Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Learned User Preference Estimator with Attention Network for Cold-Start Recommendation

One crucial challenge in the recommendation research field is the cold-start problem. Meta-learning is a feasible algorithm to reduce the error of cold-start recommendation because it can adjust to new tasks rapidly through relatively few updates. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. We proposed a recommendation model called attentional meta-learned user preference estimator that combines attention network and meta-learning. This method enhances the ability of modelling the personalized user interest by learning the weights between users and items based on attention mechanism, then improves the performance of cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%.
A single-stage automatic license plate recognition network with Balanced-IoU loss

In this paper, we propose a license plate recognition model, which can detect and recognize the license plate in a single forward. The features of the input image are extracted by our 15-layer convolutional neural network. In detection branch, we use a loss function with better nonlinear to fit the detection process of license plate. To catch the location of license plate with less information loss, we add Intersection over Ground-truth (IoG) into the Intersection over Union (IoU) and get Balanced-IoU loss (BIoU loss). The combination of these two loss functions can make the model get a better predictive result. In recognition branch, we introduce an attention mechanism to make better recognition results. The experimental results show that our network can get 99.91% recognition precision, which is over 3% higher than state-of-the-art single-stage ALPR network RPnet.
Fast Cluster Exploration of BCI data using Riemannian Geometry

Classifying Brain Computer-Interface (BCI) data can require identification and labeling of relevant signal characteristics. To aid this process, the mode-seeking algorithm Quick Shift creates a hierarchical structure of clusters from covariance matrices of BCI data; enabling data exploration at different levels of granularity. For accurate clustering, covariance matrices are measured in their Riemannian space. To approximate and speed up calculations of Quick Shift, Metric Trees are used in a Dual Tree approach. Presenting new construction methods in the Metric Tree’s Agglomeration Phase, a high performance exploration tool was developed, testing these methods on our newly recorded data set. Results showed that the presented methods greatly reduced computation time, mostly on lower channel dimensions. Quantitative clustering capabilities, using our tool, show that relevant signal patterns can be found from clustered BCI data.
Development and validation of explainable machine learning models for risk of mortality in transcatheter aortic valve implantation: TAVI risk machine scores

Abstract Aims Identification of high-risk patients and individualized decision support based on objective criteria for rapid discharge after transcatheter aortic valve implantation (TAVI) are key requirements in the context of contemporary TAVI treatment. This study aimed to predict 30-day mortality following TAVI based on machine learning (ML) using data from the German Aortic Valve Registry. Methods and results Mortality risk was determined using a random forest ML model that was condensed in the newly developed TAVI Risk Machine (TRIM) scores, designed to represent clinically meaningful risk modelling before (TRIMpre) and in particular after (TRIMpost) TAVI. Algorithm was trained and cross-validated on data of 22 283 patients (729 died within 30 days post-TAVI) and generalisation was examined on data of 5864 patients (146 died). TRIMpost demonstrated significantly better performance than traditional scores [C-statistics value, 0.79; 95% confidence interval (CI)] [0.74; 0.83] compared to Society of Thoracic Surgeons (STS) with C-statistics value 0.69; 95%-CI [0.65; 0.74]). An abridged (aTRIMpost) score comprising 25 features (calculated using a web interface) exhibited significantly higher performance than traditional scores (C-statistics value, 0.74; 95%-CI [0.70; 0.78]). Validation on external data of 6693 patients (205 died within 30 days post-TAVI) of the Swiss TAVI Registry confirmed significantly better performance for the TRIMpost (C-statistics value 0.75, 95%-CI [0.72; 0.79]) compared to STS (C-statistics value 0.67, CI [0.63; 0.70]). Conclusion TRIM scores demonstrate good performance for risk estimation before and after TAVI. Together with clinical judgement, they may support standardised and objective decision-making before and after TAVI.
DIRECT LINEAR TRANSFORMATION IN THE CONTEXT OF DIFFERENT SCALING CRITERIA

The projective transformation of 3D object points to 2D image points can be expressed in homogeneous coordinates by the 3 by 4 “Direct Linear Transformation” (DLT) matrix. This projective transformation leads to a set of homogeneous equations, leaves the DLT matrix defined up to a scale factor. The most natural idea to account for this scale factor is to fix one of the DLT parameters to unity. Traditionally this scale factor is fixed by setting the depth to unity. This article presents another criterion to fix the scale factor. The norm of the DLT parameters is set to one. The DLT parameters are recovered through the Sigular-Vlaue Decomposition (SVD) of the normal matrix. The consequences of this parameterization will be presented in this article accommodated with a detailed statistical analysis.
A Pilot Study In to the Effects of Cervical Manual Therapy Plus Conventional Physical Therapy on Clinical Outcomes and Electrodiagnostic Findings in People With Carpal Tunnel Syndrome

