Miracle-dz/UniFAR-FTUs huggingface dataset:
Consistent Smile Intensity Estimation from Wearable Optical Sensors

Smiling plays a crucial role in human communication. It is the most frequent expression shown in daily life. Smile analysis usually employs computer vision-based methods that use data sets annotated by experts. However, cameras have space constraints in most realistic scenarios due to occlusions. Wearable electromyography is a promising alternative; however, issue of user comfort is a barrier to long-term use. Other wearable-based methods can detect smiles, but they lack consistency because they use subjective criteria without expert annotation. We investigate a wearable-based method that uses optical sensors for consistent smile intensity estimation while reducing manual annotation cost. First, we use a state-of-art computer vision method (OpenFace) to train a regression model to estimate smile intensity from sensor data. Then, we compare the estimation result to that of OpenFace. We also compared their results to human annotation. The results show that the wearable method has a higher matching coefficient (r=0.67) with human annotated smile intensity than OpenFace (r=0.56). Also, when the sensor data and OpenFace output were fused, the multimodal method produced estimates closer to human annotation (r=0.74). Finally, we investigate how the synchrony of smile dynamics among subjects and their average smile intensity are correlated to assess the potential of wearable smile intensity estimation.
A Bijection for Some Paths on the Slit Plane

This paper deals with a study of the class P of lattice paths, made of north, east, south, and west unit steps, which being at (? 1,0) and end at (0,0), avoiding the non-negative x-axis. P is bijectively proved to be enumerated by odd index Catalan numbers according to the number of steps.
Robust Markov Decision Processes without Model Estimation

Robust Markov Decision Processes (MDPs) are receiving much attention in learning a robust policy which is less sensitive to environment changes. There are an increasing number of works analyzing sample-efficiency of robust MDPs. However, there are two major barriers to applying robust MDPs in practice. First, most works study robust MDPs in a model-based regime, where the transition probability needs to be estimated and requires a large amount of memories $\mathcal{O}(|\mathcal{S}|^2|\mathcal{A}|)$. Second, prior work typically assumes a strong oracle to obtain the optimal solution as an intermediate step to solve robust MDPs. However, in practice, such an oracle does not exist usually. To remove the oracle, we transform the original robust MDPs into an alternative form, which allows us to use stochastic gradient methods to solve the robust MDPs. Moreover, we prove the alternative form still plays a similar role as the original form. With this new formulation, we devise a sample-efficient algorithm to solve the robust MDPs in a model-free regime, which does not require an oracle and trades off a lower storage requirement $\mathcal{O}(|\mathcal{S}||\mathcal{A}|)$ with being able to generate samples from a generative model or Markovian chain. Finally, we validate our theoretical findings via numerical experiments, showing the efficiency with the alternative form of robust MDPs.
Deep HyFeat Based Attention in Attention Model for Face Super-Resolution

Face super-resolution (SR) is the task of generating high-resolution (HR) face images from the low-resolution (LR) inputs. Recently, deep learning-based methods have shown remarkable progress in the SR field. Most of the methods perform additional tasks such as face parsing, landmark, and attention to generate the HR images. However, parsing maps and landmark-guided models require the supplementary labeled dataset, which is difficult to obtain in real life. The attention mechanism does not require the datasets extra labeling and is also beneficial for face SR. These methods focus on a few critical features and ignore the remaining ones, which sometime causes to ignore the valuable features. Therefore, this article proposes a novel deep hybrid feature (HyFeat)-based Attention in Attention model for face SR. Moreover, the proposed model uses the coarse SR network and deep convolutional neural network (CNN) to generate the HR image. A coarse SR network is applied to upsample the LR image and generate the coarse super-resolved image, which is further sent to the deep CNN model. The proposed work incorporates the HyFeat attention in attention unit (HyFA <inline-formula> <tex-math notation="LaTeX">$^{\mathrm{ 2}}\text{U}$ </tex-math></inline-formula>), which consists of HyFeat block and attention in attention block (A <inline-formula> <tex-math notation="LaTeX">$^{\mathrm{ 2}}\text{B}$ </tex-math></inline-formula>) in the deep CNN model to improve the visual quality of the output face images. HyFeat block assists the model in extracting the coarse features and learning the enriched contextual information to enhance the details of coarse features. A <inline-formula> <tex-math notation="LaTeX">$^{\mathrm{ 2}}\text{B}$ </tex-math></inline-formula> preserves the attentive and non-attentive beneficial features while suppressing the unwanted features. The attention branch focuses on specific facial features and ignores the rest of the features. The non-attention branch aims to learn the informative features that the attention branch ignores. The proposed model repeats the HyFA <inline-formula> <tex-math notation="LaTeX">$^{\mathrm{ 2}}\text{U}$ </tex-math></inline-formula> units to focus on different facial components and enhance the features to improve the quality of resultant faces. Experimental outcomes exhibit that the proposed model gains state-of-the-art performance on the standard datasets, namely CelebAHQ, Helen, FFHQ, and LFW face. The proposed method achieves an improvement of more than 0.35 dB in peak signal-to-noise ratio (PSNR) and 0.012 in structure similarity (SSIM) on different datasets over the best models available in the literature.
An Adaptive Deep Q-Learning Strategy for Routing Schemes in SDN-Based Data Centre Networks

