Miracle-dz/UniFAR-FTUs huggingface dataset:
Overleaf Example

In Active Domain Adaptation (ADA), one uses Active Learning (AL) to select a subset of images from the target domain, which are then annotated and used for supervised domain adaptation (DA). Given the large performance gap between supervised and unsupervised DA techniques, ADA allows for an excellent trade-off between annotation cost and performance. Prior art makes use of measures of uncertainty or disagreement of models to identify ‘regions’ to be annotated by the human oracle. However, these regions frequently comprise of pixels at object boundaries which are hard and tedious to annotate. Hence, even if the fraction of image pixels annotated reduces, the overall annotation time and the resulting cost still remain high. In this work, we propose an ADA strategy, which given a frame, identifies a set of classes that are hardest for the model to predict accurately, thereby recommending semantically meaningful regions to be annotated in a selected frame. We show that these set of ‘hard’ classes are context-dependent and typically vary across frames, and when annotated help the model generalize better. We propose two ADA techniques: the Anchor-based and Augmentation-based approaches to select complementary and diverse regions in the context of the current training set. Our approach achieves 66.6 mIoU on GTA5→Cityscapes dataset with an annotation budget of 4.7% in comparison to 64.9 mIoU by MADA [22] using 5% of annotations. Our technique can also be used as a decorator for any existing frame-based AL technique, e.g., we report 1.5% performance improvement for CDAL [1] on Cityscapes using our approach.
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation

The automatic classiﬁcation of radar waveform is a fundamental technique in electronic countermeasures (ECM). Recent supervised deep learning-based methods have achieved great success in a such classiﬁcation task.However, those methods require enough labeled samples to work properly and in many circumstances, it is not available.To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels.Firstly, a pretext model is trained in a self-supervised way with the help of several data augmentation techniques to extract the class-dependent features.Next,the pseudo-supervised contrastive training is in-volved to further promote the separation between the extracted class-dependent features.And ﬁnally, the unsupervised problem is converted to a semi-supervised classiﬁcation problem via pseudo label generation. The simulation results show that the proposed algorithm can eﬀectively extract class-dependent features, outperforming several unsupervised clustering methods, even reaching performance on par with the supervised deep learning-based methods.
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks

Text image super-resolution is a unique and important task to enhance readability of text images to humans. It is widely used as pre-processing in scene text recognition. However, due to the complex degradation in natural scenes, recovering high-resolution texts from the low-resolution inputs is ambiguous and challenging. Existing methods mainly leverage deep neural networks trained with pixel-wise losses designed for natural image reconstruction, which ignore the unique character characteristics of texts. A few works proposed content-based losses. However, they only focus on text recognizers' accuracy, while the reconstructed images may still be ambiguous to humans. Further, they often have weak generalizability to handle cross languages. To this end, we present TATSR, a Text-Aware Text Super-Resolution framework, which effectively learns the unique text characteristics using Criss-Cross Transformer Blocks (CCTBs) and a novel Content Perceptual (CP) Loss. The CCTB extracts vertical and horizontal content information from text images by two orthogonal transformers, respectively. The CP Loss supervises the text reconstruction with content semantics by multi-scale text recognition features, which effectively incorporates content awareness into the framework. Extensive experiments on various language datasets demonstrate that TATSR outperforms state-of-the-art methods in terms of both recognition accuracy and human perception.
JWST's PEARLS: A new lens model for ACT-CL J0102-4915, "El Gordo," and the first red supergiant star at cosmological distances discovered by JWST

