Miracle-dz/UniFAR-FTUs huggingface dataset:
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers

Annotating bounding boxes for object detection is expensive, time-consuming, and error-prone. In this work, we propose a DETR based framework called ComplETR that is designed to explicitly complete missing annotations in partially annotated dense scene datasets. This reduces the need to annotate every object instance in the scene thereby reducing annotation cost. ComplETR augments object queries in DETR decoder with patch information of objects in the image. Combined with a matching loss, it can effectively find objects that are similar to the input patch and complete the missing annotations. We show that our framework outperforms the state-of-the-art methods such as Soft Sampling and Unbiased Teacher by itself, while at the same time can be used in conjunction with these methods to further improve their performance. Our framework is also agnostic to the choice of the downstream object detectors; we show performance improvement for several popular detectors such as Faster R-CNN, Cascade R-CNN, CenterNet2, and Deformable DETR on multiple dense scene datasets.
Joint Constraints on Exoplanetary Orbits from Gaia DR3 and Doppler Data

The third Gaia data release includes a catalog of exoplanets and exoplanet candidates identified via the star’s astrometric motion. This paper reports on tests for consistency between the Gaia two-body orbital solutions and precise Doppler velocities for stars currently amenable to such a comparison. For BD-17 0063, HD 81040, and HD 132406, the Gaia orbital solution and the Doppler data were found to be consistent and were fitted jointly to obtain the best possible constraints on the planets’ orbits and masses. Inconsistencies were found for four stars: HD 111232, probably due to additional planets that were not included in the astrometric model; HD 175167 and HR 810, possibly due to inaccurate treatment of non-Gaussian uncertainties in the Gaia orbital solutions; and HIP 66074, for unknown reasons. Consistency tests were also performed for HD 114762, which was reported in 1989 to have a brown dwarf or exoplanet but has since been shown to be a binary star. The joint Gaia–Doppler analysis shows the secondary mass to be 0.215 ± 0.013 M ⊙ and the orbital inclination to be 3. °63 ± 0. °06.
Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography

Sparse-view Computed Tomography (SVCT) has great potential for decreasing patient radiation exposure dose during scanning. In this work, we propose a Self-supervised COordinate Projection nEtwork (SCOPE) to reconstruct the artifact-free CT image from the acquired SV sinogram by solving the inverse problem of tomography imaging. To solve the under-determined inverse imaging problem, we first introduce an implicit neural representation (INR) network to constrain the solution space via image continuity prior. And inspired by the relationship between linear algebra and inverse problems, we propose a novel re-projection strategy to generate a dense view sinogram from the initial solution, which significantly improves the rank of the linear equation system and produces a more stable CT image solution space. Specially, the desired CT image is represented as an implicit function of the two-dimensional spatial coordinate to directly approximate the SV sinogram through the CT imaging forward model. Then, a dense-view sinogram is generated from the fine-trained INR network. The final CT reconstruction is reconstructed by applying Filtered Back Projection (FBP) to the generated dense-view sinogram. Additionally, we integrate the recent hash encoding into our SCOPE model, which efficiently accelerates the model training process. We evaluate SCOPE in parallel and fan X-ray beam SVCT reconstruction tasks. Our experiment results demonstrate that the re-projection strategy significantly improves the image reconstruction quality (+3 dB for PSNR at least). The proposed SCOPE model provides state-of-the-art reconstruction results compared to two latest INR-based methods and two well-popular supervised DL methods for the SV CT image reconstruction.
The Influence of Disk Composition on the Evolution of Stars in the Disks of Active Galactic Nuclei

