Miracle-dz/UniFAR-FTUs huggingface dataset:
Architecture Search of Dynamic Cells for Semantic Video Segmentation

In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames. To this end, recent approaches have been reliant on manually arranged operations applied on top of static semantic segmentation networks – with the most prominent building block being the optical flow able to provide information about scene dynamics. Related to that is the line of research concerned with speeding up static networks by approximating expensive parts of them with cheaper alternatives, while propagating information from previous frames. In this work we attempt to come up with generalisation of those methods, and instead of manually designing contextual blocks that connect per-frame outputs, we propose a neural architecture search solution, where the choice of operations together with their sequential arrangement are being predicted by a separate neural network. We showcase that such generalisation leads to stable and accurate results across common benchmarks, such as CityScapes and CamVid datasets. Importantly, the proposed methodology takes only 2 GPU-days, finds high-performing cells and does not rely on the expensive optical flow computation.
Rock glacier inventory of the western Nyainqêntanglha Range, Tibetan Plateau, supported by InSAR time series and automated classification

The western Nyainqêntanglha Range on the Tibetan Plateau reaches an elevation of 7,162 m and is characterized by an extensive periglacial environment under semi‐arid climatic conditions. Rock glaciers play an important part of the water budget in high mountain areas and recent studies suggest that they may even act as climate‐resistant water storages. In this study we present the first rock glacier inventory of this region containing 1,433 rock glaciers over an area of 4,622 km. To create the most reliable inventory we combine manually created rock glacier outlines with an automated classification approach. The manual outlines were generated based on surface elevation data, optical satellite imagery and a surface velocity estimation. This estimation was generated via InSAR time series analysis with Sentinel‐1 data from 2016 to 2019. Our pixel‐based automated classification was able to correctly identify 87.8% of all rock glaciers in the study area at a true positive rate of 69.5%. In total, 65.9% of rock glaciers are classified as transitional with surface velocities of 1–10 cm/yr. In total, 18.5% are classified as active with higher velocities of up to 87 cm/yr. The southern windward side of the mountain range contains more numerous and more active rock glaciers. We attribute this to higher moisture availability supplied by the Indian Monsoon.
Enabling Uneven Task Difficulty in Micro-Task Crowdsourcing

In micro-task crowdsourcing markets such as Amazon's Mechanical Turk, how to obtain high quality result without exceeding the limited budgets is one main challenge. The existing theory and practice of crowdsourcing suggests that uneven task difficulty plays a crucial role to task quality. Yet, it lacks a clear identifying method to task difficulty, which hinders effective and efficient execution of micro-task crowdsourcing. This paper explores the notion of task difficulty and its influence to crowdsourcing, and presents a difficulty-based crowdsourcing method to optimize the crowdsourcing process. We firstly identify task difficulty feature based on a local estimation method in the real crowdsourcing context, followed by proposing an optimization method to improve the accuracy of results, while reducing the overall cost. We conduct a series of experimental studies to evaluate our method, which show that our difficulty-based crowdsourcing method can accurately identify the task difficulty feature, improve the quality of task performance and reduce the cost significantly, and thus demonstrate the effectiveness of task difficulty as task modeling property.
Hierarchical Trajectory Planning for Autonomous Driving in Low-speed Driving Scenarios Based on RRT and Optimization

Though great effort has been put into the study of path planning on urban roads and highways, few works have studied the driving strategy and trajectory planning in low-speed driving scenarios, e.g., driving on a university campus or driving through a housing or industrial estate. The study of planning in these scenarios is crucial as these environments often cover the first or the last one kilometer of a daily travel or logistic system. Additionally, it is essential to treat these scenarios differently as, in most cases, the driving environment is narrow, dynamic, and rich with obstacles, which also causes the planning in such environments to continue to be a challenging task. This paper proposes a hierarchical planning approach that separates the path planning and the temporal planning. A path that satisfies the kinematic constraints is generated through a modified bidirectional rapidly exploring random tree (bi-RRT) approach. Following that, the timestamp of each node of the path is optimized through sequential quadratic programming (SQP) with the feasible searching bounds defined by safe intervals (SIs). Simulations and real tests in different driving scenarios prove the effectiveness of this method.
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem.

