Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Errors as a Source of Mass Discrepancy in Black Hole Microlensing Event OGLE-2011-BLG-0462

Two independent groups reported the discovery of an isolated dark stellar remnant in the microlensing event OGLE-2011-BLG-0462 based on photometric ground-based observations coupled with astrometric measurements taken with the Hubble Space Telescope. These two analyses yielded discrepant mass measurements, with the first group reporting that the lensing object is a black hole of 7.1 ± 1.3 M ⊙ whereas the other concluded that the microlensing event was caused by either a neutron star or a low-mass black hole (1.6–4.4 M ⊙). Here, we scrutinize the available photometric and astrometric data and conclude that systematic errors are a cause of the discrepant measurements. We find that the lens is an isolated black hole with a mass of 7.88 ± 0.82 M ⊙ located at a distance of 1.49 ± 0.12 kpc. We also study the impact of blending on the accuracy of astrometric microlensing measurements. We find that low-level blending by source companions is a major, previously unrecognized, challenge to astrometric microlensing measurements of black hole masses.
Strong electron-phonon coupling and predicted highest known $T_{c}$ of MXenes revealed in 2H-Mo$_{2}$N under biaxial stress

This letter reports the unexpectedly strong electron-phonon coupling (EPC) and the highest T c record ( ≈ 38 K) among the MXenes revealed in the 2H-Mo 2 N under biaxial stress. At ﬁrst, its excellent mechanical properties are demonstrated with ideal strength of 37 GPa and elastic modulus of 438 GPa. Subsequently, EPC and corresponding T c are elucidated upon the dynamically stable range of strain. For strain-free 2H-Mo 2 N, the EPC constant ( λ ) and T c are 1.3 and 22.7 K, respectively. This T c is higher than those of 2H-Mo 2 C (4.3 K), 1T-Mo 2 N (16.8 K), and other pristine MXenes. The material exhibits remarkable enhancement in λ and T c when subject to compressive and tensile stresses. The obvious strong EPC with λ over 2.0 occurs at strains of -4%, -2.5%, and 5%, yielding T c ’s of 37.8, 35.4, and 28.9 K, respectively. Our ﬁndings suggest that the strain-dependent feature and energy levels of electronic bands play an essential role in enhancing EPC. Moreover, the stronger EPC in Mo 2 N compared with Mo 2 C is clariﬁed based on lattice vibrations. Therefore, this work paves a practical way for designing 2D superconducting materials using tuning atomic recipes and strain-dependent engineering.
Relative graphs and pullbacks of relative Toeplitz graph algebras

In this note we generalize a result from a recent paper of Hajac, Reznikoﬀ and Tobolski ([5]). In that paper they give conditions they call admissibility on a pushout diagram in the category of directed graphs implying that the C ∗ -algebras of the graphs form a pullback diagram. We consider a larger category of relative graphs that correspond to relative Toeplitz graph algebras. In this setting we give necessary and suﬃcient conditions on the pushout to get a pullback of C ∗ -algebras.
Majorization requires infinitely many second laws

Majorization is a fundamental model of uncertainty that possesses several applications in areas like thermodynamics or entanglement theory, and constitutes one of the pillars of the modern resource-theoretic approach to physics. Here, we improve on its relation to measurement apparatus. In particular, after discussing what the proper notion of second law in this scenario is, we show that, for a sufficiently large state space, any family of entropy-like functions constituting a second law is necessarily countably infinite. Moreover, we provide an analogous result for a variation of majorization known as thermo-majorization which, in fact, does not require any constraint on the state space provided the equilibrium distribution is not uniform. Lastly, we conclude by discussing the applicability of our results to molecular diffusion.
Invariance properties of limiting point processes and applications to clusters of extremes

Motivated by examples from extreme value theory, but without using the theory of regularly varying time series or any assumptions about the marginal distribution, we introduce the general notion of a cluster process as a limiting point process of returns of a certain event in a time series. We explore general invariance properties of cluster processes that are implied by stationarity of the underlying time series. Of particular interest in applications are the cluster size distributions, and we derive general properties and interconnections between the size of an inspected and a typical cluster. While the extremal index commonly used in extreme value theory is often interpreted as the inverse of a “mean cluster size”, we point out that this only holds true for the expected value of the typical cluster size, caused by an effect very similar to the inspection paradox in renewal theory.
GesSure - A Robust Face-Authentication enabled Dynamic Gesture Recognition GUI Application

