Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Emotion Recognition In Images and Videos

Human expression recognition has important applications in mental analysis, behavior analysis etc. for human-ization of artiﬁcial intelligence systems. It is challenging for a machine to detect and understand facial emotions. Previous semantic facial feature analysis techniques mostly suffer from high computation time and currently deep learning techniques using CNN improve a lot. State-of-the-art methods explore mainly human expression in static images recently and have average test accuracy around 60% in several images datasets. The more challenging goal is detecting facial emotions in videos. In this case, we found the test accuracy in many video datasets are less than 40%. For this purpose, we propose an emotion recognition system by using existing CNN models as features. We explore human recognition system to identify 7 types of emotions by us-ing FER2013 dataset. Furthermore, we utilize recently proposed state-of-the-art preprocessing techniques(a modiﬁed VGG model) on facial images for manually crafting features and then use them as a CNN pretrained model features. In addition to the importance of feature extraction, we choose Long Short-Term Memory(LSTM) networks and using two datasets(JAFFE and AFEW) to train it. Finally, we use this model to test on image datasets and video datasets to compare the performance and get relative high accuracy with CNN pretrained model features.
Cyclization reaction of amines with dialkyl carbonates to yield 1,3-oxazinan-2-ones

A number of six-membered cyclic carbamates (oxazinanones) were synthesized from the reaction of a primary amine or hydrazine with a dicarbonate derivative of 1,3-diols in a one-pot reaction, in good yield, short time span, and in the absence of a solvent. The reaction proceeds in two steps: an intermolecular reaction to give a linear intermediate and an intramolecular cyclization to yield the cyclic carbamate. This is the first example of a carbonate reacting selectively and sequentially, firstly at the carbonyl center to form a linear carbamate and then as a leaving group to yield a cyclic carbamate.
Texture Profile Method

SUMMARY A texture profile method was developed that uses the A. D. Little flavor profile method as a model. A texture profile is defined as the organoleptic analysis of the texture complex of a food in terms of its mechanical, geometrical, fat, and moisture characteristics, the degree of each present, and the order in which they appear from first bite through complete mastication. The texture profile analysis requires a panel of judges with prior knowledge of the texture classification system, use of standard rating scales, and proper panel procedures with regard to the mechanics of testing and sample control.
Fast Privacy-Preserving Text Classification Based on Secure Multiparty Computation

We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340ms in the case where the dictionary size of Bob’s model includes all words (<inline-formula> <tex-math notation="LaTeX">$n = 5200$ </tex-math></inline-formula>) and Alice’s SMS has at most <inline-formula> <tex-math notation="LaTeX">$m = 160$ </tex-math></inline-formula> unigrams. In the case with <inline-formula> <tex-math notation="LaTeX">$n = 369$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$m = 8$ </tex-math></inline-formula> (the average of a spam SMS in the database), our solution takes only 21ms.
On Newton strata in the $B_{dR}^+$-Grassmannian

We study parabolic reductions and Newton points of G-bundles on the Fargues-Fontaine curve and the Newton stratification on the $B_{dR}^+$-Grassmannian for any reductive group G. Let $Bun_G$ be the stack of G-bundles on the Fargues-Fontaine curve. Our first main result is to show that under the identification of the points of $Bun_G$ with Kottwitz's set B(G), the closure relations on the topological space $|Bun_G|$ coincide with the opposite of the usual partial order on B(G). Furthermore, we prove that every non-Hodge-Newton decomposable Newton stratum in a minuscule affine Schubert cell in the $B_{dR}^+$-Grassmannian intersects the weakly admissible locus, proving a conjecture of Chen. On the way, we study several interesting properties of parabolic reductions of $G$-bundles, and determine which Newton strata have classical points.
Multiwavelength dissection of a massive heavily dust-obscured galaxy and its blue companion at z∼2