Digital camera pipelines employ color constancy methods to estimate an unknown scene illuminant, in order to re-illuminate images as if they were acquired under an achromatic light source. Fully-supervised learning approaches exhibit state-of-the-art estimation accuracy with camera-specific labelled training imagery. Resulting models typically suffer from domain gaps and fail to generalise across imaging devices. In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets. We present a general approach that utilizes the concept of color temperature to frame color constancy as a set of distinct, homogeneous few-shot regression tasks, each associated with an intuitive physical meaning. We integrate this novel formulation within a meta-learning framework, enabling fast generalisation to previously unseen cameras using only handfuls of camera specific training samples. Consequently, the time spent for data collection and annotation substantially diminishes in practice whenever a new sensor is used. To quantify this gain, we evaluate our pipeline on three publicly available datasets comprising 12 different cameras and diverse scene content. Our approach delivers competitive results both qualitatively and quantitatively while requiring a small fraction of the camera-specific samples compared to standard approaches.
Libra R-CNN: Towards Balanced Learning for Object Detection

Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level. To mitigate the adverse effects caused thereby, we propose Libra R-CNN, a simple but effective framework towards balanced learning for object detection. It integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level. Benefitted from the overall balanced design, Libra R-CNN significantly improves the detection performance. Without bells and whistles, it achieves 2.5 points and 2.0 points higher Average Precision (AP) than FPN Faster R-CNN and RetinaNet respectively on MSCOCO.
The High-resolution Transmission Spectrum of HD 189733b Interpreted with Atmospheric Doppler Shifts from Three-dimensional General Circulation Models

The signature of wind patterns caused by the interplay of rotation and energy redistribution in hot Jupiters is detectable at high spectral resolution, yet no direct comparison has been attempted between predictions from general circulation models (GCMs) and observed high-resolution spectra. We present the first such comparison on near-infrared transmission spectra of the hot Jupiter HD 189733b. Exploring 12 rotation rates and two chemical regimes, we have created model spectra from 3D GCMs and cross-correlated them with the observed spectra. Comparing our models against those of HD 189733b, we obtain three key results: (1) we confirm CO and H2O in the planet’s atmosphere at a detection significance of 8. 2σ; (2) we recover the signature of day-to-night winds with speeds of several km s−1 at pressures of several millibars; and (3) we constrain the rotation period of the planet to between 1.2 and 4.69 days (synchronous rotation (2.2 days) remains consistent with existing observations). Our results do not suffer from the shortcomings of 1D models as cross-correlation templates—these models mainly tend to overconstrain the slower rotation rates and show evidence for anomalous blueshifts. Our 3D models instead match the observed line-of-sight velocity of this planet by self-consistently including the effects of high-altitude day-to-night winds. Overall, we find a high degree of consistency between observations of HD 189733b and our GCM-based spectra, implying that the physics and chemistry are adequately described in current 3D forward models for the purpose of interpreting observations at high spectral resolution.
Advancing NLP with Cognitive Language Processing Signals

When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks. We analyze whether using such human features can show consistent improvement across tasks and data sources. We present an extensive investigation of the benefits and limitations of using cognitive processing data for NLP. Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. These methods significantly outperform the baselines and show the potential and current limitations of employing human language processing data for NLP.
The Effect of Executive Share Ownership, Executive Compensation, and Independent Commissioners on Tax Avoidance

Tax is a contribution that mandatory to be paid by personal and corporate taxpayers. The government used that tax for national development. Tax becomes a burden for companies that it is mandatory to be paid. If the companies got larger income so that the taxes that must be paid become larger too. On the other hand, if the companies got smaller income, the taxes that must be paid will become smaller. This leads the companies to avoid taxes that mandatory to be paid by reducing their amount of taxes. This is called tax avoidance. Tax avoidance influenced by several factors such as corporate governance. This study aims to determine the effect of executive share ownership, executive compensation and independent commissioners on tax avoidance. This study uses manufacturing companies listed on the Indonesia Stock Exchange (BEI) for the period 2015-2019. The sampling technique used was purposive sampling method. The sample used in this study were 52 manufacturing companies listed on the Indonesia Stock Exchange (BEI) during the 2015-2019 period. Hypothesis testing is done using multiple linear regression analysis with the SPSS 26 program and a significance value of 5%. The results of this study indicate that: (1) Executive share ownership has no significant effect on tax avoidance, (2) Executive compensation has significant positive effect on tax avoidance, (3) Independent commissioners have no significant effect on tax avoidance, (4) Executive share ownership, executive compensation, and independent commissioners are only able to explain tax avoidance by 3,1%.
Stochastic work extraction in a colloidal heat engine in the presence of colored noise

