Miracle-dz/UniFAR-FTUs huggingface dataset:
The BlueTides mock image catalogue: Simulated observations of high-redshift galaxies and predictions for JWST imaging surveys

We present a mock image catalogue of ∼100,000 MUV ≃ −22.5 to −19.6 mag galaxies at z = 7–12 from the BlueTides cosmological simulation. We create mock images of each galaxy with the James Webb (JWST), Hubble, Roman, and Euclid Space Telescopes, as well as Subaru, and VISTA, with a range of near- and mid-infrared filters. We perform photometry on the mock images to estimate the success of these instruments for detecting high-z galaxies. We predict that JWST will have unprecedented power in detecting high-z galaxies, with a 95 per cent completeness limit at least 2.5 magnitudes fainter than VISTA and Subaru, 1.1 magnitudes fainter than Hubble and 0.9 magnitudes fainter than Roman, for the same wavelength and exposure time. Focusing on JWST, we consider a range of exposure times and filters, and find that the NIRCam F356W and F277W filters will detect the faintest galaxies, with 95 per cent completeness at m ≃ 27.4 mag in 10ks exposures. We also predict the number of high-z galaxies that will be discovered by upcoming JWST imaging surveys. We predict that the COSMOS-Web survey will detect ∼1000 M1500Å < −20.1 mag galaxies at 6.5 < z < 7.5, by virtue of its large survey area. JADES-Medium will detect almost $100{{\%}}$ of M1500Å ≲ −20 mag galaxies at z < 8.5 due to its significant depth, however with its smaller survey area it will detect only ∼100 of these galaxies at 6.5 < z < 7.5. Cosmic variance results in a large range in the number of predicted galaxies each survey will detect, which is more evident in smaller surveys such as CEERS and the PEARLS NEP and GOODS-S fields.
RendNet: Unified 2D/3D Recognizer with Latent Space Rendering

Vector graphics (VG) have been ubiquitous in our daily life with vast applications in engineering, architecture, designs, etc. The VG recognition process of most existing methods is to first render the VG into raster graphics (RG) and then conduct recognition based on RG formats. However, this procedure discards the structure of geometries and loses the high resolution of VG. Recently, another category of algorithms is proposed to recognize directly from the original VG format. But it is affected by the topological errors that can be filtered out by RG rendering. Instead of looking at one format, it is a good solution to utilize the formats of VG and RG together to avoid these shortcomings. Besides, we argue that the VG-to-RG rendering process is essential to effectively combine VG and RG information. By specifying the rules on how to transfer VG primitives to RG pixels, the rendering process depicts the interaction and correlation between VG and RG. As a result, we propose RendNet, a unified architecture for recognition on both 2D and 3D scenarios, which considers both VG/RG representations and exploits their interaction by incorporating the VG-to-RG rasterization process. Experiments show that Rend-Net can achieve state-of-the-art performance on 2D and 3D object recognition tasks on various VG datasets.
Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation Techniques

Accurate depth estimation with lowest compute and energy cost is a crucial requirement for unmanned and battery operated autonomous systems. Robotic applications require real time depth estimation for navigation and decision making under rapidly changing 3D surroundings. A high accuracy algorithm may provide the best depth estimation but may consume tremendous compute and energy resources. A general trade-off is to choose less accurate methods for initial depth estimate and a more accurate yet compute intensive method when needed. Previous work has shown this trade-off can be improved by developing a state-of-the-art method [12] to improve stereo depth estimation. We studied both the monocular and stereo vision depth estimation methods and investigated methods to reduce computational complexity of these methods. This was our baseline. Consequently, our experiments show reduction of monocular depth estimation model size by ∼ 75% reduces accuracy by less than 2% (SSIM metric). Our experiments with the novel stereo vision method [12] show that accuracy of depth estimation does not degrade more than 3% (three pixel error metric) in spite of reduction in model size by 20%. We have shown that smaller models can indeed perform competitively. The source code is available at https://github.com/rajeevpatwari/AnyNet and https://github.com/rajeevpatwari/Monocular-Depth
Analytic Approach to the Late Stages of Giant Planet Formation

This paper constructs an analytic description for the late stages of giant planet formation. During this phase of evolution, the planet gains the majority of its final mass through gas accretion at a rapid rate. This work determines the density and velocity fields for material falling onto the central planet and its circumplanetary disk, and finds the corresponding column density of this infalling envelope. We derive a steady-state solution for the surface density of the disk as a function of its viscosity (including the limiting case where no disk accretion occurs). Planetary magnetic fields truncate the inner edge of the disk and determine the boundary conditions for mass accretion onto the planet from both direct infall and from the disk. The properties of the forming planet and its circumplanetary disk are determined, including the luminosity contributions from infall onto the planet and disk surfaces, and from disk viscosity. The radiative signature of the planet formation process is explored using a quasi-spherical treatment of the emergent spectral energy distributions. The analytic solutions developed herein show how the protoplanet properties (envelope density distribution, velocity field, column density, disk surface density, luminosity, and radiative signatures) vary with input parameters (instantaneous mass, orbital location, accretion rate, and planetary magnetic field strength).
H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma

