Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised shape transformer for image translation and cross-domain retrieval.

We address the problem of unsupervised geometric image-to-image translation. Rather than transferring the style of an image as a whole, our goal is to translate the geometry of an object as depicted in different domains while preserving its appearance characteristics. Our model is trained in an unsupervised fashion, i.e. without the need of paired images during training. It performs all steps of the shape transfer within a single model and without additional post-processing stages. Extensive experiments on the VITON, CMU-Multi-PIE and our own FashionStyle datasets show the effectiveness of the method. In addition, we show that despite their low-dimensionality, the features learned by our model are useful to the item retrieval task.
Antimicrobial Blue Light Inactivation of Polymicrobial Biofilms

Polymicrobial biofilms, in which mixed microbial species are present, play a significant role in persistent infections. Furthermore, polymicrobial biofilms promote antibiotic resistance by allowing interspecies transfer of antibiotic resistance genes. In the present study, we investigated the effectiveness of antimicrobial blue light (aBL; 405 nm), an innovative non-antibiotic approach, for the inactivation of polymicrobial biofilms. Dual-species biofilms with Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus (MRSA) as well as with P. aeruginosa and Candida albicans were reproducibly grown in 96-well microtiter plates or in the CDC biofilm reactor for 24 or 48 h. The effectiveness of aBL inactivation of polymicrobial biofilms was determined through colony forming assay and compared with that of monomicrobial biofilms of each species. aBL-induced morphological changes of biofilms were analyzed with confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM). For 24-h old monomicrobial biofilms formed in 96-well microtiter plates, 6.30-log10 CFU inactivation of P. aeruginosa, 2.33-log10 CFU inactivation of C. albicans and 3.48-log10 CFU inactivation of MRSA were observed after an aBL exposure of 500 J/cm2. Under the same aBL exposure, 6.34-log10 CFU inactivation of P. aeruginosa and 3.11-log10 CFU inactivation of C. albicans were observed, respectively, in dual-species biofilms. In addition, 2.37- and 3.40-log10 CFU inactivation were obtained in MRSA and P. aeruginosa, dual-species biofilms. The same aBL treatment of the biofilms developed in the CDC-biofilm reactor for 48 h significantly decreased the viability of P. aeruginosa monomicrobial and polymicrobial biofilm when cocultured with MRSA (3.70- and 3.56-log10 CFU inactivation, respectively). 2.58-log10 CFU inactivation and 0.86-log10 CFU inactivation was detected in MRSA monomicrobial and polymicrobial biofilm when cocultured with P. aeruginosa. These findings were further supported by the CLSM and SEM experiments. Phototoxicity studies revealed a no statistically significant loss of viability in human keratinocytes after an exposure to 216 J/cm2 and a statistically significant loss of viability after 500 J/cm2. aBL is potentially an alternative treatment against polymicrobial biofilm-related infections. Future studies will aim to improve the efficacy of aBL and to investigate aBL treatment of polymicrobial biofilm-related infections in vivo.
BoLTVOS: Box-Level Tracking for Video Object Segmentation

We approach video object segmentation (VOS) by splitting the task into two sub-tasks: bounding box level tracking, followed by bounding box segmentation. Following this paradigm, we present BoLTVOS (Box-Level Tracking for VOS), which consists of an R-CNN detector conditioned on the first-frame bounding box to detect the object of interest, a temporal consistency rescoring algorithm, and a Box2Seg network that converts bounding boxes to segmentation masks. BoLTVOS performs VOS using only the firstframe bounding box without the mask. We evaluate our approach on DAVIS 2017 and YouTube-VOS, and show that it outperforms all methods that do not perform first-frame fine-tuning. We further present BoLTVOS-ft, which learns to segment the object in question using the first-frame mask while it is being tracked, without increasing the runtime. BoLTVOS-ft outperforms PReMVOS, the previously best performing VOS method on DAVIS 2016 and YouTube-VOS, while running up to 45 times faster. Our bounding box tracker also outperforms all previous short-term and longterm trackers on the bounding box level tracking datasets OTB 2015 and LTB35. A newer version of this work can be found at arXiv:1911.12836.
Stellar activity and rotation of the planet host Kepler-17 from long-term space-borne photometry

