Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Recently, anomaly detection and localization in multimedia data have received significant attention among the machine learning community. In real-world applications such as medical diagnosis and industrial defect detection, anomalies only present in a fraction of the images. To extend the reconstruction-based anomaly detection architecture to the localized anomalies, we propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization. SSM not only enhances the training of the inpainting network but also leads to great improvement in the efficiency of mask prediction at inference. Through random masking, each image is augmented into a diverse set of training triplets, thus enabling the autoencoder to learn to reconstruct with masks of various sizes and shapes during training. To improve the efficiency and effectiveness of anomaly detection and localization at inference, we propose a novel progressive mask refinement approach that progressively uncovers the normal regions and finally locates the anomalous regions. The proposed SSM method outperforms several state-of-the-arts for both anomaly detection and anomaly localization, achieving 98.3% AUC on Retinal-OCT and 93.9% AUC on MVTec AD, respectively.
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes

The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComParE and BoAW features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectrum toolkit; in addition, we add end-to-end sequential modelling, and a log-mel-128-BNN.
Assessing the Linguistic Quality of REST APIs for IoT Applications

Internet of Things (IoT) is a growing technology that relies on connected ‘things’ that gather data from peer devices and send data to servers via APIs (Appli-cation Programming Interfaces). The design quality of those APIs has a direct impact on their understandability and reusability. This study focuses on the linguistic design quality of REST APIs for IoT applications and assesses their linguistic quality by performing the detection of linguistic patterns and antipatterns in REST APIs for IoT applications. Linguistic antipatterns are considered poor practices in the naming, documentation, and choice of identiﬁers. In con-trast, linguistic patterns represent best practices to APIs design. The linguistic patterns and their corresponding antipatterns are hence contrasting pairs. We propose the SARAv2 (Semantic Analysis of REST APIs version two) approach to perform syntactic and semantic analyses of REST APIs for IoT applications. Based on the SARAv2 approach, we develop the REST-Ling tool and empirically validate the detection results of nine linguistic antipatterns. We analyse 19 REST APIs for IoT applications. Our detection results show that the linguistic antipatterns are prevalent and the REST-Ling tool can detect linguistic patterns and antipatterns in REST APIs for IoT applications with an average accuracy of over 80%. Moreover, the tool performs the detection of linguistic antipatterns on average in the order of seconds, i.e. , 8.396 seconds. We found that APIs generally follow good linguistic practices, although the prevalence of poor practices exists. Summary on RQ 1 : Linguistic antipatterns are prevalent in IoT APIs. In the analysed IoT APIs we have detected some instances of poor design practices, being the most prevalent Non-pertinent Documentation or Unversioned URIs . We also observed the presence of good design practices, i.e. , linguistic patterns, which suggests that the developers are aware of the need for linguistic quality on their APIs.
FRIH: Fine-grained Region-aware Image Harmonization

Image harmonization aims to generate a more realistic appearance of foreground and background for a composite image. Existing methods perform the same harmonization process for the whole foreground. However, the implanted foreground always contains different appearance patterns. All the existing solutions ignore the difference of each color block and losing some specific details. Therefore, we propose a novel global-local two stages framework for Fine-grained Region-aware Image Harmonization (FRIH), which is trained end-to-end. In the first stage, the whole input foreground mask is used to make a global coarse-grained harmonization. In the second stage, we adaptively cluster the input foreground mask into several submasks by the corresponding pixel RGB values in the composite image. Each submask and the coarsely adjusted image are concatenated respectively and fed into a lightweight cascaded module, adjusting the global harmonization performance according to the region-aware local feature. Moreover, we further designed a fusion prediction module by fusing features from all the cascaded decoder layers together to generate the final result, which could utilize the different degrees of harmonization results comprehensively. Without bells and whistles, our FRIH algorithm achieves the best performance on iHarmony4 dataset (PSNR is 38.19 dB) with a lightweight model. The parameters for our model are only 11.98 M, far below the existing methods.
Multi-step phase transitions and gravitational waves in the inert doublet model

