Miracle-dz/UniFAR-FTUs huggingface dataset:
N-Step Pre-Training and Décalcomanie Data Augmentation for Micro-Expression Recognition

Facial expressions are divided into micro- and macro-expressions. Micro-expressions are low-intensity emotions presented for a short moment of about 0.25 s, whereas macro-expressions last up to 4 s. To derive micro-expressions, participants are asked to suppress their emotions as much as possible while watching emotion-inducing videos. However, it is a challenging process, and the number of samples collected tends to be less than those of macro-expressions. Because training models with insufficient data may lead to decreased performance, this study proposes two ways to solve the problem of insufficient data for micro-expression training. The first method involves N-step pre-training, which performs multiple transfer learning from action recognition datasets to those in the facial domain. Second, we propose Décalcomanie data augmentation, which is based on facial symmetry, to create a composite image by cutting and pasting both faces around their center lines. The results show that the proposed methods can successfully overcome the data shortage problem and achieve high performance.
On the Robustness of Economic Models

We investigate the different ways in which the results of theoretical models can be ‘robust’. We identify three kinds of ‘robustness’: (1) robustness to changes in the model’s idealisations; (2) robustness to changes in the ‘background’ conditions; (3) robustness to changes in the implied causal mechanism. Each of these is discussed and illustrated by means of examples from economic practice.
Aurora Image Classification with Deep Metric Learning

In recent years, neural networks have been increasingly used for classifying aurora images. In particular, convolutional neural networks have been actively studied. However, there are not many studies on the application of deep learning techniques that take into account the characteristics of aurora images. Therefore, in this study, we propose the use of deep metric learning as a suitable method for aurora image classification. Deep metric learning is one of the deep learning techniques. It was developed to distinguish human faces. Identifying human faces is a more difficult task than standard classification tasks because this task is characterized by a small number of sample images for each class and poor feature variation between classes. We thought that the face identification task is similar to aurora image classification in that the number of labeled images is relatively small and the feature differences between classes are small. Therefore, we studied the application of deep metric learning to aurora image classification. As a result, our experiments showed that deep metric learning improves the accuracy of aurora image classification by nearly 10% compared to previous studies.
Wearable Real-Time Gesture Recognition Scheme Based on A-Mode Ultrasound

A-mode ultrasound has the advantages of high resolution, easy calculation and low cost in predicting dexterous gestures. In order to accelerate the popularization of A-mode ultrasound gesture recognition technology, we designed a human-machine interface that can interact with the user in real-time. Data processing includes Gaussian filtering, feature extraction and PCA dimensionality reduction. The NB, LDA and SVM algorithms were selected to train machine learning models. The whole process was written in C++ to classify gestures in real-time. This paper conducts offline and real-time experiments based on HMI-A (Human-machine interface based on A-mode ultrasound), including ten subjects and ten common gestures. To demonstrate the effectiveness of HMI-A and avoid accidental interference, the offline experiment collected ten rounds of gestures for each subject for ten-fold cross-validation. The results show that the offline recognition accuracy is 96.92% ± 1.92%. The real-time experiment was evaluated by four online performance metrics: action selection time, action completion time, action completion rate and real-time recognition accuracy. The results show that the action completion rate is 96.0% ± 3.6%, and the real-time recognition accuracy is 83.8% ± 6.9%. This study verifies the great potential of wearable A-mode ultrasound technology, and provides a wider range of application scenarios for gesture recognition.
Spectral Graph Theory of the Hypercube

Abstract : In Graph Theory, every graph can be expressed in terms of certain real, symmetric matrices derived from the graph, most notably the adjacency or Laplacian matrices. Spectral Graph Theory focuses on the set of eigenvalues and eigenvectors, called the spectrum, of these matrices and provides several interesting areas of study. One of these is the inverse eigenvalue problem of a graph, which tries to determine information about the possible eigenvalues of the real symmetric matrices whose pattern of nonzero entries is described by a given graph. A second area is the energy of a graph, defined to be the sum of the absolute values of the eigenvalues of the adjacency matrix of that graph.
Production of ostrich meat pâtés: Design of a food safety management system