Context. The study of young Sun-like stars is fundamental to understanding the magnetic activity and rotational evolution of the Sun. Space-borne photometry by the Kepler telescope provides unprecedented datasets to investigate these phenomena in Sun-like stars. Aims. We present a new analysis of the entire Kepler photometric time series of the moderately young Sun-like star Kepler-17 accompanied by a transiting hot Jupiter. Methods. We applied a maximum-entropy spot model to the long-cadence out-of-transit photometry of the target to derive maps of the starspot filling factor versus the longitude and the time. These maps are compared to the spots occulted during transits to validate our reconstruction and derive information on the latitudes of the starspots. Results. We find two main active longitudes on the photosphere of Kepler-17, one of which has a lifetime of at least ∼1400 days although with a varying level of activity. The latitudinal differential rotation is of solar type, that is, with the equator rotating faster than the poles. We estimate a minimum relative amplitude ΔΩ/Ω between ∼0.08 ± 0.05 and 0.14 ± 0.05, our determination being affected by the finite lifetime of individual starspots and depending on the adopted spot model parameters. We find marginal evidence of a short-term intermittent activity cycle of ∼48 days and an indication of a longer cycle of 400−600 days characterized by an equatorward migration of the mean latitude of the spots as in the Sun. The rotation of Kepler-17 is likely to be significantly affected by the tides raised by its massive close-by planet. Conclusion. We confirm the reliability of maximum-entropy spot models to map starspots in young active stars and characterize the activity and differential rotation of this young Sun-like planetary host.
Cyclegan-VC2: Improved Cyclegan-based Non-parallel Voice Conversion

Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data. This is an important task, but it has been challenging due to the disadvantages of the training conditions. Recently, CycleGAN-VC has provided a breakthrough and performed comparably to a parallel VC method without relying on any extra data, modules, or time alignment procedures. However, there is still a large gap between the real target and converted speech, and bridging this gap remains a challenge. To reduce the gap, we propose CycleGAN-VC2, which is an improved version of CycleGAN-VC incorporating three new techniques: an improved objective (two-step adversarial losses), improved generator (2-1-2D CNN), and improved discriminator (PatchGAN). We evaluated our method on a non-parallel VC task and analyzed the effect of each technique in detail. An objective evaluation showed that these techniques help bring the converted feature sequence closer to the target in terms of both global and local structures, which we assess by using Mel-cepstral distortion and modulation spectra distance, respectively. A subjective evaluation showed that CycleGAN-VC2 outperforms CycleGAN-VC in terms of naturalness and similarity for every speaker pair, including intra-gender and inter-gender pairs.1
Kernelized Complete Conditional Stein Discrepancy

Much of machine learning relies on comparing distributions with discrepancy measures. Stein's method creates discrepancy measures between two distributions that require only the unnormalized density of one and samples from the other. Stein discrepancies can be combined with kernels to define the kernelized Stein discrepancies (KSDs). While kernels make Stein discrepancies tractable, they pose several challenges in high dimensions. We introduce kernelized complete conditional Stein discrepancies (KCC-SDs). Complete conditionals turn a multivariate distribution into multiple univariate distributions. We prove that KCC-SDs detect convergence and non-convergence, and that they upper-bound KSDs. We empirically show that KCC-SDs detect non-convergence where KSDs fail. Our experiments illustrate the difference between KCC-SDs and KSDs when comparing high-dimensional distributions and performing variational inference.
UG 2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments

The UG 2+ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can beneﬁt the high-level automatic visual recognition in various scenarios. In its second track, we focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. While existing enhancement methods are empirically expected to help the high-level end task, that is observed to not always be the case in practice. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotate objects/faces annotated. To our best knowledge, this is the ﬁrst and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. We expect a large participation from the broad research community to address these challenges together.
Physical Modeling and Numerical Analysis of Tsunami Inundation in a Coastal City

This study conducted a physical model experiment to investigate inundation processes in a complex coastal city model using the Hybrid Tsunami Open Flume in Ujigawa Open Laboratory (HyTOFU). The physical model was constructed at a 1:250 scale as an idealization of the coastal town of Onagawa. Two tsunami waveforms were used, hydraulic bore and solitary wave, which were produced by a pump-type wave generator and a mechanical piston-type wave generator, respectively, at HyTOFU. Tsunami wave generation similar to the 2011 Tohoku earthquake tsunami in Onagawa was successfully reproduced, and the spatial distribution of the tsunami wave propagation and inundation processes on land was clearly observed. A comparison of experimental and numerical models was performed using two-dimensional (2D) and quasi-three-dimensional (Q3D) models. The 2D and Q3D results agreed well with the experimental results in terms of maximum water surface elevations and arrival times for hydraulic bore conditions. For solitary wave trials, the maximum water surface elevations of the 2D results were underestimated, and the arrival times in the numerical models were slower than those in the experimental results. In this study, as a benchmark for the 2011 Tohoku earthquake tsunami, the dataset of tsunami inundation is provided, which will be useful to validate other numerical tsunami models.
Ideal Cereals With Lower Arsenic and Cadmium by Accurately Enhancing Vacuolar Sequestration Capacity

