Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Image BRDF Parameter Estimation with a Conditional Adversarial Network

Creating plausible surfaces is an essential component in achieving a high degree of realism in rendering. To relieve artists, who create these surfaces in a time-consuming, manual process, automated retrieval of the spatially-varying Bidirectional Reflectance Distribution Function (SVBRDF) from a single mobile phone image is desirable. By leveraging a deep neural network, this casual capturing method can be achieved. The trained network can estimate per pixel normal, base color, metallic and roughness parameters from the Disney BRDF. The input image is taken with a mobile phone lit by the camera flash. The network is trained to compensate for environment lighting and thus learned to reduce artifacts introduced by other light sources. These losses contain a multi-scale discriminator with an additional perceptual loss, a rendering loss using a differentiable renderer, and a parameter loss. Besides the local precision, this loss formulation generates material texture maps which are globally more consistent. The network is set up as a generator network trained in an adversarial fashion to ensure that only plausible maps are produced. The estimated parameters not only reproduce the material faithfully in rendering but capture the style of hand-authored materials due to the more global loss terms compared to previous works without requiring additional post-processing. Both the resolution and the quality is improved.
Rapid adjustment and post‐processing of temperature forecast trajectories

Modern weather forecasts are commonly issued as consistent multi‐day forecast trajectories with a time resolution of 1–3 hours. Prior to issuing, statistical post‐processing is routinely used to correct systematic errors and misrepresentations of the forecast uncertainty. However, once the forecast has been issued, it is rarely updated before it is replaced in the next forecast cycle of the numerical weather prediction (NWP) model. This paper shows that the error correlation structure within the forecast trajectory can be utilized to substantially improve the forecast between the NWP forecast cycles by applying additional post‐processing steps each time new observations become available. The proposed rapid adjustment is applied to temperature forecast trajectories from the UK Met Office's convective‐scale ensemble MOGREPS‐UK. MOGREPS‐UK is run four times daily and produces hourly forecasts for up to 36 hours ahead. Our results indicate that the rapidly adjusted forecast from the previous NWP forecast cycle outperforms the new forecast for the first few hours of the next cycle, or until the new forecast itself can be rapidly adjusted, suggesting a new strategy for updating the forecast cycle.
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages

For language documentation initiatives, transcription is an expensive resource: one minute of audio is estimated to take one hour and a half on average of a linguist's work (Austin and Sallabank, 2013). Recently, collecting aligned translations in well-resourced languages became a popular solution for ensuring posterior interpretability of the recordings (Adda et al. 2016). In this paper we investigate language-related impact in automatic approaches for computational language documentation. We translate the bilingual Mboshi-French parallel corpus (Godard et al. 2017) into four other languages, and we perform bilingual-rooted unsupervised word discovery. Our results hint towards an impact of the well-resourced language in the quality of the output. However, by combining the information learned by different bilingual models, we are only able to marginally increase the quality of the segmentation.
Productivity Improvements in Public Organisations

In this article we examine the scope a principal in a public organisation has for motivating agents for productivity improvements where standard stick and carrot incentives cannot be used. The principal's only incentive device is a reallocation of budgets and tasks across agents depending on the extent of productivity improvements revealed by each agent. We first show that as long as agents do not collude, the principal can use rotation and tournament schemes to eliminate all slack in the organisation. Second, to break collusion between agents, the principal must use discriminatory tournament schemes. In some cases, however, there is no incentive scheme that can overcome collusion.
Ergodic BSDEs with Multiplicative and Degenerate Noise

In this paper we study an Ergodic Markovian BSDE involving a forward process $X$ that solves an infinite dimensional forward stochastic evolution equation with multiplicative and possibly degenerate diffusion coefficient. A concavity assumption on the driver allows us to avoid the typical quantitative conditions relating the dissipativity of the forward equation and the Lipschitz constant of the driver. Although the degeneracy of the noise has to be of a suitable type we can give a stochastic representation of a large class of Ergodic HJB equations; morever our general results can be applied to get the synthesis of the optimal feedback law in relevant examples of ergodic control problems for SPDEs.
Information Robust Dirichlet Networks for Predictive Uncertainty Estimation

Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone to over-confident predictions. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a novel method, Information Robust Dirichlet networks, that learn an explicit Dirichlet prior distribution on predictive distributions by minimizing the expected $L_p$ norm of the prediction error and penalizing information flow associated with incorrect outcomes. Properties of the new cost function are derived to indicate how improved uncertainty estimation is achieved. Experiments using real datasets show that our technique outperforms by a large margin state-of-the-art neural networks for estimating within-distribution and out-of-distribution uncertainty, and detecting adversarial examples.
Almost square permutations are typically square

A record in a permutation is a maximum or a minimum, from the left or from the right. The entries of a permutation can be partitioned into two types: the ones that are records are called external points, the others are called internal points. Permutations without internal points have been studied under the name of square permutations. Here, we explore permutations with a fixed number of internals points, called almost square permutations. Unlike with square permutations, a precise enumeration for the total number of almost square permutations of size $n+k$ with exactly $k$ internal points is not known. However, using a probabilistic approach, we are able to determine the asymptotic enumeration. This allows us to describe the permuton limit of almost square permutations with $k$ internal points, both when $k$ is fixed and when $k$ tends to infinity along a negligible sequence with respect to the size of the permutation. Finally, we show that our techniques are quite general by studying the set of $321$-avoiding permutations of size $n+k$ with exactly $k$ internal points ($k$ fixed). In this case we obtain an interesting asymptotic enumeration in terms of the Brownian excursion area. As a consequence, we show that the points of a uniform permutation in this set concentrate on the diagonal and the fluctuations of these points converge in distribution to a biased Brownian excursion.
Recalibrating the cosmic star formation history

The calibrations linking observed luminosities to the star formation rate (SFR) depend on the assumed stellar population synthesis model, initial mass function, star formation and metal enrichment history, and whether reprocessing by dust and gas is included. Consequently the shape and normalization of the inferred cosmic star formation history is sensitive to these assumptions. Using v2.2. 1 of the Binary Population and Spectral Synthesis (bpass) model we determine a new set of calibration coefficients for the ultraviolet, thermal infrared, and hydrogen recombination lines. These ultraviolet and thermal infrared coefficients are 0.15–0.2 dex higher than those widely utilized in the literature while the H α coefficient is ∼0.35 dex larger. These differences arise in part due to the inclusion binary evolution pathways but predominantly reflect an extension in the IMF to 300 M⊙ and a change in the choice of reference metallicity. We use these new coefficients to recalibrate the cosmic star formation history, and find improved agreement between the integrated cosmic star formation history and the in situ measured stellar mass density as a function of redshift. However, these coefficients produce new tension between SFR densities inferred from the ultraviolet and thermal infrared and those from H α.
Automating Dynamic Consent Decisions for the Processing of Social Media Data in Health Research

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a “middle-ground” approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this article, we present an exploratory user study (n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimizing undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.
Central Server Free Federated Learning over Single-sided Trust Social Networks

Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not applicable (e.g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable). In this paper, we consider a generic setting: 1) the central server may not exist, and 2) the social network is unidirectional or of single-sided trust (i.e., user A trusts user B but user B may not trust user A). We propose a central server free federated learning algorithm, named Online Push-Sum (OPS) method, to handle this challenging but generic scenario. A rigorous regret analysis is also provided, which shows very interesting results on how users can benefit from communication with trusted users in the federated learning scenario. This work builds upon the fundamental algorithm framework and theoretical guarantees for federated learning in the generic social network scenario.
Incorporating Fine-grained Events in Stock Movement Prediction

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.
Generative One-Shot Face Recognition

