Miracle-dz/UniFAR-FTUs huggingface dataset:
Chunking of Control: An Unrecognized Aspect of Cognitive Resource Limits

Why do we divide (‘chunk’) long tasks into a series of shorter subtasks? A popular view is that limits in working memory (WM) prevent us from simultaneously maintaining all task relevant information in mind. We therefore chunk the task into smaller units so that we only maintain information in WM that is relevant to the current unit. In contrast to this view, we show that long tasks that are not constrained by WM limits are nonetheless chunked into smaller units. Participants executed long sequences of standalone but demanding trials that were not linked to any WM representation and whose execution was not constrained by how much information could be simultaneously held in WM. Using signs well-known to reflect beginning of new task units, we show that such trial sequences were not executed as a single task unit but were spontaneously chunked and executed as series smaller units. We also found that sequences made of easier trials were executed as longer task units and vice-versa, further suggesting that the length of task executed as one unit may be constrained by cognitive limits other than WM. Cognitive limits are typically seen to constrain how many things can be done simultaneously e.g., how many events can be maintained in WM or attended at the same time. We show a new aspect of these limits that constrains the length of behaviour that can be executed sequentially as a single task-unit.
Random trees, graphs and recursive partitions

Je presente dans ce memoire mes travaux sur les limites d'echelle de grandes structures aleatoires. Il s'agit de decrire les structures combinatoires dans la limite des grandes tailles en prenant un point de vue objectif dans le sens ou on cherche des limites des objets, et non pas seulement de parametres caracteristiques (meme si ce n'est pas toujours le cas dans les resultats que je presente). Le cadre general est celui des structures critiques pour lesquelles on a typiquement des distances caracteristiques polynomiales en la taille, et non concentrees. Sauf exception, ces structures ne sont en general pas adaptees aux applications informatiques. Elles sont cependant essentielles de part l'universalite de leurs proprietes asymptotiques, prouvees ou attendues. Je parle en particulier d'arbres uniformement choisis, de graphes aleatoires, d'arbres couvrant minimaux et de partitions recursives de domaines du plan: Arbres aleatoires uniformes. Il s'agit ici de mieux comprendre un objet limite essentiel, l'arbre continu brownien (CRT). Je presente quelques resultats de convergence pour des modeles combinatoires ''non-branchants'' tels que des arbres sujets aux symetries et les arbres a distribution de degres fixee. Je decris enfin une nouvelle decomposition du CRT basee sur une destruction partielle. Graphes aleatoires. J'y decris la construction algorithmique de la limite d'echel-le des graphes aleatoires du modele d'Erdos--Renyi dans la zone critique, et je fais le lien avec le CRT et donne des constructions de l'espace metrique limite. Arbres couvrant minimaux. J'y montre qu'une connection avec les graphes aleatoires permet de quantifier les distances dans un arbre convrant aleatoire. On obtient non seulement l'ordre de grandeur de l'esperance du diametre, mais aussi la limite d'echelle en tant qu'espace metrique mesure. Partitions recursives. Sur deux exemples, les arbres cadrant et les laminations du disque, je montre que des idees basees sur des theoremes de point fixe conduisent a des convergences de processus, ou les limites sont inhabituelles, et caracterisees par des decompositions recursives.
The SPICA mission

SPICA is a mid and far-infrared space mission to be submitted as a candidate to ESA's fifth medium class mission call, due in early 2016. This will be a joint project between ESA and JAXA, with ESA taking the lead role. If selected, SPICA will launch in ˜2029 and operate for a goal lifetime of 5 years. The spacecraft will house a 2.5 m telescope actively cooled to 8 K, providing unprecedented sensitivity at mid-far infrared wavelengths. The low background environment and wavelength coverage provided by SPICA will make it possible to conduct detailed spectroscopic surveys of sources in both the local and distant Universe, deep into the most obscured regions. Using these data the evolution of galaxies over a broad and continuous range of cosmic time can be studied, spanning the era of peak star forming activity. SPICA will also provide unique access to, among others, the deep-lying water-ice spectral features and HD lines within planet forming discs. SPICA will conduct an extensive survey of both planet forming discs and evolved planetary systems, with the aim of providing the missing link between planet formation models and the large number of extrasolar planetary systems now being discovered.
Persistent Non-Suicidal Self-Injury and Suicidality in Referred Adolescents: A Longitudinal Study Exploring the Role of Cyclothymic Temperament

