Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of memory thresholds for planar qudit geometries

We introduce and analyze a new type of decoding algorithm called general color clustering, based on renormalization group methods, to be used in qudit color codes. The performance of this decoder is analyzed under a generalized bit-flip error model, and is used to obtain the first memory threshold estimates for qudit 6-6-6 color codes. The proposed decoder is compared with similar decoding schemes for qudit surface codes as well as the current leading qubit decoders for both sets of codes. We find that, as with surface codes, clustering performs sub-optimally for qubit color codes, giving a threshold of 5.6 % compared to the 8.0 % obtained through surface projection decoding methods. However, the threshold rate increases by up to 112% for large qudit dimensions, plateauing around 11.9 % . All the analysis is performed using QTop, a new open-source software for simulating and visualizing topological quantum error correcting codes.
Electro-mechanical Casimir effect

The dynamical Casimir effect is an intriguing phenomenon in which photons are generated from vacuum due to a non-adiabatic change in some boundary conditions. In particular, it connects the motion of an accelerated mechanical mirror to the generation of photons. While pioneering experiments demonstrating this effect exist, a conclusive measurement involving a mechanical generation is still missing. We show that a hybrid system consisting of a piezoelectric mechanical resonator coupled to a superconducting cavity may allow to electro-mechanically generate measurable photons from vacuum, intrinsically associated to the dynamical Casimir effect. Such an experiment may be achieved with current technology, based on film bulk acoustic resonators directly coupled to a superconducting cavity. Our results predict a measurable photon generation rate, which can be further increased through additional improvements such as using superconducting metamaterials.
Cluster-based Diversity Over-sampling: A Density and Diversity Oriented Synthetic Over-sampling for Imbalanced Data

: In many real-life classification tasks, the issue of imbalanced data is commonly observed. The workings of mainstream machine learning algorithms typically assume the classes amongst underlying datasets are relatively well-balanced. The failure of this assumption can lead to a biased representation of the models’ performance. This has encouraged the incorporation of re-sampling techniques to generate more balanced datasets. However, mainstream re-sampling methods fail to account for the distribution of minority data and the diversity within generated instances. Therefore, in this paper, we propose a data-generation algorithm, Cluster-based Diversity Over-sampling (CDO), to consider minority instance distribution during the process of data generation. Diversity optimisation is utilised to promote diversity within the generated data. We have conducted extensive experiments on synthetic and real-world datasets to evaluate the performance of CDO in comparison with SMOTE-based and diversity-based methods (DADO, DIWO, BL-SMOTE, DB-SMOTE, and MAHAKIL). The experiments show the superiority of CDO.
In situ defect detection and feedback control with three-dimensional extrusion-based bioprinter-associated optical coherence tomography

Extrusion-based three-dimensional (3D) bioprinting is one of the most common methods used for tissue fabrication and is the most widely used additive manufacturing technique in all industries. In extrusion-based bioprinting, printing defects related to material deposition errors lead to a significant deviation from shape to function between the printed construct and design model. Using 3D extrusion-based bioprinter-associated optical coherence tomography (3D P-OCT), an in situ defect detection and feedback system was presented based on the accurate defect analysis and location, and a pre-built feedback mechanism. Using 3D P-OCT, multi-parameter quantification of the material deposition was carried out in real time, including the filament size, layer thickness, and layer fidelity. The material deposition errors under different paths were quantified and located specifically, including the start-stop points, straight-line path, and turnarounds. The pre-built feedback mechanism involving the control inputs, such as printing path, pressure, and velocity, provided the basis for in situ defect detection and real-time feedback control. In particular, the second printing repair can be performed after the broken filament defect is detected and located. After printing, fidelity can be quantitatively analyzed based on the point cloud registration between the 3D P-OCT result and the design model. In conclusion, 3D P-OCT enables in situ defect detection and feedback control, broken filament repair, and 3D fidelity analysis to achieve high-fidelity printing from shape to function.
DRAINING THE LOCAL VOID

Two galaxies that lie deep within the Local Void provide a test of the expectation that voids expand. The modest ( M B ∼ − 14 ) HI bearing dwarf galaxies ALFAZOAJ1952+1428 and KK246 have been imaged with Hubble Space Telescope in order to study the stellar populations and determine distances from the luminosities of stars at the tip of the red giant branch. The mixed age systems have respective distances of 8.39 Mpc and 6.95 Mpc and inferred line-of-sight peculiar velocities of −114 km s−1 and −66 km s−1 toward us and away from the void center. These motions compound on the Milky Way motion of ∼230 km s−1 away from the void. The orbits of the two galaxies are reasonably constrained by a numerical action model encompassing an extensive region that embraces the Local Void. It is unambiguously confirmed that these two void galaxies are moving away from the void center at several hundred km s−1.
Single mothers by choice in German-speaking countries: Attachment style, social support, personality traits, and motives from an online survey

