Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Supervised VQA: Answering Visual Questions using Images and Captions

Methodologies for training VQA models assume the availability of datasets with human-annotated Image-Question-Answer(I-Q-A) triplets for training. This has led to a heavy reliance and overfitting on datasets and a lack of generalization to new types of questions and scenes. Moreover, these datasets exhibit annotator subjectivity, biases, and errors, along with linguistic priors, which percolate into VQA models trained on such samples. We study whether models can be trained without any human-annotated Q-A pairs, but only with images and associated text captions which are descriptive and less subjective. We present a method to train models with procedurally generated Q-A pairs from captions using techniques, such as templates and annotation frameworks like QASRL. As most VQA models rely on dense and costly object annotations extracted from object detectors, we propose spatial-pyramid image patches as a simple but effective alternative to object bounding boxes, and demonstrate that our method uses fewer human annotations. We benchmark on VQA-v2, GQA, and on VQA-CP which contains a softer version of label shift. Our methods surpass prior supervised methods on VQA-CP and are competitive with methods without object features in fully supervised setting.
A method based on encryption and node rating for securing the RPL protocol communications in the IoT ecosystem

Internet of Things (IoT) provides the possibility for milliards of devices throughout the world to communicate with each other, and data are collected autonomously. The big data generated by the devices should be managed securely. Due to security challenges, like malicious nodes, many approaches cannot respond to these concerns. In this paper, a robust hybrid method, including encryption, is used as an efficient approach for resolving the Low‐Power and Lossy Networks (RPL) protocol concerns so that the devices are connected securely. Therefore, the proposed Detection of sinkholes in RPL (DSH‐RPL) method for securing the RPL protocol comprises the four following phases: the first phase creates a reliable RPL. The second phase detects the sinkhole attack. The third phase quarantines the detected malicious node, and the fourth phase transmits data through encryption. The simulation results show that the DSH‐RPL reduces the false‐positive rate more than 18.2% and 23.1% and reduces the false‐negative rate more than 16.1% and 22.78% and it also increases the packet delivery rate more than 19.68% and 25.32% and increases the detection rate more than 26% and 31% compared to SecTrust‐RPL and IBOOS‐RPL.
Additive energy of regular measures in one and higher dimensions, and the fractal uncertainty principle

We obtain new bounds on the additive energy of (Ahlfors-David type) regular measures in both one and higher dimensions, which implies expansion results for sums and products of the associated regular sets, as well as more general nonlinear functions of these sets. As a corollary of the higher-dimensional results we obtain some new cases of the fractal uncertainty principle in odd dimensions.
A Vertex Model for LLT Polynomials

We describe a novel Yang–Baxter integrable vertex model. From this vertex model we construct a certain class of partition functions that we show are essentially equal to the LLT polynomials of Lascoux, Leclerc, and Thibon. Using the vertex model formalism, we give alternate proofs of many properties of these polynomials, including symmetry and a Cauchy identity.
Deep Learning for Medical Anomaly Detection – A Survey

Machine learning–based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.
Integrated Magnetometry Platform with Stackable Waveguide-Assisted Detection Channels for Sensing Arrays

The negatively-charged NV$^-$-center in diamond has shown great success in nanoscale, high-sensitivity magnetometry. Efficient fluorescence detection is crucial for improving the sensitivity. Furthermore, integrated devices enable practicable sensors. Here, we present a novel architecture which allows us to create NV$^-$-centers a few nanometers below the diamond surface, and at the same time in the mode field maximum of femtosecond-laser-written type-II waveguides. We experimentally verify the coupling efficiency, showcase the detection of magnetic resonance signals through the waveguides and perform first proof-of-principle experiments in magnetic field and temperature sensing. The sensing task can be operated via the waveguide without direct light illumination through the sample, which marks an important step for magnetometry in biological systems which are fragile to light. In the future, our approach will enable the development of two-dimensional sensing arrays facilitating spatially and temporally correlated magnetometry.
Electric field tunable unconventional superconductivity in alternating twist magic-angle trilayer graphene.