Disks of gas accreting onto supermassive black holes, powering active galactic nuclei (AGN), can capture stars from nuclear star clusters or form stars in situ via gravitational instability. The density and thermal conditions of these disks can result in rapid accretion onto embedded stars, dramatically altering their evolution in comparison to stars in the interstellar medium. Theoretical models predict that, when subjected to sufficiently rapid accretion, fresh gas replenishes hydrogen in the cores of these stars as quickly as it is burned into helium, reaching a quasi-steady state. Such massive, long-lived (“immortal”) stars may be capable of dramatically enriching AGN disks with helium, and would increase the helium abundance in AGN broad-line regions relative to that in the corresponding narrow-line regions and hosts. We investigate how the helium abundance of AGN disks alters the evolution of stars embedded therein. We find, in agreement with analytical arguments, that stars at a given mass are more luminous at higher helium mass fractions, and so undergo more radiation-driven mass loss. We further find that embedded stars tend to be less massive in disks with higher helium mass fractions, and that immortal stars are less common in such disks. Thus, disk composition can alter the rates of electromagnetic and gravitational wave transients as well as further chemical enrichment by embedded stars.
Moments of moments of the characteristic polynomials of random orthogonal and symplectic matrices

By using asymptotics of Toeplitz+Hankel determinants, we establish formulae for the asymptotics of the moments of the moments of the characteristic polynomials of random orthogonal and symplectic matrices, as the matrix size tends to infinity. Our results are analogous to those that Fahs obtained for random unitary matrices in (Fahs B. 2021 Communications in Mathematical Physics 383, 685–730. (doi:10.1007/s00220-021-03943-0)). A key feature of the formulae we derive is that the phase transitions in the moments of moments are seen to depend on the symmetry group in question in a significant way.
The Iron Yield of Core-collapse Supernovae

We present a systematic analysis of 191 stripped-envelope supernovae (SE SNe), aimed at computing their 56Ni masses from the luminosity in their radioactive tails ( MNitail ) and/or in their maximum light, and the mean 56Ni and iron yields of SE SNe and core-collapse SNe. Our sample consists of SNe IIb, Ib, and Ic from the literature and from the Zwicky Transient Facility Bright Transient Survey. To calculate luminosities from optical photometry, we compute bolometric corrections using 49 SE SNe with optical and near-IR photometry, and develop corrections to account for the unobserved UV and IR flux. We find that the equation of Khatami & Kasen for radioactive 56Ni-powered transients with a single free parameter does not fit the observed peak time–luminosity relation of SE SNe. Instead, we find a correlation between MNitail , peak time, peak luminosity, and decline rate, which allows for measuring individual 56Ni masses to a precision of 14%. Applying this method to the whole sample, we find, for SNe IIb, Ib, and Ic, mean 56Ni masses of 0.066 ± 0.006, 0.082 ± 0.009, and 0.132 ± 0.011 M ⊙, respectively. After accounting for their relative rates, for SE SNe as a whole, we compute mean 56Ni and iron yields of 0.090 ± 0.005 and 0.097 ± 0.007 M ⊙, respectively. Combining these results with the recent Type II SN mean 56Ni mass derived by Rodríguez et al., core-collapse SNe, as a whole, have mean 56Ni and iron yields of 0.055 ± 0.006 and 0.058 ± 0.007 M ⊙, respectively. We also find that radioactive 56Ni-powered models typically underestimate the peak luminosity of SE SNe by 60%–70%, suggesting the presence of an additional power source contributing to the luminosity at peak.
Phase Dependent Loss Analysis for RIS Systems

In this paper we focus on phase dependent loss (PDL), an important aspect of reconfigurable intelligent surfaces (RIS) where the signals reflected from the RIS elements are attenuated by varying amounts depending on the phase rotation provided by the element. To evaluate the effects of PDL, we analyse the SNR of a SIMO RIS-aided wireless link. We assume that the channel between the base station (BS) and RIS is a rank-1 LOS channel, while the user (UE)-BS and UE-RIS are correlated Rayleigh channels. The RIS design is optimal in the absence of PDL and maximizes the SNR in this scenario. Specifically, we derive an exact expression for the mean SNR in the presence of PDL. The attenuation function used for PDL was developed from a detailed circuit analysis of RIS elements. Leveraging the derived results, we analytically characterise the impact of PDL on the mean SNR. Numerical results are conducted to validate the derived expressions and verify the analysis.
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation

Recent advances in text-to-image synthesis have led to large pretrained transformers with excellent capabilities to generate visualizations from a given text. However, these models are ill-suited for specialized tasks like story visualization, which requires an agent to produce a sequence of images given a corresponding sequence of captions, forming a narrative. Moreover, we find that the story visualization task fails to accommodate generalization to unseen plots and characters in new narratives. Hence, we first propose the task of story continuation, where the generated visual story is conditioned on a source image, allowing for better generalization to narratives with new characters. Then, we enhance or 'retro-fit' the pretrained text-to-image synthesis models with task-specific modules for (a) sequential image generation and (b) copying relevant elements from an initial frame. Then, we explore full-model finetuning, as well as prompt-based tuning for parameter-efficient adaptation, of the pre-trained model. We evaluate our approach StoryDALL-E on two existing datasets, PororoSV and FlintstonesSV, and introduce a new dataset DiDeMoSV collected from a video-captioning dataset. We also develop a model StoryGANc based on Generative Adversarial Networks (GAN) for story continuation, and compare it with the StoryDALL-E model to demonstrate the advantages of our approach. We show that our retro-fitting approach outperforms GAN-based models for story continuation and facilitates copying of visual elements from the source image, thereby improving continuity in the generated visual story. Finally, our analysis suggests that pretrained transformers struggle to comprehend narratives containing several characters. Overall, our work demonstrates that pretrained text-to-image synthesis models can be adapted for complex and low-resource tasks like story continuation.
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks

We present evidence for the existence and effectiveness of adversarial attacks on graph neural networks (GNNs) that aim to degrade fairness. These attacks can disadvantage a particular subgroup of nodes in GNN-based node classification, where nodes of the underlying network have sensitive attributes, such as race or gender. We conduct qualitative and experimental analyses explaining how adversarial link injection impairs the fairness of GNN predictions. For example, an attacker can compromise the fairness of GNN-based node classification by injecting adversarial links between nodes belonging to opposite subgroups and opposite class labels. Our experiments on empirical datasets demonstrate that adversarial fairness attacks can significantly degrade the fairness of GNN predictions (attacks are effective) with a low perturbation rate (attacks are efficient) and without a significant drop in accuracy (attacks are deceptive). This work demonstrates the vulnerability of GNN models to adversarial fairness attacks. We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.
Input Delay Compensation for Neuron Growth by PDE Backstepping

Neurological studies show that injured neurons can regain their functionality with therapeutics such as Chondroitinase ABC (ChABC). These therapeutics promote axon elongation by manipulating the injured neuron and its intercellular space to modify tubulin protein concentration. This fundamental protein is the source of axon elongation, and its spatial distribution is the state of the axon growth dynamics. Such dynamics often contain time delays because of biological processes. This work introduces an input delay compensation with state-feedback control law for axon elongation by regulating tubulin concentration. Axon growth dynamics with input delay is modeled as coupled parabolic diffusion-reaction-advection Partial Differential Equations (PDE) with a boundary governed by Ordinary Differential Equations (ODE), associated with a transport PDE. A novel feedback law is proposed by using backstepping method for input-delay compensation. The gain kernels are provided after transforming the interconnected PDE-ODE-PDE system to a target system. The stability analysis is presented by applying Lyapunov analysis to the target system in the spatial H1-norm, thereby the local exponential stability of the original error system is proved by using norm equivalence.
Improving Information Freshness via Backbone-Assisted Cooperative Access Points