Cereals are a staple food for many people around the world; however, they are also a major dietary source of toxic metal(loid)s. Many agricultural regions throughout the world are contaminated with toxic metal(loid)s, which can accumulate to high levels in the grains of cereals cultivated in these regions, posing serious health risks to consumers. Arsenic (As) and cadmium (Cd) are efficiently accumulated in cereals through metal transport pathways. Therefore, there is an urgent need to develop crops that contain greatly reduced levels of toxic metal(loid)s. Vacuolar sequestration of toxic metal(loid)s is a primary strategy for reducing toxic metal(loid)s in grains. However, until recently, detailed strategies and mechanisms for reducing toxic metal(loid)s in grain were limited by the lack of experimental data. New strategies to reduce As and Cd in grain by enhancing vacuolar sequestration in specific tissues are critical to develop crops that lower the daily intake of As and Cd, potentially improving human health. This review provides insights and strategies for developing crops with strongly reduced amounts of toxic metal(loid)s without jeopardizing agronomic traits.
An open source toolkit for the tracking, termination and recovery of high altitude balloon flights and payloads

We present an open source toolkit of flight-proven electronic devices which can be used to track, terminate and recover high altitude balloon flights and payloads. Comprising a beacon, pyrotechnic and non-pyrotechnic cut-down devices plus associated software, the toolkit can be used to: (i) track the location of a flight via Iridium satellite communication; (ii) release lift and/or float balloons manually or at pre-defined altitudes; (iii) locate the payload after descent. The size and mass of the toolkit make it suitable for use on weather or sounding balloon flights. We describe the technology readiness level of the toolkit, based on over 20 successful flights to altitudes of typically 32,000 m.
A Multi-year Search for Transits of Proxima Centauri. II. No Evidence for Transit Events with Periods between 1 and 30 days

Using a global network of small telescopes, we have obtained light curves of Proxima Centauri at 329 observation epochs from 2006 to 2017. The planet Proxima b discovered by Anglada-Escudé et al. with an orbital period of 11.186 days has an a priori transit probability of ∼1.5%; if it transits, the predicted transit depth is about 5 mmag. In Blank et al., we analyzed 96 of our light curves that overlapped with predicted transit ephemerides from previously published tentative transit detections and found no evidence in our data that would corroborate claims of transits with a period of 11.186 days. Here we broaden our analysis, using 262 high-quality light curves from our data set to search for any periodic transit-like events over a range of periods from 1 to 30 days. We also inject a series of simulated planet transits and find that our data are sufficiently sensitive to have detected transits of 5 mmag depth, with recoverability ranging from ∼100% for an orbital period of 1 day to ∼20% for an orbital period of 20 days for the parameter spaces tested. Specifically, at the 11.186-day period and 5 mmag transit depth, we rule out transits in our data with high confidence. We are able to rule out virtually all transits of other planets at periods shorter than 5 days and depths greater than 3 mmag; however, we cannot confidently rule out transits at the period of Proxima b due to incomplete orbital phase coverage and a lack of sensitivity to transits shallower than 4 mmag.
HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition

In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-level GRU to capture the contexts of utterance-level embeddings. Moreover, we promote the framework to two variants, Hi-GRU with individual features fusion (HiGRU-f) and HiGRU with self-attention and features fusion (HiGRU-sf), so that the word/utterance-level individual inputs and the long-range contextual information can be sufficiently utilized. Experiments on three dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate that our proposed Hi-GRU models attain at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset, respectively. Particularly, by utilizing only the textual feature in IEMOCAP, our HiGRU models gain at least 3.8% improvement over the state-of-the-art conversational memory network (CMN) with the trimodal features of text, video, and audio.
User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks

We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure a dataset coverage, we use an adversarial loss function that penalizes for incorrect reproductions of a given texture. In experiments, we show that our model can learn descriptive texture manifolds for large datasets and from raw data such as a collection of high-resolution photos. Moreover, we apply our method to produce 3D textures and show that it outperforms existing baselines.
Lagrangian Floer homology on symplectic blow ups

We show how to compute the Lagrangian Floer homology in the one-point blow up of the proper transform of Lagrangians submanifolds, solely in terms of information of the base manifold. As an example we present an alternative computation of the Lagrangian quantum homology in the one-point blow up of (CP^2,\omega) of the proper transform of the Clifford torus.
Does shallow geological knowledge help neural-networks to predict deep units?