The enhancing size of uses on the cloud has improvised the requirement for dependable and elite execution network engineering in Datacentres. Programming Defined Networking has worked on the adaptability, postponement, and throughput of networks in contrast with static arrangements. To adjust to the quick advancement of distributed computing, enormous information, and different innovations, the mix of server farm rout and SDN is anticipated to type network the executives more advantageous and adaptable. With this benefit, routing methodologies have been widely concentrated by specialists. In any case, the systems in the regulator chiefly depend on manual plan, the ideal arrangements are hard to be acquired in the powerful network climate. A few routing calculations, for example, network geographies with repetitive connections, for example, Fat-tree to give proficient burden adjusting, be that as it may, the failure of these plans to adjust to quickly changing traffic conduct restricts their exhibition. This research suggests DL-based networking plan for SDN. We utilize an adaptive deep Q-network (ADQN) to fabricate the deep reinforcement learning routing plan. We exhibit the adequacy of the proposed framework through broad reproductions. Profiting from nonstop learning with a worldwide view, the proposed framework has lower stream fruition time, throughput and better burden stability as well as better vigor, contrasted with OSPF.
Bounds on the distribution of amplitudes in ground motion prediction models

In current ground motion models, the uncertainty in predicted ground motion is modelled with a lognormal distribution. One consequence of this is that predicted ground motions do not have an upper limit. In reality, there probably exist physical conditions that limit the ground motion. Use of unbounded models in probabilistic seismic hazard analysis leads to ground motion estimates that may be unrealistically large, especially at the low annual probabilities considered for important structures, such as dams or nuclear reactors. Due to the limited size of earlier strong motion data sets, statistical analysis of the distribution of earthquake ground motion amplitudes was unable to provide a clear point at which to truncate the lognormal distribution. Recently, very large data sets of strong motion recordings have become available, making statistical analysis more viable. We have analysed very large strong motion data sets from the K-net and Kik-net strong motion networks in Japan. Preliminary analyses by us and other investigators using normal probability plots show departures from lognormal behaviour at about 2.5 standard deviations above the median. Our basic approach is to calculate residuals of the recorded data from the ground motion model developed for Japan by Zhao et al. (2006) using the K-net and Kik-net data. These residuals are then used to construct normal probability plots, and the significance of departures of the residuals from lognormal distributions is quantified. The significance of departures is assessed in light of earthquake source, wave propagation, and site response effects that may be present in the data but not taken into account in the ground motion model of Zhao et al. (2006) that form the basis for measurement of residuals.
Future supply of phosphorus in agriculture and the need to maximise efficiency of use and reuse