Non-suicidal self-injury (NSSI) is deliberate harm to the body surface without suicidal intent, though it may be a predictor of suicide attempts. Our aim was to test the hypothesis that persisting and recovering NSSI may have a different longitudinal risk for suicidal ideation and behavior and that the intensity of Cyclothymic Hypersensitive Temperament (CHT) may increase this risk. Fifty-five patients (mean age 14.64 ± 1.77 years) referred for mood disorders according to the DSM-5 were consecutively recruited and followed-up for a mean of 19.79 ± 11.67 months and grouped according to the presence/absence of NSSI at baseline and follow-up into three groups: without NSSI (non-NSSI; n = 22), with NSSI recovered at follow-up (past-NSSI; n = 19), and with persistent NSSI at follow-up (pers-NSSI; n = 14). At follow-up, both NSSI groups were more severely impaired and failed to improve internalizing problems and dysregulation symptoms. Both NSSI groups reported higher scores in suicidal ideation compared to non-NSSI, but only pers-NSSI presented higher scores in suicidal behavior. CHT was higher in pers-NSSI, followed by past-NSSI and then by non-NSSI. Our data support a continuity between NSSI and suicidality, and they suggest the prognostic validity of persistent NSSI, associated with highest CHT scores.
Fitted finite element discretization of two‐phase Stokes flow

We propose a novel fitted finite element method for two‐phase Stokes flow problems that uses piecewise linear finite elements to approximate the moving interface. The method can be shown to be unconditionally stable. Moreover, spherical stationary solutions are captured exactly by the numerical approximation. In addition, the meshes describing the discrete interface in general do not deteriorate in time, which means that in numerical simulations, a smoothing or a remeshing of the interface mesh is not necessary. We present several numerical experiments for our numerical method, which demonstrate the accuracy and robustness of the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.
Change in intestinal alkaline phosphatase activity is a hallmark of antibiotic-induced intestinal dysbiosis

Objective Intestinal alkaline phosphatase (IAP) maintains intestinal homeostasis by detoxifying bacterial endotoxins and regulating gut microbiota, and lipid absorption. Antibiotics administered to animals can cause gut dysbiosis and barrier disruption affecting animal health. Therefore, the present study sought to investigate the role of IAP in the intestinal environment in dysbiosis. Methods Young male mice aged 9 weeks were administered a high dose of antibiotics to induce dysbiosis. They were then sacrificed after 4 weeks to collect the serum and intestinal organs. The IAP activity in the ileum and the level of cytokines in the serum samples were measured. Quantitative real-time polymerase chain reaction analysis of RNA from the intestinal samples was performed using primers for tight junction proteins (TJPs) and proinflammatory cytokines. The relative intensity of IAP and toll-like receptor 4 (TLR4) in intestinal samples was evaluated by western blotting. Results The IAP activity was significantly lower in the ileum samples of the dysbiosis-induced group compared to the control. The interleukin-1 beta, interleukin-6, and tumor necrosis factor-alpha concentrations were significantly higher in the ileum samples of the dysbiosis-induced group. The RNA expression levels of TJP2, claudin-3, and claudin-11 showed significantly lower values in the intestinal samples from the dysbiosis-induced mice. Results from western blotting revealed that the intensity of IAP expression was significantly lower in the ileum samples of the dysbiosis-induced group, while the intensity of TLR4 expression was significantly higher compared to that of the control group without dysbiosis. Conclusion The IAP activity and relative mRNA expression of the TJPs decreased, while the levels of proinflammatory cytokines increased, which can affect intestinal integrity and the function of the intestinal epithelial cells. This suggests that IAP is involved in mediating the intestinal environment in dysbiosis induced by antibiotics and is an enzyme that can potentially be used to maintain the intestinal environment in animal health care.
Fast transmission of computer-generated hologram with compressed sensing and quantum-inspired neural network