Lung cancer is the leading cause of cancer death worldwide, with lung adenocarcinoma being the most prevalent form of lung cancer. EGFR positive lung adenocarcinomas have been shown to have high response rates to TKI therapy, underlying the essential nature of molecular testing for lung cancers. Despite current guidelines consider testing necessary, a large portion of patients are not routinely profiled, resulting in millions of people not receiving the optimal treatment for their lung cancer. Sequencing is the gold standard for molecular testing of EGFR mutations, but it can take several weeks for results to come back, which is not ideal in a time constrained scenario. The development of alternative screening tools capable of detecting EGFR mutations quickly and cheaply while preserving tissue for sequencing could help reduce the amount of sub-optimally treated patients. We propose a multi-modal approach which integrates pathology images and clinical variables to predict EGFR mutational status achieving an AUC of 84% on the largest clinical cohort to date. Such a computational model could be deployed at large at little additional cost. Its clinical application could reduce the number of patients who receive sub-optimal treatments by 53.1% in China, and up to 96.6% in the US.
Measuring Gender Bias in Educational Videos: A Case Study on YouTube

Students are increasingly using online materials to learn new subjects or to supplement their learning process in educational institutions. Issues regarding gender bias have been raised in the context of formal education and some measures have been proposed to mitigate them. However, online educational materials in terms of possible gender bias and stereotypes which may appear in diﬀerent forms are yet to be investigated in the context of search bias in a widely-used search platform. As a ﬁrst step towards measuring possible gender bias in online platforms, we have investigated YouTube educational videos in terms of the perceived gender of their narrators. We adopted bias measures for ranked search results to evaluate educational videos returned by YouTube in response to queries related to STEM (Science, Technology, Engineering, and Mathematics) and NON-STEM ﬁelds of education. Gender is a research area by itself in social sciences which is beyond the scope of this work. In this respect, for annotating the perceived gender of the narrator of an instructional video we used only a crude classiﬁcation of gender into Male, and Female. Then, for analysing perceived gender bias we utilised bias measures that have been inspired by search platforms and further incorporated rank information into our analysis. Our preliminary results demonstrate that there is a signiﬁcant bias towards the male gender on the returned YouTube educational videos, and the degree of bias varies when we compare STEM and NON-STEM queries. Finally, there is a strong evidence that rank information might aﬀect the results.
Radiation resistance in simulated metallic core-shell nanoparticles

We present molecular dynamics (MD) simulations of radiation damage in pure Fe nanoparticles (NP) and bimetallic FeCu core-shell nanoparticles (CSNP). The CSNP includes a perfect body centered cubic (bcc) Fe core coated with a face-centered cubic (fcc) Cu shell. Irradiation with Fe Primary Knock-on Atoms (PKA) with energies between 1 and 7 keV leads to point defects, without clustering beyond divacancies and very few slightly larger vacancy clusters, and without interstitial clusters, unlike what happens in bulk at the same PKA energies. The Fe-Cu interface and shell can act as a defect sink, absorbing radiation-induced damage and, therefore, the final number of defects in the Fe core is significantly lower than in the Fe NP. In addition, the Cu shell substantially diminishes the number of sputtered Fe atoms, acting as a barrier for recoil ejection. Structurally, the Cu shell responds to the stress generated by the collision cascade by creating and destroying stacking faults across the shell width, which could also accommodate further irradiation defects. We compare our MD results to Monte Carlo Binary Collision Approximation (BCA) simulations using SRIM code, for the irradiation of an amorphous 3-layer thin film with thickness equal to the CSNP diameter. BCA does not include defect recombination, so the number of Frenkel pairs is significantly higher than in MD, as expected. Sputtering yield (Y) is underestimated by BCA, which is also expected since the simulation is for a thin film at normal incidence. We also compare MD defect production to bulk predictions of the analytic Athermal Recombination Corrected Displacements Per Atom (arc-dpa) model. The number of vacancies in the Fe core is only slightly lower than arc-dpa predictions, but the number of interstitials is reduced by about one order of magnitude compared to vacancies, at 5 keV. According to the radiation resistance found for FeCu CSNP in our simulations, this class of nanomaterial could be suitable for developing new radiation resistant coatings, nanostructured components, and shields for use in extreme environments, for instance, in nuclear energy and astrophysical applications.
Theory of Radiation Pressure on a Diffractive Solar Sail

