Miracle-dz/UniFAR-FTUs huggingface dataset:
Exemplar-Based Image Colorization with A Learning Framework

Image learning and colorization are hot spots in multimedia domain. Inspired by the learning capability of humans, in this paper, we propose an automatic colorization method with a learning framework. This method can be viewed as a hybrid of exemplar-based and learning-based method, and it decouples the colorization process and learning process so as to generate various color styles for the same gray image. The matching process in the exemplar-based colorization method can be regarded as a parameterized function, and we employ a large amount of color images as the training samples to fit the parameters. During the training process, the color images are the ground truths, and we learn the optimal parameters for the matching process by minimizing the errors in terms of the parameters for the matching function. To deal with images with various compositions, a global feature is introduced, which can be used to classify the images with respect to their compositions, and then learn the optimal matching parameters for each image category individually. What's more, a spatial consistency based post-processing is design to smooth the extracted color information from the reference image to remove matching errors. Extensive experiments are conducted to verify the effectiveness of the method, and it achieves comparable performance against the state-of-the-art colorization algorithms.
Vision Transformers for Action Recognition: A Survey

Vision transformers are emerging as a powerful tool to solve computer vision problems. Recent techniques have also proven the efficacy of transformers beyond the image domain to solve numerous video-related tasks. Among those, human action recognition is receiving special attention from the research community due to its widespread applications. This article provides the first comprehensive survey of vision transformer techniques for action recognition. We analyze and summarize the existing and emerging literature in this direction while highlighting the popular trends in adapting transformers for action recognition. Due to their specialized application, we collectively refer to these methods as ``action transformers''. Our literature review provides suitable taxonomies for action transformers based on their architecture, modality, and intended objective. Within the context of action transformers, we explore the techniques to encode spatio-temporal data, dimensionality reduction, frame patch and spatio-temporal cube construction, and various representation methods. We also investigate the optimization of spatio-temporal attention in transformer layers to handle longer sequences, typically by reducing the number of tokens in a single attention operation. Moreover, we also investigate different network learning strategies, such as self-supervised and zero-shot learning, along with their associated losses for transformer-based action recognition. This survey also summarizes the progress towards gaining grounds on evaluation metric scores on important benchmarks with action transformers. Finally, it provides a discussion on the challenges, outlook, and future avenues for this research direction.
Moral Hazard on Productivity Among Work-From-Home Workers Amid the COVID-19 Pandemic (preprint)

After the outbreak of COVID 19, firms appear to monitor Work From Home (WFH) workers more than ever out of anxiety that workers may shirk at home or implement moral hazard at home. Using the Survey of Working Arrangements and Attitudes (SWAA, Barrero et al., 2021), the evidence of WFH workers' ex post moral hazard as well as its specific aspects are examined. The results show that the ex post moral hazard among the WFH workers is generally found. Interestingly, however, the moral hazard on specific type of productivity, efficiency, is not detected for the workers at firms with WFH friendly policy for long term. Moreover, the advantages &challenges for the WFH culture report that workers with health or disability issues improve their productivity, whereas certain conditions specific to the WFH environment must be met.
Standard Stars for Linear Polarization Observed with FORS1

We present the analysis of the observations of standard stars for linear polarization obtained from 1999 to 2005 within the context of the calibration plan of the FORS1 instrument of the ESO VLT. We have considered observations carried out both in imaging polarimetric and in spectropolarimetric mode. Broadband polarization was obtained in the Bessel BV RI filters; spectropolarimetry was obtained with various grisms covering different optical ranges and with a typical resolution of a few hundreds. Spectropolarimetric data have been convolved with the transmission functions of the Bessel filters, which enabled us to calculate polarization values equivalent to broadband polarization measurements in imaging mode. Finally, for each star, instrument mode, and for each Bessel filter band, we have calculated an average polarization value obtained after filtering all available data with a kσ-clipping algorithm.
04 10 03 7 v 2 2 M ay 2 00 5 Analysis of dephasing mechanisms in a standing wave dipole trap