We construct a van der Waals heterostructure consisting of three graphene layers stacked with alternating twisting angles $\pm\theta$. At the average twist angle $\theta\sim 1.56^{\circ}$, a theoretically predicted magic angle for the formation of flat electron bands, narrow conduction and valence moir\'e bands appear together with a linearly dispersing Dirac band. Upon doping the half-filled moir\'e valence band with holes, or the half-filled moir\'e conduction band with electrons, displacement field tunable superconductivity emerges, reaching a maximum critical temperature of 2.1 K at optimal doping and displacement field. By tuning the doping level and displacement fields, we find that superconducting regimes occur in conjunction with flavour polarization of moir\'e bands bounded by a van Hove singularity (vHS) at high displacement fields. This experimental observation is found to be inconsistent with a weak coupling description, suggesting that the observed moir\'e superconductivity has an unconventional nature.
On C0-persistent homology and trees

In this paper we give a metric construction of a tree which correctly identifies connected components of superlevel sets of $\mathbb{R}$-valued continuous functions $f$ on $X$ and show that it is possible to retrieve the $H_0$-persistent diagram from this tree. We revisit the notion of homological dimension previously introduced by Schweinhart and give some bounds for the latter in terms of the upper-box dimension of $X$, thereby partially answering a question of the same author. We prove a quantitative version of the Wasserstein stability theorem valid for regular enough $X$ and $\alpha$-H\"older functions and discuss some applications of this theory to random fields and the topology of their superlevel sets.
Spectrochemical Analysis of Liquids Using the Laser Spark

A laser-generated spark was used to analyze liquids spectroscopically for elemental constituents The spark was produced directly in liquids by a focused laser pulse of 15 ns duration and an energy of about 45 mJ/pulse The size, temperature, and electron density of the spark are reported Emissions from once-ionized and neutral atoms and simple molecules were observed Limits of detection for Li, Na, K, Rb, Cs, Be, Mg, Ca, B, and Al in aqueous solutions were established with the use of a 10 Hz repetitive single spark (RSS) Most of these elements were only detectable at levels above 1 µg/mL, although the detection limit for Li was 0006 µg/mL The relative standard deviation for replicate sample analysis was 4–8% The detectability of some species was enhanced with the use of a 10 Hz repetitive spark pair (RSP): a pair of sparks separated in time by about 18 µs The detection limits for B/H2O with the RSP and the RSS were 80 and 1200 µg/mL, respectively Species were also detected in organic solvents and in flowing samples The laser spark method of liquid analysis would be useful in situations requiring noninvasive monitoring of species at high or moderate concentrations
Compositionally Generalizable 3D Structure Prediction

Single-image 3D shape reconstruction is an important and long-standing problem in computer vision. A plethora of existing works is constantly pushing the state-of-the-art performance in the deep learning era. However, there remains a much difficult and largely under-explored issue on how to generalize the learned skills over novel unseen object categories that have very different shape geometry distribution. In this paper, we bring in the concept of compositional generalizability and propose a novel framework that factorizes the 3D shape reconstruction problem into proper sub-problems, each of which is tackled by a carefully designed neural sub-module with generalizability guarantee. The intuition behind our formulation is that object parts (slates and cylindrical parts), their relationships (adjacency, equal-length, and parallelism) and shape substructures (T-junctions and a symmetric group of parts) are mostly shared across object categories, even though the object geometry may look very different (chairs and cabinets). Experiments on PartNet show that we achieve superior performance than baseline methods, which validates our problem factorization and network designs.
The Role of Core-collapse Physics in the Observability of Black Hole Neutron Star Mergers as Multimessenger Sources

Recent 1D core-collapse simulations indicate a nonmonotonicity of the explodability of massive stars with respect to their precollapse core masses, which is in contrast to commonly used prescriptions. In this work, we explore the implications of these results on the formation of coalescing black hole (BH)–neutron star (NS) binaries. Furthermore, we investigate the effects of natal kicks and the NS’s radius on the synthesis of such systems and potential electromagnetic counterparts (EMCs) linked to them. Models based on 1D core-collapse simulations result in a BH–NS merger detection rate ( ∼ 2.3 yr−1), 5–10 times larger than the predictions of “standard” prescriptions. This is primarily due to the formation of low-mass BHs via direct collapse, and hence no natal kicks, favored by the 1D simulations. The fraction of observed systems that will produce an EMC, with the supernova engine from 1D simulations, ranges from 2% to 25%, depending on the NS equation of state. Notably, in most merging systems with EMCs, the NS is the first-born compact object, as long as the NS’s radius is ≲ 12 km. Furthermore, models with negligible kicks for low-mass BHs increase the detection rate of GW190426_152155-like events to ∼ 0.6 yr−1, with an associated probability of EMC ≤10% for all supernova engines. Finally, models based on 1D core-collapse simulations predict a ratio of BH–NSs to binary BHs’ merger rate density that is at least twice as high as other prescriptions, but at the same time overpredicting the measured local merger density rate of binary black holes.
Bumblebee gravity and particle motion in Snyder noncommutative spacetime structures

