Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Anomalies using Generative Adversarial Networks on Images

Automatic detection of anomalies such as weapons or threat objects in baggage security, or detecting impaired items in industrial production is an important computer vision task demanding high eﬃciency and accuracy. Most of the available data in the anomaly detection task is imbalanced as the number of positive/anomalous instances is sparse. Inadequate availability of the data makes training of a deep neural network architecture for anomaly detection challenging. This paper proposes a novel Generative Adversarial Network (GAN) based model for anomaly detection. It uses normal (non-anomalous) images to learn about the normality based on which it detects if an input image contains an anomalous/threat object. The proposed model uses a generator with an encoder-decoder network having dense convolutional skip connections for enhanced reconstruction and to capture the data distribution. A self-attention augmented discriminator is used having the ability to check the consistency of detailed features even in distant portions. We use spectral normalisation to facilitate stable and improved training of the GAN. Experiments are performed on three datasets, viz. CIFAR-10, MVTec AD (for industrial applications) and SIXray (for Xray baggage security). On the MVTec AD and SIXray datasets, our model achieves an improvement of upto 21% and 4.6%, in Area Under Curve (AUC) of the Receiver Operating Characteris-tic (ROC) curve, outperforming prior works. Due to the sensitivity to the false negatives of the X-ray baggage screening task, addition-ally, we obtain upto 11% improvement in Recall on the SIXray dataset compared to the state-of-the-art anomaly detection models using GANs.
Lightweight Event-based Optical Flow Estimation via Iterative Deblurring

Inspired by frame-based methods, state-of-the-art event-based optical ﬂow networks rely on the explicit computation of correlation volumes, which are expensive to compute and store on systems with limited processing budget and memory. To this end, we introduce IDNet (Iterative De-blurring Network), a lightweight yet well-performing event-based optical ﬂow network without using correlation volumes. IDNet leverages the unique spatiotemporally continuous nature of event streams to propose an alternative way of implicitly capturing correlation through iterative reﬁne-ment and motion deblurring. Our network does not compute correlation volumes but rather utilizes a recurrent network to maximize the spatiotemporal correlation of events iteratively. We further propose two iterative update schemes: “ID” which iterates over the same batch of events, and “TID” which iterates over time with streaming events in an online fashion. Benchmark results show the former “ID” scheme can reach close to state-of-the-art performance with 33% of savings in compute and 90% in memory footprint, while the latter “TID” scheme is even more efﬁcient promising 83% of compute savings and 15 times less latency at the cost of 18% of performance drop.
Using Focal Loss to Fight Shallow Heuristics: An Empirical Analysis of Modulated Cross-Entropy in Natural Language Inference

There is no such thing as a perfect dataset. In some datasets, deep neural networks discover underlying heuristics that allow them to take shortcuts in the learning process, resulting in poor generalization capability. Instead of using standard cross-entropy, we explore whether a modulated version of cross-entropy called focal loss can constrain the model so as not to use heuristics and improve generalization performance. Our experiments in natural language inference show that focal loss has a regularizing impact on the learning process, increasing accuracy on out-of-distribution data, but slightly decreasing performance on in-distribution data. Despite the improved out-of-distribution performance, we demonstrate the shortcomings of focal loss and its inferiority in comparison to the performance of methods such as unbiased focal loss and self-debiasing ensembles. Agree to disagree: Diversity through disagreement for better transferability.
Compact binaries through a lens: Silent versus detectable microlensing for the LIGO-Virgo-KAGRA gravitational wave observatories

Massive objects located between Earth and a compact binary merger can act as a magnifying glass improving the sensitivity of gravitational wave detectors to distant events. Depending on the parameters of the system, a point mass lens between the detector and the source can either lead to a smooth frequency-dependent amplification of the gravitational wave signal, or magnification combined with the appearance of a second image that interferes with the first creating a regular, predictable pattern. We map the increase in the signal to noise ratio for upcoming LVK observations as a function of the mass of the lens $M_L$ and dimensionless source position $y$ for any point mass lens between the detector and the binary source. To quantify detectability, we compute the optimal match between the lensed waveform and the waveforms in the unlensed template bank. The higher the mismatch with unlensed templates, the more detectable lensing is. Furthermore, we estimate the probability of lensing, and find that the redshift to which binary mergers are visible with the LVK increases from z = 1 to about 3.2 for a total detected binary mass of 120 $M_\odot$. The overall probability of lensing is $<20\%$ of all detectable events above the threshold SNR for $120 M_\odot$ and $<5\%$ for more common events with $60 M_\odot$. We find that there is a selection bias for detectable lensing that favors events that are close to the line of sight $y \lesssim 0.5$. Black hole binary searches could thus improve their sensitivity by taking this bias into account. Moreover, the match, the SNR increase due to lensing, and the probability of lensing are only weakly dependent on the noise curve of the detector with very similar results for both the O3 and predicted O4 noise power spectral densities. These results are upper limits that assume all dark matter is composed of $300 M_\odot$ point mass lenses.
Introducing TIGRESS-NCR. I. Coregulation of the Multiphase Interstellar Medium and Star Formation Rates