Aims. We study a system of two galaxies, Astarte and Adonis, at z ∼ 2. At this time, the Universe was undergoing the peak of its star formation activity. Astarte is a dusty star-forming galaxy at the massive end of the main sequence (MS), and Adonis is a less massive companion galaxy that is bright in the ultraviolet and has an optical spectroscopic redshift. We investigate whether this ultramassive galaxy is quenching, and whether it has always been on the MS of star-forming galaxies. Methods. We used the code CIGALE to model the spectral energy distribution. The code relies on the energetic balance between the ultraviolet and the infrared. We derived some of the key physical properties of Astarte and Adonis, mainly their star formation rates (SFRs), stellar masses, and dust luminosities. We inspected the variation of the physical parameters depending on the assumed dust-attenuation law. We also estimated the molecular gas mass of Astarte from its CO emission, using different αCO and transition ratios (r31), and we discuss the implication of the various assumptions on the gas-mass derivation. Reults. We find that Astarte exhibits a MS-like star formation activity, and Adonis is undergoing a strong starburst phase. The molecular gas mass of Astarte is far lower than the gas fraction of typical star-forming galaxies at z = 2. This low gas content and high SFR result in a depletion time of 0.22 ± 0.07 Gyr, which is slightly shorter than expected for a MS galaxy at this redshift. The CO luminosity relative to the total infrared luminosity suggests a MS-like activity when we assume a galactic conversion factor and a low transition ratio. The SFR of Astarte is on the same order when different attenuation laws are used, unlike its stellar mass, which increases when shallow attenuation laws are used (∼1 × 1011 M⊙ assuming a Calzetti relation, versus ∼4 × 1011 M⊙ assuming a shallow attenuation law). We discuss these properties and suggest that Astarte might be experiencing a recent decrease in star formation activity and is quenching through the MS following a starburst epoch.
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning

As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this article, we propose MORL-BD, a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning to provide the optimal bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the perspective of cyber-physical systems and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching. We define the multi-route bicycle dispatching problem as a multi-objective optimization problem by considering the optimization objectives of dispatching costs, dispatch truck's initial load, workload balance among the trucks, and the dynamic balance of bicycle supply and demand. On this basis, the collaborative multi-route bicycle dispatching problem among multiple dispatch trucks is modeled as a multi-agent and multi-objective reinforcement learning model. All dispatch paths between parking spots are defined as state spaces, and the reciprocal of dispatching costs is defined as a reward. Each dispatch truck is equipped with an agent to learn the optimal dispatch path in the dynamic DL-PBS network. We create an elite list to store the Pareto optimal solutions of bicycle dispatch paths found in each action, and finally get the Pareto frontier. Experimental results on the actual DL-PBS show that compared with existing methods, MORL-BD can find a higher quality Pareto frontier with less execution time.
COTORRA: COntext-aware Testbed fOR Robotic Applications

Edge computing have received considerable attention as a promising candidate for the evolution of robotic systems. In this work, we propose COTORRA, an Edge driven robotic testbed that combines context information with robot sensor data to validate innovative concepts for robotic systems prior to being applied in a production environment. We have tested COTORRA in a controlled university environment as an easy applicable, serverless, and modular testbed on top of commodity network infrastructure. COTORRA supports pluggable robotic applications. To verify its feasibility and assess its performance, we ran a set of experiments that show how autonomous navigation applications can achieve target latencies bellow 15 ms, and perform an inter-domain Distributed Ledger Technology (DLT) federation within 19 seconds.
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks

Training Graph Convolutional Networks (GCNs) is expensive as it needs to aggregate data recursively from neighboring nodes. To reduce the computation overhead, previous works have proposed various neighbor sampling methods that estimate the aggregation result based on a small number of sampled neighbors. Although these methods have successfully accelerated the training, they mainly focus on the single-machine setting. As real-world graphs are large, training GCNs in distributed systems is desirable. However, we found that the existing neighbor sampling methods do not work well in a distributed setting. Specifically, a naive implementation may incur a huge amount of communication of feature vectors among different machines. To address this problem, we propose a communication-efficient neighbor sampling method in this work. Our main idea is to assign higher sampling probabilities to the local nodes so that remote nodes are accessed less frequently. We present an algorithm that determines the local sampling probabilities and makes sure our skewed neighbor sampling does not affect much the convergence of the training. Our experiments with node classification benchmarks show that our method significantly reduces the communication overhead for distributed GCN training with little accuracy loss.
Edge-Featured Graph Attention Network