: Solar sails propelled by radiation pressure enable space missions that cannot be achieved using chemical rockets alone. Signiﬁcant in-space propulsion for missions such as a solar polar orbiter may be achieved with a sail that deviates sunlight at a large average angular direction. The momentum transfer eﬃciency of sunlight diﬀracted from a sun-facing diﬀractive sail comprised of periodic sawtooth prism elements is examined here. The spectrally averaged eﬃciency is found to approach that of a monolithic prism when the grating period is much longer than the peak of the solar spectrum. This idealized diﬀraction analysis predicts a greater transverse radiation pressure force compared to an idealized reﬂective sail. With modern optical design and fabrication techniques, diﬀractive solar sails may one day replace reﬂective sails.
Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation

It has been found that temporal action proposal generation, which aims to discover the temporal action instances within the range of the start and end frames in the untrimmed videos, can largely benefit from proper temporal and semantic context exploitation. The latest efforts were dedicated to considering the temporal context and similarity-based semantic contexts through self-attention modules. However, they still suffer from cluttered background information and limited contextual feature learning. In this paper, we propose a novel Pyramid Region-based Slot Attention (PRSlot) module to address these issues. Instead of using the similarity computation, our PRSlot module directly learns the local relations in an encoder-decoder manner and generates the representation of a local region enhanced based on the attention over input features called \textit{slot}. Specifically, upon the input snippet-level features, PRSlot module takes the target snippet as \textit{query}, its surrounding region as \textit{key} and then generates slot representations for each \textit{query-key} slot by aggregating the local snippet context with a parallel pyramid strategy. Based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention Network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation. Extensive experiments are conducted on two widely adopted THUMOS14 and ActivityNet-1.3 benchmarks. Our PRSA-Net outperforms other state-of-the-art methods. In particular, we improve the AR@100 from the previous best 50.67% to 56.12% for proposal generation and raise the mAP under 0.5 tIoU from 51.9\% to 58.7\% for action detection on THUMOS14. \textit{Code is available at} \url{https://github.com/handhand123/PRSA-Net}
Q-linear Convergence of Distributed Optimization with Barzilai-Borwein Step Sizes

The growth in sizes of large-scale systems and data in machine learning have made distributed optimization a naturally appealing technique to solve decision problems in different contexts. In such methods, each agent iteratively carries out computations on its local objective using information received from its neighbors, and shares relevant information with neighboring agents. Though gradient-based methods are widely used because of their simplicity, they are known to have slow convergence rates. On the other hand, though Newton-type methods have better convergence properties, though they are not as applicable because of the enormous computation and memory requirements. In this work, we introduce a distributed quasi-Newton method with Barzilai-Borwein step-sizes. We prove a Q-linear convergence to the optimal solution, present conditions under which the algorithm is superlinearly convergent and validate our results via numerical simulations.
ViralBERT: A User Focused BERT-Based Approach to Virality Prediction

Recently, Twitter has become the social network of choice for sharing and spreading information to a multitude of users through posts called ‘tweets’. Users can easily re-share these posts to other users through ‘retweets’, which allow information to cascade to many more users, increasing its outreach. Clearly, being able to know the extent to which a post can be retweeted has great value in advertising, influencing and other such campaigns. In this paper we propose ViralBERT, which can be used to predict the virality of tweets using content- and user-based features. We employ a method of concatenating numerical features such as hashtags and follower numbers to tweet text, and utilise two BERT modules: one for semantic representation of the combined text and numerical features, and another module purely for sentiment analysis of text, as both the information within text and it’s ability to elicit an emotional response play a part in retweet proneness. We collect a dataset of 330k tweets to train ViralBERT and validate the efficacy of our model using baselines from current studies in this field. Our experiments show that our approach outperforms these baselines, with a 13% increase in both F1 Score and Accuracy compared to the best performing baseline method. We then undergo an ablation study to investigate the importance of chosen features, finding that text sentiment and follower counts, and to a lesser extent mentions and following counts, are the strongest features for the model, and that hashtag counts are detrimental to the model.
Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering

In this paper, we present a Geometry-aware Neural Interpolation (Geo-NI) framework for light field rendering. Previous learning-based approaches either rely on the capability of neural networks to perform direct interpolation, which we dubbed Neural Interpolation (NI), or explore scene geometry for novel view synthesis, also known as Depth Image-Based Rendering (DIBR). Instead, we incorporate the ideas behind these two kinds of approaches by launching the NI with a novel DIBR pipeline. Specifically, the proposed Geo-NI first performs NI using input light field sheared by a set of depth hypotheses. Then the DIBR is implemented by assigning the sheared light fields with a novel reconstruction cost volume according to the reconstruction quality under different depth hypotheses. The reconstruction cost is interpreted as a blending weight to render the final output light field by blending the reconstructed light fields along the dimension of depth hypothesis. By combining the superiorities of NI and DIBR, the proposed Geo-NI is able to render views with large disparity with the help of scene geometry while also reconstruct non-Lambertian effect when depth is prone to be ambiguous. Extensive experiments on various datasets demonstrate the superior performance of the proposed geometry-aware light field rendering framework.
LordNet: Learning to Solve Parametric Partial Differential Equations without Simulated Data

Neural operators, as a powerful approximation to the non-linear operators between inﬁnite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE). However, it requires a large amount of simulated data which can be costly to collect, resulting in a chicken-egg dilemma and limiting its usage in solving PDEs. To jump out of the dilemma, we propose a general data-free paradigm where the neural network directly learns physics from the mean squared residual (MSR) loss constructed by the discretized PDE. We investigate the physical information in the MSR loss and identify the challenge that the neural network must have the capacity to model the long range entanglements in the spatial domain of the PDE, whose patterns vary in different PDEs. Therefore, we propose the low-rank decomposition network (LordNet) which is tunable and also efﬁcient to model various entanglements. Speciﬁcally, LordNet learns a low-rank approximation to the global entanglements with simple fully connected layers, which extracts the dominant pattern with reduced computational cost. The experiments on solving Poisson’s equation and Navier-Stokes equation demonstrate that the physical constraints by the MSR loss can lead to better accuracy and generalization ability of the neural network. In addition, LordNet outperforms other modern neural network architectures in both PDEs with the fewest parameters and the fastest inference speed. For Navier-Stokes equation, the learned operator is over 50 times faster than the ﬁnite difference solution with the same computational resources.
Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations

Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit high learning capabilities at the cost of compute-intensive operations. To enable their deployment on edge devices, we propose to leverage approximate computing for designing approximate variants of the complex operations like softmax and squash. In our experiments, we evaluate tradeoffs between area, power consumption, and critical path delay of the designs implemented with the ASIC design flow, and the accuracy of the quantized CapsNets, compared to the exact functions.
Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information

Image transformation, a class of vision and graphics problems whose goal is to learn the mapping between an input image and an output image, develops rapidly in the context of deep neural networks. In Computer Vision (CV), many problems can be regarded as the image transformation task, e.g., semantic segmentation and style transfer. These works have different topics and motivations, making the image transformation task flourishing. Some surveys only review the research on style transfer or image-to-image translation, all of which are just a branch of image transformation. However, none of the surveys summarize those works together in a unified framework to our best knowledge. This paper proposes a novel learning framework including Independent learning, Guided learning, and Cooperative learning, called the IGC learning framework. The image transformation we discuss mainly involves the general image-to-image translation and style transfer about deep neural networks. From the perspective of this framework, we review those subtasks and give a unified interpretation of various scenarios. We categorize related subtasks about the image transformation according to similar development trends. Furthermore, experiments have been performed to verify the effectiveness of IGC learning. Finally, new research directions and open problems are discussed for future research.
Polarized image of equatorial emission in horizonless spacetimes: Traversable wormholes

We study the linear polarization from the accretion disk around a class of static traversable wormholes. Applying the simplified model of a magnetized fluid ring orbiting in the equatorial plane, we search for characteristic signatures, which could distinguish wormhole from black hole spacetimes by their polarization properties. For the purpose we analyse the direct polarized images for different inclination angles, the strongly lensed indirect images, and the polarized radiation which reaches the asymptotic observer through the wormhole throat, and compare to the Schwarzschild black hole. For small inclination angles the two types of compact objects lead to a very similar polarization pattern of the direct images. More significant distinctions are observed for the strongly lensed indirect images, where the polarization intensity in the wormhole spacetimes can grow up to an order of magnitude compared to the Schwarzschild black hole. Detecting radiation from the region across the wormhole throat leads to the formation of an additional structure of ring images with distinct polarization properties. The twist of the polarization vector around the ring is less pronounced, thus modifying the polarization pattern, and the polarization intensity can increase with an order of magnitude compared to the radiation from our universe. Thus, while it could be difficult to distinguish wormhole spacetimes by their direct polarized images, the strongly lensed images and the polarization of the radiation through the wormhole throat provide characteristic signatures which can serve as probes for horizonless objects.