Miracle-dz/UniFAR-FTUs huggingface dataset:
JWST/NIRSpec Observations of the Planetary Mass Companion TWA 27B

We present 1–5 μm spectroscopy of the young planetary mass companion TWA 27B (2M1207B) performed with NIRSpec on board the James Webb Space Telescope. In these data, the fundamental band of CH4 is absent, and the fundamental band of CO is weak. The nondetection of CH4 reinforces a previously observed trend of weaker CH4 with younger ages among L dwarfs, which has been attributed to enhanced nonequilibrium chemistry among young objects. The weakness of CO may reflect an additional atmospheric property that varies with age, such as the temperature gradient or cloud thickness. We are able to reproduce the broad shape of the spectrum with an ATMO cloudless model that has T eff = 1300 K, nonequilibrium chemistry, and a temperature gradient reduction caused by fingering convection. However, the fundamental bands of CH4 and CO are somewhat stronger in the model. In addition, the model temperature of 1300 K is higher than expected from evolutionary models given the luminosity and age of TWA 27B (T eff = 1200 K). Previous models of young L-type objects suggest that the inclusion of clouds could potentially resolve these issues; it remains to be seen whether cloudy models can provide a good fit to the 1–5 μm data from NIRSpec. TWA 27B exhibits emission in Paschen transitions and the He I triplet at 1.083 μm, which are signatures of accretion that provide the first evidence of a circumstellar disk. We have used the NIRSpec data to estimate the bolometric luminosity of TWA 27B (log L/L ⊙ = −4.466 ± 0.014), which implies a mass of 5–6 M Jup according to evolutionary models.
Quantum duality maps, skein algebras and their ensemble compatibility

We generalize the quantum duality map $\mathbb{I}_{\mathcal{A}}$ of Allegretti--Kim [AK17] for punctured closed surfaces to general marked surfaces. When the marked surface has no interior marked points, we investigate its compatibility with the quantum duality map $\mathbb{I}_{\mathcal{X}}$ on the dual side based on the quantum bracelets basis [Thu14, MQ23]. Our construction factors through reduced stated skein algebras, based on the quantum trace maps [L\^e18] together with an appropriate way of \emph{skein lifting} of integral $\mathcal{A}$-laminations. We also give skein theoretic proofs for some expected properties of Laurent expressions, and positivity of structure constants for marked disks and a marked annulus.
Asymptotic behavior of Kawahara equation with memory effect

In this work, we are interested in a detailed qualitative analysis of the Kawahara equation, a model that has numerous physical motivations such as magneto-acoustic waves in a cold plasma and gravity waves on the surface of a heavy liquid. First, we design a feedback control law, which combines a damping component and another one of finite memory-type. Then, we are capable of proving that the problem is well-posed under a condition involving the feedback gains of the boundary control and the memory kernel. Afterwards, it is shown that the energy associated with this system exponentially decays.
Unconditionally Anonymous Controllable ID-Based Ring Signatures

Abstract ID-Based ring signatures are result of application of ID-Based cryptosystems to ring signature schemes. The contribution of this paper is twofold. At first the paper introduces a new variant of ID-based ring signatures named as controllable ID-Based ring signatures (CIBRS). This new primitive has additional properties like control linkability, anonymous authorship and convertibility. Formal definitions of these properties and the security model for the new primitive were presented. Secondly, the paper presents the first unconditionally anonymous CIBRS scheme from Chow et al.’s [4] scheme. The proposed scheme uses control linkability to achieve high efficiency of computation and storage. In the proposed scheme, to generate k signatures, a member of an n-member-ring needs only n+k scalar point multiplications and 2n+k-1 hash computations when compared to kn operations of Chow et al.’s scheme. Also, to verify k signatures the scheme requires only 2 pairing operations and n+k-1 hash computations compared to 2k and nk operations respectively. Link-signatures due to the proposed scheme are independent of size of the ring. Using the link-signatures our scheme achieves a significant signature size of 3(n+k)-1 compared to 2k(n+1) (in the units of size of the field element). The scheme provides unconditional anonymity and uncontrollability to the signer even in case of compromise of the key-escrow.
SANE: The phases of gradient descent through Sharpness Adjusted Number of Effective parameters

Modern neural networks are undeniably successful. Numerous studies have investigated how the curvature of loss landscapes can affect the quality of solutions. In this work we consider the Hessian matrix during network training. We reiterate the connection between the number of"well-determined"or"effective"parameters and the generalisation performance of neural nets, and we demonstrate its use as a tool for model comparison. By considering the local curvature, we propose Sharpness Adjusted Number of Effective parameters (SANE), a measure of effective dimensionality for the quality of solutions. We show that SANE is robust to large learning rates, which represent learning regimes that are attractive but (in)famously unstable. We provide evidence and characterise the Hessian shifts across"loss basins"at large learning rates. Finally, extending our analysis to deeper neural networks, we provide an approximation to the full-network Hessian, exploiting the natural ordering of neural weights, and use this approximation to provide extensive empirical evidence for our claims.
From Bardeen-boson stars to black holes without event horizon

