Miracle-dz/UniFAR-FTUs huggingface dataset:
Demand Analytics in E-Commerce Leveraging Computer Vision Algorithms

In e-commerce marketing, consumer choice is usually an uncertain process that depends on various factors. This paper introduces deep learning models, such as YOLOv3 and MASK-RCNN, for e-commerce image content detection by utilizing e-commerce sales data. The extraction of HSV color features, identification of promotional information by CTPN + CRNN + CTC, and the use of Spearman's correlation coefficient to analyze the relationship between variables provided valuable data for understanding the relation between sales and images in the e-commerce market. Furthermore, Automl and Catboost Regressor were used to determine consumers' preferences in images, and 2SLS regression analysis was performed on the influencing factors. Additionally, we established the BLP model along with GMM structure estimation to analyze the impact on consumers' purchasing decisions. In this work, an end-to-end machine vision system was designed to turn image information into quantitative coordinates in consumer choice models, and we proposed that the best image layout with deep learning models can improve consumers' willingness to buy.
The Fundamental of Leaky wave Antenna

The fundamental of leaky-wave antenna (LWAs) that have been compiled and published are presented in this paper. A leaky-wave antenna uses the guiding structural, which support the propagation of waves along the length of the structure. While the basic family of LWAs is dependent on the radiation of a propagative mode in a basic guiding structure (usually a waveguide of some sort), the radiation mechanism changes drastically from one sub-category to another. Such antennas either uniform or periodic appear in various characteristics and performances. The fundamental of the basic operating principles and physics, and discuss the design of types of LWA.
A Meta-heuristic Approach for Strategic Fair Division Problems

Fair division of resources emerges in a variety of different contexts in real-world problems, some of which can be seen through the lens of game theory. Many equilibrium notions for simple fair division problems with indivisible items have been considered, and many of these notions are hard to compute. Strategic fair division is a branch of fair division in which participants may act uncooperatively to maximize their utility. In the presence of participants who have strategic behavior, it is essential to have a suitable algorithm in place to allocate resources in a fair and equitable manner. We propose a new approach to solve strategic fair division problems where fairness is attained by finding a constrained Nash equilibrium in a specific game. We show that computational complexity barriers also hold. More broadly, the theoretical results of this paper could potentially be applied to related general game theory problems and complex fair division problems. Finally, we propose an algorithm for finding a constrained Nash equilibrium in the game that we introduce. Our focus will be on one particular meta-heuristic – the bus transportation algorithm – as an approach to improve the running time of the search.
Hybrid Pose and Velocity-Bias Estimation on $SE(3)$ Using Inertial and Landmark Measurements

This paper deals with the design of globally exponentially stable invariant observers on the Special Euclidian group <inline-formula><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula>. First, we propose a generic hybrid estimation scheme (depending on a generic potential function) evolving on <inline-formula><tex-math notation="LaTeX">$SE(3)\times \mathbb {R}^6$</tex-math></inline-formula> for pose (orientation and position) and velocity-bias estimation. Thereafter, the proposed estimation scheme is formulated explicitly in terms of inertial vectors and landmark measurements. Interestingly, the proposed estimation scheme leads to a decoupled rotational error dynamics from the translational dynamics, which is an interesting feature in practical applications with noisy measurements and disturbances.
The Creative Knife: Does Interference Dull the Edge of Creativity Effectiveness?

Abstract It is no secret that creativity is an important component of advertising. It can enhance attention, facilitate recall, and drive emotional responses. However, there is continued opportunity to explore the boundary conditions regarding creativity’s ability to break though clutter. This article looks at whether two types of interference—external interference and competitive interference—limit creativity’s ability to enhance recall. We test this across three studies investigating the effects of these types of interference on individuals’ ability to recall creative ads and brands. The first experiment tested external interference using digitally manipulated print ads and three external music conditions. The second experiment tested competitive interference through TV ads. A third study extended the second study by holding creativity constant within the interference conditions. Results indicate that creativity does indeed facilitate recall under limited interference. However, this effect is lost under conditions of high interference, implying that creativity can be dulled and lost in the noise. We find these effects under both immediate and one-week-delay recall conditions. These findings are important for researchers and managers as the positive impact of creativity may not be realized under conditions of high interference but may certainly be fully realized in conditions of low or moderate interference.
GODDeSS: a Geant4 extension for easy modelling of optical detector components

