Miracle-dz/UniFAR-FTUs huggingface dataset:
Categorical Depth Distribution Network for Monocular 3D Object Detection

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. The main challenge in monocular 3D detection lies in accurately predicting object depth, which must be inferred from object and scene cues due to the lack of direct range measurement. Many methods attempt to directly estimate depth to assist in 3D detection, but show limited performance as a result of depth inaccuracy. Our proposed solution, Categorical Depth Distribution Network (CaDDN), uses a predicted categorical depth distribution for each pixel to project rich contextual feature information to the appropriate depth interval in 3D space. We then use the computationally efficient bird’s-eye-view projection and single-stage detector to produce the final output detections. We design CaDDN as a fully differentiable end-to-end approach for joint depth estimation and object detection. We validate our approach on the KITTI 3D object detection benchmark, where we rank 1st among published monocular methods. We also provide the first monocular 3D detection results on the newly released Waymo Open Dataset. We provide a code release for CaDDN which is made available.
Systematic Analysis and Removal of Circular Artifacts for StyleGAN

StyleGAN is one of the state-of-the-art image generators which is well-known for synthesizing high-resolution and hyper-realistic face images. Though images generated by vanilla StyleGAN model are visually appealing, they sometimes contain prominent circular artifacts which severely degrade the quality of generated images. In this work, we provide a systematic investigation on how those circular artifacts are formed by studying the functionalities of different stages of vanilla StyleGAN architecture, with both mechanism analysis and extensive experiments. The key modules of vanilla StyleGAN that promote such undesired artifacts are highlighted. Our investigation also explains why the artifacts are usually circular, relatively small and rarely split into 2 or more parts. Besides, we propose a simple yet effective solution to remove the prominent circular artifacts for vanilla StyleGAN, by applying a novel pixel-instance normalization (PIN) layer.
Sample Complexity and Overparameterization Bounds for Projection-Free Neural TD Learning

We study the dynamics of temporal-difference learning with neural network-based value function approximation over a general state space, namely, Neural TD learning . Existing analysis of neural TD learning relies on either inﬁnite width-analysis or constraining the network parameters in a (random) compact set; as a result, an extra projection step is required at each iteration. This paper establishes a new convergence analysis of neural TD learning without any projection . We show that the projection-free TD learning equipped with a two-layer ReLU network of any width exceeding poly ( ν, 1 /ǫ ) converges to the true value function with error ǫ given poly ( ν, 1 /ǫ ) iterations or samples, where ν is an upper bound on the RKHS norm of the value function induced by the neural tangent kernel. Our sample complexity and overparameterization bounds are based on a drift analysis of the network parameters as a stopped random process in the lazy training regime.
Pixel-Level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments

In this letter, we present a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. Our approach does not require checkerboards but can achieve pixel-level accuracy by aligning natural edge features in the two sensors. On the theory level, we analyze the constraints imposed by edge features and the sensitivity of calibration accuracy with respect to edge distribution in the scene. On the implementation level, we carefully investigate the physical measuring principles of LiDARs and propose an efficient and accurate LiDAR edge extraction method based on point cloud voxel cutting and plane fitting. Due to the edges’ richness in natural scenes, we have carried out experiments in many indoor and outdoor scenes. The results show that this method has high robustness, accuracy, and consistency. It can promote the research and application of the fusion between LiDAR and camera. We have open sourced our code on GitHub1 to benefit the community.
Categorical enumerative invariants of the ground field

For an $S^1$-framed modular operad $P$, we introduce its"Feynman compactification"denoted by $FP$ which is a modular operad. Let $\{\mathbb{M}^{\sf fr}(g,n)\}_{(g,n)}$ be the $S^1$-framed modular operad defined using moduli spaces of smooth curves with framings along punctures. We prove that the homology operad of $F\mathbb{M}^{\sf fr}$ is isomorphic to $H_*(\overline{M})$, the homology operad of the Deligne-Mumford operad. Using this isomorphism, we obtain an explicit formula of the fundamental class of $[\overline{M}_{g,n}/S_n]$ in terms of Sen-Zwiebach's string vertices. As an immediate application, under mild assumptions, we prove that Costello's categorical enumerative invariants of the ground field match with the Gromov-Witten invariants of a point.
Extending Prolog for Quantified Boolean Horn Formulas