The inert doublet model is a well-motivated extension of the Standard Model that contains a dark matter candidate and modifies the dynamics of the electroweak symmetry breaking. In order to detail its phenomenology, we perform a comprehensive study of cosmic phase transitions and gravitational wave signals implied by the framework, accounting for the latest results of collider experiments. We require the neutral inert scalar to constitute, at least, a subdominant part of the observed dark matter abundance. While most of the phase transitions proceed through a single step, we identify regions of the parameter space where the electroweak vacuum is reached after multiple phase transitions. The resulting gravitational wave spectrum is generally dominated by single-step transitions and, in part of the parameter space, falls within the reach of future gravitational wave detectors such as LISA, BBO or DECIGO. We find that direct detection experiments efficiently probe the part of parameter space associated with multi-step phase transitions, which remain unconstrained only in the Higgs resonance region testable with future monojet searches. The implications of the new determination of the W boson mass are also discussed.
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic Cluster Expansion (ACE), which unified many of the earlier ideas around atom density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message passing neural network with equivariant features that showed state of the art accuracy. In this work, we construct a mathematical framework that unifies these models: ACE is generalised so that it can be recast as one layer of a multi-layer architecture. From another point of view, the linearised version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in the unified design space. We demonstrate this by an ablation study of NequIP via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, and shed some light on which design choices are critical for achieving high accuracy. Finally, we present BOTNet (Body-Ordered-Tensor-Network), a much-simplified version of NequIP, which has an interpretable architecture and maintains accuracy on benchmark datasets.
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A comprehensive review

Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of Earth’s surface at a distance of data acquisition devices. The recent advancement and even revolution of HS RS techniques offer opportunities to realize the potential of various applications while confronting new challenges for efficiently processing and analyzing the enormous HS acquisition data. Due to the maintenance of the 3D HS inherent structure, tensor decomposition has aroused widespread concern and spurred research in HS data processing tasks over the past decades. In this article, we aim to present a comprehensive overview of tensor decomposition, specifically contextualizing the five broad topics in HS data processing: HS restoration, compressive sensing (CS), anomaly detection (AD), HS–multispectral (MS) fusion, and spectral unmixing (SU). For each topic, we elaborate on the remarkable achievements of tensor decomposition models for HS RS, with a pivotal description of the existing methodologies and a representative exhibition of experimental results. As a result, the remaining challenges of the follow-up research directions are outlined and discussed from the perspective of actual HS RS practices and tensor decomposition merged with advanced priors and even deep neural networks. This article summarizes different tensor decomposition-based HS data processing methods and categorizes them into different classes, from simple adoptions to complex combinations with other priors for algorithm beginners. We expect that this survey provides new investigations and development trends for experienced researchers to some extent.
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints

Federated learning systems facilitate the training of global models across large numbers of distributed edge-devices with potentially heterogeneous data. Such systems operate in resource constrained settings with intermittent client availability and/or time-varying communication constraints. As a result, the global models trained by federated learning systems may be biased towards clients with higher availability. We propose <underline>F</underline>ederated <underline>A</underline>veraging <underline>A</underline>ided by an <underline>A</underline>daptive <underline>S</underline>ampling <underline>T</underline>echnique (<sc>F3ast</sc>), an unbiased algorithm that dynamically learns an availability-dependent client selection strategy which asymptotically minimizes the impact of client-sampling variance on the global model's convergence, enhancing performance of federated learning. The proposed algorithm is tested in a variety of settings for intermittently available clients operating under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets. We report up to 186% and 8% accuracy improvements over <sc>FedAvg</sc>, and 8% and 7% over <sc>FedAdam</sc>on CIFAR100 and Shakespeare, respectively.
Diagram of High-Energy Nuclear Collisions

Many new particles, mostly hadrons, are produced in high-energy collisions between atomic nuclei. The most popular models describing the hadron-production process are based on the creation, evolution and decay of resonances, strings or quark–gluon plasma. The validity of these models is under vivid discussion, and it seems that a common framework for this discussion is missing. Here, for the first time, we explicitly introduce the diagram of high-energy nuclear collisions, where domains of the dominance of different hadron-production processes in the space of laboratory-controlled parameters, the collision energy and nuclear-mass number of colliding nuclei are indicated. We argue that the recent experimental results suggest the location of boundaries between the domains, allowing for the first time to sketch an example diagram. Finally, we discuss the immediate implications for experimental measurements and model development following the proposed sketch of the diagram.
Warm-starting DARTS using meta-learning