Massive, young stars are the main source of energy that maintains multiphase structure and turbulence in the interstellar medium (ISM), and without this “feedback” the star formation rate (SFR) would be much higher than is observed. Rapid energy loss in the ISM and efficient energy recovery by stellar feedback lead to coregulation of SFRs and the ISM state. Realistic approaches to this problem should solve for the dynamical evolution of the ISM, including star formation and the input of feedback energy self-consistently and accurately. Here, we present the TIGRESS-NCR numerical framework, in which UV radiation, supernovae, cooling and heating processes, and gravitational collapse are modeled explicitly. We use an adaptive ray-tracing method for UV radiation transfer from star clusters represented by sink particles, accounting for attenuation by dust and gas. We solve photon-driven chemical equations to determine the abundances of hydrogen (time dependent) and carbon/oxygen-bearing species (steady state), which then set cooling and heating rates self-consistently. Applying these methods, we present high-resolution magnetohydrodynamics simulations of differentially rotating local galactic disks representing typical conditions of nearby star-forming galaxies. We analyze ISM properties and phase distributions and show good agreement with existing multiwavelength galactic observations. We measure midplane pressure components (turbulent, thermal, and magnetic) and the weight, demonstrating that vertical dynamical equilibrium holds. We quantify the ratios of pressure components to the SFR surface density, which we call the feedback yields. The TIGRESS-NCR framework will allow for a wide range of parameter exploration, including in low-metallicity systems.
Scaling of Entanglement-Assisted Communication in Ampliﬁed Fiber Links

—Quantum communication technology offers several advanced strategies. However, their practical use is often times still not well understood. In this work we outline the theoretical communication capacity scaling in ampliﬁed ﬁber links. We present a scenario in which the assistance via pre-shared entan-glement offers an arbitrary capacity, given enough bandwidth and spatial modes are provided by the ﬁber. The future capacity advantage over conventional classical techniques as well as non-assisted quantum techniques is potentially inﬁnite. We discuss this theoretical observation in connection to current trends in ﬁber development.
Classification of the Vacua of the Dimensionally Reduced Low‐Energy Limit of the Heterotic String Over Nearly‐Kähler Manifolds

We examine the vacua of the scalar potential of the effective 4‐d action, obtained after the dimensional reduction of the 10‐d N=1$\mathcal {N}=1$ heterotic supergravity coupled to an N=1$\mathcal {N}=1$ Yang‐Mills sector. The (Coset Space) dimensional reduction takes place over the three 6‐d nearly‐Kähler manifolds, namely the homogeneous 6‐d non‐symmetric coset spaces, G2/SU(3)$G_2/SU(3)$ , Sp4/SU(2)×U(1)$Sp_4/SU(2)\times U(1)$ and SU(3)/U(1)×U(1)$SU(3)/U(1)\times U(1)$ . The current work consists a complete catalogue of the kinds of vacua of theories obtained after the reduction of the heterotic string over the 6‐d non‐symmetric coset spaces and, moreover, a contribution to the dialogue of the possibility to result with non‐AdS vacua in the framework of string theories.
Universality of grain boundary phases in fcc metals: Case study on high-angle [111] symmetric tilt grain boundaries

Grain boundaries often exhibit ordered atomic structures. Increasing amounts of evidence have been provided by transmission electron microscopy and atomistic computer simulations that different stable and metastable grain boundary structures can occur. Meanwhile, theories to treat them thermodynamically as grain boundary phases have been developed. Whereas atomic structures were identified at particular grain boundaries for particular materials, it remains an open question if these structures and their thermodynamic excess properties are material specific or generalizable to, e.g., all fcc metals. In order to elucidate that question, we use atomistic simulations with classical interatomic potentials to investigate a range of high-angle [111] symmetric tilt grain boundaries in Ni, Cu, Pd, Ag, Au, Al, and Pb. We could indeed find two families of grain boundary phases in all of the investigated grain boundaries, which cover most of the standard fcc materials. Where possible, we compared the atomic structures to atomic-resolution electron microscopy images and found that the structures match. This poses the question if the grain boundary phases are simply the result of sphere-packing geometry or if material-specific bonding physics play a role. We tested this using simple model pair potentials and found that medium-ranged interactions are required to reproduce the atomic structures, while the more realistic material models mostly affect the grain boundary (free) energy. In addition to the structural investigation, we also report the thermodynamic excess properties of the grain boundaries, explore how they influence the thermodynamic stability of the grain boundary phases, and detail the commonalities and differences between the materials.
On Designing Light-Weight Object Trackers Through Network Pruning: Use CNNS or Transformers?