Prolog is a well known declarative programming language based on propositional Horn formulas. It is useful in various areas, including artificial intelligence, automated theorem proving, mathematical logic and so on. An active research area for many years is to extend Prolog to larger classes of logic. Some important extensions of it includes the constraint logic programming, and the object oriented logic programming. However, it cannot solve problems having arbitrary quantified Horn formulas. To be precise, the facts, rules and queries in Prolog are not allowed to have arbitrary quantified variables. The paper overcomes this major limitations of Prolog by extending it for the quantified Boolean Horn formulas. We achieved this by extending the SLD-resolution proof system for quantified Boolean Horn formulas, followed by proposing an efficient model for implementation. The paper shows that the proposed implementation also supports the first-order predicate Horn logic with arbitrary quantified variables. The paper also introduces for the first time, a declarative programming for the quantified Boolean Horn formulas.
Missing Value Imputation on Multidimensional Time Series

We present DeepMVI, a deep learning method for missing value imputation in multidimensional time-series datasets. Missing values are commonplace in decision support platforms that aggregate data over long time stretches from disparate sources, whereas reliable data analytics calls for careful handling of missing data. One strategy is imputing the missing values, and a wide variety of algorithms exist spanning simple interpolation, matrix factorization methods like SVD, statistical models like Kalman filters, and recent deep learning methods. We show that often these provide worse results on aggregate analytics compared to just excluding the missing data. DeepMVI expresses the distribution of each missing value conditioned on coarse and fine-grained signals along a time series, and signals from correlated series at the same time. Instead of resorting to linearity assumptions of conventional matrix factorization methods, DeepMVI harnesses a flexible deep network to extract and combine these signals in an end-to-end manner. To prevent over-fitting with high-capacity neural networks, we design a robust parameter training with labeled data created using synthetic missing blocks around available indices. Our neural network uses a modular design with a novel temporal transformer with convolutional features, and kernel regression with learned embeddings. Experiments across ten real datasets, five different missing scenarios, comparing seven conventional and three deep learning methods show that DeepMVI is significantly more accurate, reducing error by more than 50% in more than half the cases, compared to the best existing method. Although slower than simpler matrix factorization methods, we justify the increased time overheads by showing that DeepMVI provides significantly more accurate imputation that finally impacts quality of downstream analytics.
Model-based Personalized Synthetic Magnetic Resonance Imaging

Synthetic Magnetic Resonance (MR) imaging predicts images at new design parameter settings from a few observed MR scans. Model-based methods, that use both the physical and statistical properties underlying the MR signal and its acquisition, can predict images at any setting from as few as three scans, allowing it to be used in individualized patientand anatomyspecific contexts. However, the estimation problem in model-based synthetic MR imaging is ill-posed and so regularization, in the form of correlated Gaussian Markov Random Fields, is imposed on the voxel-wise spin-lattice relaxation time, spin-spin relaxation time and the proton density underlying the MR image. We develop theoretically sound but computationally practical matrix-free estimation methods for synthetic MR imaging. Our evaluations demonstrate excellent ability of our methods to synthetize MR images in a clinical framework and also estimation and prediction accuracy and consistency. An added strength of our model-based approach, also developed and illustrated here, is the accurate estimation of standard errors of regional means in the synthesized images.
Gender Typicality of Behavior Predicts Success on Creative Platforms

Collaboration platforms on the Internet have become crucial tools for independent creative workers, facilitating connections with collaborators, users, and buyers. Such platforms carried the promise of better opportunities for women and other under-represented groups to access markets and collaborators, but evidence is mounting for that they rather perpetuate existing biases and inequalities. In previous work, we had found that the majority of women’s disadvantage in success and survival on GitHub stems from what they do – the gender typicality of their behavior in open source programming – rather than from a categorical discrimination of their gender. In this article we replicate our findings on another platform with a markedly different focus: Behance, a community for for graphic artists. We also study attention as a new outcome on both platforms. We found that female typicality of behaviour is a significant negative predictor of attention, success, and survival on creative platforms, while the impact of categorical gender varies by outcome and field. We found support for the visibility paradox of women in technical fields: while female typicality of behaviors is negatively related to attention, being female predicts a higher level of attention. We quantified the indirect impact of gender homophily on success via gendered behaviour that accounts for 37% of the disadvantage of women in success. Our findings suggest that the negative impact of the gender typicality of behaviour is a more general phenomena than our first study indicated, underlining the scope of the challenge of countering unconscious gender bias in the platform economy.
Apples, Oranges & Fruits - Understanding Similarity of Software Projects Through The Lens of Dissimilar Artifacts

The growing availability of open source projects has facilitated developers to reuse existing software artifacts and leverage them to develop new software. However, it is hard to understand the notion of similarity as it varies from developer to developer. Some developers might search for repositories with similar source code, while some might be in search of repositories with similar requirements or issues. Existing approaches tend to find similar projects by comparing similar artifacts such as source-code to source-code, API usage to API usage, documentation to documentation, and so on. Even though there is a dissimilarity between two similar artifacts, there could be a similarity between two dissimilar artifacts. Hence, in this paper, we aim to answer the question - Can we find similarity of software repositories through dissimilar artifacts? To this end, we conduct an experiment to find similarities between three repositories, two similar and one different project comparing similar and dissimilar artifacts (documentation, commits, and source-code). We observed similarities between dissimilar artifacts such as Commits, Source Code, and Readme Files in the context of both similar and different repositories.
High-productivity, high-performance workflow for virus-scale electrostatic simulations with Bempp-Exafmm