One-shot face recognition measures the ability to identify persons with only seeing them at one glance, and is a hallmark of human visual intelligence. It is challenging for conventional machine learning approaches to mimic this way, since limited data are hard to effectively represent the data variance. The goal of one-shot face recognition is to learn a large-scale face recognizer, which is capable to fight off the data imbalance challenge. In this paper, we propose a novel generative adversarial one-shot face recognizer, attempting to synthesize meaningful data for one-shot classes by adapting the data variances from other normal classes. Specifically, we target at building a more effective general face classifier for both normal persons and one-shot persons. Technically, we design a new loss function by formulating knowledge transfer generator and a general classifier into a unified framework. Such a two-player minimax optimization can guide the generation of more effective data, which effectively promote the underrepresented classes in the learned model and lead to a remarkable improvement in face recognition performance. We evaluate our proposed model on the MS-Celeb-1M one-shot learning benchmark task, where we could recognize 94.98% of the test images at the precision of 99% for the one-shot classes, keeping an overall Top1 accuracy at $99.80\%$ for the normal classes. To the best of our knowledge, this is the best performance among all the published methods using this benchmark task with the same setup, including all the participants in the recent MS-Celeb-1M challenge at ICCV 2017\footnote{this http URL}.
From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images

Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether leaf patterns can provide sufficient information for cultivar recognition. This paper reports the first attempt on soybean cultivar recognition from plant leaves which is not only a challenging research problem but also important for soybean cultivar evaluation, selection and production in agriculture. In this paper, we propose a novel multiscale sliding chord matching (MSCM) approach to extract leaf patterns that are distinctive for soybean cultivar identification. A chord is defined to slide along the contour for measuring the synchronised patterns of exterior shape and interior appearance of soybean leaf images. A multiscale sliding chord strategy is developed to extract features in a coarse-to-fine hierarchical order. A joint description that integrates the leaf descriptors from different parts of a soybean plant is proposed for further enhancing the discriminative power of cultivar description. We built a cultivar leaf image database, SoyCultivar, consisting of 1200 sample leaf images from 200 soybean cultivars for performance evaluation. Encouraging experimental results of the proposed method in comparison to the state-of-the-art leaf species recognition methods demonstrate the availability of cultivar information in soybean leaves and effectiveness of the proposed MSCM for soybean cultivar identification, which may advance the research in leaf recognition from species to cultivar.
Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism

We present an endpoint box regression module(epBRM), which is designed for predicting precise 3D bounding boxes using raw LiDAR 3D point clouds. The proposed epBRM is built with sequence of small networks and is computationally lightweight. Our approach can improve a 3D object detection performance by predicting more precise 3D bounding box coordinates. The proposed approach requires 40 minutes of training to improve the detection performance. Moreover, epBRM imposes less than 12ms to network inference time for up-to 20 objects. The proposed approach utilizes a spatial transformation mechanism to simplify the box regression task. Adopting spatial transformation mechanism into epBRM makes it possible to improve the quality of detection with a small sized network. We conduct in-depth analysis of the effect of various spatial transformation mechanisms applied on raw LiDAR 3D point clouds. We also evaluate the proposed epBRM by applying it to several state-of-the-art 3D object detection systems. We evaluate our approach on KITTI dataset, a standard 3D object detection benchmark for autonomous vehicles. The proposed epBRM enhances the overlaps between ground truth bounding boxes and detected bounding boxes, and improves 3D object detection. Our proposed method evaluated in KITTI test server outperforms current state-of-the-art approaches.
Bulk topological proximity effect in multilayer systems

We investigate the bulk topological proximity effect in multilayer hexagonal lattice systems by which one can introduce topological properties into a system composed of multiple trivial layers by tunnel coupling to a single nontrivial layer described by the Haldane model. In a multilayer system arrangement of different layers can significantly affect its properties. This mechanism is relevant for the bulk topological proximity effect leading to complex behavior like the emergence of dark states. The response of a trivial system to the proximity of a topological insulator appears to be highly nonlocal, in contrast to the proximity effect observed in context of superconductivity. Furthermore, for a wide range of parameters our system is semimetallic with multiple Dirac points emerging in the Brillouin zone.
Time to BRIC it? Internationalization of European family firms in Europe, North America and the BRIC countries

For a sample of 1243 European companies, we analyse the link between firm type and foreign direct investment (FDI) locations. We find substantial empirical evidence that being a family firm does not only affect the overall propensity for FDI, but that this effect is also specific to target regions. Overall, family firms invest more than managerial-led firms, particularly in Europe and North America. Furthermore, the BRIC countries, Brazil, Russia, India and China do not constitute a homogeneous attractiveness cluster for FDI.
The Permanent Need for Political Economy