The first JWST data on the massive colliding cluster El Gordo confirm 23 known families of multiply lensed images and identify 8 new members of these families. Based on these families, which have been confirmed spectroscopically by MUSE, we derived an initial lens model. This model guided the identification of 37 additional families of multiply lensed galaxies, among which 28 are entirely new systems, and 9 were previously known. The initial lens model determined geometric redshifts for the 37 new systems. The geometric redshifts agree reasonably well with spectroscopic or photometric redshifts when those are available. The geometric redshifts enable two additional models that include all 60 families of multiply lensed galaxies spanning a redshift range $2<z<6$. The derived dark-matter distribution confirms the double-peak configuration of mass found by earlier work with the southern and northern clumps having similar masses. We confirm that El Gordo is the most massive known cluster at $z>0.8$ and has an estimated virial mass close the maximum mass allowed by standard cosmological models. The JWST images also reveal the presence of small-mass perturbers that produce small lensing distortions. The smallest of these is consistent with being a dwarf galaxy at $z=0.87$ and has an estimated mass of $3.8\times10^9$~\Msol, making it the smallest substructure found at $z>0.5$. The JWST images also show several candidate caustic-crossing events. One of them is detected at high significance at the expected position of the critical curve and is likely a red supergiant star at $z=2.1878$. This would be the first red supergiant found at cosmological distances. The cluster lensing should magnify background objects at $z>6$, making more of them visible than in blank fields of similar size, but there appears to be a deficiency of such objects.
glamer – II. Multiple-plane gravitational lensing

We present an extension to multiple planes of the gravitational lensing code {\small GLAMER}. The method entails projecting the mass in the observed light-cone onto a discrete number of lens planes and inverse ray-shooting from the image to the source plane. The mass on each plane can be represented as halos, simulation particles, a projected mass map extracted form a numerical simulation or any combination of these. The image finding is done in a source oriented fashion, where only regions of interest are iteratively refined on an initially coarse image plane grid. The calculations are performed in parallel on shared memory machines. The code is able to handle different types of analytic halos (NFW, NSIE, power-law, etc.), haloes extracted from numerical simulations and clusters constructed from semi-analytic models ({\small MOKA}). Likewise, there are several different options for modeling the source(s) which can be distributed throughout the light-cone. The distribution of matter in the light-cone can be either taken from a pre-existing N-body numerical simulations, from halo catalogs, or are generated from an analytic mass function. We present several tests of the code and demonstrate some of its applications such as generating mock images of galaxy and galaxy cluster lenses.
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains

Deep learning models dealing with image understanding in real-world settings must be able to adapt to a wide variety of tasks across different domains. Domain adaptation and class incremental learning deal with domain and task variability separately, whereas their unified solution is still an open problem. We tackle both facets of the problem together, taking into account the semantic shift within both input and label spaces. We start by formally introducing continual learning under task and domain shift. Then, we address the proposed setup by using style transfer techniques to extend knowledge across domains when learning incremental tasks and a robust distillation framework to effectively recollect task knowledge under incremental domain shift. The devised framework (LwS, Learning with Style) is able to generalize incrementally acquired task knowledge across all the domains encountered, proving to be robust against catastrophic forgetting. Extensive experimental evaluation on multiple autonomous driving datasets shows how the proposed method outperforms existing approaches, which prove to be ill-equipped to deal with continual semantic segmentation under both task and domain shift.
Results from the first use of low radioactivity argon in a dark matter search

Liquid argon is a bright scintillator with potent particle identification properties, making it an attractive target for direct-detection dark matter searches. The DarkSide-50 dark matter search here reports the first WIMP search results obtained using a target of low-radioactivity argon. DarkSide-50 is a dark matter detector, using two-phase liquid argon time projection chamber, located at the Laboratori Nazionali del Gran Sasso. The underground argon is shown to contain Ar-39 at a level reduced by a factor (1.4 +- 0.2) x 103 relative to atmospheric argon. We report a background-free null result from (2616 +- 43) kg d of data, accumulated over 70.9 live-days. When combined with our previous search using an atmospheric argon, the 90 % C.L. upper limit on the WIMP-nucleon spin-independent cross section based on zero events found in the WIMP search regions, is 2.0 x 10-44 cm2 (8.6 x 10-44 cm2, 8.0 x 10-43 cm2) for a WIMP mass of 100 GeV/c2 (1 TeV/c2 , 10 TeV/c2).
Shape Preserving Facial Landmarks with Graph Attention Networks