From synthetic active devices such as self-propelling Janus colloids to micro-organisms like bacteria, micro-algae, living cells in tissues, active fluctuations are ubiquitous. Thermodynamics of small systems involving thermal as well as active fluctuations are of immense importance. They can be employed to extract thermodynamic work. Here we propose a simple model system that can produce thermodynamic work exploiting active fluctuations. We consider a Brownian particle, trapped by an externally controlled harmonic confinement that contracts and expands time-periodically by modulating its spring constant e.g. optical tweezers. The system produces work by being alternately connected to two baths one passive and other active. The active bath provides exponentially time-correlated noise to the particle, that breaks the fluctuation dissipation relation. The average efficiency of the system is calculated exactly in quasistatic limit. Nonquasistatic regime is explored by numerics. Comparing with its passive counterpart, we also show that the active micro heat engine can be more efficient depending on the chosen parameter space. We also believe that our model can be realised experimentally with the help of bacterial baths.
Boosting segmentation with weak supervision from image-to-image translation

In many cases, especially with medical images, it is prohibitively challenging to produce a sufficiently large training sample of pixel-level annotations to train deep neural networks for semantic image segmentation. On the other hand, some information is often known about the contents of images. We leverage information on whether an image presents the segmentation target or whether it is absent from the image to improve segmentation performance by augmenting the amount of data usable for model training. Specifically, we propose a semi-supervised framework that employs image-to-image translation between weak labels (e.g., presence vs. absence of cancer), in addition to fully supervised segmentation on some examples. We conjecture that this translation objective is well aligned with the segmentation objective as both require the same disentangling of image variations. Building on prior image-to-image translation work, we re-use the encoder and decoders for translating in either direction between two domains, employing a strategy of selectively decoding domain-specific variations. For presence vs. absence domains, the encoder produces variations that are common to both and those unique to the presence domain. Furthermore, we successfully re-use one of the decoders used in translation for segmentation. We validate the proposed method on synthetic tasks of varying difficulty as well as on the real task of brain tumor segmentation in magnetic resonance images, where we show significant improvements over standard semi-supervised training with autoencoding.
On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures

Colonoscopy is the standard device for diagnosing colorectal cancer, which develops from little lesions on the bowel wall called polyps. The Rényi entropies-based structural entropy and spatial filling factor are two scale- and resolution-independent quantities that characterize the shape of a probability distribution with the help of characteristic curves of the structural entropy–spatial filling factor map. This alternative definition of structural entropy is easy to calculate, independent of the image resolution, and does not require the calculation of neighbor statistics, unlike the other graph-based structural entropies.The distant goal of this study was to help computer aided diagnosis in finding colorectal polyps by making the Rényi entropy based structural entropy more understood. The direct goal was to determine characteristic curves that can differentiate between polyps and other structure on the picture. After analyzing the distribution of colonoscopy picture color channels, the typical structures were modeled with simple geometrical functions and the structural entropy–spatial filling factor characteristic curves were determined for these model structures for various parameter sets. A colonoscopy image analying method, i.e., the line- or column-wise scanning of the picture, was also tested, with satisfactory matching of the characteristic curve and the image.
Fast anharmonic free energy method with an application to vacancies in ZrC

We propose an approach to calculate the anharmonic part of the volumetric-strain and temperature dependent free energy of a crystal. The method strikes an effective balance between accuracy and computational efficiency, showing a $\times10$ speed-up on comparable free energy approaches at the level of density functional theory, with average errors less than 1 meV/atom. As a demonstration we make new predictions on the thermodynamics of substoichiometric ZrC$_x$, including vacancy concentration and heat capacity.
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.4 BLEU points, and also a marked improvement over a state-of-the-art system.
Do not Omit Local Minimizer: a Complete Solution for Pose Estimation from 3D Correspondences