We study in detail the mechanisms causing dephasing of hyperfine coherences of cesium atoms confined by a far off-resonant standing wave optical dipole trap [S. Kuhr et al., Phys. Rev. Lett. 91, 213002 (2003)]. Using Ramsey spectroscopy and spin echo techniques, we measure the reversible and irreversible dephasing times of the ground state coherences. We present an analytical model to interpret the experimental data and identify the homogeneous and inhomogeneous dephasing mechanisms. Our scheme to prepare and detect the atomic hyperfine state is applied at the level of a single atom as well as for ensembles of up to 50 atoms.
PR ] 5 A pr 2 00 4 DIFFUSION-LIMITED AGGREGATION ON A TREE by

We study the following growth model on a regular d-ary tree. Points at distance n adjacent to the existing subtree are added with probabilities proportional to α, where α < 1 is a positive real parameter. The heights of these clusters are shown to increase linearly with their total size; this complements known results that show the height increases only logarithmically when α ≥ 1. Results are obtained using stochastic monotonicity and regeneration results which may be of independent interest. Our motivation comes from two other ways in which the model may be viewed: as a problem in first-passage percolation, and as a version of diffusion-limited aggregation (DLA), adjusted so that “fingering” occurs.
$Z$ boson decays $Z\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ and Higgs boson decays $h\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ with lepton flavor violation in a $U(1)$ extension of the MSSM

$U(1)_X$SSM is the extension of the minimal supersymmetric standard model (MSSM) and its local gauge group is $SU(3)_C\times SU(2)_L \times U(1)_Y \times U(1)_X$. We study lepton flavor violating (LFV) decays $Z\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$($Z\rightarrow e{\mu}$, $Z\rightarrow e{\tau}$, and $Z\rightarrow {\mu}{\tau}$) and $h\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$($h\rightarrow e{\mu}$, $h\rightarrow e{\tau}$, and $h\rightarrow {\mu}{\tau}$), in this model. In the numerical results, the branching ratios of $Z\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ are from $10^{-9}$ to $10^{-13}$ and the branching ratios of $h\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$ are from $10^{-3}$ to $10^{-9}$, which can approach the present experimental upper bounds. Based on the latest experimental data, we analyze the influence of different sensitive parameters on the branching ratio, and make reasonable predictions for future experiments. The main sensitive parameters and LFV sources are the non-diagonal elements corresponding to the initial and final generations of leptons, which can be seen from the numerical analysis.
The origin of the [C II] emission in the S140 PDRs - new insights from HIFI

Using Herschel's HIFI instrument we have observed [C II] along a cut through S140 and high-J transitions of CO and HCO+ at two positions on the cut, corresponding to the externally irradiated ionization front and the embedded massive star forming core IRS1. The HIFI data were combined with available ground-based observations and modeled using the KOSMA-tau model for photon dominated regions. Here we derive the physical conditions in S140 and in particular the origin of [C II] emission around IRS1. We identify three distinct regions of [C II] emission from the cut, one close to the embedded source IRS1, one associated with the ionization front and one further into the cloud. The line emission can be understood in terms of a clumpy model of photon-dominated regions. At the position of IRS1, we identify at least two distinct components contributing to the [C II] emission, one of them a small, hot component, which can possibly be identified with the irradiated outflow walls. This is consistent with the fact that the [C II] peak at IRS1 coincides with shocked H2 emission at the edges of the outflow cavity. We note that previously available observations of IRS1 can be well reproduced by a single-component KOSMA-tau model. Thus it is HIFI's unprecedented spatial and spectral resolution, as well as its sensitivity which has allowed us to uncover an additional hot gas component in the S140 region.
A Small but Informed and Diverse Model: The Case of the Multimodal GuessWhat!? Guessing Game