In recent years, there has been a growing interest in ostrich breeding, and the commercial breeding of these birds has attracted the attention of new breeders, and it has become a great alternative to agricultural production. The study on the production of pâtés was conducted in 2019 in Almaty (Kazakhstan). During the formation of two new formulations of pâtés, the following safety system parameters were taken into account: microflora development; oxidation number (intensity of oxidation products accumulation). The aim of the study is to obtain the optimal characteristics of ostrich meat preservation in the production of pâtés. As a result of the study, it was found that ostrich meat samples have high nutritional value. Meat is characterized by a high content of protein, low content of fat, and carbohydrates are almost absent. Also, it was found that concentration of arachidonic acid in ostrich meat was 3 times higher as compared to beef (p ≤ 0.05). Therefore, the introduction of inulin and carrageenan polysaccharides into the recipe of ostrich pâtés allows one to significantly improve their shelf life and based on the developed recipe it is possible to start the production of pâtés.
Design Optimization of a Submersible Chemiluminescent Sensor (DISCO) for Improved Quantification of Reactive Oxygen Species (ROS) in Surface Waters

Reactive oxygen species (ROS) are key drivers of biogeochemical cycling while also exhibiting both positive and negative effects on marine ecosystem health. However, quantification of the ROS superoxide (O2−) within environmental systems is hindered by its short half-life. Recently, the development of the diver-operated submersible chemiluminescent sensor (DISCO), a submersible, handheld instrument, enabled in situ superoxide measurements in real time within shallow coral reef ecosystems. Here, we present a redesigned and improved instrument, DISCO II. Similar to the previous DISCO, DISCO II is a self-contained, submersible sensor, deployable to 30 m depth and capable of measuring reactive intermediate species in real time. DISCO II is smaller, lighter, lower cost, and more robust than its predecessor. Laboratory validation of DISCO II demonstrated an average limit of detection in natural seawater of 133.1 pM and a percent variance of 0.7%, with stable photo multiplier tube (PMT) counts, internal temperature, and flow rates. DISCO II can also be optimized for diverse environmental conditions by adjustment of the PMT supply voltage and integration time. Field tests showed no drift in the data with a percent variance of 3.0%. Wand tip adaptations allow for in situ calibrations and decay rates of superoxide using a chemical source of superoxide (SOTS-1). Overall, DISCO II is a versatile, user-friendly sensor that enables measurements in diverse environments, thereby improving our understanding of the cycling of reactive intermediates, such as ROS, across various marine ecosystems.
Critical Path-Based Backdoor Detection for Deep Neural Networks

Backdoor attack to deep neural networks (DNNs) is among the predominant approaches to bring great threats into artificial intelligence. The existing methods to detect backdoor attacks focus on the perspective of distributions in DNNs, however, limited by its ability of generalization across DNN models. In this article, a critical-path-based backdoor detector (CPBD) is proposed, which approaches to detect backdoor attacks via DNN’s interpretability. CPBD is designed to efficiently discover the characteristics of backdoors, which distinguish the critical paths in the attacked DNNs. To deal with the intractably large number of neurons, we propose to simplify the neurons, and the preserved key nodes are integrated into a set of critical paths. Thus, a DNN model can be formulated as a combination of several critical paths. Afterward, the detection of backdoors is performed based on the analysis of critical paths corresponding to different classes. Then, combining all the above steps, the CPBD algorithm is integrated to present the results in a standard and systematic manner. In addition, CPBD is able to locate neurons associated with malicious triggers, the combination of which is named as trigger propagation path. Extensive experiments are conducted, which testify the efficiency of the proposed method on multiple DNNs and different trigger sizes.
Rabi spectra - a simple tool for analyzing the limitations of RWA in modelling of the selective population transfer in many-level quantum systems

The controlled manipulation of quantum systems is becoming an important technological tool. Rabi oscillations are one of the simplest, yet quite useful mechanisms for achieving such manipulation. However, the validity of simple Rabi theory is limited and its upgrades are usually both technically involved, as well as limited to the simple two-level systems. The aim of this paper is to demonstrate a simple method for analyzing the applicability of the RWA Rabi solutions to the controlled population transfer in complex many-level quantum systems.
Gross lipemia and blood bank testing