ABSTRACT The family model of single mothers by choice (SMCs) is spreading in western industrial nations in the last 30 decades. Since gender equality and new methods in reproductive medicine do not sufficiently explain this phenomenon, this exploratory quantitative study investigates the psychological factors of SMCs assessing attachment style, social support, and Big Five personality traits issuing an online survey to 131 SMCs. In addition, this study aims to gather information about SMCs in German-speaking countries to close a research gap. Findings indicate similarities in characteristics between German-speaking SMCs and internationally reported SMCs. Independent-samples t-tests showed significant differences from the corresponding German norm sample in personality traits (conscientiousness, extraversion, and openness to experience), attachment style, and social support.
Utilization of Social Media as a Means of Healthy Food Promotion by Housewives in Dumai City

The purpose of this study was to analyze the role of social media in promoting healthy food by Hanadona Catering in Dumai City. This study uses a constructivist paradigm with a descriptive qualitative approach. The research subject only involved the owner of Hanadona Catering, namely Mrs. Aldona Wulandari as the main informant. Data collection techniques are through in-depth interviews conducted directly (via the Zoom application) or indirectly (via WhatsApp chat) and non-participant observation. The results of this study indicate that Hanadona Catering uses social media WhatsApp Messenger, Instagram, and Facebook to promote its culinary products. Promotional activities are usually carried out alone, involving the community and using the services of celebgrams and influencers from the local area. The massive promotions carried out have made the existence of Hanadona Catering for the last three years quite well known to the people of Dumai city and economically it brings benefits to the owner and the surrounding community, although on the other hand there are obstacles in terms of the availability of human resources to manage social media if it is overloaded. orders.
Cooking Reviews Segmentation and Classification based on Deep Learning and Named Entity Detection

: YouTube is one of the most used online social networking (OSN) websites for exchanging recipes. It allows uploading them, searching for, downloading, as well as rating and reviewing them. Sentiment analysis for food and cooking recipes comments is to identify what people think about such cooking recipe video through users’ comments. Nowadays, users’ give their opinion not only about recipes; they also evaluate the cook through their comments, where a cook’s reputation can affect the users' opinion about his cooking recipes. Frequently, when a cook has a good reputation, his recipes receive a great success by people, and vice versa. In this paper, we propose a new approach that deal with the sentiment classification of cooking reviews. Firstly, we examine the benefit of performing named entity detection and conjunctions on our corpus for text segmentation in order to divide the comment on segments concerning the cook and segments concerning the recipe. Next, we make two sentiment classifications (about the cook and about the recipe). Finally, we incorporate the polarity of the cook sentiment classification in the recipe sentiment classification in order to analyse the effect of the opinion about the cook on the performance of the categorization of the shared cooking recipes comments in OSNs.
Automatic Feature Extraction for Bearings' Degradation Assessment using Minimally Pre-processed Time series and Multi-modal Feature Learning

: Maintenance activities can be better planned by employing machine learning technologies to monitor an asset’s health conditions. However, the variety of observable measures (e.g. temperature, vibration) and behaviours characterizing the health degradation process results in time-consuming manual feature extraction to ensure accurate degradation stage recognitions. Indeed, approaches able to provide automatic feature extraction from multiple and heterogeneous sources are more and more required in the ﬁeld of predictive maintenance. This issue can be addressed in a data-driven fashion by using feature learning technology, enabling the transformation of minimally processed time series into informative features. Given its capability of discovering meaningful patterns in data while enabling data fusion, many feature learning approaches are based on deep learning technology (e.g. autoencoders). In this work, an architecture based on autoencoders is used to automatically extract degradation-representative features from minimally preprocessed time series of vibration and temperature data. Different autoencoder architectures are implemented to compare different data fusion strategies. The proposed approach is tested considering both the recognition performances and the quality of the learned features with a publicly available real-world dataset about bearings’ progressive degradation. The proposed approach is also compared against manual feature extraction and the state-of-the-art technology in feature learning.
Distributed Robust Optimization for Networked Agent Systems With Unknown Nonlinearities

The distributed robust optimization problem is investigated in this article for a class of networked agent systems under stochastically switching communication graphs. Unlike most existing results on distributed optimization of networked agent systems, the dynamics of each agent in the present model are allowed to be subjected to matched linearly parameterized unknown nonlinearities while the underlying communication graph is stochastically switching among some matrix-weighted communication graphs. The objective here is to cooperatively optimize a holistic cost function, which is a sum of all the convex local objective functions, under the mild condition that each agent may only have access to the gradient of its local objective function and the relative local information between itself and its neighbors. To solve such an outstanding optimization problem, a neuro-adaptive optimization protocol is developed based upon parameterizations of the unknown nonlinearities where the neuro-adaptive learning algorithm for estimating the weight matrices therein acts as the key role to compensate for the unknown nonlinearities. Furthermore, the signum function-based feedback law is subtly integrated into the proposed protocol to deal with the effect of the gradient term of the local objective function. Efficient criteria are provided to guide selections of proper control parameters under which the robust optimization problem is successfully resolved in the mean-square sense. Lastly, theoretical results are validated by presenting several illustrative simulation examples.
Entanglement harvesting from multiple massless scalar fields and divergences in Unruh-DeWitt detector models