Neural architecture search (NAS) has shown great promise in the ﬁeld of automated machine learning (AutoML). NAS has outperformed hand-designed networks and made a signiﬁcant step forward in the ﬁeld of automating the design of deep neural networks, thus further reducing the need for human expertise. However, most research is done targeting a single speciﬁc task, leaving research of NAS methods over multiple tasks mostly over-looked. Generally, there exist two popular ways to ﬁnd an architecture for some novel task. Either searching from scratch, which is ineﬀective by design, or transferring discovered architectures from other tasks, which provides no performance guarantees and is probably not optimal. In this work we present a meta-learning framework to warm-start Diﬀerentiable architecture search (DARTS). DARTS is a NAS method that can be initialized with a transferred architecture and is able to quickly adapt to new tasks. A task similarity measure is used to determine which transfer architecture is selected, as transfer architectures found on similar tasks will likely perform better. Additionally, we employ a simple meta-transfer architecture that was learned over multiple tasks. Experiments show that warm-started DARTS is able to ﬁnd competitive performing architectures while reducing searching cost on average by 60%. P-DARTS. Comparison between the two warm-starting approaches, transfer architecture λ i and learned transfer architecture ˆ λ i , shows that learned transfer architecture, ˆ λ i , requires more computational resources. ˆ λ i is on average faster than P-DARTS by only 58 . 5% compared to λ i ’s 64 . 7%. This diﬀerence is especially visible on smaller datasets ( aircraft , ﬂower , birds and dtd ), whereas on tiny imagenet there was no signiﬁcant statistical diﬀerence in means at p < 0 . 05.
Brought to Light. III. Colors of Disk and Clump Substructures in Dwarf Early-type Galaxies of the Fornax Cluster

It has been well established that dwarf early-type galaxies (ETGs) can often exhibit a complex morphology, whereby faint spiral arms, bars, edge-on disks, or clumps are embedded in their main, brighter diffuse body. In our first paper (“Brought to Light I”), we developed a new method for robustly identifying and extracting substructures in deep imaging data of dwarf ETGs in the Virgo galaxy cluster. Here we apply our method to a sample of 23 dwarf ETGs in the Fornax galaxy cluster, out of which 9 have disk-like and 14 have clump-like substructures. According to Fornax Deep Survey (FDS) data, our sample constitutes 12% of all dwarf ETGs in Fornax brighter than M r = − 13 mag, and contains all cases that unequivocally exhibit substructure features. We use g- and r-band FDS images to measure the relative contribution of the substructures to the total galaxy light and to estimate their g − r colors. We find that the substructures typically contribute 8.7% and 5.3% of the total galaxy light in the g and r bands, respectively, within two effective radii. Disk substructures are usually found in dwarf ETGs with redder global colors, and they can be either as red as or bluer than their galaxy’s diffuse component. In contrast, the clump substructures are found in comparatively bluer dwarf ETGs, and they are always bluer than their galaxy’s diffuse component. These results provide further evidence that dwarf ETGs can hide diverse complex substructures, with stellar populations that can greatly differ from those of the dominant diffuse light in which they are embedded.
Optical follow-up of the tick-tock massive black hole binary candidate

The observation of a population of massive black hole binaries (MBHBs) is key for our complete understanding of galaxy mergers and for the characterization of the expected gravitational waves (GWs) signal. However, MBHBs still remain elusive with only a few candidates proposed to date. Among these, SDSSJ143016.05+230344.4 (’tick-tock’ hereafter) is the only candidate with a remarkably well sampled light curve showing a clear reduction of the modulation period and amplitude over three years of observations. This particular feature has been recently claimed to be the signature of a MBHB that is about to merge. In this paper, we provide an optical follow-up of the tick-tock source using the Rapid Eye Mount (REM) telescope. The decreasing luminosity observed in our follow up is hardly explained within the binary scenario. We speculate about an alternative scenario that might explain the observed light curve through relativistic Lense-Thirring precession of an accretion disc around a single massive black hole.
Retrodictive Quantum Computing