Top-performing landmark estimation algorithms are based on exploiting the excellent ability of large convolutional neural networks (CNNs) to represent local appearance. However, it is well known that they can only learn weak spatial relationships. To address this problem, we propose a model based on the combination of a CNN with a cascade of Graph Attention Network regressors. To this end, we introduce an encoding that jointly represents the appearance and location of facial landmarks and an attention mechanism to weigh the information according to its reliability. This is combined with a multi-task approach to initialize the location of graph nodes and a coarse-to-fine landmark description scheme. Our experiments confirm that the proposed model learns a global representation of the structure of the face, achieving top performance in popular benchmarks on head pose and landmark estimation. The improvement provided by our model is most significant in situations involving large changes in the local appearance of landmarks.
Bug Analysis in Jupyter Notebook Projects: An Empirical Study

Computational notebooks, such as Jupyter, have been widely adopted by data scientists to write code for analyzing and visualizing data. Despite their growing adoption and popularity, few studies were found to understand Jupyter development challenges from the practitioners’ point of view. This paper presents a systematic study of bugs and challenges that Jupyter practitioners face through a large-scale empirical investigation. We mined 14,740 commits from 105 GitHub open-source projects with Jupyter notebook code. Next, we analyzed 30,416 Stack Overflow posts, which gave us insights into bugs that practitioners face when developing Jupyter notebook projects. Next, we conducted nineteen interviews with data scientists to uncover more details about Jupyter bugs and to gain insight into Jupyter developers’ challenges. Finally, to validate the study results and proposed taxonomy, we conducted a survey with 91 data scientists. We also highlight bug categories, their root causes, and the challenges that Jupyter practitioners face.
Do observations offer evidence for cosmological-scale extra dimensions?

We present a case that current observations may already indicate new gravitational physics on cosmological scales. The excess of power seen in the Lyman-α forest and small-scale CMB experiments, the anomalously large bulk flows seen both in peculiar velocity surveys and in kinetic SZ, and the higher ISW cross-correlation all indicate that structure may be more evolved than expected from ΛCDM. While these anomalies may constitute a statistical fluke or may eventually disappear as systematic effects are better understood, we argue that they can be explained in models with infinite-volume (or, at least, cosmological-size) extra dimensions, where the graviton is a resonance with a tiny width. The longitudinal mode of the graviton mediates an extra scalar force which speeds up structure formation at late times, thereby accounting for the above anomalies. The required graviton Compton wavelength is relatively small compared to the present Hubble radius, of order 300-600 Mpc. Moreover, with certain assumptions about the behavior of the longitudinal mode on super-Hubble scales, our modified gravity framework can also alleviate the tension with the low quadrupole and the peculiar vanishing of the CMB correlation function on large angular scales, seen both in COBE and WMAP. This relies on a novel mechanism that cancels a late-time ISW contribution against the primordial Sachs-Wolfe amplitude.
Leibniz's law and paraconsistent models of ZFC

In this paper, we present full models for some Paraconsistent Set Theories (PSTs). These models are built over Fidel semantics where they are specific first-order structures in the sense of Model Theory. These structures are known as F-structures in the literature and they are not algebras in the universal algebra sense. We demonstrate how is possible to present paraconsistent models for ZFC for any of PSTs studied in this paper, by adapting the proofs given on the celebrated John Lane Bell's books; in general, we adapt the proofs in the mentioned book throughout the work.
Policy Gradient With Serial Markov Chain Reasoning