Abstract. A two-stage compression framework for computer-generated hologram (CGH) with compressed sensing (CS) and quantum-inspired neural network (QINN) is proposed. A deep learning-based CS model is applied to sample the CGH at sub-Nyquist rates to generate measurements, and its more compact and representative information using interblock information is further extracted by QINN. With two-stage compression framework, the CGH is greatly compressed and suitable for storage and transmission. Experimental results demonstrate that our proposed two-stage compression framework can preserve more important holographic information during the compression, thus significantly improving the overall quality of reconstructed images compared to only using CS or QINN.
A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification

It is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informative. Therefore, selecting genes to study as a starting point for cancer classification is common practice. We offer a novel approach that combines the Runge Kutta optimizer (RUN) with a support vector machine (SVM) as the classifier to select the significant genes in the detection of cancer tissues. As a means of dealing with the high dimensionality that characterizes microarray datasets, the preprocessing stage of the ReliefF method is implemented. The proposed RUN–SVM approach is tested on binary-class microarray datasets (Breast2 and Prostate) and multi-class microarray datasets in order to assess its efficacy (i.e., Brain Tumor1, Brain Tumor2, Breast3, and Lung Cancer). Based on the experimental results obtained from analyzing six different cancer gene expression datasets, the proposed RUN–SVM approach was found to statistically beat the other competing algorithms due to its innovative search technique.
The Importance of Educational Adaptability

What is the role of education for preparing the labor force of the middle of the 21st century? The most conventional response is that we need to specify the 21st century skills that will be required. These can then be translated into changes in core curriculum and instructional practices that incorporate increasing use of educational technologies to produce a capable and competitive labor force as well as measures for assessment.
Emulsion-Templated Liquid Oil Structuring with Egg White Protein Microgel- Xanthan Gum

In this study, oleogels were prepared by the emulsion-template method using egg-white protein microgel as a gelator and xanthan gum (XG) as thickener. The physicochemical properties of the emulsion and oleogels were investigated. The adsorption of protein on the surface of the oil droplet reached saturation when the protein microgel concentration reached 2%. The excess protein combined with XG and accumulated on the outer layer of the oleogel, which prevented the emulsion from flocculation, enhanced the oil-holding capacity of the oleogel, and had a positive effect on preventing the oxidation of oil. When the concentration of XG was less than 0.4%, the EWP microgel, combined with the XG, stabilized the emulsion. As the concentration of XG was greater than 0.4%, excessive XG in the emulsion improved the viscosity and mechanical properties of the emulsion to prevent the aggregation of oil droplets. However, the change in XG concentration had no significant effect on the oxidation of the oil.
Stilladsering af logbog som metode til reflekteret læring

Logbog som metode til understøttelse af refleksion kan være med til at bygge bro over kløften mellem teori og praksis som beskrevet af bl.a. nyuddannede psykologer. Studiet undersøger betydningen for de studerendes læringsoplevelse af stilladsering af logbogsmetoden gennem organiserede refleksionsøvelser og feedback i et praksisfællesskab. Skriftlige og mundtlige evalueringer blandt 30 kandidatstuderende på psykologi samt beskrivelser af egen læring i eksamensopgaverne danner sammen med en fokusgruppe datamaterialet for analysen. Undersøgelsen viste, at refleksionsøvelserne uddybede og forankrede de studerendes læringsoplevelse og bidrog som et direkte afsæt til logbogsskrivningen. Peer-feedback bidrog til inspiration og fortrolighed med logbogsmetoden via aktiv deltagelse i praksisfællesskabet. Indsatsen ved logbogsskrivningen undervejs i faget mindskede generelt fokus på eksamensopgaven som præstation og tydeliggjorde læringsprocessen samt fokus på at studere. Via organisering af refleksioner i et praksisfællesskab samt peer-feedback fremmer logbogen som metode og eksamensform de psykologistuderendes læring og understøtter relationen mellem akademisk teori og psykologfaglig praksis.
The applications of nanozymes in neurological diseases: From mechanism to design