Biomolecular electrostatics is key in protein function and the chemical processes affecting it. Implicit-solvent models via the Poisson-Boltzmann (PB) equation provide insights with less computational cost than atomistic models, making large-system studies -- at the scale of viruses -- accessible to more researchers. Here we present a high-productivity and high-performance linear PB solver based on Exafmm, a fast multipole method library, and Bempp, a Galerkin boundary element method package. The workflow integrates an easy-to-use Python interface with optimized computational kernels, and can be run interactively via Jupyter notebooks, for faster prototyping. Our results show the capability of the software, confirm code correctness, and assess performance with between 8,000 and 2 million elements. Showcasing the power of this interactive computing platform, we study the conditioning of two variants of the boundary integral formulation with just a few lines of code. Mesh-refinement studies confirm convergence as $1/N$, for $N$ boundary elements, and a comparison with results from the trusted APBS code using various proteins shows agreement. Our binding energy calculations using 9 various complexes align with the results from using five other grid-based PB solvers. Performance results include timings, breakdowns, and computational complexity. Exafmm offers evaluation speeds of just a few seconds for tens of millions of points, and $\mathcal{O}(N)$ scaling. The trend observed in our performance comparison with APBS demonstrates the advantage of Bempp-Exafmm in applications involving larger structures or requiring higher accuracy. Computing the solvation free energy of a Zika virus, represented by 1.6 million atoms and 10 million boundary elements, took 80-min runtime on a single compute node (dual 20-core).
An Earth-mass planet in a time of COVID-19: KMT-2020-BLG-0414Lb

We report the discovery of KMT-2020-BLG-0414Lb, with a planet-to-host mass ratio q 2 = 0.9–1.2 × 10−5 = 3–4 ⊕ at 1σ, which is the lowest mass-ratio microlensing planet to date. Together with two other recent discoveries (4 ≲ q/q ⊕ ≲ 6), it fills out the previous empty sector at the bottom of the triangular (log s, log q) diagram, where s is the planet-host separation in units of the angular Einstein radius θE. Hence, these discoveries call into question the existence, or at least the strength, of the break in the mass-ratio function that was previously suggested to account for the paucity of very low-q planets. Due to the extreme magnification of the event, A max ∼ 1450 for the underlying single-lens event, its light curve revealed a second companion with q 3 ∼ 0.05 and |log s 3| ∼ 1, i.e., a factor ∼ 10 closer to or farther from the host in projection. The measurements of the microlens parallax π E and the angular Einstein radius θE allow estimates of the host, planet and second companion masses, (M 1, M 2, M 3) ∼ (0.3 M ⊙, 1.0 M ⊕, 17 MJ ), the planet and second companion projected separations, (a ⊥,2, a ⊥,3) ∼ (1.5, 0.15 or 15) au, and system distance D L ∼ 1 kpc. The lens could account for most or all of the blended light (I ∼ 19.3) and so can be studied immediately with high-resolution photometric and spectroscopic observations that can further clarify the nature of the system. The planet was found as part of a new program of high-cadence follow-up observations of high-magnification events. The detection of this planet, despite the considerable difficulties imposed by COVID-19 (two KMT sites and OGLE were shut down), illustrates the potential utility of this program.
Auto-Exposure Fusion for Single-Image Shadow Removal

Shadow removal is still a challenging task due to its inherent background-dependent1 and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful deep neural networks could hardly recover traceless shadow-removed background. This paper proposes a new solution for this task by formulating it as an exposure fusion problem to address the challenges. Intuitively, we first estimate multiple over-exposure images w.r. t. the input image to let the shadow regions in these images have the same color with shadow-free areas in the input image. Then, we fuse the original input with the over-exposure images to generate the final shadow-free counterpart. Nevertheless, the spatial-variant property of the shadow requires the fusion to be sufficiently ‘smart’, that is, it should automatically select proper over-exposure pixels from different images to make the final output natural. To address this challenge, we propose the shadow-aware FusionNet that takes the shadow image as input to generate fusion weight maps across all the over-exposure images. Moreover, we propose the boundary-aware RefineNet to eliminate the remaining shadow trace further. We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method’s effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods. We release the code in https://github.com/tsingqguo/exposure-fusion-shadow-removal.
High-sensitivity radio study of the non-thermal stellar bow shock EB27