The reasons for permanent need for political economy is discussed and a note on the establishment of a separate Department of Political Economy in the School of Social and Political Sciences in the Faculty of Arts, is provided. The significance of the political economy dispute in terms of the nature of economics and the role of university ideals, is assessed.
The Driving Forces of Stock Returns in Hong Kong

We comprehensively investigate what drives stock returns in Hong Kong stock market which has been consistently ranked as one of the most important markets for IPOs. We find that Hong Kong inflation rate is a systematic pricing factor across stocks after controlling for Fama-French three-factor. It is different from the U.S. market and other developed markets that the momentum, dividend yield, cash-flow yield, earnings yield, and return-reversal factors are not significant pricing factors for stock returns in Hong Kong. Our Fama-MacBeath (1973) regressions show that a stock’s value (cash-flow yield and book-to-market ratio) is the strongest predictor of stock returns in Hong Kong after controlling for market, value, and size factors and macroeconomic factors.
Quality sorting across export markets: Alchian-Allen eects and the role of asymmetric tastes

The Alchian-Allen hypothesis postulates that due to per unit trade costs the relative demand for high-quality goods is increasing in distance. We propose a modied version of this hypothesis for the quality of goods, as perceived by domestic consumers: the relative demand for high-quality goods is increasing in the proximity in tastes, i.e. depends on how well domestic and foreign tastes accord. Based on rm-product level data on trade and production in the Danish Chocolate and confectionery industry, we infer domestic perceived product quality. We use these quality estimates to explain export market participation and export volumes across destinations. Domestic quality perceptions have a signicantly higher eect on a product's export performance if the destination market is similar to the home market in terms of culture and income.
Recent trends in health near the age of retirement: new findings from the Health Interview Survey.

In recent years, mortality rates have been falling. One result of that decline is an increase in the proportion of the population that lives beyond the age of retirement. Decreased mortality has traditionally been taken as a sign of improved health status, but the same period has also seen a steady trend toward earlier retirement. Taken together, these two trends led to a decision to raise the “normal” retirement age gradually from age 65 to age 67. This article examines whether, as assumed,
Computationally-Efficient Fuel-Economic High-Level Controller Design for a Group of Connected Vehicles in Urban Roads

This paper presents a computationally-efficient fuel-economic control strategy for a group of connected vehicles in urban roads. We assume the availability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Apart from fuel economy, the proposed higher-level controller also focuses on reducing red light idling, which improves traffic mobility and in turn improves vehicle emissions. The red light idling avoidance problem is formulated as a two-point boundary value problem and sampling-based approach is employed to evaluate a feasible solution in real-time. This leads to control solutions that can ensure avoidance of red light idling despite the number of vehicles in front of it. We have shown that sampling from a Gaussian distribution whose mean depends on the target velocity can improve fuel economy to a good extent. This higher-level control solution provides a good initial solution for any deterministic lower-level controller. Simulation results show the efficacy of the proposed method in terms of fuel economy and computational efficiency.
Engineering Good Times: Fiscal Manipulation in a Global Economy

In a global economy, a country’s international economic ties affect both how desirable pre-electoral fiscal manipulation is to the government, and how costly it is to the government to engage in such manipulation. Governments are more likely to engage in pre-electoral fiscal manipulation when the country’s exchange rate is flexible and the domestic economy is highly open to international trade, and when the exchange rate is fixed and the domestic economy is relatively closed to international trade. This argument is tested empirically through a quantitative analysis of changes in government debt in twenty OECD countries from 1974 to 2008.
On Computer-Aided Instruction Tools for Teaching College Engineering Mechanics Related Courses

In this research, questionnaires are designed to survey among teachers and students the current situation of applying Computer-Aided Instruction (CAI) tools to teaching and learning of college engineering mechanics related courses in Taiwan. The needs for CAI tools for teaching these mechanics related courses are investigated in the survey. Several prototypes of interactive multimedia tools are designed and implemented using information technologies. The applicability and effectiveness of these tools on assisting teaching of engineering mechanics related courses are discussed and evaluated. Moreover, a website for managing and sharing collected and developed CAI resources is constructed.