Geological interpretation of seismic images is a visual task that can be automated by training neural networks. While neural networks have shown to be effective at various interpretation tasks, a fundamental challenge is the lack of labeled data points in the subsurface. For example, the interpolation and extrapolation of well-based lithology using seismic images relies on a small number of known labels. Besides well-known data augmentation techniques, as well as regularization of the network output, we propose and test another approach to deal with the lack of labels. Non learning-based horizon trackers work very well in the shallow subsurface where seismic images are of higher quality and the geological units are roughly layered. We test if these segmented and shallow units can help train neural networks to predict deeper geological units that are not layered and flat. We show that knowledge of shallow geological units helps to predict deeper units when there are only a few labels for training using a dataset from the Sea of Ireland. We employ U-net based multi-resolution networks, and we show that these networks can be described using matrix-vector product notation in a similar fashion as standard geophysical inverse problems.
Milk Consumption Across Life Periods in Relation to Lower Risk of Nasopharyngeal Carcinoma: A Multicentre Case-Control Study

Background: The much higher incidence of nasopharyngeal carcinoma (NPC) in men suggests sex hormones as a risk factor, and dairy products contain measurable amounts of steroid hormones. Milk consumption has greatly increased in endemic regions of NPC. We investigated the association between NPC and milk consumption across life periods in Hong Kong. Methods: A multicentre case-control study included 815 histologically confirmed NPC incident cases and 1,502 controls who were frequency-matched on age and sex at five major hospitals in Hong Kong in 2014–2017. Odds ratios (ORs) of NPC (cases vs. controls) for milk consumption at different life periods were estimated by unconditional logistic regression, adjusting for sex, age, socioeconomic status score, smoking and alcohol drinking status, exposure to occupational hazards, family history of cancer, IgA against Epstein-Barr virus viral capsid antigen, and total energy intake. Results: Compared with abstainers, lower risks of NPC were consistently observed in regular users (consuming ≥5 glasses of milk [fresh and powdered combined] per month) across four life periods of age 6–12 (adjusted OR 0.74, 95% CI 0.54–0.86), 13–18 (0.68, 0.55–0.84), 19–30 (0.68, 0.55–0.84), and 10 years before recruitment (0.72, 0.59–0.87). Long-term average milk consumption of ≤2.5, >2.5, and ≤12.5, >12.5 glasses per month yielded adjusted OR (95% CI) of 1.00 (0.80–1.26), 0.98 (0.81–1.18), 0.95 (0.76–1.18), and 0.55 (0.43–0.70), respectively (all P-values for trend < 0.05). Conclusion: Consumption of milk across life periods was associated with lower risks of NPC. If confirmed to be causal, this has important implications for dairy product consumption and prevention of NPC.
Block Neural Autoregressive Flow

Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently. Recently, as an alternative to hand-crafted bijections, Huang et al. (2018) proposed neural autoregressive flow (NAF) which is a universal approximator for density functions. Their flow is a neural network (NN) whose parameters are predicted by another NN. The latter grows quadratically with the size of the former and thus an efficient technique for parametrization is needed. We propose block neural autoregressive flow (B-NAF), a much more compact universal approximator of density functions, where we model a bijection directly using a single feed-forward network. Invertibility is ensured by carefully designing each affine transformation with block matrices that make the flow autoregressive and (strictly) monotone. We compare B-NAF to NAF and other established flows on density estimation and approximate inference for latent variable models. Our proposed flow is competitive across datasets while using orders of magnitude fewer parameters.
The Optical to Mid-infrared Extinction Law Based on the APOGEE, Gaia DR2, Pan-STARRS1, SDSS, APASS, 2MASS, and WISE Surveys

A precise interstellar dust extinction law is critically important to interpret observations. There are two indicators of extinction: the color excess ratio (CER) and the relative extinction. Compared to the CER, the wavelength-dependent relative extinction is more challenging to be determined. In this work, we combine spectroscopic, astrometric, and photometric data to derive high-precision CERs and relative extinction from optical to mid-infrared (IR) bands. A group of 61,111 red clump (RC) stars are selected as tracers by stellar parameters from the Apache Point Observatory Galaxy Evolution Experiment survey. The multiband photometric data are collected from Gaia, APASS, Sloan Digital Sky Survey, Pan-STARRS1, Two Micron All Sky Survey, and Wide-field Infrared Survey Explorer surveys. For the first time, we calibrate the curvature of CERs in determining CERs E(λ − GRP)/E(GBP − GRP) from color excess–color excess diagrams. Through elaborate uncertainty analysis, we conclude that the precision of our CERs is significantly improved (σ < 0.015). With parallaxes from Gaia DR2, we calculate the relative extinction / for 5051 RC stars. By combining the CERs with the / , the optical–mid-IR extinction Aλ/ has been determined in a total of 21 bands. Given no bias toward any specific environment, our extinction law represents the average extinction law with the total-to-selective extinction ratio RV = 3.16 ± 0.15. Our observed extinction law supports an adjustment in parameters of the CCM RV = 3.1 curve, together with the near-IR (NIR) power-law index α = 2.07 ± 0.03. The relative extinction values of the Hubble Space Telescope (HST) and the James Webb Space Telescope (JWST) NIR bandpasses are predicted in 2.5% precision. As the observed reddening/extinction tracks are curved, the curvature correction needs to be considered when applying extinction correction.