A metric with a Lorentz violating parameter is associated with the bumblebee gravity model. We study the motion of a particle in this bumblebee background where the dynamical variables satisfy non-canonical Snyder algebra along with some critical survey on the classical observations in the bumblebee background to see how these would likely differ from Schwarzschild background. It has been found that the perihelion shift acquires a generalized expression with two independent parameters. One of these two is connected with the Lorentz violating factor and the other is involved in the Snyder algebraic formulation. We also observe that the time period of revolution, in general, acquires a Lorentz violating factor in the bumblebee background, however, for the circular orbit, it remains unchanged even in the presence of the Lorentz violating factor in the bumblebee background. The parameters used here can be constrained with the same type of conjecture used earlier.
Fully pseudospectral solution of the conformally invariant wave equation on a Kerr background

We study axisymmetric solution to the conformally invariant wave equation on a Kerr background by means of numerical and analytical methods. Our main focus is on the behaviour of the solutions near spacelike infinity, which is appropriately represented as a cylinder. Earlier studies of the wave equation on a Schwarzschild background have revealed important details about the regularity of the corresponding solutions. It was found that, on the cylinder, the solutions generically develop logarithmic singularities at infinitely many orders. Moreover, these singularities also ‘spread’ to future null infinity. However, by imposing certain regularity conditions on the initial data, the lowest-order singularities can be removed. Here we are interested in a generalisation of these results to a rotating black hole background and study the influence of the rotation rate on the properties of the solutions. To this aim, we first construct a conformal compactification of the Kerr solution which yields a suitable representation of the cylinder at spatial infinity. Besides analytical investigations on the cylinder, we numerically solve the wave equation with a fully pseudospectral method, which allows us to obtain highly accurate numerical solutions. This is crucial for a detailed analysis of the regularity of the solutions. In the Schwarzschild case, the numerical problem could effectively be reduced to solving (1 + 1)-dimensional equations. Here we present a code that can perform the full 2 + 1 evolution as required for axisymmetric waves on a Kerr background.
Planning from Pixels using Inverse Dynamics Models

Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by learning to predict sequences of future actions conditioned on task completion. These task-conditioned models adaptively focus modeling capacity on task-relevant dynamics, while simultaneously serving as an effective heuristic for planning with sparse rewards. We evaluate our method on challenging visual goal completion tasks and show a substantial increase in performance compared to prior model-free approaches.
Analog Particle Production Model for General Classes of Taub-NUT Black Holes

We derive a correspondence between the Hawking radiation spectra emitted from general classes of Taub-NUT black holes with that induced by the relativistic motion of an accelerated Dirichlet boundary condition (i.e., a perfectly reflecting mirror) in (1+1)-dimensional flat spacetime. We demonstrate that the particle and energy spectra is thermal at late times and that particle production is suppressed by the NUT parameter. We also compute the radiation spectrum in the rotating, electrically charged (Kerr–Newman) Taub-NUT scenario, and the extremal case, showing, explicitly, how these parameters affect the outgoing particle and energy fluxes.
Effect of the initial configuration of weights on the training and function of artificial neural networks

The function and performance of neural networks are largely determined by the evolution of their weights and biases in the process of training, starting from the initial configuration of these parameters to one of the local minima of the loss function. We perform the quantitative statistical characterization of the deviation of the weights of two-hidden-layer feedforward ReLU networks of various sizes trained via Stochastic Gradient Descent (SGD) from their initial random configuration. We compare the evolution of the distribution function of this deviation with the evolution of the loss during training. We observed that successful training via SGD leaves the network in the close neighborhood of the initial configuration of its weights. For each initial weight of a link we measured the distribution function of the deviation from this value after training and found how the moments of this distribution and its peak depend on the initial weight. We explored the evolution of these deviations during training and observed an abrupt increase within the overfitting region. This jump occurs simultaneously with a similarly abrupt increase recorded in the evolution of the loss function. Our results suggest that SGD’s ability to efficiently find local minima is restricted to the vicinity of the random initial configuration of weights.
Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease

Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes, which are correlated and predictive of AD progression. It is of great scientific interest to investigate the association between the outcomes and time to AD onset. We model the multiple longitudinal outcomes as multivariate sparse functional data and propose a functional joint model linking multivariate functional data to event time data. In particular, we propose a multivariate functional mixed model to identify the shared progression pattern and outcome‐specific progression patterns of the outcomes, which enables more interpretable modeling of associations between outcomes and AD onset. The proposed method is applied to the Alzheimer's Disease Neuroimaging Initiative study (ADNI) and the functional joint model sheds new light on inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model. Data used in preparation of this article were obtained from the ADNI database.
Spatial-Temporal Alignment Network for Action Recognition and Detection

This paper studies how to introduce viewpoint-invariant feature representations that can help action recognition and detection. Although we have witnessed great progress of action recognition in the past decade, it remains challenging yet interesting how to efficiently model the geometric variations in large scale datasets. This paper proposes a novel Spatial-Temporal Alignment Network (STAN) that aims to learn geometric invariant representations for action recognition and action detection. The STAN model is very light-weighted and generic, which could be plugged into existing action recognition models like ResNet3D and the SlowFast with a very low extra computational cost. We test our STAN model extensively on AVA, Kinetics-400, AVA-Kinetics, Charades, and Charades-Ego datasets. The experimental results show that the STAN model can consistently improve the state of the arts in both action detection and action recognition tasks. We will release our data, models and code.
Non-Asymptotic Analysis of Excess Risk via Empirical Risk Landscape

In this paper, we provide a unified analysis of the excess risk of the model trained by some proper algorithms in both convex and non-convex regime. In contrary to the existing results in the literature that depends on iteration steps, our bounds to the excess risk do not diverge with the number of iterations. This underscores that, at least for loss functions of certain types, the excess risk on it can be guaranteed after a period of training. Our technique relies on a non-asymptotic characterization of the empirical risk landscape. To be rigorous, under the condition that the local minima of population risk are non-degenerate, each local minimum of the smooth empirical risk is guaranteed to generalize well. The conclusion is independent of the convexity. Combining this with the classical optimization result, we derive converged upper bounds to the excess risk in both convex and non-convex regime.
Reviewing the role of the extinction coefficient in radar remote sensing.

This report revisits the role of the extinction coefficient in radar backscattering-based models for forest monitoring. A review of a number of works dealing with this issue has revealed a diversity of extinction values being unclear its dependence on the sensor frequency and the forest type. In addition, a backscattering model directly derived from the RVoG formulation is employed to analyse the saturation of backscattering level as a function of vegetation height and the presence of a decreasing trend of backscatter beyond the saturation point as suggested in previous works in the literature. According to this analysis it seems reasonable to think that further research specially focused on dedicated experimental measurements of the extinction coefficient should be carried out.
Concentrated phase emulsion with multicore morphology under shear: A numerical study

We numerically study the dynamic behavior under a symmetric shear flow of selected examples of concentrated phase emulsions with multi-core morphology confined within a microfluidic channel. A variety of new nonequilibrium steady states is reported. Under low shear rates, the emulsion is found to exhibit a solid-like behavior, in which cores display a periodic planetary-like motion with approximately equal angular velocity. At higher shear rates two steady states emerge, one in which all inner cores align along the flow and become essentially motionless and a further one in which some cores accumulate near the outer interface and produce a dynamical elliptical-shaped ring chain, reminiscent of a treadmilling-like structure, while others occupy the center of the emulsion. A quantitative description in terms of i) motion of the cores, ii) rate of deformation of the emulsion and iii) structure of the fluid flow within the channel is also provided.
Perceptual Error Optimization for Monte Carlo Rendering

Synthesizing realistic images involves computing high-dimensional light-transport integrals. In practice, these integrals are numerically estimated via Monte Carlo integration. The error of this estimation manifests itself as conspicuous aliasing or noise. To ameliorate such artifacts and improve image fidelity, we propose a perception-oriented framework to optimize the error of Monte Carlo rendering. We leverage models based on human perception from the halftoning literature. The result is an optimization problem whose solution distributes the error as visually pleasing blue noise in image space. To find solutions, we present a set of algorithms that provide varying trade-offs between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using quantitative and error metrics and provide extensive supplemental material to demonstrate the perceptual improvements achieved by our methods.