Background Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy that has a significant impact on patients’ quality of life. Current physical therapy treatment options show limited effects or low-quality evidence, especially in the long term. To date, there has been little research to look at the effects of treating the cervical spine on decreasing symptoms distally to the carpal tunnel. This study aimed to evaluate the effects of cervical manual therapy plus conventional physical therapy on patients with carpal tunnel syndrome. Methods This pilot pretest/posttest and six-month follow-up clinical study included 15 adult patients with CTS. For two weeks, each patient received 10 sessions of supervised intervention treatment. The efficacy of the therapies was assessed at baseline (T0), immediately after treatment (T1), and six months after treatment (T2). The visual analog scale (VAS), a symptom severity scale, the functional capacity scale of the Boston Carpal Tunnel Questionnaire (BCTQ), the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire, median nerve motor distal latency (mMDL), and median sensory nerve conduction velocity (mSNCV) were outcome measures. Results There were significant improvements in all measures between the baseline values at T0 and those recorded immediately after the treatment at T1 or six months later at T2 (p<.05). Conclusion This pilot study indicates that cervical manual therapy plus conventional physical therapy applied for two weeks improves clinical outcomes and electrodiagnostic findings in people with CTS.
The Influence of a Celebrity Chef on Customer Repurchase Behavior: Empirical Study of Taiwan’s F&B Industry During COVID-19 Pandemic

The spread of COVID-19 pandemic has inflicted severe blows on the global hospitality industry. In Taiwan, revenue from the food and beverage (F&B) department has decreased by more than 90%. This study aims to understand whether celebrity chefs can effectively help and enhance their corporates’ business performance under COVID-19′s severe impacts via leveraging their personal brand value, explores the influence of a celebrity chef on customer repurchase behavior during the epidemic and examines whether such a chef has a mediation effect on the relationship between corporate brand and customer satisfaction. The primary data were collected from the respondents through online questionnaire in Taiwan to get 245 respondents as a sample size of the research from Nov. 10 to Nov. 25 in 2021, and through validity and reliability analysis that processed by statistical software using factor analysis and structural equation modeling to see if celebrity chefs’ personality branding could influence customer repurchase behavior, and also examine the relationship between corporate brand and celebrity chef. The findings show that corporate brand enhances both a celebrity chef’s personal brand and customer satisfaction, and that a celebrity chef has a positive effect on both customer satisfaction and loyalty, which can partially mediate the effect of corporate brand; furthermore, a celebrity chef has a positive effect on customer repurchase behavior. In Taiwan relative studies into aspects of a celebrity chef’s effect on consumer behavior are limited, and so this research offers new insights into the celebrity chef phenomenon there as well as elsewhere.
Generation of rectangular duals of a planar triangulated graph by elementary transformations

In the rectangular dual approach to the floorplan design, it is important to incorporate efficient rectangular dual transformation techniques in the iterative optimal-search algorithms. A solution to the rectangular dual transformation problem by directly using the properties of oriented graphs is proposed. The definitions of elementary components and elementary transformation are given. It is shown that any two rectangular duals of a graph can be converted into each other by a finite sequence of elementary transformations. As a result, all rectangular duals of a graph are derivable from each other. <<ETX>>
Generative adversarial network based digital stain conversion for generating RGB EVG stained image from hyperspectral H&E stained image

Abstract. Significance Quantification of elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. Though hematoxylin and eosin (H&E) staining is a routinely used and less expensive tissue staining technique, elastic and collagen fibers cannot be differentiated using it. So, in conventional pathology, special staining technique, such as Verhoeff’s van Gieson (EVG), is applied physically for this purpose. However, the procedure of EVG staining is very expensive and time-consuming. Aim The goal of our study is to propose a deep-learning-based computerized method for the generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Approach H&E stained hyperspectral image and EVG stained RGB whole slide image of human pancreatic tissue have been leveraged for this experiment. CycleGAN-based deep learning model has been proposed for digital stain conversion while images from source and target domains are of different modalities (hyperspectral and RGB) with different channel dimensions. A set of three basis functions have been introduced for calculating one of the losses of the proposed method, which retains the relevant features of EVG stained image within the reduced channel dimension of the H&E stained one. Results The experimental results showed that a set of three basis functions including linear discriminant function and transmittance spectrum of eosin and hematoxylin better retained the essential properties of the elastic fiber to be discriminated from collagen fiber within the reduced dimension of the hyperspectral H&E stained image. Also, only a smaller number of paired training data with our proposed training method contributed significantly to the generation of more realistic EVG stained image with more precise identification of elastic fiber. Conclusions RGB EVG stained image is generated from hyperspectral H&E stained image for which our model has performed two types of image conversion simultaneously: hyperspectral to RGB and H&E to EVG. The experimental results show that the intentionally designed set of three basis functions contains more relevant information and prove the effectiveness of our proposed method in generating realistic RGB EVG stained image from hyperspectral H&E stained one.
Unravelling the genetic diversity of water yam (Dioscorea alata L.) accessions from Tanzania using simple sequence repeat (SSR) markers