Lots of neural network architectures have been proposed to deal with learning tasks on graph-structured data. However, most of these models concentrate on only node features during the learning process. The edge features, which usually play a similarly important role as the nodes, are often ignored or simplified by these models. In this paper, we present edge-featured graph attention networks, namely EGATs, to extend the use of graph neural networks to those tasks learning on graphs with both node and edge features. These models can be regarded as extensions of graph attention networks (GATs). By reforming the model structure and the learning process, the new models can accept node and edge features as inputs, incorporate the edge information into feature representations, and iterate both node and edge features in a parallel but mutual way. The results demonstrate that our work is highly competitive against other node classification approaches, and can be well applied in edge-featured graph learning tasks.
Knowledge Graph for Microdata of Statistics Netherlands

Statistics Netherlands (CBS) hosted a huge amount of data not only on the statistical level but also on the individual level. With the development of data science technologies, more and more researchers request to conduct their research by using high-quality individual data from CBS (called CBS Microdata) or combining them with other data sources. Making great use of these data for research and scientific purposes can tremendously benefit the whole society. However, CBS Microdata has been collected and maintained in different ways by different departments in and out of CBS. The representation, quality, metadata of datasets are not sufficiently harmonized. The project converts the descriptions of all CBS microdata sets into one knowledge graph with comprehensive metadata in Dutch and English using text mining and semantic web technologies. Researchers can easily query the metadata, explore the relations among multiple datasets, and find the needed variables. For example, if a researcher searches a dataset about "Age at Death" in the Health and Well-being category, all information related to this dataset will appear including keywords and variable names. "Age at Death" dataset has a keyword - "Death". This keyword will lead to other datasets such as "Date of Death". "Cause of Death", "Production statistics Health and welfare" from Population, Business categories, and Health and well-being categories. This will tremendously save time and costs for the data requester but also data maintainers.
Efficient Polymer Solar Cells Based on M3EH−PPV

We report on polymer blend solar cells with an external quantum efficiency of more than 30% and a high overall energy conversion efficiency (ECE) under white light illumination (100 mW/cm2) of up to 1.7% using a blend of M3EH−PPV (poly[2,5-dimethoxy-1,4-phenylene-1,2-ethenylene-2-methoxy-5-(2-ethylhexyloxy)−(1,4-phenylene-1,2-ethenylene)]) and CN−ether−PPV (poly[oxa-1,4-phenylene-1,2-(1-cyano)ethenylene-2,5-dioctyloxy-1,4-phenylene-1,2-(2-cyano)ethenylene-1,4-phenylene]). We attribute these high efficiencies to the formation of a vertically composition graded structure during spin coating. Photoluminescence measurements performed on the blend layers indicated the formation of exciplexes between both types of polymers, which we propose to be one factor preventing even higher efficiencies.
Accelerating Deep Learning Inference via Learned Caches

Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains owing to their high accuracy in solving real-world problems. However, this high accuracy has been achieved by building deeper networks, posing a fundamental challenge to the low latency inference desired by user-facing applications. Current low latency solutions trade-off on accuracy or fail to exploit the inherent temporal locality in prediction serving workloads. We observe that caching hidden layer outputs of the DNN can introduce a form of late-binding where inference requests only consume the amount of computation needed. This enables a mechanism for achieving low latencies, coupled with an ability to exploit temporal locality. However, traditional caching approaches incur high memory overheads and lookup latencies, leading us to design learned caches - caches that consist of simple ML models that are continuously updated. We present the design of GATI, an end-to-end prediction serving system that incorporates learned caches for low-latency DNN inference. Results show that GATI can reduce inference latency by up to 7.69X on realistic workloads.
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments

This work developed a meta-learning approach that adapts the control policy on the fly to different changing conditions for robust locomotion. The proposed method constantly updates the interaction model, samples feasible sequences of actions of estimated the state-action trajectories, and then applies the optimal actions to maximize the reward. To achieve online model adaptation, our proposed method learns different latent vectors of each training condition, which are selected online given the newly collected data. Our work designs appropriate state space and reward functions, and optimizes feasible actions in an MPC fashion which are then sampled directly in the joint space considering constraints, hence requiring no prior design of specific walking gaits. We further demonstrate the robot's capability of detecting unexpected changes during interaction and adapting control policies quickly. The extensive validation on the SpotMicro robot in a physics simulation shows adaptive and robust locomotion skills under varying ground friction, external pushes, and different robot models including hardware faults and changes.
Reliable Spanners: Locality-Sensitive Orderings Strike Back