Information freshness, characterized by Age of Information (AoI), is important for sensor applications involving timely status updates. In many cases, the wireless signals from one sensor can be received by multiple access points (APs). This article investigates the average AoI for cooperative APs (Co-APs), in which they can share information through a wired backbone network. We first study a basic backbone-assisted Co-AP system where APs share only decoded packets. Experimental results on software-defined radios (SDRs) indicate that Co-AP significantly improves the average AoI performance over a single-AP system. Next, we investigate an improved Co-AP system, called Soft-Co-AP. In addition to sharing decoded packets, Soft-Co-AP shares and collects soft information of packets that the APs fail to decode for further joint decoding. A critical issue in Soft-Co-AP is determining the number of quantization bits that represent the soft information (each soft bit) shared over the backbone. While more quantization bits per soft bit improves the joint decoding performance, it leads to higher backbone delay. We experimentally study the average AoI of Soft-Co-AP by evaluating the tradeoff between the backbone delay and the number of quantization bits. SDR experiments show that when the number of sensors is large, Soft-Co-AP further reduces the average AoI by 12% compared with Co-AP. Interestingly, good average AoI performance is usually achieved when the number of quantization bits per soft bit is neither too large nor too small.
Thermodynamic approach to liquid-to-glass transformation as an arrest transition in polydisperse solution

Thermodynamic multi-component solution solidification approach to liquid-to-glass transition is proposed and actual mechanisms underlying vitrification, other than viscous slowdown, are identified. Due to polydisperse aggregation in liquid state, glass-forming liquids, irrespective of chemical composition, appear to be mixtures of various quasi-components whose thermodynamic quantities shall be expressed not in terms of molar concentrations of actual chemical components, but in terms of relative concentrations of dominant structural units. Thermodynamically, any glass-former is expected to behave as multi-component solution and solidify in continuous temperature range between apparent liquidus and solidus temperatures that can be identified as glass-transition range. Using extended irreversible thermodynamics of polydisperse solutions it is demonstrated that upon quenching, diffusional and Brownian mass transport in such solutions is negated within heat removal timescale, which results in dynamical arrest of nucleation and growth in clusters and solid-liquid phase separation. Rapid solution solidification proceeds via successive cluster freezing in continuous temperature range, in line with cluster size dispersity, which can be described in terms of percolation in static polydisperse fractal ensemble where glass transition temperature naturally emerges as percolation threshold. Multi-component solution solidification framework is shown to be reconcilable qualitatively and quantitatively with the Mode Coupling - Random First Order Transition scenario. Finally, it is demonstrated that liquid-to-glass transformation is thermodynamic liquid-to-solid phase transition, glassy state of matter appears to be solid supersaturated solution of defects in otherwise perfect matrix, and the true equilibrium structure which glass is unable access is a crystalline one.
On MDS Condition and Erased Lines Recovery of Generalized Expanded-Blaum-Roth Codes and Generalized Blaum-Roth Codes

Generalized Expanded-Blaum-Roth (GEBR) codes [1] are designed for large-scale distributed storage systems that have larger recoverability for single-symbol failures, multi-column failures and multi-row failures, compared with locally recoverable codes (LRC). GEBR codes encode an α × k information array into a pτ × ( k + r ) array such that lines of slope i with 0 ≤ i ≤ r − 1 have even parity and each column contains pτ − α local parity symbols, where p is an odd prime and k + r ≤ pτ . Necessary and sufﬁcient conditions for GEBR codes to be ( n, k ) recoverable (i.e., any k out of n = k + r columns can retrieve all information symbols) are given in [2] for α = ( p − 1) τ . However, the ( n, k ) recoverable condition of GEBR codes is unknown when α < ( p − 1) τ . In this paper, we present the ( n, k ) recoverable condition for GEBR codes for α < ( p − 1) τ . In addition, we present a sufﬁcient condition for enabling GEBR codes to recover some erased lines of any slope i ( 0 ≤ i ≤ pτ − 1 ) for any parameter r when τ is a power of p . Moreover, we present the construction of Generalized Blaum-Roth (GBR) codes that encode an α × k information array into an α × ( k + r ) array. We show that GBR codes share the same MDS condition as the ( n, k ) recoverable condition of GEBR codes, and we also present a sufﬁcient condition for GBR codes to recover some erased lines of any slope i ( 0 ≤ i ≤ α − 1 ).
Suppressing Recoil Heating in Levitated Optomechanics Using Squeezed Light