Water yam (Dioscorea alata L.) is among the most cultivated species used as a source of food and income for small-scale farmers in Tanzania. However, little is documented about Dioscorea species available in Tanzania, including their genetic diversity. This study used ten polymorphic microsatellite markers to determine the genetic diversity and relationship of 63 D. alata accessions from six major producing regions. Results revealed a polymorphic information content (PIC) of 0.63, while the number of alleles per locus ranged from 4 to 12 with a mean of 7.60. The expected heterozygosity ranged from 0.20to 0.76, with a mean of 0.53, which suggests moderate genetic diversity of D. alata accessions. Kagera region had the highest mean number of (1.5) private alleles. Analysis of molecular variance revealed that 54% of the variation was attributed to within individual, 39% was attributed to among individual while among population contributed 7% of the total variation. The highest Nei’s genetic distance (0.43) was for accessions sampled from Kilimanjaro and Mtwara regions. Principal coordinate analysis and cluster analysis using Unweighted Paired Group Method using Arithmetic (UPGMA) grouped D. alata accessions into two major clusters regardless of geographical origin and local names. The Bayesian structure analysis confirmed the two clusters obtained in UPGMA and revealed an admixture of D. alata accessions in all six regions suggesting farmers’ extensive exchange of planting materials. These results are helpful in the selection of D. alata accessions for breeding programs in Tanzania.
Leisure Sedentary Time is Associated with Self-Reported Falls in Middle-aged and Older Females and Males: an Analysis of the CLSA

Aim The purpose of this analysis was to report the prevalence of falls and falls-related injuries among those reporting different volumes of weekly sedentary time, and to understand the association of sedentary time and falls, accounting for functional fitness. Methods Baseline and first follow-up data from the Canadian Longitudinal Study on Aging (CSLA) were analyzed (n=22,942). Participants self-reported whether they had a fall in the past 12 months (at baseline) and whether they had an injury that was a result of a fall (follow-up). In-home interviews collected self-reported leisure sedentary time using the Physical Activity Scale for Elderly. Functional fitness was assessed using grip strength, timed-up-and-go, and chair rise tests during clinic visits. Results The prevalence of falls was higher among those who reported higher sedentary time. For example, among males aged 65 and older who reported lower sedentary time (<1,080 min/week), the prevalence of falls in the past 12 months (at baseline) was 7.8% compared to 9.8% in those reporting higher sedentary time. The odds of reporting a fall (at baseline) was 21% higher in those who reported higher sedentary time (OR: 1.21; 95%CI: 1.11–1.33) in adjusted models. No associations were found between sedentary time and injuries due to a fall. Conclusions Reporting high volumes of sedentary time may increase the risk of falls. Future research using device-based estimates of total sedentary time and breaks in sedentary time is needed to further elucidate this association.
Guidelines for repeated measures statistical analysis approaches with basic science research considerations

Introduction A study design with repeated measures data occurs when each experimental unit has multiple dependent variable observations collected at several time points. Often longitudinal data (1, 2) are used interchangeably to describe repeated measures data. This study design also includes two or more experimental groups, and each experimental group receives varying levels of experimental conditions. The experimental group variable is treated as the primary independent variable in the statistical analysis. Study designs with repeated measurements are common in basic science research. A systematic review of preclinical animal research studies found that approximately 50% of studies within biomedical research domains of toxicology and brain trauma reported a study design with repeated measurements (3). Planned statistical analyses for this type of research design should incorporate all observed repeated measurements to evaluate the research hypotheses. However, often statistical analyses aggregate repeated measures data by computing an average for each experimental unit. When the repeated measurements are aggregated, ANOVA is generally applied. Using an ANOVA with aggregated repeated measurements violates the key ANOVA assumption of independence. Repeated measurements are correlated observations, given that they are observed from the same experimental unit. Repeated measurements collected closer in time are more correlated than measurements collected further in time (4). Correlation within an experimental unit is ignored when repeated measurements are aggregated. This violation of independence leads to biased analysis results and incorrect interpretations (1). An extension of an ANOVA is a repeated measures ANOVA. A repeated measures ANOVA accounts for the correlation within and between experimental groups (5) along with the time of the measurements (time point 1, time point 2, etc.). Similar to an ANOVA, time is treated as a categorical variable (6) rather than a continuous variable in a repeated measures ANOVA. Assumptions of the repeated measures ANOVA should be carefully considered when determining if it is an appropriate statistical approach. Statistical method alternatives to a repeated measures ANOVA are applicable when repeated measures assumptions are not met. The purpose of this Viewpoint is to provide guidance on statistical analysis options for repeated measurements within the context of basic science. Analysis guidance is given considering various assumptions and repeated measures data structures.
Directional Radiation Dosimeter for Area and Environmental Monitoring