Pre-trained Vision and Language Transformers achieve high performance on downstream tasks due to their ability to transfer representational knowledge accumulated during pretraining on substantial amounts of data. In this paper, we ask whether it is possible to compete with such models using features based on transferred (pre-trained, frozen) representations combined with a lightweight architecture. We take a multimodal guessing task as our testbed, GuessWhat? An ensemble of our lightweight model matches the performance of the finetuned pre-trained transformer (LXMERT). An uncertainty analysis of our ensemble shows that the lightweight transferred representations close the data uncertainty gap with LXMERT, while retaining model diversity leading to ensemble boost. We further demonstrate that LXMERT’s performance gain is due solely to its extra V&L pretraining rather than because of architectural improvements. These results argue for flexible integration of multiple features and lightweight models as a viable alternative to large, cumbersome, pre-trained models.
Analysis of Similes in Serbian Literary Texts (1860-1920) using computational methods

Similes are rhetorical figures which play an important role in literary texts. This paper presents a finite-state methodology developed for the description of adjectival similes, which enables their retrieval and annotation in Serbian novels written in the mid-19th and early 20th centuries. The results of a textometric analysis reveal the most frequent adjectival similes and the specificity of their usage, with respect to the author, title, or publication date, in a subset of the SrpELTeC corpus.
An Auxiliary/main Verb Distinction in the Grammar of Faroese Children

Faroese is a Scandinavian language spoken as a first language by the approximately 49,000 inhabitants of the Faroe Islands, a partly autonomous dependency of Denmark, and by several thousand Faroese living outside the islands, mainly in Denmark.i All of the Scandinavian languages are SVO and exhibit verb-second (V2) word order in main clauses, and Faroese is not an exception. One point on which the syntax of these languages does differ, however, is that Icelandic exhibits the phenomenon sometimes knows as “V-to-I” or “V-toT” in subordinate clauses, while in the Mainland Scandinavian languages (at least, in the standard varieties) even the finite verb remains in a low position, as diagnosed by its order with respect to negation or to sentence-medial adverbs. Thus while a simple main clause in Icelandic shows the same word order as the corresponding example in Danish (1a–b), in a subordinate clause in Icelandic the finite verb appears above negation while in Danish it appears below it (2a–b).
Mechanisms of inkjet printing in a liquid environment

Abstract Inkjet printing, originally invented for text and pattern printing, is now central to many industrial applications, such as printed electronics, flexible electronics, 3-D printing of mechanical and even biological devices. However, constrained by the droplets’ ejection mechanism, the accuracy of traditional inkjet printing is limited by the size of its orifice, and it is difficult to achieve a volume of droplets at the femtolitre scale, which hinders its further application in the above fields. To this end, we propose the confined interface vibration inkjet printing (CIVIJP) technique, which is capable of printing patterns in a liquid environment with droplet size much smaller than the orifice from which they are dispensed. Here, further systematic study of the mechanism of printing in a liquid environment was carried out with the assistance of a high-speed imaging technique. It is found for the first time that a single pulse stimulation applied on the piezo-ceramic of the inkjet nozzle can trigger damping oscillations of the oil/water interface confined by the orifice, which can last more than 500 $\mathrm {\mu }$s. By adjusting the intensity of single pulse stimulation, the size and quantity of the dispensed droplets can be controlled in a wide range, which is obviously different from traditional droplet ejection in a gaseous environment. This work reveals the underlying physics between the pulse stimulation and the interface behaviours, as well as the physics between the interface behaviours and the size and number of dispensed droplets, enriching the fundamental theory of the inkjet printing in liquid phase.
A design-space minimum sampling strategy based on proper-orthogonal-decomposition projection