We present a deep radio-polarimetric observation of the stellar bow shock EB27 associated to the massive star BD+43◦3654. This is the only stellar bow shock confirmed to have non-thermal radio emission. We used the Jansky Very Large Array in S band (2–4 GHz) to test whether this synchrotron emission is polarised. The unprecedented sensitivity achieved allowed us to map even the fainter regions of the bow shock, revealing that the more diffuse emission is steeper and the bow shock brighter than previously reported. No linear polarisation is detected in the bow shock above 0.5%, although we detected polarised emission from two southern sources, probably extragalactic in nature. We modeled the intensity and morphology of the radio emission to better constrain the magnetic field and injected power in relativistic electrons. Finally, we derived a set of more precise parameters for the system EB27– BD+43◦3654 using Gaia Early Data Release 3, including the spatial velocity. The new trajectory, back in time, intersects the core of the CygOB2 association.
Low-redshift compact star-forming galaxies as analogues of high-redshift star-forming galaxies

We compare the relations among various integrated characteristics of ∼ 25,000 low-redshift (z <∼ 1.0) compact star-forming galaxies (CSFGs) from Data Release 16 (DR16) of the Sloan Digital Sky Survey (SDSS) and of high-redshift (z >∼ 1.5) star-forming galaxies (SFGs) with respect to oxygen abundances, stellar masses M? , far-UV absolute magnitudes MFUV, star-formation rates SFR and specific star-formation rates sSFR, Lyman-continuum photon production efficiencies (ξion), UV continuum slopes β, [O III] λ5007/[O II] λ3727 and [Ne III] λ3868/[O II] λ3727 ratios, and emission-line equivalent widths EW([O II] λ3727), EW([O III] λ5007), and EW(Hα). We find that the relations for low-z CSFGs with high equivalent widths of the Hβ emission line, EW(Hβ) ≥ 100Å, and high-z SFGs are very similar, implying close physical properties in these two categories of galaxies. Thus, CSFGs are likely excellent proxies for the SFGs in the high-z Universe. They also extend to galaxies with lower stellar masses, down to ∼ 106 M , and to absolute FUV magnitudes as faint as −14 mag. Thanks to their proximity, CSFGs can be studied in much greater detail than distant SFGs. Therefore, the relations between the integrated characteristics of the large sample of CSFGs studied here can prove very useful for our understanding of high-z dwarf galaxies in future observations with large ground-based and space telescopes.
Multiclass Burn Wound Image Classification Using Deep Convolutional Neural Networks

Millions of people are affected by acute and chronic wounds yearly across the world. Continuous wound monitoring is important for wound specialists to allow more accurate diagnosis and optimization of management protocols. Machine Learning-based classification approaches provide optimal care strategies resulting in more reliable outcomes, cost savings, healing time reduction, and improved patient satisfaction. In this study, we use a deep learning-based method to classify burn wound images into two or three different categories based on the wound conditions. A pre-trained deep convolutional neural network, AlexNet, is fine-tuned using a burn wound image dataset and utilized as the classifier. The classifier's performance is evaluated using classification metrics such as accuracy, precision, and recall as well as confusion matrix. A comparison with previous works that used the same dataset showed that our designed classifier improved the classification accuracy by more than 8%.
Nonthermal leptogenesis and UV freeze-in of dark matter: Impact of inflationary reheating

We study a minimal scenario to realize non-thermal leptogenesis and UV freeze-in of a Standard Model (SM) gauge singlet fermionic dark matter (DM) simultaneously, with inflaton field playing a non-trivial role in their yields. The renormalizable interactions are restricted to the SM fields, two right handed neutrinos (RHN) and inflaton coupling exclusively to the RHNs, while the DM couples to both the SM and the RHNs only via operators of dimension $d>4$. Considering two separate cases of $d=\{5,6\}$, we show that for $d=5$, inflaton decay into RHNs followed by their subsequent decay into SM particles lead to both reheating as well as DM production from the SM bath. This requires a cut-off scale as large as $\Lambda\sim 10^{17}~\rm GeV$ depending on the DM mass. On the other hand, for $d=6$, DM production happens directly from scattering of RHNs (for $\Lambda\gtrsim 10^{14}~\rm GeV$) that results in a very non-trivial evolution of the DM yield. In both these cases, it is possible to explain the observed baryon asymmetry through successful non-thermal leptogenesis via the decay of the RHNs, together with the PLANCK observed relic density of the DM via pure UV freeze-in mechanism. Taking into account both instantaneous as well as non-instantaneous reheating separately, we constrain the parameter space of this minimal scenario from relevant phenomenological requirements including sub-eV scale active neutrino masses and their mixing.