Commercially viable reserves of rock phosphate are limited and only a few countries are significant producers. China and the US will play a much smaller role within 50 years time and the bulk of the world's mined phosphorus will come from Morocco. A conservative estimate of longevity of the resource shows that at a 1% exponential increase for the next 50 years followed by zero increase, the global reserves would last 235 years. If one uses the UN global population growth rate to determine future demand, with a stabilisation by 2100, the current global reserves would last 172 years. This estimate can be further reduced to 126 years if Africa develops its agriculture and to just 48 years if in addition bio-energy crops are given higher priority. The phosphorus losses are significant in the mining/beneficiation/ /fertiliser production steps (35% of what is mined is not converted into usable product) and in agriculture (30% of what is added as fertiliser is not contained in agricultural output, with most being retained in the soil) but they are even higher within the areas of food processing, distribution and consumption (60% of the P in food is lost). To reduce phosphorus losses the questions of erosion from farm fields and more effective handling of manure from high density livestock feedlots need to be addressed. When it comes to food processing, improvements in crop storage, processing facilities and trade methods are needed. At present most of the excreted phosphorus from humans ends up lost in the environment. Phosphorus extraction from wastewater, sludge, manures and other organic sources is only starting and needs worldwide promotion. About one billion people are under-nourished and many are smallholder farmers that cannot afford chemical fertilisers. Food production in developing countries will probably have to double by 2050. More conservative policies and measures are required in the management of fertilisers to feed a world with 9 billion people. Countries need to further develop productive sanitation systems in order to safely reuse human and animal excreta. Guidelines now exist for the use of human urine as a substitute for chemical fertiliser in agriculture. There is a higher chance that food security can be achieved by maintaining soil fertility if all available sources of fertiliser resources are better managed – animal manure, crop and food residues, chemical fertilisers and human excreta. For these reasons a much more conservative approach is needed in the exploitation of fossil phosphate-rock and that reuse and more efficient systems should be promoted and developed.
Exponential Evolutionary Programming Without Self-Adaptive Strategy Parameter

Evolutionary programming (EP) uses strategy parameter with self-adaptation. This strategy parameter corresponds to a search step size in solution search algorithm. Exponential evolutionary programming (EEP) uses exponential mutation instead of Gaussian mutation of conventional evolutionary programming (CEP). Therefore, the search step size of EEP depends on the parameter value of exponential distribution as well as self-adaptation. Generally, the strategy parameter has to decrease its value with evolution progress. For the sake of this purpose, the parameter value of EEP has to augment the self-adaptation of EP. However, it is not so easy to find the line tuning parameter value of EEP with linkage to the self-adaptation in actual computation. Considering these situations, we propose here new EEP (nsEEP) without self-adaptive strategy parameter. Instead of self-adaptation, the parameter value of EEP changes automatically with evolution progress. In this paper, we present new EEP algorithm without self-adaptive strategy parameter. Experimental results show that this new EEP outperforms to other existing EP and obtains excellent high quality solutions with fine tuning parameter value.
Binary recycled pulsars, as a most precise physical laboratory

The following problems are discussed. 1. Pulsars and close binaries. 2. Hulse-Taylor pulsar. 3. Disrupted pulsar pairs. 4. RP statistics. 5. Enhanced evaporation: formation of single RP. 6. General relativity effects: NS+NS. 7. A Double pulsar system. 8. Checking general relativity. 9. Variability of the gravitational constant. 10. Space Watch.
Cross-Cultural Preferences in Spatial Reasoning

How do people reason about spatial relations? Do people with different cultural backgrounds differ in how they reason about space? The aim of our cross-cultural study on spatial reasoning is to strengthen this link between spatial cognition and culture. We conducted two reasoning experiments, one in Germany and one in Mongolia. Topological relations, such as “A overlaps B” or “B lies within C”, were presented to the participants as premises and they had to find a conclusion that was consistent with the premises (“What is the relation between A and C? ”). The problem description allowed multiple possible “conclusions”. Our results, however, indicate that the participants had strong preferences: They consistently preferred one of the possible conclusions and neglected other conclusions, although they were also logically consistent with the premises. The preferred and neglected conclusions were similar in Germany and Mongolia. We argue that the preferences are caused by universal cognitive principles that work the same way in the western culture and Mongolia.
Fast Mapping and Slow Mapping in Children's Word Learning

