

We present a set of parallel algorithms for computing exact k-nearest neighbors in low dimensions. Many k-nearest neighbor algorithms use either a kd-tree or the Morton ordering of the point set; our algorithms combine these approaches using a data structure we call the \textit{zd-tree}. We show that this combination is both theoretically efficient under common assumptions, and fast in practice. For point sets of size $n$ with bounded expansion constant and bounded ratio, the zd-tree can be built in $O(n)$ work with $O(n^{\epsilon})$ span for constant $\epsilon<1$, and searching for the $k$-nearest neighbors of a point takes expected $O(k\log k)$ time. We benchmark our k-nearest neighbor algorithms against existing parallel k-nearest neighbor algorithms, showing that our implementations are generally faster than the state of the art as well as achieving 75x speedup on 144 hyperthreads. Furthermore, the zd-tree supports parallel batch-dynamic insertions and deletions; to our knowledge, it is the first k-nearest neighbor data structure to support such updates. On point sets with bounded expansion constant and bounded ratio, a batch-dynamic update of size $k$ requires $O(k \log n/k)$ work with $O(k^{\epsilon} + \text{polylog}(n))$ span.
GdV6Sn6: A Multi-carrier Metal with Non-magnetic 3d-electron Kagome Bands and 4f-electron Magnetism

Electronic properties of the single crystal of GdV6Sn6, where non-magnetic V-kagome layers are separated by magnetic Gd-triangular lattice, are investigated. GdV6Sn6 exhibits unique magnetotransport properties at low-temperature such as non-linear Hall resistivity and increase of resistance R in magnetic field H as R ~ H up to 56 T with Shubnikov–De Haas oscillations. Investigation of the non-magnetic analogue YV6Sn6 and the first principles calculations reveal these properties are relevant to the bands arising from the V-kagome layer. A magnetic transition at 5 K in GdV6Sn6 modifies the transport properties, pointing to a coupling between Gd-spins on the triangular lattice and carriers in the V-kagome layer.
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods

In reinforcement learning, continuous time is often discretized by a time scale $\delta$, to which the resulting performance is known to be highly sensitive. In this work, we seek to find a $\delta$-invariant algorithm for policy gradient (PG) methods, which performs well regardless of the value of $\delta$. We first identify the underlying reasons that cause PG methods to fail as $\delta \to 0$, proving that the variance of the PG estimator can diverge to infinity in stochastic environments under a certain assumption of stochasticity. While durative actions or action repetition can be employed to have $\delta$-invariance, previous action repetition methods cannot immediately react to unexpected situations in stochastic environments. We thus propose a novel $\delta$-invariant method named Safe Action Repetition (SAR) applicable to any existing PG algorithm. SAR can handle the stochasticity of environments by adaptively reacting to changes in states during action repetition. We empirically show that our method is not only $\delta$-invariant but also robust to stochasticity, outperforming previous $\delta$-invariant approaches on eight MuJoCo environments with both deterministic and stochastic settings. Our code is available at https://vision.snu. ac.kr/projects/sar.
Anisotropic Multimessenger Signals from Black Hole Neutrino-dominated Accretion Flows with Outflows in Binary Compact Object Mergers

A black hole (BH) hyperaccretion system might be born after the merger of a BH and a neutron star (NS) or a binary NS (BNS). In the case of a high mass accretion rate, the hyperaccretion disk is in a state of neutrino-dominated accretion flow (NDAF) and emits numerous anisotropic MeV neutrinos. Only a small fraction of these neutrinos annihilates in the space outside of the disk and then launches ultrarelativistic jets that break away from the merger ejecta to power gamma-ray bursts. Mergers and their remnants are generally considered sources of gravitational waves (GWs), neutrinos, and kilonovae. Anisotropic neutrino emission and anisotropic high-velocity material outflows from central BH–NDAF systems can also trigger strong GWs and luminous disk-outflow-driven (DOD) kilonovae, respectively. In this paper, the anisotropic multimessenger signals from NDAFs with outflows, including DOD kilonovae, MeV neutrinos, and GWs, are presented. According to the results, the typical AB magnitude of the DOD kilonovae is lower than that of astronomical transient AT 2017gfo at the same distance, and it decreases with increasing viewing angles and its anisotropy is not sensitive to the outflow mass distribution but mainly determined by the velocity distribution. Since neutrinos with ≳10 MeV are mainly produced in the inner region of the disk, they will be dramatically deflected to a large viewing angle by relativity effects. Moreover, the strains of GWs induced by anisotropic neutrinos increase with increasing viewing angles. The accumulation of multimessenger detection of the BNS/BH–NS mergers with different viewing angles might further verify the existence of NDAFs with outflows.
RAIL: Robust Acoustic Indoor Localization for Drones

Navigating in environments where the GPS signal is unavailable, weak, purposefully blocked, or spoofed has become crucial for a wide range of applications. A prime example is autonomous navigation for drones in indoor environments: to fly fully or partially autonomously, drones demand accurate and frequent updates of their locations. This paper proposes a Robust Acoustic Indoor Localization (RAIL) scheme for drones designed explicitly for GPS-denied environments. Instead of depending on GPS, RAIL leverages ultrasonic acoustic signals to achieve precise localization using a novel hybrid Frequency Hopping Code Division Multiple Access (FH-CDMA) technique. Contrary to previous approaches, RAIL is able to both overcome the multipath fading effect and provide precise signal separation in the receiver. Comprehensive simulations and experiments using a prototype implementation demonstrate that RAIL provides high-accuracy three-dimensional localization with an average error of less than 1.5 cm.
Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?

In this work, we examine the robustness of state-of-the-art semi-supervised learning (SSL) algorithms when applied to morphological classification in modern radio astronomy. We test whether SSL can achieve performance comparable to the current supervised state of the art when using many fewer labelled data points and if these results generalise to using truly unlabelled data. We find that although SSL provides additional regularisation, its performance degrades rapidly when using very few labels, and that using truly unlabelled data leads to a significant drop in performance.