A 44-year-old G6P3 female at 25 weeks gestational age was transferred to our tertiary care center for threatened preterm birth and a stat cesarean section was performed upon presentation for abnormal fetal tracing. A group and screen specimen was found to be grossly lipemic (Figure 1). Intraoperatively, the on-call obstetrician made note of thick, oily, and milky appearing blood at the surgical and venipuncture sites. On the group and screen testing by gel, the reverse typing and antibody screen could not be read by the OrthoVision analyzer. (Figures 2A and 2B). As per our institution's policy, if there is interference detected due to lipemia on the automated analyzer, the results are repeated by tube testing and interpreted manually. The gel cards were manually read and testing using saline IAT confirmed the patient's blood group and negative antibody screen. The patient had an oral glucose tolerance test the day prior, which was suspected to have precipitated diabetic ketoacidosis (DKA) and exacerbated her underlying hypercholesterolemia. Post-operative bloodwork demonstrated an elevated triglyceride level at 154.8 mmol/L, which had been normal at 2.6 mmol/L 2 months prior. Sample interferences such as gross lipemia, hemolysis, and icterus can lead to erroneous results and delays in blood product administration. Gross lipemia can be due to sample collection shortly after parenteral administration or eating a large meal, underlying disease states (diabetes mellitus, pancreatitis, chronic kidney disease), and propofol infusions. In addition, interference of gel testing may be due to the presence of excess paraproteins, fibrin, cold-reacting antibodies, particulate matter in the plasma, and in this case, lipemia. Grossly hemolyzed or icteric samples may also lead to difficulties in interpretation of results. This was the first presentation of DKA and hypertriglyceridemia in a pregnant patient with no history of diabetes after an oral glucose challenge test. This acute FIGURE 1 Patient sample demonstrating gross lipemia after centrifugation Received: 10 December 2021 Revised: 18 August 2022 Accepted: 20 August 2022
Relativistic Bose gases at finite density

Nordita,Blegdamsvej 17, DK-2100 Copenhagen O, Denmark(Dated: February 2, 2008)We consider a massive relativistic Bose gas with N complex scalars at ﬁnite density. At zerotemperature, we calculate the pressure, charge density and the speed of sound in the one-loopapproximation. In the nonrelativistic limit, we obtain the classic results for the dilute Bose gas.We also discuss ﬁnite-temperature eﬀects. In particular, we consider the problem of calculatingthe critical temperature for Bose-Einstein condensation. Dimensional reduction and eﬀective-ﬁeld-theory methods are used to perturbatively calculate the eﬀects of the nonstatic Matsubara modes.Calculations of T
Additive and non-additive epigenetic signatures of natural hybridization between fish species with different mating systems

ABSTRACT Hybridization is a major source of evolutionary innovation. In plants, epigenetic mechanisms can help to stabilize hybrid genomes and contribute to reproductive isolation, but the relationship between genetic and epigenetic changes in animal hybrids is unclear. We analysed the relationship between genetic background and methylation patterns in natural hybrids of two genetically divergent fish species with different mating systems, Kryptolebias hermaphroditus (self-fertilizing) and K. ocellatus (outcrossing). Co-existing parental species displayed highly distinct genetic (SNPs) and methylation patterns (37,000 differentially methylated cytosines). Hybrids had predominantly intermediate methylation patterns (88.5% of the sites) suggesting additive effects, as expected from hybridization between genetically distant species. The large number of differentially methylated cytosines between hybrids and parental species (n = 5,800) suggests that hybridization may play a role in increasing genetic and epigenetic variation. Although most of the observed epigenetic variation was additive and had a strong genetic component, we also found a small percentage of non-additive, potentially stochastic, methylation differences that might act as an evolutionary bet-hedging strategy and increase fitness under environmental instability.
Morphological, Chemical, and Physical–Mechanical Properties of a Clumping Bamboo (Thyrsostachys oliveri) for Construction Applications

In view of the long-term utilization history as a building and furniture making material in southeast Asian countries, Thyrsostachys oliveri is considered to have great utilization potential. However, little is known about the quantitative morphological characteristics and comprehensive material properties of its culm. In this study, we systematically investigated the morphological characteristics, the chemical components, and the physical–mechanical properties of the three-year-old culm of T. oliveri. The morphological analysis result showed that the internode length, the diameter of internodes and the wall thickness changed with the culm height. The volume of the culm wall of a single internode increased before the 10th internode, and then it decreased to a significant level at the 20th internode. The basic chemical compositions (cellulose, hemicellulose, lignin and silicon content) of the culm wall were 346.19 mg/g, 95.32 mg/g, 33.17%, and 3.39 mg/g, respectively. These component contents were relatively stable in the bottom and middle part of the culm, but changed significantly in the upper part of the culm. The moisture content and the base density of the culm wall were 73.01% and 0.64 g/cm3, respectively. The culm wall shrinkage rate in the radial, tangential direction as well as the volumetric shrinkage reached the minimum value in the middle part of the culm. The average compressive strength, modulus of rupture and modulus of elasticity of the culm wall were 67.03 MPa, 143.74 MPa, and 7.99 GPa, respectively. These results provide valuable reference data for more rational use of this bamboo resources.
Analysis of interpurchase intervals of cars using truncated and censored data