Object trackers deployed on low-power devices need to be light-weight, however, most of the current state-of-the-art (SOTA) methods rely on using compute-heavy backbones built using CNNs or Transformers. Large sizes of such models do not allow their deployment in low-power conditions and designing compressed variants of large tracking models is of great importance. This paper demonstrates how highly compressed light-weight object trackers can be designed using neural architectural pruning of large CNN and Transformer based trackers. Further, a comparative study on architectural choices best suited to design lightweight trackers is provided. A comparison between SOTA trackers using CNNs, Transformers as well as the combination of the two is presented to study their stability at various compression ratios. Finally results for extreme pruning scenarios going as low as 1% in some cases are shown to study the limits of network pruning in object tracking. This work provides deeper insights into designing highly efficient trackers from existing SOTA methods.1
MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs

Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to pooling operations, encoder-decoder networks used for pixel-level predictions also require unpooling. It is common to combine unpooling with convolution or deconvolution for up-sampling. However, using its morphological properties, unpooling can be generalised and improved. Extensive experimentation on two tasks and three large-scale datasets shows that morphological pooling and unpooling lead to improved predictive performance at much reduced parameter counts.
FIXED POINTS SUBGROUPS Gσ, σ′ BY TWO INVOLUTIVE AUTOMORPHISMS σ, σ′ OF EXCEPTIONAL COMPACT LIE GROUP G. PART II: G = E8

For the simply connected compact exceptional Lie group E8, we determine the structure of subgroup (E8)σ,σ′ of E8 that is the intersection (E8)σ ∩ (E8)σ′ . Then the space E8/(E8)σ,σ′ is the exceptional Z2 × Z2- symmetric space of type EVIII-VIII-VIII, and we give two involutions σ, σ′ for the space E8/(E8)σ,σ′ concretely.
Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications

Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimposed exponential functions, deep learning has achieved fast and high-fidelity signal reconstruction by training a mapping from the undersampled exponentials to the fully sampled ones. However, the mismatch, such as the sampling rate of undersampling, the organ and the contrast of imaging, between the training and target data will heavily compromise the reconstruction. To address this issue, we propose Alternating Deep Low-Rank (ADLR), which combines deep learning solvers and classic optimization solvers. Experiments on the reconstruction of synthetic and realistic biomedical magnetic resonance signals demonstrate that ADLR can effectively mitigate the mismatch issue and achieve lower reconstruction errors than state-of-the-art methods.
Pulsar Glitches: A Review

∼6% of all known pulsars have been observed to exhibit sudden spin-up events, known as glitches. For more than fifty years, these phenomena have played an important role in helping to understand pulsar (astro)physics. Based on the review of pulsar glitches search method, the progress made in observations in recent years is summarized, including the achievements obtained by Chinese telescopes. Glitching pulsars demonstrate great diversity of behaviours, which can be broadly classified into four categories: normal glitches, slow glitches, glitches with delayed spin-ups, and anti-glitches. The main models of glitches that have been proposed are reviewed and their implications for neutron star structure are critically examined regarding our current understanding. Furthermore, the correlations between glitches and emission changes, which suggest that magnetospheric state-change is linked to the pulsar-intrinsic processes, are also described and discussed in some detail.
A Hierarchical Variable Autonomy Mixed-Initiative Framework for Human-Robot Teaming in Mobile Robotics

This paper presents a Mixed-Initiative (MI) framework for addressing the problem of control authority transfer between a remote human operator and an AI agent when cooperatively controlling a mobile robot. Our Hierarchical Expert-guided Mixed-Initiative Control Switcher (HierEMICS) leverages information on the human operator’s state and intent. The control switching policies are based on a criticality hierarchy. An experimental evaluation was conducted in a high-fidelity simulated disaster response and remote inspection scenario, comparing HierEMICS with a state-of-the-art Expert-guided Mixed-Initiative Control Switcher (EMICS) in the context of mobile robot navigation. Results suggest that HierEMICS reduces conflicts for control between the human and the AI agent, which is a fundamental challenge in both the MI control paradigm and also in the related shared control paradigm. Additionally, we provide statistically significant evidence of improved, navigational safety (i.e., fewer collisions), LOA switching efficiency, and conflict for control reduction.
A study on the electrical property of HgSe under high pressure

Using a microcircuit fabricated on a diamond anvil cell, we have measured in-situ conductivity of HgSe under high pressures, and investigated the temperature dependence of conductivity under several different pressures. The result shows that HgSe has a pressure-induced transition sequence from a semimetal to a semiconductor to a metal, similar to that in HgTe. Several discontinuous changes in conductivity are observed at around 1.5, 17, 29 and 49GPa, corresponding to the phase transitions from zinc-blende to cinnabar to rocksalt to orthorhombic to an unknown structure, respectively. In comparison with HgTe, it is speculated that the unknown structure may be a distorted CsCl structure. For the cinnabar-HgSe, the energy gap as a function of pressure is obtained according to the temperature dependence of conductivity. The plot of the temperature dependence of conductivity indicates that the unknown structure of HgSe has an electrical property of a conductor.
A brief introduction to the mathematics of Landau damping