Using physical interactive devices like mouse and keyboards hinders naturalistic human-machine interaction and increases the probability of surface contact during a pandemic. Existing gesturerecognition systems do not possess user authentication, making them unreliable. Static gestures in current gesture-recognition technology introduce long adaptation periods and reduce user compatibility. Our technology places a strong emphasis on user recognition and safety. We use meaningful and relevant gestures for task operation, resulting in a better user experience. This paper aims to design a robust, faceverification-enabled gesture recognition system that utilizes a graphical user interface and primarily focuses on security through user recognition and authorization. The face model uses MTCNN and FaceNet to verify the user, and our LSTM-CNN architecture for gesture recognition, achieving an accuracy of 95% with five classes of gestures. The prototype developed through our research has successfully executed context-dependent tasks like save, print, control video-player operations and exit, and context-free operating system tasks like sleep, shut-down, and unlock intuitively. Our application and dataset are available as open source.
On Higher Adversarial Susceptibility of Contrastive Self-Supervised Learning

Contrastive self-supervised learning (CSL) has managed to match or surpass the performance of supervised learning in image and video classification. However, it is still largely unknown if the nature of the representation induced by the two learning paradigms is similar. We investigate this under the lens of adversarial robustness. Our analytical treatment of the problem reveals intrinsic higher sensitivity of CSL over supervised learning. It identifies the uniform distribution of data representation over a unit hypersphere in the CSL representation space as the key contributor to this phenomenon. We establish that this increases model sensitivity to input perturbations in the presence of false negatives in the training data. Our finding is supported by extensive experiments for image and video classification using adversarial perturbations and other input corruptions. Building on the insights, we devise strategies that are simple, yet effective in improving model robustness with CSL training. We demonstrate up to 68% reduction in the performance gap between adversarially attacked CSL and its supervised counterpart. Finally, we contribute to robust CSL paradigm by incorporating our findings in adversarial self-supervised learning. We demonstrate an average gain of about 5% over two different state-of-the-art methods in this domain.
Higher-Derivative Corrections and AdS$_5$ Black Holes

Using recent results in four-derivative 5d N = 2 minimal gauged supergravity, we evaluate the regularized on-shell action of the Euclidean solution in this theory that admits a Lorentzian continuation to an AdS 5 black hole with one electric charge and two angular momenta. We focus on the supersymmetric limit of this solution and employ holography to show that the result can be expressed purely in terms of angular momentum fugacities and the ’t Hooft anomalies for the U(1) R R-symmetry of the dual 4d N = 1 SCFT. This holographic calculation is in perfect agreement with recent studies of the 4d N = 1 superconformal index “on the second sheet”. We illustrate the utility of these results in two classes of 4d N = 1 holographic SCFTs that have ’t Hooft anomalies with suitable large N behavior that leads to non-trivial corrections at ﬁrst subleading order in the 1 /N expansion. We also explicitly calculate the Wald entropy of the black hole solution and delineate the leading four-derivative corrections to the Bekenstein-Hawking entropy.
Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data

: Classification on long-tailed distributed data is a challenging problem, which suffers from serious class-imbalance and hence poor performance on tail classes with only a few samples. Owing to this paucity of samples, learning on the tail classes is especially challenging for the fine-tuning when transferring a pretrained model to a downstream task. In this work, we present a simple modification of standard fine-tuning to cope with these challenges. Specifically, we propose a two-stage fine-tuning: we first fine-tune the final layer of the pretrained model with class-balanced reweighting loss, and then we perform the standard fine-tuning. Our modification has several benefits: (1) it leverages pretrained representations by only fine-tuning a small portion of the model parameters while keeping the rest untouched; (2) it allows the model to learn an initial representation of the specific task; and importantly (3) it protects the learning of tail classes from being at a disadvantage during the model updating. We conduct extensive experiments on synthetic datasets of both two-class and multi-class tasks of text classification as well as a real-world application to ADME (i.e., absorption, distribution, metabolism, and excretion) semantic labeling. The experimental results show that the proposed two-stage fine-tuning outperforms both fine-tuning with conventional loss and fine-tuning with a reweighting loss on the above datasets.
Test-based Patch Clustering for Automatically-Generated Patches Assessment