Quantum models of computation are widely believed to be more powerful than classical ones. Efforts center on proving that, for a given problem, quantum algorithms are more resource efficient than any classical one. All this, however, assumes a standard predictive paradigm of reasoning where, given initial conditions, the future holds the answer. How about bringing information from the future to the present and exploit it to one's advantage? This is a radical new approach for reasoning, so-called Retrodictive Computation, that benefits from the specific form of the computed functions. We demonstrate how to use tools of symbolic computation to realize retrodictive quantum computing at scale and exploit it to efficiently, and classically, solve instances of the quantum Deutsch-Jozsa, Bernstein-Vazirani, Simon, Grover, and Shor's algorithms.
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning

Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer. We show that the emergent production, consumption, and pricing behaviors respond to environmental conditions in the directions predicted by supply and demand shifts in Microeconomics. We also demonstrate settings where the agents' emergent prices for goods vary over space, reflecting the local abundance of goods. After the price disparities emerge, some agents then discover a niche of transporting goods between regions with different prevailing prices -- a profitable strategy because they can buy goods where they are cheap and sell them where they are expensive. Finally, in a series of ablation experiments, we investigate how choices in the environmental rewards, bartering actions, agent architecture, and ability to consume tradable goods can either aid or inhibit the emergence of this economic behavior. This work is part of the environment development branch of a research program that aims to build human-like artificial general intelligence through multi-agent interactions in simulated societies. By exploring which environment features are needed for the basic phenomena of elementary microeconomics to emerge automatically from learning, we arrive at an environment that differs from those studied in prior multi-agent reinforcement learning work along several dimensions. For example, the model incorporates heterogeneous tastes and physical abilities, and agents negotiate with one another as a grounded form of communication.
Statistics of diffusive encounters with a small target: three complementary approaches

Diffusive search for a static target is a common problem in statistical physics with numerous applications in chemistry and biology. We look at this problem from a different perspective and investigate the statistics of encounters between the diffusing particle and the target. While an exact solution of this problem was recently derived in the form of a spectral expansion over the eigenbasis of the Dirichlet-to-Neumann operator, the latter is generally difficult to access for an arbitrary target. In this paper, we present three complementary approaches to approximate the probability density of the rescaled number of encounters with a small target in a bounded confining domain. In particular, we derive a simple fully explicit approximation, which depends only on a few geometric characteristics such as the surface area and the harmonic capacity of the target, and the volume of the confining domain. We discuss the advantages and limitations of three approaches and check their accuracy. We also deduce an explicit approximation for the distribution of the first-crossing time, at which the number of encounters exceeds a prescribed threshold. Its relations to common first-passage time problems are discussed.
Understanding Generation Z Students to Promote a Contemporary Learning Environment

These groups have been characterized both positively and negatively in the popular press. A fresh understanding of the newer generations can help instructors better meet current students’ educational needs. This article shares brief generational profiles based on recent research and then presents questions and recommendations for improving course assignments and their effectiveness. Ways of communicating about assignments and their benefits are also shared. The goal is to equip college-level instructors with ways to relate to and support the newest generation of learners.
Search for baryon junctions in photonuclear processes and isobar collisions at RHIC

During the early development of Quantum Chromodynamics, it was proposed that baryon number could be carried by a non-perturbative Y-shaped topology of gluon fields, called the gluon junction, rather than by the valence quarks as in the QCD standard model. A puzzling feature of ultra-relativistic nucleus-nucleus collisions is the apparent substantial baryon excess in the midrapidity region that could not be adequately accounted for in most conventional models of quark and diquark transport. The transport of baryonic gluon junctions is predicted to lead to a characteristic exponential distribution of net-baryon density with rapidity and could resolve the puzzle. In this context we point out that the rapidity density of net-baryons near midrapidity indeed follows an exponential distribution with a slope of $-0.61\pm0.03$ as a function of beam rapidity in the existing global data from A+A collisions at AGS, SPS and RHIC energies. To further test if quarks or gluon junctions carry the baryon quantum number, we propose to study the absolute magnitude of the baryon vs. charge stopping in isobar collisions at RHIC. We also argue that semi-inclusive photon-induced processes ($\gamma+p$/A) at RHIC kinematics provide an opportunity to search for the signatures of the baryon junction and to shed light onto the mechanisms of observed baryon excess in the mid-rapidity region in ultra-relativistic nucleus-nucleus collisions. Such measurements can be further validated in A+A collisions at the LHC and $e+p$/A collisions at the EIC.