Scintillator- and fibre-based particle detectors with SiPM readout are an indispensable tool in high-energy particle physics, medical physics and other fields of application. For designing and understanding these detectors, very detailed simulations are necessary, which require an accurate modelling of the optical physics (optics, scintillation, wavelength-shifting effects, . ), of the optical material properties, and of the geometry. To allow for a reliable usage also by less experienced users, the necessary complexity and flexibility of a suitable simulation framework must not lead to an increasing danger of user mistakes. Additionally, the required effort for creating or modifying a detailed simulation has to be minimised in order to allow for the fast creation of flexible simulation setups. These challenges have been addressed by developing GODDeSS (Geant4 Objects for Detailed Detectors with Scintillators and SiPMs). It is an extension of the particle-physics simulation tool Geant4 and allows for the easy simulation of optical detector components, especially combinations of scintillators, optical fibres, and photodetectors. GODDeSS enables the user to create extensive setups for Geant4 simulations with a few lines of source code. At the same time, GODDeSS helps to avoid typical user mistakes. This paper introduces the basic concepts of the GODDeSS framework, its object classes, and its functionality. Furthermore, test measurements with prototype modules will be presented, which were performed to validate simulation results of the GODDeSS framework.
Psychiatrische Erkrankungen im Vorfeld der Anorexia nervosa

In einer retrospektiven Untersuchung wurden prämorbide psychiatrische Störungen von 40 stationär behandelten Anorexia nervosa-Patientell und einer Vergleichsgruppe (Patienten mit Neurosen und Emotionalstörungen) erfaßt. Die ErkrankungshHufigkeit stimmte in beiden Gruppen überein : Bei den magersüchtigen Patienten standen Angstund Affektsyndrome im Vordergrund, die sich besonders in neuen Lebenssituationen manifestierten. Die These der . "Musterkindheit" magersüchtiger Patienten mit einem erst durch die Pubertät bedingten Krankheitsaushruch muß daher bezweifelt werden.
Impact of operating temperature on the electrical and magnetic properties of the bottom-pinned perpendicular magnetic tunnel junctions

Analogous device parameters in both the parallel (P) and anti-parallel (AP) states ensure a symmetric spin-transfer-torque magnetic random-access memory operation scheme. In this study, however, we observe an increasing asymmetry in the performance metrics with operating temperature of the bottom-pinned perpendicular magnetic tunnel junction (p-MTJ) devices. A temperature-dependent increase in the contribution of the stray field is observed in the tunneling magnetoresistance loop analysis. The switching current for P-to-AP decreases by 30% in the thermally activated switching regime by increasing the temperature from 300 K to 400 K, while it remains similar for AP-to-P. In addition, with the same temperature range, the thermal stability factor for the P state decreases 20% more than that for the AP state. We attribute those observations to the increase in the overcompensation of the stray field from the synthetic anti-ferromagnet structure. Saturation magnetization (MS) of the [Co/Pt]x-based multilayers i.
A Unit-Consistent Error Measure for Mixed Linear Complementarity Problems in Multibody Dynamics With Contact

Modeling multibody systems subject to unilateral contacts and friction efficiently is challenging, and dynamic formulations based on the mixed linear complementarity problem (MLCP) are commonly used for this purpose. The accuracy of the MLCP solution method can be evaluated by determining the error introduced by it. In this paper, we find that commonly used MLCP error measures suffer from unit inconsistency leading to the error lacking any physical meaning. We propose a unit-consistent error measure which computes energy error components for each constraint dependent on the inverse effective mass and compliance. It is shown by means of a simple example that the unit consistency issue does not occur using this proposed error measure. Simulation results confirm that the error decreases with convergence toward the solution. If a pivoting algorithm does not find a solution of the MLCP due to an iteration limit, e.g. in real-time simulations, choosing the result with the least error can reduce the risk of simulation instabilities.
Human primary motor cortex indexes the onset of subjective intention in brain-machine-interface mediated actions

Self-initiated behavior is accompanied by the experience of willing our actions. Here, we leverage the unique opportunity to examine the full intentional chain – from will (W) to action (A) to environmental effects (E) - in a tetraplegic person fitted with a primary motor cortex (M1) brain machine interface (BMI) generating hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (W, A, and E) while performing extra-cellular recordings and probing subjective experience. Our results reveal single-cell, multi-unit, and population-level dynamics in human M1 that encode W and may predict its subjective onset. Further, we show that the proficiency of a neural decoder in M1 reflects the degree of W-A binding, tracking the participant’s subjective experience of intention in (near) real time. These results point to M1 as a critical node in forming the subjective experience of intention and demonstrate the relevance of intention-related signals for translational neuroprosthetics.
A control engineering perspective on the advantages of efference copies