We introduce a new framework that performs decision-making in reinforcement learning (RL) as an iterative reasoning process. We model agent behavior as the steady-state distribution of a parameterized reasoning Markov chain (RMC), optimized with a new tractable estimate of the policy gradient. We perform action selection by simulating the RMC for enough reasoning steps to approach its steady-state distribution. We show our framework has several useful properties that are inherently missing from traditional RL. For instance, it allows agent behavior to approximate any continuous distribution over actions by parameterizing the RMC with a simple Gaussian transition function. Moreover, the number of reasoning steps to reach convergence can scale adaptively with the difficulty of each action selection decision and can be accelerated by re-using past solutions. Our resulting algorithm achieves state-of-the-art performance in popular Mujoco and DeepMind Control benchmarks, both for proprioceptive and pixel-based tasks.
Flare7K: A Phenomenological Nighttime Flare Removal Dataset

Artificial lights commonly leave strong lens flare artifacts on images captured at night. Nighttime flare not only affects the visual quality but also degrades the performance of vision algorithms. Existing flare removal methods mainly focus on removing daytime flares and fail in nighttime. Nighttime flare removal is challenging because of the unique luminance and spectrum of artificial lights and the diverse patterns and image degradation of the flares captured at night. The scarcity of nighttime flare removal datasets limits the research on this crucial task. In this paper, we introduce, Flare7K, the first nighttime flare removal dataset, which is generated based on the observation and statistics of real-world nighttime lens flares. It offers 5,000 scattering and 2,000 reflective flare images, consisting of 25 types of scattering flares and 10 types of reflective flares. The 7,000 flare patterns can be randomly added to flare-free images, forming the flare-corrupted and flare-free image pairs. With the paired data, we can train deep models to restore flare-corrupted images taken in the real world effectively. Apart from abundant flare patterns, we also provide rich annotations, including the labeling of light source, glare with shimmer, reflective flare, and streak, which are commonly absent from existing datasets. Hence, our dataset can facilitate new work in nighttime flare removal and more fine-grained analysis of flare patterns. Extensive experiments show that our dataset adds diversity to existing flare datasets and pushes the frontier of nighttime flare removal.
SQuAT: Sharpness- and Quantization-Aware Training for BERT

Quantization is an effective technique to reduce memory footprint, inference latency, and power consumption of deep learning models. However, existing quantization methods suffer from accuracy degradation compared to full-precision (FP) models due to the errors introduced by coarse gradient estimation through non-differentiable quantization layers. The existence of sharp local minima in the loss landscapes of overparameterized models (e.g., Transformers) tends to aggravate such performance penalty in low-bit (2, 4 bits) settings. In this work, we propose sharpness- and quantization-aware training (SQuAT), which would encourage the model to converge to flatter minima while performing quantization-aware training. Our proposed method alternates training between sharpness objective and step-size objective, which could potentially let the model learn the most suitable parameter update magnitude to reach convergence near-flat minima. Extensive experiments show that our method can consistently outperform state-of-the-art quantized BERT models under 2, 3, and 4-bit settings on GLUE benchmarks by 1%, and can sometimes even outperform full precision (32-bit) models. Our experiments on empirical measurement of sharpness also suggest that our method would lead to flatter minima compared to other quantization methods.
Radiolysis of amino acids by heavy and energetic cosmic ray analogues in simulated space environments: α-glycine zwitterion form

In this work, we studied the stability of the glycine molecule in the crystalline zwitterion form, known as {\alpha}-glycine ($^{+}$NH$_{3}$CH$_{2}$COO$^{-}$) under action of heavy cosmic ray analogs. The experiments were conducted in a high vacuum chamber at heavy ions accelerator GANIL, in Caen, France. The samples were bombarded at two temperatures (14 K and 300 K) by $^{58}$Ni$^{11+}$ ions of 46 MeV until the final fluence of $10^{13}$ ions cm$^{-2}$. The chemical evolution of the sample was evaluated in-situ using Fourrier Transformed Infrared (FTIR) spectrometer. The bombardment at 14 K produced several daughter species such as OCN$^-$, CO, CO$_2$, and CN$^-$. The results also suggest the appearing of peptide bonds during irradiation but this must be confirmed by further experiments. The halflives of glycine in Interstellar Medium were estimated to be 7.8 $\times 10^3$ years (300 K) and 2.8 $\times 10^3$ years (14 K). In the Solar System the values were 8.4 $\times 10^2$ years (300 K) and 3.6 $\times 10^3$ years (14 K). It is believed that glycine could be present in space environments that suffered aqueous changes such as the interior of comets, meteorites and planetesimals. This molecule is present in proteins of all alive beings. So, studying its stability in these environments provides further understanding about the role of this specie in the prebiotic chemistry on Earth.
Electronic Structure and Properties of the Organic Semi Conductor Material Anthracene in Gas Phase and Ethanol: An ab Initio and DFT Study