Previous studies have shown that Automated Program Repair (APR) techniques suffer from the overfitting problem. Overfitting happens when a patch is run and the test suite does not reveal any error, but the patch actually does not fix the underlying bug or it introduces a new defect that is not covered by the test suite. Therefore, the patches generated by APR tools need to be validated by human programmers, which can be very costly, and prevents APR tools adoption in practice.Our work aims at increasing developer trust in automated patch generation by minimizing the number of plausible patches that they have to review, thereby reducing the time required to find a correct patch. We introduce a novel light-weight test-based patch clustering approach called xTestCluster, which clusters patches based on their dynamic behavior. xTestCluster is applied after the patch generation phase in order to analyze the generated patches from one or more repair tools. The novelty of xTestCluster lies in using information from execution of newly generated test cases to cluster patches generated by multiple APR approaches. A cluster is formed with patches that fail on the same generated test cases. The output from xTestCluster gives developers a) a way of reducing the number of patches to analyze, as they can focus on analyzing a sample of patches from each cluster, b) additional information attached to each patch. After analyzing 1910 plausible patches from 25 Java APR tools, our results show that xTestCluster is able to reduce the number of patches to review and analyze with a median of 50%. xTestCluster can save a significant amount of time for developers that have to review the multitude of patches generated by APR tools, and provides them with new test cases that show the differences in behavior between generated patches.
Divergence anomaly and Schwinger terms: Towards a consistent theory of anomalous classical fluids

Anomaly, a generic feature of relativistic quantum field theory, is shown to be present in non-relativistic classical ideal fluid. A new result is the presence of anomalous terms in current algebra, an obvious analogue of Schwinger terms present in quantum field theory. We work in Hamiltonian framework where Eulerian dynamical variables obey an anomalous algebra (with Schwinger terms) that is inherited from modified Poisson brackets, with Berry curvature corrections, among Lagrangian discrete coordinates. The divergence anomaly appears in the Hamiltonian equations of motion. A generalized form of fluid velocity field can be identified with the"anomalous velocity"of Bloch band electrons appearing in quantum Hall effect in condensed matter physics. We finally show that the divergence anomaly and Schwinger terms satisfy well known Adler consistency condition. Lastly we mention possible scenarios where this new anomalous fluid theory can impact.
First light and reionisation epoch simulations (FLARES) VI: The colour evolution of galaxies z = 5 − 15

With its exquisite sensitivity, wavelength coverage, and spatial and spectral resolution, the James Webb Space Telescope (JWST) is poised to revolutionise our view of the distant, high-redshift (z > 5) Universe. While Webb’s spectroscopic observations will be transformative for the field, photometric observations play a key role in identifying distant objects and providing more comprehensive samples than accessible to spectroscopy alone. In addition to identifying objects, photometric observations can also be used to infer physical properties and thus be used to constrain galaxy formation models. However, inferred physical properties from broadband photometric observations, particularly in the absence of spectroscopic redshifts, often have large uncertainties. With the development of new tools for forward modelling simulations it is now routinely possible to predict observational quantities, enabling a direct comparison with observations. With this in mind, in this work, we make predictions for the colour evolution of galaxies at z = 5 − 15 using the First Light And Reionisation Epoch Simulations (Flares) cosmological hydrodynamical simulation suite. We predict a complex evolution with time, driven predominantly by strong nebular line emission passing through individual bands. These predictions are in good agreement with existing constraints from Hubble and Spitzer as well as some of the first results from Webb. We also contrast our predictions with other models in the literature: while the general trends are similar we find key differences, particularly in the strength of features associated with strong nebular line emission. This suggests photometric observations alone should provide useful discriminating power between different models.
Strong c-concavity and stability in optimal transport

The stability of solutions to optimal transport problems under variation of the measures is fundamental from a mathematical view-point: it is closely related to the convergence of numerical approaches to solve optimal transport problems and justiﬁes many of the applications of optimal transport. In this article, we introduce the notion of strong c -concavity, and we show that it plays an important role for proving stability results in optimal transport for general cost functions c . We then introduce a diﬀerential criterion for proving that a function is strongly c -concave, under an hypothesis on the cost introduced originally by Ma-Trudinger-Wang for establishing regularity of optimal transport maps. Finally, we provide two examples where this stability result can be applied, for cost functions taking value + ∞ on the sphere: the reﬂector problem and the Gaussian curvature measure prescription problem.
TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priors

We introduce TIDEE, an embodied agent that tidies up a disordered scene based on learned commonsense object placement and room arrangement priors. TIDEE explores a home environment, detects objects that are out of their natural place, infers plausible object contexts for them, localizes such contexts in the current scene, and repositions the objects. Commonsense priors are encoded in three modules: i) visuo-semantic detectors that detect out-of-place objects, ii) an associative neural graph memory of objects and spatial relations that proposes plausible semantic receptacles and surfaces for object repositions, and iii) a visual search network that guides the agent's exploration for efficiently localizing the receptacle-of-interest in the current scene to reposition the object. We test TIDEE on tidying up disorganized scenes in the AI2THOR simulation environment. TIDEE carries out the task directly from pixel and raw depth input without ever having observed the same room beforehand, relying only on priors learned from a separate set of training houses. Human evaluations on the resulting room reorganizations show TIDEE outperforms ablative versions of the model that do not use one or more of the commonsense priors. On a related room rearrangement benchmark that allows the agent to view the goal state prior to rearrangement, a simplified version of our model significantly outperforms a top-performing method by a large margin. Code and data are available at the project website: https://tidee-agent.github. io/.
JAWS: Predictive Inference Under Covariate Shift