Biological systems have evolved to perform high-speed voluntary movements whilst maintaining robustness and stability. This paper examines a control architecture based on the principles of efference copies found in insect sensorimotor control which we call the inner-outer loop (IOL) controller. Within a control engineering framework, we benchmark the advantages of this control architecture against two common engineering control schemes: a pure feedback (PFB) and a two-degree-of-freedom (2DoF) controller. Our results show that the architecture performs particularly well for systems with noisy sensors, and systems with time-delays. We explain a hypothesis that the structure of the insect sensorimotor system allows the sensors to increase their operating range. Finally, we explore the robustness of the IOL controller and show that it achieves this increased performance with equal stability margins and stability robustness. Finally, we discuss the benefits of the IOL controller in relation to the properties of sensors employed in insect sensorimotor control.
Disentangling the multiple drivers of ecological adaptation in a microorganism

The survival and reproduction of living organisms depend on their ability to achieve an adequate balance between energy intake and energy expenditure. Multiple quantities contribute to this energetic balance, such as the feeding rate, and the allocation of available energy to growth, maintenance, movement and reproduction. Given that many of these quantities scale in a predictable way with the size of the organism and with environmental parameters, it should be possible to predict how organisms may adapt to novel environmental conditions in terms of adjusting their morphology, physiology, and behaviour. In a series of experiments, we adapted axenic experimental populations of the ciliate Tetrahymena pyriformis to different environmental conditions of temperature (15°C, 20°C and 25°C) and resource levels (50%, 100%, and 200% of a standard protein solution). We measured population growth, metabolic rate (from respiration), cell size, and movement speed (from video-tracking). On a very short time scale, movement speed and metabolic rate increased with environmental temperature in a way that can be predicted from simple physical scaling relations such as the Boltzmann-Arrhenius equation and the ‘viscous drag’ impacting movement. However, soon after the introduction of Tetrahymena into a novel environment, all the measured quantities were further modulated in a direction that likely provided better biological fitness in the new environment. Changes in cell size played a central role in mediating these adaptations: in small organisms with short generation times, cell size regulations can be a fast and effective way to mediate environmental adaptation by simultaneously affecting multiple phenotypic traits, such as metabolic rate and the energetic costs of movement.
Fine-scale contemporary recombination variation and its fitness consequences in adaptively diverging stickleback fish

Despite deep evolutionary conservation, recombination varies greatly across the genome, among individuals, sexes, and populations and can be a major evolutionary force in the wild. Yet this variation in recombination and its impact on adaptively diverging populations is not well understood. To elucidate the nature and potential consequences of recombination rate variation, we characterized fine-scale recombination landscapes by combining pedigrees, functional genomics and field fitness measurements in an adaptively divergent pair of marine and freshwater threespine stickleback populations from River Tyne, Scotland. Through whole-genome sequencing of large nuclear families, we identified the genomic location of almost 50,000 crossovers and built recombination maps for 36 marine, freshwater, and hybrid individuals at 3.8 kilobase resolution. Using these maps, we quantified the factors driving variation in recombination rate: we find strong heterochiasmy between sexes (68% of the variation) but also differences among ecotypes (21.8%). Hybrids show evidence of significant recombination suppression, both in overall map length and in individual loci. We further tested and found reduced recombination rates both within single marine–freshwater adaptive loci and between loci on the same chromosome, suggestive of selection on linked ‗cassettes‘. We tested theory supporting the evolution of linked selection using temporal sampling along a natural hybrid zone, and found that recombinants with shuffled alleles across loci show traits associated with reduced fitness. Our results support predictions that divergence in cis-acting recombination modifiers whose mechanisms are disrupted in hybrids, may have an important role to play in the maintenance of differences among adaptively diverging populations.
Quantifying Microbial Guilds

The ecological role of microorganisms is of utmost importance due to their multiple interactions with the environment. However, assessing the contribution of individual taxonomic groups has proven difficult despite the availability of high throughput data, hindering our understanding of such complex systems. Here, we propose a quantitative definition of guild that is readily applicable to metagenomic data. Our framework focuses on the functional character of protein sequences, as well as their diversifying nature. First, we discriminate functional sequences from the whole sequence space corresponding to a gene annotation to then quantify their contribution to the guild composition across environments. In addition, we identify and distinguish functional implementations, which are sequence spaces that have different ways of carrying out the function. We demonstrate the value of our approach with two case studies: the ammonia oxidation and polyamine uptake guilds from the Malaspina circumnavigation cruise, revealing novel ecological dynamics of the latter in marine ecosystems. Thus, the quantification of guilds helps to assess the functional role of different taxonomic groups with profound implications on the study of microbial communities.
Single-cell RNA-seq reveals TCR clonal expansion and a high frequency of transcriptionally distinct double-negative T cells in NOD mice