A proper-orthogonal-decomposition (POD)-based procedure has been developed to identify the tests/experiments strictly needed for modelling a complex system. The case here studied concerns the aerodynamic efficiency response of a low-pressure-turbine cascade to the variation of the most influencing flow parameters. A large number of experimental tests have been carried out to sample the design space. POD has been applied to reduce data dimensionality, identifying the rank of the problem and defining a vector subspace where data are as compact as possible. Lasso and Gaussian process regressions, complemented by cross-validation and marginal-likelihood criteria, have been used to learn parsimonious efficiency trends in the POD subspace. It will be shown that a model educated with the most orthogonal combination of data projected in the POD space performs as well as the model educated with the entire dataset. Then, it will be shown that the strictly needed tests can be iteratively localized (a priori) in the design space by means of the data-driven procedure here proposed, thus allowing for an active application. This will provide significant time reduction in the execution of tests and can be generalized to other complex systems, either of an experimental or numerical nature, even pertaining to other disciplines.
Fundamental constraints on broadband passive acoustic treatments in unidimensional scattering problems

In a passive lossy acoustical system, sum rules derived from passivity explicitly relate the broadband response to the spatial dimension of the system, which provide important design criteria as well. In this work, the theory of Herglotz function is applied systematically to derive sum rules for unidimensional scattering problems relying on passive acoustic treatments which are generally made of rigid, motionless and subwavelength structures saturated by air. The rigid-boundary reflection, soft-boundary reflection and transmission problems are analysed. The derived sum rules are applied for guiding the designs of passive absorbers and mufflers: the required minimum space is directly predicted from the target (i.e. the desired absorption or transmission-loss spectra) without any specific design. Besides, it is possible to break this type of sum rules and fundamental constraints in particular cases. This property, if well used, could result in ultra-compact absorbers working at long wavelength up to infinity.
Fitness of evolving bacterial populations is contingent on deep and shallow history but only shallow history creates predictable patterns

Long-term evolution experiments have tested the importance of genetic and environmental factors in influencing evolutionary outcomes. Differences in phylogenetic history, recent adaptation to distinct environments and chance events, all influence the fitness of a population. However, the interplay of these factors on a population's evolutionary potential remains relatively unexplored. We tracked the outcome of 2000 generations of evolution of four natural isolates of Escherichia coli bacteria that were engineered to also create differences in shallow history by adding previously identified mutations selected in a separate long-term experiment. Replicate populations started from each progenitor evolved in four environments. We found that deep and shallow phylogenetic histories both contributed significantly to differences in evolved fitness, though by different amounts in different selection environments. With one exception, chance effects were not significant. Whereas the effect of deep history did not follow any detectable pattern, effects of shallow history followed a pattern of diminishing returns whereby fitter ancestors had smaller fitness increases. These results are consistent with adaptive evolution being contingent on the interaction of several evolutionary forces but demonstrate that the nature of these interactions is not fixed and may not be predictable even when the role of chance is small.
Dynamics of forced and unforced autophoretic particles

Abstract Chemically active, or autophoretic, particles that isotropically emit or absorb solute molecules undergo spontaneous self-propulsion when their activity is increased beyond a critical Péclet number ($Pe$). Here, we conduct numerical computations, using a spectral element based method, of a rigid, spherical autophoretic particle in unsteady rectilinear translation. The particle can be freely suspended (or ‘unforced’) or subject to an external force field (or ‘forced’). The motion of an unforced particle progresses through four regimes as $Pe$ is increased: quiescent, steady, stirring and chaos. The particle is stationary in the quiescent regime, and the solute profile is isotropic about the particle. At $Pe=4$ the fore–aft symmetry in the solute profile is broken, resulting in its steady self-propulsion. Our computations indicate that the self-propulsion speed scales linearly with $Pe-4$ near the onset of self-propulsion, as has been predicted in previous studies. A further increase in $Pe$ gives rise to the stirring regime at $Pe\approx 27$, where the fluid undergoes recirculation, while the particle remains essentially stationary. As $Pe$ is increased even further, the particle dynamics is marked by chaotic oscillations at $Pe\approx 55$ and higher, which we characterize in terms of the mean square displacement and velocity autocorrelation of the particle. Our results for an autophoretic particle under a weak external force are in good agreement with recent asymptotic predictions (Saha, Yariv & Schnitzer, J. Fluid Mech., vol. 916, 2021, p. A47). Additionally, we demonstrate that the strength and temporal scheduling of the external force may be tuned to modulate the chaotic dynamics at large $Pe$.
Strategy-proof Preference Aggregation