Estimating pose from given 3D correspondences, including point-to-point, point-to-line and point-to-plane correspondences, is a fundamental task in computer vision with many applications. We present a complete solution for this task, including a solution for the minimal problem and the least-squares problem of this task. Previous works mainly focused on finding the global minimizer to address the least-squares problem. However, existing works that show the ability to achieve global minimizer are still unsuitable for real-time applications. Furthermore, as one of contributions of this paper, we prove that there exist ambiguous configurations for any number of lines and planes. These configurations have several solutions in theory, which makes the correct solution may come from a local minimizer. Our algorithm is efficient and able to reveal local minimizers. We employ the Cayley-Gibbs-Rodriguez (CGR) parameterization of the rotation to derive a general rational cost for the three cases of 3D correspondences. The main contribution of this paper is to solve the resulting equation system of the minimal problem and the first-order optimality conditions of the least-squares problem, both of which are of complicated rational forms. The central idea of our algorithm is to introduce intermediate unknowns to simplify the problem. Extensive experimental results show that our algorithm significantly outperforms previous algorithms when the number of correspondences is small. Besides, when the global minimizer is the solution, our algorithm achieves the same accuracy as previous algorithms that have guaranteed global optimality, but our algorithm is applicable to real-time applications.
Internships and promises of diversity: how anticipatory psychological contracts shape employment intentions in the U.S

Abstract Our paper bridges psychological contract and recruitment literatures, examining the relationship between diversity messages on recruitment websites and internship outcomes, through the lens of anticipatory psychological contracts (APCs). We focus on interns as a distinct segment of the job market, with data collected in the Midwest region of the U.S. Study 1, using a lab experiment (n = 579), indicates that recruitment websites with diversity messages have a significant impact on job applicants’ perceptions of organizational obligations for offering a diverse and inclusive work environment, in comparison to neutral and exclusive recruitment websites. Moreover, Study 2 (n = 196), based on a field study using two-wave surveys, shows that APC fulfillment mediates the relationship between diversity-related organizational obligations and intention to pursue fulltime employment with the organization. These results suggest that when recruiting interns, employers should be intentional regarding promises made during recruitment to promote accurate APC formation. This is particularly important regarding diversity messaging, because interns hold promises made about diversity more strictly than promises of exclusivity and competitiveness.
Polypodium vulgare L.: Polyphenolic Profile, Cytotoxicity and Cytoprotective Properties in Different Cell Lines

Pteridophytes, represented by ferns and allies, are an important phytogenetic bridge between lower and higher plants (gymnosperms and angiosperms). Ferns have evolved independently of any other species in the plant kingdom being its secondary metabolism a reservoir of phytoconstituents characteristic of this taxon. The study of the possible medicinal uses of Polypodium vulgare L. (Polypodiaceae), PV, has increased particularly when in 2008 the European Medicines Agency published a monograph about the rhizome of this species. Thus, our objective is to provide scientific knowledge on the methanolic extract from the fronds of P. vulgare L. , one of the main ferns described in the Prades Mountains, to contribute to the validation of certain traditional uses. Specifically, we have characterized the methanolic extract of PV fronds (PVM) by HPLC-DAD and investigated its potential cytotoxicity, phototoxicity, ROS production and protective effects against oxidative stress by using in vitro methods. Our results show that PVM is not cytotoxic against the different cell lines assessed, but we found potential cytoprotective and cellular repair activity in 3T3 fibroblast cells. This biological activity could be attributed to the high content of polyphenolic compounds; thus, this extract is positioned as a potential candidate for pharmaceutical uses.
WATCAT: a tale of wide-angle tailed radio galaxies

We present a catalog of 47 wide-angle tailed radio galaxies (WATs), the WATCAT, mainly built including a radio morphological classification; WATs were selected by combining observations from the National Radio Astronomy Observatory/Very Large Array Sky Survey (NVSS), the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST), and the Sloan Digital Sky Survey (SDSS). We included in the catalog only radio sources showing two-sided jets with two clear “warmspots” (i.e., jet knots as bright as 20% of the nucleus) lying on the opposite side of the radio core, and having classical extended emission resembling a plume beyond them. The catalog is limited to redshifts z ≤ 0.15, and lists only sources with radio emission extended beyond 30 kpc from the host galaxy. We found that host galaxies of WATCAT sources are all luminous (−20.5 ≳ Mr ≳ −23.7), red early-type galaxies with black hole masses in the range 108 ≲ MBH ≲ 109 M⊙. The spectroscopic classification indicates that they are all low-excitation galaxies (LEGs). Comparing WAT multifrequency properties with those of FR I and FR II radio galaxies at the same redshifts, we conclude that WATs show multifrequency properties remarkably similar to FR I radio galaxies, having radio power of typical FR IIs.