A highly desirable property of networks is robustness to failures. Consider a metric space ( X,d X ) , a graph H over X is a ϑ -reliable t -spanner if, for every set of failed vertices B ⊂ X , there is a superset B + ⊇ B such that the induced subgraph H [ X ∖ B ] preserves all the distances between points in X ∖ B + up to a stretch factor t , while the expected size of B + is as most ( 1 + ϑ )∣ B ∣ . Such a spanner could withstand a catastrophe: failure of even 90% of the network. Buchin, Har-Peled, and Ol´ah [2019,2020], constructed very sparse reliable spanners with stretch 1 + (cid:15) for Euclidean space using locality-sensitive orderings. Har-Peled and Ol´ah [2020] constructed reliable spanners for various non-Euclidean metric spaces using sparse covers. However, this second approach has an inherent dependency on the aspect ratio (a.k.a. spread) and gives sub-optimal stretch and sparsity parameters. Our contribution is twofold:
Hyperdimensional computing as a framework for systematic aggregation of image descriptors

Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbolic processing with distributed representations in numerical vectors with thousands of dimensions. We present a HDC implementation that is suitable for processing the output of existing and future (deep learning based) image descriptors. We discuss how this can be used as a framework to process descriptors together with additional knowledge by simple and fast vector operations. A concrete outcome is a novel HDC-based approach to aggregate a set of local image descriptors together with their image positions in a single holistic descriptor. The comparison to available holistic descriptors and aggregation methods on a series of standard mobile robotics place recognition experiments shows a 20% improvement in average performance and > 2× better worst-case performance compared to runner-up.
Developing a Deep Neural Network to Denoise Time-Resolved In Situ ETEM Movies of Catalyst Nanoparticles

A deep learning-based convolutional neural network has been developed to denoise atomic-resolution in situ TEM image datasets of catalyst nanoparticles acquired on high speed, direct electron counting detectors, where the signal is severely limited by shot noise. The network was applied to a model catalyst of CeO2-supported Pt nanoparticles. We leverage multislice simulation to generate a large and flexible dataset for training and testing the network. The proposed network outperforms state-of-the-art denoising methods by a significant margin both on simulated and experimental test data. Factors contributing to the performance are identified, including most importantly (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms used by the network to denoise experimental images. This shows the network exploits information on the surrounding structure and that it adapts its filtering approach when it encounters atomic-level defects at the catalyst surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the catalyst surface. Finally, we develop an approach based on the log-likelihood ratio test that provides an quantitative measure of uncertainty regarding the atomic-level structure in the network-denoised image.
A Novel Cluster Classify Regress Model Predictive Controller Formulation; CCR-MPC

In this work, we develop a novel data-driven model predictive controller using advanced techniques in the field of machine learning. The objective is to regulate control signals to adjust the desired internal room setpoint temperature, affected indirectly by the external weather states. The methodology involves developing a time-series machine learning model with either a Long Short Term Memory model (LSTM) or a Gradient Boosting Algorithm (XGboost), capable of forecasting this weather states for any desired time horizon and concurrently optimising the control signals to the desired set point. The supervised learning model for mapping the weather states together with the control signals to the room temperature is constructed using a previously developed methodology called Cluster Classify regress (CCR), which is similar in style but scales better to high dimensional dataset than the well-known Mixture-of-Experts. The overall method called CCR-MPC involves a combination of a time series model for weather states prediction, CCR for forwarding and any numerical optimisation method for solving the inverse problem. Forward uncertainty quantification (Forward-UQ) leans towards the regression model in the CCR and is attainable using a Bayesian deep neural network or a Gaussian process (GP). For this work, in the CCR modulation, we employ K-means clustering for Clustering, XGboost classifier for Classification and 5th order polynomial regression for Regression. Inverse UQ can also be obtained by using an I-ES approach for solving the inverse problem or even the well-known Markov chain Monte Carlo (MCMC) approach. The developed CCR-MPC is elegant, and as seen on the numerical experiments is able to optimise the controller to attain the desired setpoint temperature.