We theoretically show that laser recoil heating in free-space levitated optomechanics can be arbitrarily suppressed by shining squeezed light onto an optically trapped nanoparticle. The presence of squeezing modifies the quantum electrodynamical light-matter interaction in a way that enables us to control the amount of information that the scattered light carries about a given mechanical degree of freedom. Moreover, we analyze the trade-off between measurement imprecision and back-action noise and show that optical detection beyond the standard quantum limit can be achieved. We predict that, with state-of-the-art squeezed light sources, laser recoil heating can be reduced by at least 60% by squeezing a single Gaussian mode with an appropriate incidence direction, and by 98% by squeezing a properly mode-matched mode. Our results, which are valid both for motional and librational degrees of freedom, will lead to improved feedback cooling schemes as well as boost the coherence time of optically levitated nanoparticles in the quantum regime.
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation

Adversarial patch attacks are an emerging security threat for real world deep learning applications. We present Demasked Smoothing, the first approach (up to our knowledge) to certify the robustness of semantic segmentation models against this threat model. Previous work on certifiably defending against patch attacks has mostly focused on image classification task and often required changes in the model architecture and additional training which is undesirable and computationally expensive. In Demasked Smoothing, any segmentation model can be applied without particular training, fine-tuning, or restriction of the architecture. Using different masking strategies, Demasked Smoothing can be applied both for certified detection and certified recovery. In extensive experiments we show that Demasked Smoothing can on average certify 64% of the pixel predictions for a 1% patch in the detection task and 48% against a 0.5% patch for the recovery task on the ADE20K dataset.
Verified Compositions of Neural Network Controllers for Temporal Logic Control Objectives

This paper presents a new approach to design verified compositions of Neural Network (NN) controllers for autonomous systems with tasks captured by Linear Temporal Logic (LTL) formulas. Particularly, the LTL formula requires the system to reach and avoid certain regions in a temporal/logical order. We assume that the system is equipped with a finite set of trained NN controllers. Each controller has been trained so that it can drive the system towards a specific region of interest while avoiding others. Our goal is to check if there exists a temporal composition of the trained NN controllers - and if so, to compute it - that will yield composite system behaviors that satisfy a user-specified LTL task for any initial system state belonging to a given set. To address this problem, we propose a new approach that relies on a novel integration of automata theory and recently proposed reachability analysis tools for NN-controlled systems. We note that the proposed method can be applied to other controllers, not necessarily modeled by NNs, by appropriate selection of the reachability analysis tool. We focus on NN controllers due to their lack of robustness. The proposed method is demonstrated on navigation tasks for aerial vehicles.
Phenomenology of scalar leptoquarks at the LHC in explaining the radiative neutrino masses, muon 
g−2
, and lepton flavor violating observables

We study the phenomenology of a particular leptoquark extension of the Standard Model (SM), namely the doublet-singlet scalar leptoquark extension of the SM (DSL-SM). Besides generating Majorana mass for neutrinos, these leptoquarks contribute to muon and electron $(g-2)$ and various lepton flavour violating processes. Collider signatures of the benchmark points (BPs), consistent with the neutrino oscillation data, anomalous muon/electron magnetic moments, experimental bounds on the charged lepton flavour violation observables, etc., are studied at the LHC/FCC with centre-of-mass energies of 14, 27 and 100 TeV. While the two $-1/3$ charged colored scalars from singlet and doublet leptoquark mix with each other, the charge $2/3$ colored scalar from the doublet leptoquark remains pure. With a near-degenerate mass spectrum, the pure and mixed leptoquark states are shown to be distinguishable from multiple finalstates, while discerning between the two mixed states remain very challenging.
Blurring Diffusion Models

Recently, Rissanen et al., (2022) have presented a new type of diffusion process for generative modeling based on heat dissipation, or blurring, as an alternative to isotropic Gaussian diffusion. Here, we show that blurring can equivalently be defined through a Gaussian diffusion process with non-isotropic noise. In making this connection, we bridge the gap between inverse heat dissipation and denoising diffusion, and we shed light on the inductive bias that results from this modeling choice. Finally, we propose a generalized class of diffusion models that offers the best of both standard Gaussian denoising diffusion and inverse heat dissipation, which we call Blurring Diffusion Models.