We analyze scenarios in which pairs of Unruh-DeWitt-like detectors interact with scalar field(s) with bi-linear and quadratic coupling. For all cases -- real scalar fields, charged scalar fields, and even for couplings mixing two different scalar fields -- we find that the detectors' dynamics depends on the Wightman function of a real scalar field with the same functional form up to a constant multiple. Consequently, entanglement harvesting exhibits low sensitivity to either the kind of scalar field or their charged nature. Furthermore, we show on general grounds that pairs of Unruh-DeWitt-like detectors exhibit persistent divergences when they interact with scalar field(s) with a quadratic or bi-linear coupling and discuss possible avenues to improve the models through the regularization of such divergences.
Wearable Magnetic Flexonic Sensor Nodes for Simultaneous Normal Force and Displacement Measurements

Illustrated in the context of home-based stroke rehabilitation, this paper presents the design and development of magnetic flexonic sensor nodes (FSNs) with configurable parameters for simultaneous sensing of normal forces and displacement. The FSN is composed of a magnetic sensor in flexible elements that can be embedded in wearable devices and rapid-prototyped at a low cost to facilitate distributed force sensing applications. As an illustration, a shoe insole with 19 embedded FSN for monitoring the plantar force distribution and arch height has been designed, prototyped, and experimentally evaluated by comparing it with a commercial dynamometric platform. The FSN-embedded insole directly measures the arch height and captures the transients of the plantar forces in five different zones in real-time. Because of the simplicity and low cost, FSNs have the potential to facilitate fabricating low-cost sensing systems, for example, for home-based stroke rehabilitation where the insole stiffness and arch height of the embeddable electronic sensors can be configured according to the patient's specific abnormal plantar distribution to adapt to different range and distribution and wearable configuration during different rehabilitation state.
Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution

The potential to perform attenuation and scatter compensation (ASC) in single-photon emission computed tomography (SPECT) without a separate transmission scan is highly significant. In this context, attenuation in SPECT is primarily due to Compton scattering, where the probability of Compton scatter is proportional to the attenuation coefficient of the tissue and the energy of the scattered photon and the scattering angle are related. Based on this premise, we investigated whether the SPECT emission data, including the scatter-window data, acquired in list-mode (LM) format and including the energy information, can be used to estimate the attenuation map. For this purpose, we propose a Fisher-information-based method that yields the Cramér–Rao bound (CRB) for the task of jointly estimating the activity and attenuation distribution using only the SPECT emission data. In the process, a path-based formalism to process the LM SPECT emission data, including the scattered-photon data, is proposed. The Fisher information method was implemented on NVIDIA graphics processing units (GPUs) for acceleration. The method was applied to quantify the information content of SPECT LM emission data, which contains up to first-order scattered events, in a simulated SPECT system with parameters modeling a clinical system using realistic computational studies with 2D digital synthetic and anthropomorphic phantoms. Experiments with anthropomorphic phantoms simulated myocardial perfusion and dopamine transporter (DaT)-Scan SPECT studies. The method was also applied to LM data containing up to second-order scatter for a synthetic phantom. The results show that the CRB obtained for the attenuation and activity coefficients was typically much lower than the true value of these coefficients. An increase in the number of detected photons yielded lower CRB for both the attenuation and activity coefficients. Further, we observed that systems with better energy resolution yielded a lower CRB for the attenuation coefficient. Overall, the results provide evidence that LM SPECT emission data, including the scatter-window data, contains information to jointly estimate the activity and attenuation coefficients.
Functional clustering of water quality data in Scotland

Assessing quality and quantity of water is of crucial importance to identify risks to the environment, society and human health. The European Community Water Framework Directive establishes guidelines for the classification of all water bodies across Europe and requires that all sites attain ‘good’ status by 2015. Classifications are made on the basis of a range of chemical and biological determinands. Within the directive, standing waters can be grouped, and the classifications of all members of the group are then based on the classification of a single representative lake within that grouping. Classification is based on different chemical and biological determinands. A key question is therefore how to determine ‘appropriate’ groups. We investigate and develop univariate and multivariate functional clustering models to investigate the spatiotemporal structure of determinands in a set of 21 Scottish lakes. These approaches enable sites to be grouped on the basis of one or more determinands; however, unlike with standard clustering methods, the temporal dynamics of the determinands are also taken into account in the formation of the groups. Copyright © 2012 John Wiley & Sons, Ltd.
Trends in dark matter research