T cells primarily drive the autoimmune destruction of pancreatic beta cells in Type 1 diabetes (T1D). However, the profound yet uncharacterized diversity of the T cell populations in vivo has hindered obtaining a clear picture of the T cell changes that occur longitudinally during T1D onset. This study aimed to identify T cell clonal expansion and distinct transcriptomic signatures associated with T1D progression in Non-Obese Diabetic (NOD) mice. Here we profiled the transcriptome and T cell receptor (TCR) repertoire of T cells at single-cell resolution from longitudinally collected peripheral blood and pancreatic islets of NOD mice using single-cell RNA sequencing technology. Surprisingly, we detected a considerable high frequency of islet-matching T cell clones in the peripheral circulation and blood-matching T cell clones in the islets. Our analysis showed that transcriptional signatures of the T cells are associated with the matching status of the T cells, suggesting potential future applications as a marker for early prediction of diabetes onset using peripheral T cells. In addition, we discovered a high frequency of transcriptionally distinct double negative (DN) T cells that might arise from naïve and effector backgrounds through the loss of CD4 or CD8 in a yet unknown biological pathway. This study provides a single-cell level transcriptome and TCR repertoire atlas of T cells in NOD mice and opens the door for more research into the causes of type 1 diabetes and inflammatory autoimmune disease using mouse models.
CellSNAP: A fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging

Quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. In particular, three-dimensional (3D) tomographic imaging of live cells has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw 3D tomograms is not well-developed. This work focuses on a critical, yet often underappreciated, step of the analysis pipeline, that of 3D cell segmentation from the acquired tomograms. The current method employed for such tasks is the Otsu-based 3D watershed algorithm, which works well for isolated cells; however, it is very challenging to draw boundaries when the cells are clumped. This process is also memory intensive since the processing requires computation on a 3D stack of images. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the segmentation of QPI images, which outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 seconds per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused segmentation tools. We envision our work will lead to the broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.
Interpersonal heart rate synchrony predicts effective group 1 performance in a naturalistic collective decision-making task

21 Groups often outperform individuals in problem-solving. However, failure of group mem-22 bers to critically evaluate ideas in a discussion risks sub-optimal outcomes – a phenomenon 23 called “groupthink”. While recent studies have found interpersonal physiological synchrony to 24 correlate with shared attention and group cohesion, whether it can track group efficacy in a 25 collective decision-making task with an objectively defined performance measure remains con-26 troversial. To address this gap, we collected heart rate data from 58 groups (n=271) performing 27 a task based on the hidden profile paradigm. Using multi-dimensional recurrence quantifica-28 tion analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted 29 the probability of groups overriding groupthink and reaching correct consensus with more than 30 70% cross-validation accuracy—significantly higher than that predicted by subjective assess-31 ment of team function or baseline heart rates alone. These findings demonstrate that heart 32 rate synchrony during a naturalistic group discussion could be a biomarker of effective collective 33 decision-making. 34
FLECS Technology for High-Throughput Screening of Hypercontractile Cellular Phenotypes in Fibrosis: A Function-First Approach to Anti-Fibrotic Drug Discovery

The pivotal role of myofibroblast contractility in the pathophysiology of fibrosis is widely recognized, yet HTS approaches are not available to quantify this critically important function in drug discovery. We develop, validate, and scale-up a HTS platform that quantifies contractile function of primary human lung myofibroblasts upon treatment with pro-fibrotic TGF-β1. With the fully automated assay we screened a library of 40,000 novel small molecules in under 80 h of total assay run-time. We identified 42 hit compounds that inhibited the TGF-β1-induced contractile phenotype of myofibroblasts, and enriched for 19 that specifically target myofibroblasts but not phenotypically related smooth muscle cells. Selected hits were validated in an ex vivo lung tissue models for their inhibitory effects on fibrotic gene upregulation by TGF-β1. Our results demonstrate that integrating a functional contraction test into the drug screening process is key to identify compounds with targeted and diverse activity as potential anti-fibrotic agents.