When young children encounter a word they do not know, their guesses about what the word might mean are often surprisingly accurate. This is true not only with respect to the particular instance that the speaker refers to at that moment but also with respect to the entire category of things, states, situations, or events to which the word may refer in the language. For more than 30 years, understanding how this is possible has been the central empirical and theoretical concern of most of the developmental psychologists and linguists who study the process of word learning experimentally. This attention has not been misplaced. The domain of word learning has provided a fertile ground for testing competing accounts of children’s understanding of reference in language, their use of ontological divisions and other world knowledge in categorization, and their grasp of syntactic regularities. On the whole, children have performed surprisingly well in these experiments—by the age of two or three, they make efficient and appropriate use of a wide range of sources of information in determining what speakers are referring to in the moment, and in evaluating how novel words may be used in future situations. Most such tests have taken place in contrived but well-controlled experimental situations in which a brief exposure to a novel word, in a particular social or linguistic context, is revealed to lead children to choose one object or scene rather than another as a referent of the word. What gives such studies their force is the careful manipulation of the precise content of the introducing event, and the selection of the alternatives offered to the child, which pit one possibly tempting interpretation against another. The point is not that the experimental situation closely mimics children’s daily lives but rather that children’s interpretations reveal antecedent knowledge either innately specified or gained in development. The experiments acknowledged as the primary intellectual ancestors to this research tradition are those reported in Carey and Bartlett (1978; see also Brown, 1957; Katz, Baker, & MacNamara, 1974). Carey and Bartlett (1978) introduced the term “fast mapping,” which has become central to developmental psychology’s narrative about how words are learned. In this narrative, it is children’s accuracy in fast mapping that cries out for explanation. How can children arrive at the correct meaning of a word given only indirect and incomplete evidence? Yet in Carey and Bartlett’s famous “chromium” study, fast mapping was not so successful. Fewer than one in ten of the 3-year-olds appeared to have linked the word to its intended meaning (olive green). The children who had been exposed to the word in the study’s naturalistic teaching context (“bring
Component Analysis Based Approach to Support the Design of Meta-Heuristics for MLCLSP Providing Guidelines

Today's supply chain are highly complex and globally set-up underlying a constant change with increasing speed. This has to be reflected by the planning processes and algorithms being utilized in the different stages of a supply chain. In the context of production planning, meta-heuristics are usually applied due to their ability to handle high complex problems. As a consequence, these algorithms require adaptation to the new scenario or even new solution approaches/strategies have to be devised. However, designing a meta-heuristic of good performance for a problem is a hard task, since it requires deep knowledge on the problem, as well as on the meta-heuristic side. Therefore, the existence of supporting guidelines for meta-heuristics might ease and speed-up the adaptation or design of these algorithms to better cope with the problem. In this paper, meta-heuristics are deconstructed into its components and an approach for component-based analysis is proposed to gain knowledge about their performance and how they perform the search. Based on the results of this analysis, guidelines can be devised. The proposed approach is applied for analyzing components of a good performing Genetic Algorithm (GA) for multi-level capacitated lot sizing problem (MLCLSP) and initial guidelines for the construction of GA in the domain of MLCLSP are generated.
The Legal Regime for Transboundary Water Pollution: Between Discretion and Constraint.

Abbreviations. 1. Introduction. 2. Principles and policies. 3. Specific obligations. 4. Procedural obligations. 5. Legal and non-legal rules. 6. Compliance control. 7. From discretion to constraint? Summary. Table of Cases. Table of Materials.
A Complete Software-Based IF GNSS Signal Generator for Software Receiver Development

The recent development of Global Navigation Satellite Systems (GNSS) software receivers has brought a new perspective to receiver design. The first step necessary to use them is to have access to sampled data at an Intermediate Frequency (IF). These sampled data can be provided through two different ways. First, an RF frontend that would sample data coming from either the real satellites or from a hardware signal generator, if the latter exists at all for the signals of interest. Real data might not be suitable when a specific aspect of GNSS is studied, especially in a research and development context, or might not yet be available as in the case of GPS L5 and Galileo signals. Hardware signal simulators were created to fill that gap, and are now widely used. However, they are very expensive, may not be available for the signals of interest and consequently might not be always suitable for specialized research due to their lack of flexibility. Sampled data can also be provided by a software IF signal generator. Such a tool includes signal simulation as well as front-end filtering and sampling, fully software-based, and can fed directly into a software receiver. Such programs are already used and are often created by the research groups themselves to fit their research programs. Although they offer a total control of all the parameters for simulation purpose, they are often highly specialized and, as such tend to model only a part of the error sources of interest, e.g., jamming or multipath. The Position, Location And Navigation (PLAN) research group of the Department of Geomatics Engineering has developed a complete IF signal generator that can model the new GPS and Galileo signals propagation channels while being versatile and customizable. This signal generator allows for the modeling of pre-defined environments for specific applications. It offers a direct access to IF sampled data that can be directly used by a software receiver. From an ephemeris file, an entire constellation of satellites can be easily simulated. The entire GNSS propagation channel is then modeled. Finally, the front-end filter and the Analog-to-Digital Converter (ADC) (1-bit quantization) are simulated. A first Matlab/C, GPS-only version of this IF signal generator modeled thermal noise, satellite clock errors, atmospheric errors, the front-end filter, and an ADC. The current enhanced version, written in C++ to improve processing speed, now includes multipath modeling,