Preface Modification of the Field Theory and the Dark Matter Problem The Hypothesis of Scalar Field Dark Matter: The Cosmological Evolution Challenge Analytical Methods for the Formation of Dark Matter Haloes Dark Matter as a Quantum Foam In-Flow Effect Scalar Field as Dark Energy and Machina Cosmological Solution in the Generalised Scalar-Tensor Theory of Gravitation Dark Matter and Dark Energy Production in Quantum Model of the Universe HI Shells as Tracers of the Dark Matter Distribution Index.
Dust emission profiles of DustPedia galaxies

Most radiative transfer models assume that dust in spiral galaxies is distributed exponentially. In this paper our goal is to verify this assumption by analysing the two-dimensional large-scale distribution of dust in galaxies from the DustPedia sample. For this purpose, we have made use of Herschel imaging in five bands, from 100 to 500 μm, in which the cold dust constituent is primarily traced and makes up the bulk of the dust mass in spiral galaxies. For a subsample of 320 disc galaxies, we successfully performed a simultaneous fitting with a single Sérsic model of the Herschel images in all five bands using the multi-band modelling code GALFITM. We report that the Sérsic index n, which characterises the shape of the Sérsic profile, lies systematically below 1 in all Herschel bands and is almost constant with wavelength. The average value at 250 μm is 0.67 ± 0.37 (187 galaxies are fitted with n250 ≤ 0.75, 87 galaxies have 0.75 < n250 ≤ 1.25, and 46 – with n250 > 1.25). Most observed profiles exhibit a depletion in the inner region (at r < 0.3−0.4 of the optical radius r25) and are more or less exponential in the outer part. We also find breaks in the dust emission profiles at longer distances (0.5−0.6) r25 which are associated with the breaks in the optical and near-infrared. We assumed that the observed deficit of dust emission in the inner galaxy region is related to the depression in the radial profile of the HI surface density in the same region because the atomic gas reaches high enough surface densities there to be transformed into molecular gas. If a galaxy has a triggered star formation in the inner region (for example, because of a strong bar instability, which transfers the gas inwards to the centre, or a pseudobulge formation), no depletion or even an excess of dust emission in the centre is observed.
Association between Barthel Index, Grip Strength, and Physical Activity Level at Admission and Prognosis in Community-Acquired Pneumonia: A Prospective Cohort Study

Background: Impaired functional status is a risk factor for hospitalization in patients with community-acquired pneumonia (CAP). The aim was to determine the influence of functional status and physical activity level on severe outcomes, including length of stay, admission to the intensive care unit (ICU), readmission, and mortality in patients with CAP. Methods: A prospective cohort study among patients hospitalized with CAP. Functional status was assessed with the Barthel index and grip strength, and physical activity level was assessed using the international physical activity questionnaire. Linear regression was used to assess the association with length of stay, and logistic regression was used to assess the risk of severe outcomes. Results: Among 355 patients admitted with CAP, 18% had a low Barthel index (<80), 45% had a low grip strength, and 75% had a low physical activity level. Low Barthel index was associated with increased risk of ICU admission (OR 3.6, 95% CI 1.2–10.9), longer length of stay (27.9%, 95% CI 2.3–59.7%), readmission within 30, 90, and 180 days (OR 2.1–2.4, p < 0.05), and mortality within 90 and 180 days (OR 4.2–5.0, p < 0.05). Low grip strength was associated with increased risk of 90 days readmission (OR 1.6, 95% CI 1.0–2.6, p < 0.05) and mortality within 30, 90, and 180 days (OR 2.6–3.2, p < 0.05). Low physical activity level was associated with increased risk of readmission within 90 and 180 days (OR 1.8–2.1, p < 0.05) and mortality within 30, 90, and 180 days (OR 3.3–5.5, p < 0.05). Conclusions: Impaired functional status and low physical activity level were associated with a longer length of stay and increased risk of ICU admission, readmission, and mortality in patients hospitalized with CAP. Routine assessment of functional status and physical activity level in clinical care could enable early identification of individuals with excess risk for a poor prognosis. Trial registration: ClinicalTrials.gov, NCT03795662.
On the stabilization in finite time of locally controllable systems by means of continuous time-vary

It is proven that, if, for any positive time $T$, there exists an open-loop control $u(a,t)$ depending continuously on the initial data $a$, vanishing for $a=0$, and steering a small neighborhood of $0$ into $0$ in time $T$, then the control system can be locally stabilized in small time by means of continuous time-varying feedback law, provided that the dimension of the state space is at least 4 and the strong accessibility rank condition holds.