We propose JAWS , a series of wrapper methods for distribution-free uncertainty quantiﬁcation tasks under covariate shift, centered on our core method JAW , the JA ckknife+ W eighted with likelihood-ratio weights. JAWS also includes computationally efﬁcient A pproximations of JAW using higher-order inﬂuence functions: JAWA . Theoretically, we show that JAW relaxes the jackknife+’s assumption of data exchangeability to achieve the same ﬁnite-sample coverage guarantee even under covariate shift. JAWA further approaches the JAW guarantee in the limit of either the sample size or the inﬂuence function order under mild assumptions. Moreover, we propose a general approach to repurposing any distribution-free uncertainty quantiﬁcation method and its guarantees to the task of risk assessment: a task that generates the estimated probability that the true label lies within a user-speciﬁed interval. We then propose JAW-R and JAWA-R as the repurposed versions of proposed methods for R isk assessment. Practically, JAWS outperform the state-of-the-art predictive inference baselines in a variety of biased real world data sets for both interval-generation and risk-assessment auditing tasks.
Learning Unsupervised Hierarchies of Audio Concepts

Music signals are difficult to interpret from their low-level features, perhaps even more than images: e.g. highlighting part of a spectrogram or an image is often insufficient to convey high-level ideas that are genuinely relevant to humans. In computer vision, concept learning was therein proposed to adjust explanations to the right abstraction level (e.g. detect clinical concepts from radiographs). These methods have yet to be used for MIR. In this paper, we adapt concept learning to the realm of music, with its particularities. For instance, music concepts are typically non-independent and of mixed nature (e.g. genre, instruments, mood), unlike previous work that assumed disentangled concepts. We propose a method to learn numerous music concepts from audio and then automatically hierarchise them to expose their mutual relationships. We conduct experiments on datasets of playlists from a music streaming service, serving as a few annotated examples for diverse concepts. Evaluations show that the mined hierarchies are aligned with both ground-truth hierarchies of concepts -- when available -- and with proxy sources of concept similarity in the general case.
Network analysis of the human structural connectome including the brainstem

The underlying anatomical structure is fundamental to the study of brain networks and is likely to play a key role in the generation of conscious experience. We conduct a computational and graph-theoretical study of the human structural connectome incorporating a variety of subcortical structures including the brainstem, which is typically not considered in similar studies. Our computational scheme involves the use of Python DIPY and Nibabel libraries to develop an averaged structural connectome comprised of 100 healthy adult subjects. We then compute degree, eigenvector, and betweenness centralities to identify several highly connected structures and find that the brainstem ranks highest across all examined metrics. Our results highlight the importance of including the brainstem in structural network analyses. We suggest that structural network-based methods can inform theories of consciousness, such as global workspace theory (GWT), integrated information theory (IIT), and the thalamocortical loop theory.
Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners

Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi-based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.
Tactile Gym 2.0: Sim-to-Real Deep Reinforcement Learning for Comparing Low-Cost High-Resolution Robot Touch

High-resolution optical tactile sensors are increasingly used in robotic learning environments due to their ability to capture large amounts of data directly relating to agent-environment interaction. However, there is a high barrier of entry to research in this area due to the high cost of tactile robot platforms, specialised simulation software, and sim-to-real methods that lack generality across different sensors. In this letter we extend the Tactile Gym simulator to include three new optical tactile sensors (TacTip, DIGIT and DigiTac) of the two most popular types, Gelsight-style (image-shading based) and TacTip-style (marker based). We demonstrate that a single sim-to-real approach can be used with these three different sensors to achieve strong real-world performance despite the significant differences between real tactile images. Additionally, we lower the barrier of entry to the proposed tasks by adapting them to an inexpensive 4-DoF robot arm, further enabling the dissemination of this benchmark. We validate the extended environment on three physically-interactive tasks requiring a sense of touch: object pushing, edge following and surface following. The results of our experimental validation highlight some differences between these sensors, which may help future researchers select and customize the physical characteristics of tactile sensors for different manipulations scenarios.