It is presented a dosimeter that is able to measure the photon exposure and the direction from where the radiation came from. Preliminary measurements performed by this new directional radiation dosimeter demonstrate its application. This dosimeter consists of a small lead cube with thermoluminescent discs on each face, placed in well known coordinates. Only one dosimeter of this kind indicates the direction of the radiation beam, if it came from a unique position. This study was conducted inside the radiation room of a Cobalt-60 Gamma Irradiator and the dosimeter indicated the source position.
Laser-induced breakdown spectroscopy for the remote detection of explosives at level of fingerprints

We report the results of the application of Laser-Induced Breakdown Spectroscopy (LIBS) for the detection of some common military explosives and theirs precursors deposited on white varnished car’s external and black car’s internal or external plastic. The residues were deposited by an artificial silicon finger, to simulate material manipulation by terrorists when preparing a car bomb, leaving traces of explosives on the parts of a car. LIBS spectra were acquired by using a first prototype laboratory stand-off device, developed in the framework of the EU FP7 313077 project EDEN (End-user driven DEmo for CBRNe). The system operates at working distances 8-30 m and collects the LIBS in the spectral range 240-840 nm. In this configuration, the target was moved precisely in X-Y direction to simulate the scanning system, to be implemented successively. The system is equipped with two colour cameras, one for wide scene view and another for imaging with a very high magnification, capable to discern fingerprints on a target. The spectral features of each examined substance were identified and compared to those belonging to the substrate and the surrounding air, and those belonging to possible common interferents. These spectral differences are discussed and interpreted. The obtained results show that the detection and discrimination of nitro-based compounds like RDX, PETN, ammonium nitrate (AN), and urea nitrate (UN) from organic interfering substances like diesel, greasy lubricants, greasy adhesives or oils in fingerprint concentration, at stand-off distance of some meters or tenths of meters is feasible.
An Evaluation of the Xu-Randall Cloud Fraction Parameterization Using ASTEX Data

A dearth of detailed cloud observations has encouraged large-scale modelers to utilize methods that employ cloud resolving models (CRMs) to evaluate the utility of cloud parameterizations. However, cloud radars are now making detailed observations of clouds. In this paper, we use such observations to evaluate a parameterization based on CRM results. Herein we examine a particular cloud parameterization developed by Xu and Randall (1996). Unlike cloud parameterizations involving probability distribution functions (PDFs), which require knowledge of the higher order moments of the subgrid variables, the Xu and Randall (1996) technique depends only upon the large-scale relative humidity (RH), the saturation water vapor mixing ratio, and cloud and ice water mixing ratios. “Largescale” refers to space and time scales resolved by a large-scale model, such as a global numerical weather prediction (NWP) model or a global climate model. A large-scale quantity typically represents a spatial average over a 250 km by 250 km area and, under quiescent conditions, a time variation on the order of 3 hours. Herein, we apply the Xu and Randall (1996) stratus cloud parameterization to Atlantic Stratocumulus Transition Experiment (ASTEX) observations of the RH field obtained from sounding data and liquid water content (LWC) profiles derived from a combination of the liquid water path (LWP) measured by a Microwave Radiometer (MWR), and reflectivity Z from a millimeter wavelength cloud radar. The parameterized cloud fraction is then compared with observed cloud fraction profiles determined from cloud radar measurements.
Materials Overview for 2-Photon 3D Printing Applications

Interest in microscale 3D lithography continues to grow with the development of new tools and new patterning materials. To date most available materials are rigid, glassy systems derived from stereolithography studies. Soft materials lack the same level of sophistication, but there is a drive to improve this situation with the introduction of new elastomers and hydrogels. A general strategy is lacking for these 3D patterning systems as there is for the production of chemically amplified photoresists. This paper describes both positive and negative tone chemically amplified 3D materials as well and photocured systems based on radical and cationic crosslinking systems.