Miracle-dz/UniFAR-FTUs huggingface dataset:
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning

We present 360-MLC, a self-training method based on multi-view layout consistency for finetuning monocular room-layout models using unlabeled 360-images only. This can be valuable in practical scenarios where a pre-trained model needs to be adapted to a new data domain without using any ground truth annotations. Our simple yet effective assumption is that multiple layout estimations in the same scene must define a consistent geometry regardless of their camera positions. Based on this idea, we leverage a pre-trained model to project estimated layout boundaries from several camera views into the 3D world coordinate. Then, we re-project them back to the spherical coordinate and build a probability function, from which we sample the pseudo-labels for self-training. To handle unconfident pseudo-labels, we evaluate the variance in the re-projected boundaries as an uncertainty value to weight each pseudo-label in our loss function during training. In addition, since ground truth annotations are not available during training nor in testing, we leverage the entropy information in multiple layout estimations as a quantitative metric to measure the geometry consistency of the scene, allowing us to evaluate any layout estimator for hyper-parameter tuning, including model selection without ground truth annotations. Experimental results show that our solution achieves favorable performance against state-of-the-art methods when self-training from three publicly available source datasets to a unique, newly labeled dataset consisting of multi-view of the same scenes.
Fates of the dense cores formed by fragmentation of filaments: do they fragment again or not?

Fragmentation of filaments into dense cores is thought to be an important step in forming stars. The bar-mode instability of spherically collapsing cores found in previous linear analysis invokes a possibility of re-fragmentation of the cores due to their ellipsoidal (prolate or oblate) deformation. To investigate this possibility, here we perform three-dimensional self-gravitational hydrodynamics simulations that follow all the way from filament fragmentation to subsequent core collapse. We assume the gas is polytropic with index \gamma, which determines the stability of the bar-mode. For the case that the fragmentation of isolated hydrostatic filaments is triggered by the most unstable fragmentation mode, we find the bar mode grows as collapse proceeds if \gamma < 1.1, in agreement with the linear analysis. However, it takes more than ten orders-of-magnitude increase in the central density for the distortion to become non-linear. In addition to this fiducial case, we also study non-fiducial ones such as the fragmentation is triggered by a fragmentation mode with a longer wavelength and it occurs during radial collapse of filaments and find the distortion rapidly grows. In most of astrophysical applications, the effective polytropic index of collapsing gas exceeds 1.1 before ten orders-of-magnitude increase in the central density. Thus, supposing the fiducial case of filament fragmentation, re-fragmentation of dense cores would not be likely and their final mass would be determined when the filaments fragment.
Interplay between geostrophic vortices and inertial waves in precession-driven turbulence

The properties of rotating turbulence driven by precession are studied using direct numerical simulations and analysis of the underlying dynamical processes in Fourier space. The study is carried out in the local rotating coordinate frame, where precession gives rise to a background shear flow, which becomes linearly unstable and breaks down into turbulence. We observe that this precession-driven turbulence is in general characterized by coexisting two-dimensional (2D) columnar vortices and three-dimensional (3D) inertial waves, whose relative energies depend on the precession parameter Po. The vortices resemble the typical condensates of geostrophic turbulence, are aligned along the rotation axis (with zero wavenumber in this direction, kz = 0), and are fed by the 3D waves through nonlinear transfer of energy, while the waves (with [Formula: see text]) in turn are directly fed by the precessional instability of the background flow. The vortices themselves undergo inverse cascade of energy and exhibit anisotropy in Fourier space. For small Po < 0.1 and sufficiently high Reynolds numbers, the typical regime for most geo- and astrophysical applications, the flow exhibits strongly oscillatory (bursty) evolution due to the alternation of vortices and small-scale waves. On the other hand, at larger Po > 0.1 turbulence is quasi-steady with only mild fluctuations, the coexisting columnar vortices and waves in this state give rise to a split (simultaneous inverse and forward) cascade. Increasing the precession magnitude causes a reinforcement of waves relative to vortices with the energy spectra approaching the Kolmogorov scaling, and therefore, the precession mechanism counteracts the effects of the rotation.
Symmetry preserving contact interaction treatment of the kaon

A symmetry-preserving regularization procedure for dealing with the contact interaction model is proposed in this work. This regularization procedure follows a series of consistency conditions which are necessary to maintain gauge symmetry. Under this regularization, proofs for the preservation of the Ward-Takahashi identities are given and the loop integrals in the contact interaction model are systematically computed. As an application example, the kaon electromagnetic form factor and K l 3 transition form factor are computed and self-consistent results are obtained. Since the proposed regularization properly handles the divergence, one is freed from the inconsistencies caused by the regularization and can concentrate more on the physical discussion.
Near-infrared spectra of ISO selected Chamaeleon I young stellar objects