Nanozymes are a class of nanomaterials with enzyme-like catalytic activities. Due to their multiple catalytic activities, as well as their good stability, modifiable activity and other advantages over natural enzymes, they have a wide range of application prospects in sterilization, the treatment of inflammation, cancer, and neurological diseases, and other fields. In recent years, it has been found that various nanozymes have antioxidant activity, allowing them to simulate the endogenous antioxidant system and play an important role in cell protection. Therefore, nanozymes can be applied in the treatment of reactive oxygen species (ROS)-related neurological diseases. Another advantage of nanozymes is that they can be customized and modified in a variety of ways to increase their catalytic activity beyond that of classical enzymes. In addition, some nanozymes have unique properties, such as the ability to effectively penetrate the blood‒brain barrier (BBB) or to depolymerize or otherwise eliminate misfolded proteins, making them potentially useful therapeutic tools for the treatment of neurological diseases. Here, we review the catalytic mechanisms of antioxidant-like nanozymes, as well as the latest research progress and strategies for designing therapeutic nanozymes, aiming to promote the development of more effective nanozymes for the treatment of neurological diseases in the future.
The entrepreneurial gender gap: The role of in-group support and national embeddedness values in young women’s entrepreneurship

This article uses a global multilevel sample to advance our understanding of the gender gap in youth entrepreneurship by investigating the joint moderating influence of in-group support and national embeddedness values on young women’s entrepreneurial activity relative to that of young men. Based on a mixed embeddedness theoretical lens, our moderation analysis demonstrates the importance of in-group support in narrowing the gender gap in youth entrepreneurship. Moreover, in-group support enhances young women’s entrepreneurship vis-à-vis that of young men primarily in countries with strong embeddedness values. Our findings contribute to the entrepreneurial gender gap literature as well as to the comparative entrepreneurship literature, by providing evidence of the joint role of micro-level and macro-level cultural layers in reducing the entrepreneurial gender gap. Implications for theory, practice and policy are provided.
Mixed Warm and Cold Autoimmune Hemolytic Anemia With Concomitant Immune Thrombocytopenia Following Recent SARS-CoV-2 Infection and Ongoing Rhinovirus Infection

Mixed-type autoimmune hemolytic anemia (AIHA) is a term used to describe hemolysis occurring in the context of both warm and cold reactive autoantibodies to red blood cells. Immune thrombocytopenia (ITP) is an acquired form of thrombocytopenia potentially complicated by hemorrhage due to autoantibodies reactive with platelets and megakaryocytes. Diagnosis of ITP requires exclusion of other known causes of thrombocytopenia. AIHA and ITP may be primary disorders or associated with lymphoproliferative, autoimmune, or viral infections. Here, we report a rare case of simultaneous mixed-type autoimmune hemolytic anemia with immune thrombocytopenia following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection treated with Paxlovid followed by Rhinovirus infection.
Capacitive wearable tactile sensor based on smart textile substrate with carbon black /silicone rubber composite dielectric

To achieve the wearable comfort of electronic skin (e-skin), a capacitive sensor printed on a flexible textile substrate with a carbon black (CB)/silicone rubber (SR) composite dielectric was demonstrated in this paper. Organo-silicone conductive silver adhesive serves as a flexible electrodes/shielding layer. The structure design, sensing mechanism and the influence of the conductive filler content and temperature variations on the sensor performance were investigated. The proposed device can effectively enhance the flexibility and comfort of wearing the device asthe sensing element has achieved a sensitivity of 0.02536%/KPa, a hysteresis error of 5.6%, and a dynamic response time of ~89 ms at the range of 0–700 KPa. The drift induced by temperature variations has been calibrated by presenting the temperature compensation model. The research on the time–space distribution of plantar pressure information and the experiment of the manipulator soft-grasping were implemented with the introduced device, and the experimental results indicate that the capacitive flexible textile tactile sensor has good stability and tactile perception capacity. This study provides a good candidate for wearable artificial skin.
Distillation and Ordinary Federated Learning Actor-Critic Algorithms in Heterogeneous UAV-Aided Networks