The molecular geometry of Anthracene in gas phase and ethanol are studied using ab initio quantum chemical calculations at the Restricted Hartree-Fock (RHF) level of theory by employing 6-31G basis set. Density functional calculations at the Becke3LYP (B3LYP) are carried out using 6-31G basis set for inclusion of electron correlations. The molecular structure, dipole moment, quadrupole moment, charge transfer, polarizability, energy, and vibrational frequencies with IR and Raman intensities have been studied. At both levels of theory, it is predicted that the solvent (ethanol) has an effect of expanding the molecule as there is a slight increase in most of the bond lengths, bond angles and dihedral angles. The predicted dipole moment for Anthracene is almost zero at RHF/6-31G level and zero at B3LYP/6-31G level indicating that the molecule is not polar and the charge distribution is fairly symmetrical. The quadrupole moment predicts that the molecule is slightly elongated along the ZZ axis. Using the total energies, it is predicted that the molecule is slightly more stable in gas phase than in ethanol. The change in polarizability tensors is found to be more pronounced in solution than in the gas phase.
Patterns of structural reflection in the large-cardinal hierarchy

We unveil new patterns of Structural Reflection in the large-cardinal hierarchy below the first measurable cardinal. Namely, we give two different characterizations of strongly unfoldable and subtle cardinals in terms of a weak form of the principle of Structural Reflection, and also in terms of weak product structural reflection. Our analysis prompts the introduction of the new notion of C ( n ) C^{(n)} -strongly unfoldable cardinal for every natural number n n , and we show that these cardinals form a natural hierarchy between strong unfoldable and subtle cardinals analogous to the known hierarchies of C ( n ) C^{(n)} -extendible and Σ n \Sigma _n -strong cardinals. These results show that the relatively low region of the large-cardinal hierarchy comprised between the first strongly unfoldable and the first subtle cardinals is completely analogous to the much higher region between the first strong and the first Woodin cardinals, and also to the much further upper region of the hierarchy ranging between the first supercompact and the first Vopěnka cardinals.
FARE: Provably Fair Representation Learning

Fair representation learning (FRL) is a popular class of methods that can replace 1 the original dataset with a debiased synthetic one, which is then to be used to train 2 fair classiﬁers. However, recent work has shown that prior methods achieve worse 3 accuracy-fairness tradeoffs than originally suggested, dictating the need for FRL 4 methods that provide provable bounds on unfairness of any downstream classi- 5 ﬁer, a challenge yet unsolved. In this work we address this challenge and propose 6 Fairness with Restricted Encoders (FARE), the ﬁrst FRL method with provable 7 fairness guarantees. Our key insight is that restricting the representation space 8 of the encoder enables us to derive fairness guarantees, while allowing empiri- 9 cal accuracy-fairness tradeoffs comparable to prior work. FARE instantiates this 10 idea with a tree-based encoder, a choice motivated by advantages of decision trees 11 when applied in our setting. Crucially, we develop and apply a practical statisti- 12 cal procedure that computes a high-conﬁdence upper bound on the unfairness of 13 any downstream classiﬁer. In our experimental evaluation on several datasets we 14 demonstrate that FARE produces tight upper bounds, often comparable with em- 15 pirical results of prior methods, establishing the practical value of our approach. 16