We present 0.95{2.5 mm oderate (R 500) resolution spectra of 19 ISOCAM detected sources in the Chamaeleon I dark cloud. Thirteen of these stars are candidate very low mass members of the cloud proposed by Persi et al. (2000) on basis of the mid-IR color excess. The sample also includes a bona-de young brown dwarf (Cha H 1), a transition { stellar/sub-stellar { object (Cha H 2), one previously known T Tauri star (Sz 33) and three ISOCAM sources with no mid-IR excess. The spectra of the mid-IR color excess sources are relatively flat and featureless in this wavelength range. Both atomic and molecular lines (when in absorption) are partially veiled suggesting the presence of continuum emission from circumstellar dust. In addition some of the sources show Paschen and Brackett lines in emission. We apply the 2 m water vapor index dened by Wilking et al. (1999) to estimate spectral types. These stars have spectral types M0{8. We use Persi et al.'s stellar luminosity determinations, in combination with D'Antona & Mazzitelli latest pre-main sequence evolutionary tracks, to estimate masses and ages. The ISOCAM detected mid-IR excess sources have sub-solar masses down to the H-burning limit and a median age of few10 6 yr, in good agreement with the higher mass members of this cloud.
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions

One of the most important AI research questions is to trade off computation versus performance since ``perfect rationality"exists in theory but is impossible to achieve in practice. Recently, Monte-Carlo tree search (MCTS) has attracted considerable attention due to the significant performance improvement in various challenging domains. However, the expensive time cost during search severely restricts its scope for applications. This paper proposes the Virtual MCTS (V-MCTS), a variant of MCTS that spends more search time on harder states and less search time on simpler states adaptively. We give theoretical bounds of the proposed method and evaluate the performance and computations on $9 \times 9$ Go board games and Atari games. Experiments show that our method can achieve comparable performances to the original search algorithm while requiring less than $50\%$ search time on average. We believe that this approach is a viable alternative for tasks under limited time and resources. The code is available at \url{https://github.com/YeWR/V-MCTS.git}.
Investigating Input Modality and Task Geometry on Precision-first 3D Drawing in Virtual Reality

Accurately drawing non-planar 3D curves in immersive Virtual Reality (VR) is indispensable for many precise 3D tasks. However, due to lack of physical support, limited depth perception, and the non-planar nature of 3D curves, it is challenging to adjust mid-air strokes to achieve high precision. Instead of creating new interaction techniques, we investigated how task geometric shapes and input modalities affect precision-first drawing performance in a within-subject study (n=12) focusing on 3D target tracing in commercially available VR headsets. We found that compared to using bare hands, VR controllers and pens yield nearly 30% of precision gain, and that the tasks with large curvature, forward-backward or left-right orientations perform best. We finally discuss opportunities for designing novel interaction techniques for precise 3D drawing. We believe that our work will benefit future research aiming to create usable toolboxes for precise 3D drawing.
Precision Machine Learning

We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data. We find that neural networks (NNs) can often outperform classical approximation methods on high-dimensional examples, by (we hypothesize) auto-discovering and exploiting modular structures therein. However, neural networks trained with common optimizers are less powerful for low-dimensional cases, which motivates us to study the unique properties of neural network loss landscapes and the corresponding optimization challenges that arise in the high precision regime. To address the optimization issue in low dimensions, we develop training tricks which enable us to train neural networks to extremely low loss, close to the limits allowed by numerical precision.
Exploring Representation-level Augmentation for Code Search

Code search, which aims at retrieving the most relevant code fragment for a given natural language query, is a common activity in software development practice. Recently, contrastive learning is widely used in code search research, where many data augmentation approaches for source code (e.g., semantic-preserving program transformation) are proposed to learn better representations. However, these augmentations are at the raw-data level, which requires additional code analysis in the preprocessing stage and additional training cost in the training stage. In this paper, we explore augmentation methods that augment data (both code and query) at representation level which does not require additional data processing and training, and based on this we propose a general format of representation-level augmentation that unifies existing methods. Then, we propose three new augmentation methods (linear extrapolation, binary interpolation, and Gaussian scaling) based on the general format. Furthermore, we theoretically analyze the advantages of the proposed augmentation methods over traditional contrastive learning methods on code search. We experimentally evaluate the proposed representation-level augmentation methods with state-of-the-art code search models on a large-scale public dataset consisting of six programming languages. The experimental results show that our approach can consistently boost the performance of the studied code search models.
Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning

Current methods for 3D object reconstruction from a set of planar cross-sections still struggle to capture detailed topology or require a considerable number of cross-sections. In this paper, we present, to the best of our knowledge the first 3D shape reconstruction network to solve this task which additionally uses orthographic projections of the shape. Our method is based on applying a Reinforcement Learning algorithm to learn how to effectively parse the shape using a trial-and-error scheme relying on scalar rewards. This method cuts a part of a 3D shape in each step which is then approximated as a polygon mesh. The agent aims to maximize the reward that depends on the accuracy of surface reconstruction for the approximated parts. We also consider pre-training of the network for faster learning using demonstrations generated by a heuristic approach. Experiments show that our training algorithm which benefits from both imitation learning and also self exploration, learns efficient policies faster, which results the agent to produce visually compelling results.
The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative

Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics. Rather than rules that operate on lexical items, it posits constructions as the central building blocks of language, i.e., linguistic units of different granularity that combine syntax and semantics. As a first step towards assessing the compatibility of CxG with the syntactic and semantic knowledge demonstrated by state-of-the-art pretrained language models (PLMs), we present an investigation of their capability to classify and understand one of the most commonly studied constructions, the English comparative correlative (CC). We conduct experiments examining the classification accuracy of a syntactic probe on the one hand and the models’ behaviour in a semantic application task on the other, with BERT, RoBERTa, and DeBERTa as the example PLMs. Our results show that all three investigated PLMs are able to recognise the structure of the CC but fail to use its meaning. While human-like performance of PLMs on many NLP tasks has been alleged, this indicates that PLMs still suffer from substantial shortcomings in central domains of linguistic knowledge.
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices

Behaviour biometrics are being explored as a viable alternative to overcome the limitations of traditional authentication methods such as passwords and static biometrics. Also, they are being considered as a viable authentication method for IoT devices such as smart headsets, wearables, and erables, that do not have a large form factor or the ability to seamlessly interact with the user. Recent behavioural biometric solutions use deep learning models that require large amounts of annotated training data. Collecting such volumes of behaviour biometrics data raises privacy and usability concerns. To this end, we propose using SimSiam-based non-contrastive self-supervised learning to improve the label efficiency of behavioural biometric systems. The key idea is to use large volumes of unlabelled (and anonymised) data to build good feature extractors that can be subsequently used in supervised settings. Using two EEG datasets, we show that at lower amounts of labelled data, non-contrastive learning performs 4%--11% more than conventional methods such as supervised learning and data augmentation. We also show that self-supervised learning methods perform better than other baselines.
Observations of Upward Propagating Waves in the Transition Region and Corona above Sunspots

We present observations of persistent oscillations of some bright features in the upper-chromosphere/transition region above sunspots taken by IRIS SJ 1400 Å and upward propagating quasi-periodic disturbances along coronal loops rooted in the same region taken by the AIA 171 Å passband. The oscillations of the features are cyclic oscillatory motions without any obvious damping. The amplitudes of the spatial displacements of the oscillations are about 1″. The apparent velocities of the oscillations are comparable to the sound speed in the chromosphere, but the upward motions are slightly larger than that of the downward. The intensity variations can take 24%–53% of the background, suggesting nonlinearity of the oscillations. The FFT power spectra of the oscillations show a dominant peak at a period of about 3 minutes, which is consistent with the omnipresent 3 minute oscillations in sunspots. The amplitudes of the intensity variations of the upward propagating coronal disturbances are 10%–15% of the background. The coronal disturbances have a period of about 3 minutes, and propagate upward along the coronal loops with apparent velocities in a range of 30 ∼ 80 km s−1. We propose a scenario in which the observed transition region oscillations are powered continuously by upward propagating shocks, and the upward propagating coronal disturbances can be the recurrent plasma flows driven by shocks or responses of degenerated shocks that become slow magnetic-acoustic waves after heating the plasma in the coronal loops at their transition-region bases.
Interactive Inference: A Multi-Agent Model of Cooperative Joint Actions