In these short, rather informal, notes I review the current state of the ﬁeld regarding the mathematics of Landau damping, based on lectures given at the CIRM Research School on Kinetic Theory , November 14–18, 2022. These notes are mainly on Vlasov-Poisson in ( x, v ) ∈ T d × R d however a brief discussion of the important case of ( x, v ) ∈ R d × R d is included at the end. The focus will be nonlinear and these notes include a proof of Landau damping on ( x, v ) ∈ T d × R d in the Vlasov–Poisson equations meant for graduate students, post-docs, and others to learn the basic ideas of the methods involved. The focus is also on the mathematical side, and so most references are from the mathematical literature with only a small number of the many important physics references included. A few open problems are included at the end. These notes are not currently meant for publication so they may not be perfectly proof-read and the reference list might not be complete. If there is an error or you have some references which you think should be included, feel free to send me an email and I will correct it when I get a chance.
Some monotonicity and dependence properties of self-exciting point processes

Point processes on the positive real axis which are positively self- exciting in a sense expressed by their martingale dynamics are studied in this paper. It is shown that such processes can be realized as increasing mappings of Poisson processes and are therefore associated in appropriate manners. Some examples are presented, including Hawkes, renewal, P(cid:19)olya{Lundberg, Markov dependent, semi-Markov, in addition to other point processes. As corollaries an extension of the Burton{Waymire as- sociation result and a solution of the Glasserman conjecture are obtained. Some results on dependence in stochastic processes of interest in queueing are given as a by product.
Hybrid α-attractors, primordial black holes and gravitational wave backgrounds

We investigate the two-stage inflation regime in the theory of hybrid cosmological α-attractors. The spectrum of inflationary perturbations is compatible with the latest Planck/BICEP/Keck Array results, thanks to the attractor properties of the model. However, at smaller scales, it may have a very high peak of controllable width and position, leading to a copious production of primordial black holes (PBH) and generation of a stochastic background of gravitational waves (SGWB).
Modeling Hadronic Gamma-Ray Emissions from Solar Flares and Prospects for Detecting Nonthermal Signatures from Protostars

We investigate gamma-ray emission in the impulsive phase of solar flares and the detectability of nonthermal signatures from protostellar flares. Energetic solar flares emit high-energy gamma rays of GeV energies, but their production mechanism and emission site are still unknown. Young stellar objects, including protostars, also exhibit luminous X-ray flares, but the triggering mechanism of the flaring activity is still unclear owing to the strong obscuration. Nonthermal signatures in millimeter/submillimeter and gamma-ray bands are useful to probe protostellar flares owing to their strong penetration power. We develop a nonthermal emission model of the impulsive phase of solar flares, where cosmic-ray protons accelerated at the termination shock produce high-energy gamma rays via hadronuclear interaction with the evaporation plasma. This model can reproduce gamma-ray data in the impulsive phase of a solar flare. We apply our model to protostellar flares and show that the Cherenkov Telescope Array will be able to detect gamma rays of TeV energies if particle acceleration in protostellar flares is efficient. Nonthermal electrons accelerated together with protons can emit strong millimeter and submillimeter signals via synchrotron radiation, whose power is consistent with the energetic millimeter/submillimeter transients observed from young stars. Future gamma-ray and millimeter/submillimeter observations from protostars, coordinated with a hard X-ray observation, will unravel the nonthermal particle production and triggering mechanism of protostellar flares.
Strategyproofness and Proportionality in Party-Approval Multiwinner Elections

In party-approval multiwinner elections the goal is to allocate the seats of a fixed-size committee to parties based on the approval ballots of the voters over the parties. In particular, each voter can approve multiple parties and each party can be assigned multiple seats. Two central requirements in this setting are proportional representation and strategyproofness. Intuitively, proportional representation requires that every sufficiently large group of voters with similar preferences is represented in the committee. Strategyproofness demands that no voter can benefit by misreporting her true preferences. We show that these two axioms are incompatible for anonymous party-approval multiwinner voting rules, thus proving a far-reaching impossibility theorem. The proof of this result is obtained by formulating the problem in propositional logic and then letting a SAT solver show that the formula is unsatisfiable. Additionally, we demonstrate how to circumvent this impossibility by considering a weakening of strategyproofness which requires that only voters who do not approve any elected party cannot manipulate. While most common voting rules fail even this weak notion of strategyproofness, we characterize Chamberlin-Courant approval voting within the class of Thiele rules based on this strategyproofness notion.