An aggregation rule maps each profile of individual strict preference orderings over a set of alternatives into a social ordering over that set. We call such a rule strategyproof if misreporting one’s preference never produces a social ordering that is strictly between the original ordering and one’s own preference. After describing a few examples of manipulable rules, we study in some detail three classes of strategy-proof rules: (i) rules based on a monotonic alteration of the majority relation generated by the preference profile; (ii) rules improving upon a fixed status-quo; and (iii) rules generalizing the Condorcet-Kemeny aggregation method.
Study of Emotion Regulation Based on Yogic Personality Using Implicit, Explicit, and Eye Tracking Techniques

Context: Emotion regulation (ER) is vital for well-being. Yogic personality traits, gunas, influence the expression of ER. Aims: To investigate the expression of ER based on guna-related personality traits employing explicit, implicit, and objective eye-tracking techniques. Settings and Design: Quota sampling was used, with a cross-sectional design with three groups, sattva, rajas, and tamas. Three hundred and ninety-four female students were screened and finally, 30 from sattva, 34 from rajas, and 36 from tamas were analyzed. Methods: Participants were given State and Trait Mindfulness Scales, Gita Inventory, Social Desirability Scale, state-trait anxiety inventory, Adult Attachment Scale, and Emotional Regulation Questionnaire. ER-Implicit Association Test (ER-IAT) and Eye Tracker were also used to evaluate the participants. Statistical Analysis Used: One-way repeated-measures analysis of variance was used to examine differences between sattva, rajas, and tamas. Results: Sattva group adopted a higher level of regulation strategy, ER-IAT scores were positive for sattva, negative for rajas and tamas. Mindfulness was highest and anxiety was least in sattva. In the eye tracker, within the non-emotional areas of interest (eAOI) regions of pleasant condition, and eAOI regions of unpleasant condition, sattva group had considerably higher dwell time. Pupil dilation was lowest in sattva for all the conditions, both within and outside eAOIs. Conclusions: Sattva guna was found to foster healthy ER, and other psychological attributes. Conscious cultivation of sattva is needed for healthy emotional life.
Mechanisms of change of a cognitive-existential group intervention for fear of cancer recurrence: mediation analyses of the FORT trial

Abstract Background: Meta-analyses have demonstrated that brief interventions can address fear of cancer recurrence (FCR), but their mechanisms of action are largely unknown. Our goal was to identify the mediators of treatment efficacy of the Fear Of Recurrence Therapy (FORT) intervention using data from a multisite randomized controlled trial targeting FCR. That randomized controlled trial compared a 6-week cognitive-existential group intervention with an active control group. Methods: Participants (n = 135) were women diagnosed with stage I-III breast or gynecological cancer who were assessed at 4 time points (pretherapy, post-therapy, 3-month, and 6-month follow-up). The primary outcome, changes in FCR at 6 months, was measured with the Fear of Cancer Recurrence Inventory. We examined 6 mediators based on our theoretical model of FCR: perceived risk of recurrence, uncertainty in illness, intolerance of uncertainty, positive beliefs about worrying, reassurance-seeking, and cognitive avoidance. Changes in the possible mediator variables were simultaneously investigated to predict changes in FCR using Generalized Structural Equation Models with robust variance estimation. Results: FORT predicted FCR at 6 months in univariate analyses (β = −8.93, P = .0001). In the model including the 6 possible mediators, changes in uncertainty in illness (β = −8.72, P < .0001) and cognitive avoidance (β = −8.36, P < .0001) mediated the relationship between treatment and changes in FCR. However, FORT still predicted changes in FCR at 6 months (β = −6.35, P = .02), suggesting partial mediation. Conclusions: We identified 2 mechanisms of action that can be incorporated in future interventions. However, other processes that underlie the efficacy of these interventions need to be uncovered.