This thesis contributes in several ways to the existing knowledge on estimation of truncated, censored, and left truncated right censored (LTRC) regression models. Three new semiparametric estimators are proposed, allowing for asymmetric error distributions. A bootstrap method for estimation of the covariance matrix of the quadratic mode estimator (QME) is proposed and studied. In addition, finite sample properties of estimators for truncated, censored, and LTRC data are studied within simulation studies and applications with real data. The first paper consists of a simulation study of the QME and other estimators of truncated regression models. The paper contributes with results suggesting the bootstrap technique being potentially useful for estimation of the QME covariance matrix. In the second paper estimators of truncated and censored semiparametric regression models are proposed. These estimators are generalizations of the QME and the winsorized mean estimator (WME) by allowing asymmetric ``trimming'' of observations. Consistency and asymptotic normality of the estimators are shown. By combining the two moment restrictions used to derive the estimators in the second paper, a consistent estimator of LTRC regression models is proposed in the third paper. The fourth paper contains an application where LTRC interpurchase intervals of cars are analysed. Results regarding the interpurchase behaviour of consumers are provided, as are results on estimator properties.
An update on teratogens for pediatric healthcare providers

Purpose of review To review updates regarding teratogens and give pediatric healthcare providers insight into the prevention of teratogenic exposures. Recent findings Application of the principles of teratology can help to assess the potential for exposures to be teratogenic. Identification of Zika virus as a teratogen, the most recent teratogenic agent identified, allowed public health measures to be put in place to mitigate its spread. Risk management strategies for teratogenic medications have resulted in a decrease but often not elimination of prenatal exposures. The failure to include pregnant persons in clinical trials results in their being less likely to receive needed medications and vaccines in a timely manner. Summary Pediatricians play an important role in the prevention of teratogenic exposures. Ensuring optimal management of patients with chronic illnesses that might increase their risk of birth defects during pregnancy due to the illness itself or its treatment is essential. For patients with pregnancy potential who are on teratogenic medications, ensuring effective contraception is also important. Inclusion of pregnant persons in clinical trials and research studies will be critical to advancing our knowledge of the safety of medications and other exposures during pregnancy.
Simulation-Based Analysis of “What-If” Scenarios with Connected and Automated Vehicles Navigating Roundabouts

Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the “what-if” traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs’ capabilities to increase roundabouts’ throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.
Dribbling skill determinants in youth soccer players

Dribbling and pass-kick are the most frequently performed techniques during match play. While an extensive knowledge is available about kicking biomechanics, little is known about dribbling kinematics. Given that dribbling performance can assist in the talent identification process [1], we aim at finding the biomechanics determinants of this technique. A motion analysis system recorded the 3-D coordinates of 21 reflective markers placed upon the body of 10 youth sub-elite soccer players (12.6±0.37 years, 42.9±6.15 kg, 1.54 ± 0.07 m). Each participant performed 5 slalom-dribbling tests consisting of a 180° turning and four changes of direction. Since dribbling speed is a separating performance factor, players were split into two groups of 5 according to the execution time (“fast” and “slow”). Center of Mass (CoM) trajectory, Range of Motion (RoM), velocity and acceleration were computed along the three directions [2]. Mann-Whitney’s non-parametric tests were used for comparisons. Fast and slow players did not differ in terms of age, weight, body height, BMI, playing position and number of foot-ball contacts (p>0.05). While CoM average velocity and root mean square acceleration were similar in the two groups, CoM mediolateral and craniocaudal RoM were significantly lower (p<0.05) in faster players. Skilled players took less time in completing the test because they were able to drive the ball while running through a shorter path. Their CoM vertical and lateral displacements were significantly lower, meaning that, at a higher skill level, dribbling technique is delivered in a more effective and economical way.
Selective enrichment of energetic ions in impulsive solar flares

We have shown previously that heavy ions as well as 3He can be accelerated to MeV energies by obliquely propagating electromagnetic proton cyclotron waves in impulsive solar flares. Impulsive flares are characterized by an increase of up to four orders of magnitude in the abundance of energetic 3He ions and up to one order of magnitude in energetic heavy ions compared to the abundances in long duration events and in the solar corona. 3He is accelerated when the first harmonic resonance is satisfied, while the heavier ions can be accelerated by the same wave at the second harmonic resonance when the wave frequency is twice the ion gyrofrequency. We postulate that obliquely propagating proton cyclotron waves exist in impulsive solar flares in analogy with the Earth’s aurora, where such waves are often the most intense waves present. The heating of 3He and of heavy ions can be substantially more efficient in a nonuniform plasma than in a uniform plasma. The heating efficiencies are nearly equal at higher ene.