In recent years, there has been growing enthusiasm for employing Unmanned Aerial Vehicles (UAVs) as an innovative technology with significant potential for the next generation of wireless networks. Hence, the Quality of Service (QoS) of clients within the Internet of Things (IoT) infrastructure can be considerably enhanced by integrating UAVs into wireless systems. However, maximizing QoS and ensuring user information security poses significant challenges in determining the trajectory of UAVs, as they need to account for the mobility and density of users. This article addresses this issue and considers a heterogeneous UAV-assisted network in which UAVs as Base Stations (BSs) have the role of finding the best trajectory based on the movement and density of Ground Users (GUs) in a way that the enhancement of the security of the covered users’ information and QoS is guaranteed. To achieve these objectives, we first introduce the Actor-Critic (AC) scheme for the optimal trajectory of UAVs due to the use of a continuous environment with a large number of states and actions. In addition, the accuracy and learning speed of the agent in AC algorithms is higher than in other Reinforcement Learning (RL) schemes. Then, the combination of ordinary Federated Learning (FL) and Distillation Federated Learning (DFL) algorithms with AC is presented to increase the security of users’ data, the learning speed of UAVs, and improve the QoS of GUs. In the FL scheme, access to the principal information of users is limited, but it cannot prevent reverse engineering techniques to attain users’ data. To intercept such incidents, we use the DFL scheme. Due to the dynamic nature of weight updating in the neural network, this method is associated with a low hacking probability. Simulation results demonstrate that utilizing FL- and DFL-AC algorithms can significantly enhance the learning speed of the proposed schemes, while increasing the downlink rate of users by approximately 1.1% and 1.5%, respectively, compared to the AC method.
Lecture Notes 1

In a perfect economy people don't need to understand all the implications of their consumption on different resources; they only need to know the price. The price is the sole sufficient indicator of scarcity. So much energy is expended by modern consumers trying to balance off different criteria, even for simple choices like a lightbulb. An incandescent bulb uses 'too much' energy relative to a fluorescent, but fluorescent bulbs usually contain mercury (hazardous disposal), other types of bulb might consume particular resources (rare earth metals) in being made. How ought consumers to trade off greater electricity usage versus mercury contamination? A consumer can be left swamped with information! But in a perfect economy consumers only need to look at the price. Clearly we don't live in a perfect economy.
Numerical Integration in Meshfree Methods

The issue of finding efficient numerical integration schemes for integrals arising in the Galerkin approximation of partial differential equations using compactly supported radial basis functions is studied. In this dissertation, the model problem of the Helmholtz equation with natural boundary conditions will be considered. Different quadrature rules will be discussed and studied as well as their influence on the convergence and stability of the approximation process.
Digital Technologies’ Risks and Opportunities: Case Study of an RFID System

Smart technologies have been the subject of a growing interest for the past few years due to the growing market demand. They are believed to improve human life, existence, and companies’ performance. Considering the recent advances, X.0 concept has proven to be a mindset changing so that companies can now see that they can improve their competitiveness, ensure an innovative, sustainable and resilient environment, and smarten and develop their lean manufacturing tools. Nevertheless, if X.0 adoption is still not at its highest level, it is because of the relevant challenges and difficulties that occur during the implementation process. Within this scope, this paper aims, through a systematic literature review, to identify risks and opportunities of X.0 technologies to constitute a referential to be taken into consideration for a successful implementation. Results are validated by the modelling and simulation of an RFID system applied within the automotive industry, for which we identified risks and opportunities from one side and the system contribution in terms of smart Lean Manufacturing. From one hand, the value added of this paper, on the contrary of previous researches, is mainly regrouping risks and opportunities of most relevant digital technologies to conclude on those of X.0 revolution as a concept as described in following sections. From another hand, we were able to prove, through a real case study, that X.0 concept directly contribute in smartening and improving lean manufacturing principles.