We advance a novel computational model of multi-agent, cooperative joint actions that is grounded in the cognitive framework of active inference. The model assumes that to solve a joint task, such as pressing together a red or blue button, two (or more) agents engage in a process of interactive inference. Each agent maintains probabilistic beliefs about the joint goal (e.g., Should we press the red or blue button? ) and updates them by observing the other agent’s movements, while in turn selecting movements that make his own intentions legible and easy to infer by the other agent (i.e., sensorimotor communication). Over time, the interactive inference aligns both the beliefs and the behavioral strategies of the agents, hence ensuring the success of the joint action. We exemplify the functioning of the model in two simulations. The first simulation illustrates a “leaderless” joint action. It shows that when two agents lack a strong preference about their joint task goal, they jointly infer it by observing each other’s movements. In turn, this helps the interactive alignment of their beliefs and behavioral strategies. The second simulation illustrates a “leader–follower” joint action. It shows that when one agent (“leader”) knows the true joint goal, it uses sensorimotor communication to help the other agent (“follower”) infer it, even if doing this requires selecting a more costly individual plan. These simulations illustrate that interactive inference supports successful multi-agent joint actions and reproduces key cognitive and behavioral dynamics of “leaderless” and “leader–follower” joint actions observed in human–human experiments. In sum, interactive inference provides a cognitively inspired, formal framework to realize cooperative joint actions and consensus in multi-agent systems.
Generic curves and non-coprime Catalans

We compute the Poincar\'e polynomials of the compactified Jacobians for plane curve singularities with Puiseaux exponents $(nd,md,md+1)$, and relate them to the combinatorics of $q,t$-Catalan numbers in the non-coprime case. We also confirm a conjecture of Cherednik and Danilenko for such curves.
A First Look at CI/CD Adoptions in Open-Source Android Apps

Continuous Integration (CI) and Continuous Delivery (CD) have been demonstrated to be effective in facilitating software building, testing, and deployment. Many research studies have investigated and subsequently improved their working processes. Unfortunately, such research efforts have largely not touched on the usage of CI/CD in the development of Android apps. We fill this gap by conducting an exploratory study of CI/CD adoption in open-source Android apps. We start by collecting a set of 84,475 open-source Android apps from the most popular three online code hosting sites, namely Github, GitLab, and Bitbucket. We then look into those apps and find that (1) only around 10% of apps have leveraged CI/CD services, i.e., the majority of open-source Android apps are developed without accessing CI/CD services, (2) a small number of apps (291) has even adopted multiple CI/CD services, (3) nearly half of the apps adopted CI/CD services have not really used them, and (4) CI/CD services are useful to improve the popularity of projects.
Formation and evolution of dwarf galaxies in the CDM Universe

We first review the results of the tidal stirring model for the transformation of gas-rich dwarf irregulars into dwarf spheroidals, which turns rotationally supported stellar systems into pressure supported ones. We emphasize the importance of the combined effect of ram pressure stripping and heating from the cosmic ultraviolet background in removing the gas and converting the object into a gas poor system as dSphs. We discuss how the timing of infall of dwarfs into the primary halo determines the final mass-to-light ratio and star formation history. Secondly we review the results of recent cosmological simulations of the formation of gas-rich dwarfs. These simulations are finally capable to produce a realistic object with no bulge, an exponential profile and a slowly rising rotation curve. The result owes to the inclusion of an inhomogeneous ISM and a star formation scheme based on regions having the typical density of molecular cloud complexes. Supernovae-driven winds become more effective in such mode, driving low angular momentum baryons outside the virial radius at high redshift and turning the dark matter cusp into a core. Finally we show the first tidal stirring experiments adopting dwarfs formed in cosmological simulations as initial conditions. The latter are gas dominated and have turbulent thick gaseous and stellar disks that cannot develop strong bars, yet they are efficiently heated into spheroids by tidal shocks.
A BROKEN SOLAR TYPE II RADIO BURST INDUCED BY A CORONAL SHOCK PROPAGATING ACROSS THE STREAMER BOUNDARY

We discuss an intriguing type II radio burst that occurred on 2011 March 27. The dynamic spectrum was featured by a sudden break at about 43 MHz on the well-observed harmonic branch. Before the break, the spectrum drifted gradually with a mean rate of about -0.05 MHz s(-1). Following the break, the spectrum jumped to lower frequencies. The post-break emission lasted for about 3 minutes. It consisted of an overall slow drift which appeared to have a few fast-drift sub-bands. Simultaneous observations from the Solar TErrestrial RElations Observatory and the Solar Dynamics Observatory were also available and are examined for this event. We suggest that the slow-drift period before the break was generated inside a streamer by a coronal eruption driven shock, and the spectral break as well as the relatively wide spectrum after the break is a consequence of the shock crossing the streamer boundary where density drops abruptly. It is suggested that this type of radio bursts can be taken as a unique diagnostic tool for inferring the coronal density structure, as well as the radio-emitting source region.