In a talk given in 2013, S. Hawking conjectured that the event horizon of black holes does not exist and suggested redefining black holes as bound states of the gravitational field. Inspired by this idea, we investigated the coupling of the Bardeen action and a complex scalar field model. Numerically, we obtained a class of boson stars solutions with the magnetic monopole charge $q$. When the constant $q$ exceeds a certain threshold, we observed that as the frequency approaches zero, a critical position $r_c$ emerges where the scalar field concentrates within its interior. Outside this critical position, these boson star solutions tend to infinitely approach what is known as an extreme black hole. However, there is no event horizon present. While our results are model-dependent and their generality remains uncertain, they align well with Hawking's conjecture that real, regular black holes do not have an event horizon and provided valuable insights into the understanding and development of concepts such as fuzzballs, firewalls and black hole soft hairs.
An Improved Coronary Model for One-Dimensional Pressure-Flow Analysis

Estimation of fractional flow reserve (FFR) from computed tomography coronary angiograms (CTCA) can be a useful guide in clinical decision-making for revascularization of specific lesions. FFR appears as one of the most accurate index to assess the functional severity of coronary stenoses [2]. If resistance is minimal and constant, then pressure can be used as a surrogate of flow during maximal hyperaemia (Poiseuille’s Law) [3]. Estimation of FFR from CTCA data can provide a noninvasive method for identifying ischaemia-causing stenosis [4]. The diagnostic performance of this new method relies on the accurate calculation of coronary flow and pressure from acquired CTCA scans, using computational fluid dynamics (CFD), and requires the construction of a realistic physiological model.
Using maximum weighted likelihood to derive Lehmer and H\"older mean families

In this paper, we establish the links between the Lehmer and H\"older mean families and maximum weighted likelihood estimator. Considering the regular one-parameter exponential family of probability density functions, we show that the maximum weighted likelihood of the parameter is a generalized weighted mean family from which Lehmer and H\"older mean families are derived. Some of the outcomes obtained provide a probabilistic interpretation of these mean families and could therefore broaden their uses in various applications.
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm

Multi-step learning applies lookahead over multiple time steps and has proved valuable in policy evaluation settings. However, in the optimal control case, the impact of multi-step learning has been relatively limited despite a number of prior efforts. Fundamentally, this might be because multi-step policy improvements require operations that cannot be approximated by stochastic samples, hence hindering the widespread adoption of such methods in practice. To address such limitations, we introduce doubly multi-step off-policy VI (DoMo-VI), a novel oracle algorithm that combines multi-step policy improvements and policy evaluations. DoMo-VI enjoys guaranteed convergence speed-up to the optimal policy and is applicable in general off-policy learning settings. We then propose doubly multi-step off-policy actor-critic (DoMo-AC), a practical instantiation of the DoMo-VI algorithm. DoMo-AC introduces a bias-variance trade-off that ensures improved policy gradient estimates. When combined with the IMPALA architecture, DoMo-AC has showed improvements over the baseline algorithm on Atari-57 game benchmarks.
DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification

Text simplification is an intralingual translation task in which documents, or sentences of a complex source text are simplified for a target audience. The success of automatic text simplification systems is highly dependent on the quality of parallel data used for training and evaluation. To advance sentence simplification and document simplification in German, this paper presents DEplain, a new dataset of parallel, professionally written and manually aligned simplifications in plain German “plain DE” or in German: “Einfache Sprache”. DEplain consists of a news-domain (approx. 500 document pairs, approx. 13k sentence pairs) and a web-domain corpus (approx. 150 aligned documents, approx. 2k aligned sentence pairs). In addition, we are building a web harvester and experimenting with automatic alignment methods to facilitate the integration of non-aligned and to be-published parallel documents. Using this approach, we are dynamically increasing the web-domain corpus, so it is currently extended to approx. 750 document pairs and approx. 3.5k aligned sentence pairs. We show that using DEplain to train a transformer-based seq2seq text simplification model can achieve promising results. We make available the corpus, the adapted alignment methods for German, the web harvester and the trained models here: https://github.com/rstodden/DEPlain.
Template-free Articulated Neural Point Clouds for Reposable View Synthesis

Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality when synthesizing novel views of time-evolving 3D scenes. However, the common reliance on backward deformation fields makes reanimation of the captured object poses challenging. Moreover, the state of the art dynamic models are often limited by low visual fidelity, long reconstruction time or specificity to narrow application domains. In this paper, we present a novel method utilizing a point-based representation and Linear Blend Skinning (LBS) to jointly learn a Dynamic NeRF and an associated skeletal model from even sparse multi-view video. Our forward-warping approach achieves state-of-the-art visual fidelity when synthesizing novel views and poses while significantly reducing the necessary learning time when compared to existing work. We demonstrate the versatility of our representation on a variety of articulated objects from common datasets and obtain reposable 3D reconstructions without the need of object-specific skeletal templates. Code will be made available at https://github.com/lukasuz/Articulated-Point-NeRF.
Wide & deep learning for spatial & intensity adaptive image restoration

Most existing deep learning-based image restoration methods usually aim to remove degradation with uniform spatial distribution and constant intensity, making insufficient use of degradation prior knowledge. Here we bootstrap the deep neural networks to suppress complex image degradation whose intensity is spatially variable, through utilizing prior knowledge from degraded images. Specifically, we propose an ingenious and efficient multi-frame image restoration network (DparNet) with wide&deep architecture, which integrates degraded images and prior knowledge of degradation to reconstruct images with ideal clarity and stability. The degradation prior is directly learned from degraded images in form of key degradation parameter matrix, with no requirement of any off-site knowledge. The wide&deep architecture in DparNet enables the learned parameters to directly modulate the final restoring results, boosting spatial&intensity adaptive image restoration. We demonstrate the proposed method on two representative image restoration applications: image denoising and suppression of atmospheric turbulence effects in images. Two large datasets, containing 109,536 and 49,744 images respectively, were constructed to support our experiments. The experimental results show that our DparNet significantly outperform SoTA methods in restoration performance and network efficiency. More importantly, by utilizing the learned degradation parameters via wide&deep learning, we can improve the PSNR of image restoration by 0.6~1.1 dB with less than 2% increasing in model parameter numbers and computational complexity. Our work suggests that degraded images may hide key information of the degradation process, which can be utilized to boost spatial&intensity adaptive image restoration.
CDJUR-BR - A Golden Collection of Legal Document from Brazilian Justice with Fine-Grained Named Entities

A basic task for most Legal Artificial Intelligence (Legal AI) applications is Named Entity Recognition (NER). However, texts produced in the context of legal practice make references to entities that are not trivially recognized by the currently available NERs. There is a lack of categorization of legislation, jurisprudence, evidence, penalties, the roles of people in a legal process (judge, lawyer, victim, defendant, witness), types of locations (crime location, defendant's address), etc. In this sense, there is still a need for a robust golden collection, annotated with fine-grained entities of the legal domain, and which covers various documents of a legal process, such as petitions, inquiries, complaints, decisions and sentences. In this article, we describe the development of the Golden Collection of the Brazilian Judiciary (CDJUR-BR) contemplating a set of fine-grained named entities that have been annotated by experts in legal documents. The creation of CDJUR-BR followed its own methodology that aimed to attribute a character of comprehensiveness and robustness. Together with the CDJUR-BR repository we provided a NER based on the BERT model and trained with the CDJUR-BR, whose results indicated the prevalence of the CDJUR-BR.
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods

Supervised training of deep neural networks on pairs of clean image and noisy measurement achieves state-of-the-art performance for many image reconstruction tasks, but such training pairs are difficult to collect. Self-supervised methods enable training based on noisy measurements only, without clean images. In this work, we investigate the cost of self-supervised training in terms of sample complexity for a class of self-supervised methods that enable the computation of unbiased estimates of gradients of the supervised loss, including noise2noise methods. We analytically show that a model trained with such self-supervised training is as good as the same model trained in a supervised fashion, but self-supervised training requires more examples than supervised training. We then study self-supervised denoising and accelerated MRI empirically and characterize the cost of self-supervised training in terms of the number of additional samples required, and find that the performance gap between self-supervised and supervised training vanishes as a function of the training examples, at a problem-dependent rate, as predicted by our theory.
Ultraviolet sensitivity of Peccei-Quinn inflation

The radial direction of the Peccei--Quinn field can drive cosmic inflation, given a non-minimal coupling to gravity. This scenario has been considered to simultaneously explain inflation, the strong $CP$ problem, and dark matter. We argue that Peccei--Quinn inflation is extremely sensitive to higher-dimensional operators. Further combining with the discussion on the axion quality required for solving the strong $CP$ problem, we examine the validity of this scenario. We also show that after Peccei--Quinn inflation, resonant amplifications of the field fluctuation is inevitably triggered.
A Schnyder-type drawing algorithm for 5-connected triangulations

We define some Schnyder-type combinatorial structures on a class of planar triangulations of the pentagon which are closely related to 5-connected triangulations. The combinatorial structures have three incarnations defined in terms of orientations, corner-labelings, and woods respectively. The wood incarnation consists in 5 spanning trees crossing each other in an orderly fashion. Similarly as for Schnyder woods on triangulations, it induces, for each vertex, a partition of the inner triangles into face-connected regions (5~regions here). We show that the induced barycentric vertex-placement, where each vertex is at the barycenter of the 5 outer vertices with weights given by the number of faces in each region, yields a planar straight-line drawing.
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization

The popularity of bi-level optimization (BO) in deep learning has spurred a growing interest in studying gradient-based BO algorithms. However, existing algorithms involve two coupled learning rates that can be affected by approximation errors when computing hypergradients, making careful fine-tuning necessary to ensure fast convergence. To alleviate this issue, we investigate the use of recently proposed adaptive step-size methods, namely stochastic line search (SLS) and stochastic Polyak step size (SPS), for computing both the upper and lower-level learning rates. First, we revisit the use of SLS and SPS in single-level optimization without the additional interpolation condition that is typically assumed in prior works. For such settings, we investigate new variants of SLS and SPS that improve upon existing suggestions in the literature and are simpler to implement. Importantly, these two variants can be seen as special instances of general family of methods with an envelope-type step-size. This unified envelope strategy allows for the extension of the algorithms and their convergence guarantees to BO settings. Finally, our extensive experiments demonstrate that the new algorithms, which are available in both SGD and Adam versions, can find large learning rates with minimal tuning and converge faster than corresponding vanilla SGD or Adam BO algorithms that require fine-tuning.
Sensitivity of Slot-Based Object-Centric Models to their Number of Slots

Self-supervised methods for learning object-centric representations have recently been applied successfully to various datasets. This progress is largely fueled by slot-based methods, whose ability to cluster visual scenes into meaningful objects holds great promise for compositional generalization and downstream learning. In these methods, the number of slots (clusters) $K$ is typically chosen to match the number of ground-truth objects in the data, even though this quantity is unknown in real-world settings. Indeed, the sensitivity of slot-based methods to $K$, and how this affects their learned correspondence to objects in the data has largely been ignored in the literature. In this work, we address this issue through a systematic study of slot-based methods. We propose using analogs to precision and recall based on the Adjusted Rand Index to accurately quantify model behavior over a large range of $K$. We find that, especially during training, incorrect choices of $K$ do not yield the desired object decomposition and, in fact, cause substantial oversegmentation or merging of separate objects (undersegmentation). We demonstrate that the choice of the objective function and incorporating instance-level annotations can moderately mitigate this behavior while still falling short of fully resolving this issue. Indeed, we show how this issue persists across multiple methods and datasets and stress its importance for future slot-based models.
Optimizing Airbnb Search Journey with Multi-task Learning

At Airbnb, an online marketplace for stays and experiences, guests often spend weeks exploring and comparing multiple items before making a final reservation request. Each reservation request may then potentially be rejected or cancelled by the host prior to check-in. The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking. In this paper, we present Journey Ranker, a new multi-task deep learning model architecture that addresses these challenges. Journey Ranker leverages intermediate guest actions as milestones, both positive and negative, to better progress the guest towards a successful booking. It also uses contextual information such as guest state and search query to balance guest and host preferences. Its modular and extensible design, consisting of four modules with clear separation of concerns, allows for easy application to use cases beyond the Airbnb search ranking context. We conducted offline and online testing of the Journey Ranker and successfully deployed it in production to four different Airbnb products with significant business metrics improvements.
Variational Quantum Algorithm based circuit that implements the Toffoli gate with multi inputs

The prime objective of this study is to seek a circuit diagram for a multi-inputs Toffoli gate including only single qubit gates and CNOTs. In this regard, we have developed two variational quantum algorithms that can be used to implement a multi-inputs Toffoli gate. The cost functions of these two VQAs are derived by using the Hilbert Schmidt inner product and the expected value of an observable that can capture the difference between the inputs and outputs of a Toffoli gate. We employ two ansatz circuit architectures and use the PennyLane package to execute the optimization.
Fashion Object Detection for Tops & Bottoms

Fashion is one of the largest world's industries and computer vision techniques have been becoming more popular in recent years, in particular, for tasks such as object detection and apparel segmentation. Even with the rapid growth in computer vision solutions, specifically for the fashion industry, many problems are far for being resolved. Therefore, not at all times, adjusting out-of-the-box pre-trained computer vision models will provide the desired solution. In the present paper is proposed a pipeline that takes a noisy image with a person and specifically detects the regions with garments that are bottoms or tops. Our solution implements models that are capable of finding human parts in an image e.g. full-body vs half-body, or no human is found. Then, other models knowing that there's a human and its composition (e.g. not always we have a full-body) finds the bounding boxes/regions of the image that very likely correspond to a bottom or a top. For the creation of bounding boxes/regions task, a benchmark dataset was specifically prepared. The results show that the Mask RCNN solution is robust, and generalized enough to be used and scalable in unseen apparel/fashion data.