Simple Search Research Articles

Greedy knot selection algorithm for restricted cubic spline regression.

Non-linear regression modeling is common in epidemiology for prediction purposes or estimating relationships between predictor and response variables. Restricted cubic spline (RCS) regression is one such method, for example, highly relevant to Cox proportional hazard regression model analysis. RCS regression uses third-order polynomials joined at knot points to model non-linear relationships. The standard approach is to place knots by a regular sequence of quantiles between the outer boundaries. A regression curve can easily be fitted to the sample using a relatively high number of knots. The problem is then overfitting, where a regression model has a good fit to the given sample but does not generalize well to other samples. A low knot count is thus preferred. However, the standard knot selection process can lead to underperformance in the sparser regions of the predictor variable, especially when using a low number of knots. It can also lead to overfitting in the denser regions. We present a simple greedy search algorithm using a backward method for knot selection that shows reduced prediction error and Bayesian information criterion scores compared to the standard knot selection process in simulation experiments. We have implemented the algorithm as part of an open-source R-package, knutar.

PROVENANCE STUDIES IN ARCHAEOLOGY – MORE REFLECTIVE OF ‘QUALITY CONTROL’ WITHIN A RESOURCESCAPE THAN GEOCHEMISTRY?

SummaryDespite the obvious methodological similarities between archaeology and geology, we argue here that the fundamental assumption in scientific provenance studies of inorganic artefacts provides an insufficient basis for the methodology. That assumption is that there is a geochemical link between the source of the raw material and the finished object. Although this is undoubtedly necessary, it is not sufficient. We argue that, particularly for highly processed materials such as metal, glass, or ceramics, an equally (if not more) important factor is the sequence of human activities which are applied to the raw material during processing to become an object. In fact, we suggest that the main requirement for successful provenance work is the existence of sustained and repeatable quality assurance processes during production, carried out within a defined resourcescape. Ironically, this makes provenance studies more relevant to archaeology than does the simple search for sources.

Fifteen Hypervelocity Stars Whose Gaia G Magnitudes are Less Than 16.0 and Radial Velocities Less Than—600 km per second

From Gaia DR3 data using a simple search criteria fifteen hypervelocity stars (HVS) are detected. These stars have radial velocities less than −600 km s−1, Gaia G magnitudes less than 16.0 and Renormalized Unit Weight Error values less than 1.4. The U, V, W velocities also indicate that these stars are HVS. The effective temperatures of these stars indicate that they are F and G type stars. Gaia DR3 409420157955913856 and Gaia DR3 2198292118993038464 seems to be metal-poor HVS. High resolution spectroscopy of these stars is needed to derive the atmospheric parameters, chemical composition and to understand their evolutionary status and origin. The progenitors of these stars may be close binary systems with white dwarf companions. When the white dwarf companions became Type Ia supernovae and their F-G companion stars have become HVS. Or they may be the result of other unknown processes.

Roadside Assistance Finder

A growing number of cars on the road are vulnerable to emergencies or breakdowns in today's fast-paced world, making roadside assistance timely and dependable. The goal of the Roadside Assistance Finder project is to create a clever and practical way to easily link drivers in need with local assistance services. The system makes use of cutting-edge technologies to improve service providers' and drivers' overall experiences. The suggested application facilitates the quick and simple search for mechanics. Locating mechanics in a nearby area is a challenging task when traveling. This system offers mechanics details with a single click, which helps to solve the problem

The difference between molecules and materials: Reassessing the role of exact conditions in density functional theory.

Exact conditions have long been used to guide the construction of density functional approximations. However, hundreds of empirical-based approximations tailored for chemistry are in use, of which many neglect these conditions in their design. We analyze well-known conditions and revive several obscure ones. Two crucial distinctions are drawn: that between necessary and sufficient conditions and that between all electronic densities and the subset of realistic Coulombic ground states. Simple search algorithms find that many empirical approximations satisfy many exact conditions for realistic densities and non-empirical approximations satisfy even more conditions than those enforced in their construction. The role of exact conditions in developing approximations is revisited.

ROOF3D: A REAL AND SYNTHETIC DATA COLLECTION FOR INDIVIDUAL BUILDING ROOF PLANE AND BUILDING SECTIONS DETECTION

Abstract. Deep learning is a powerful tool to extract both individual building and roof plane polygons. But deep learning requires a large amount of labeled data. Hence, publicly available level of detail (LoD)-2 datasets are a natural choice to train fully convolutional neural networks (FCNs) models for both building section and roof plane instance segmentation. Since publicly available datasets are often automatically derived, e.g. based on laser scanning, they lack on annotation accuracy. To complement such a dataset, we introduce manually annotated and synthetically generated data. Manually annotated data is domain-specific and has a high annotation quality but is expensive to obtain. Synthetically generated data has high-quality annotations by definition, but lacks domain-specificity. Moreover, we not only detect individual building section instances, but also roof plane instances. We predict separations not only between individual buildings, but also by a class that describes the line which separates roof planes. The predicted building and roof plane instances are polygonized by a simple tree search algorithm. To achieve more regular polygons, we utilize the Douglas-Peucker polygon simplification algorithm. We describe our dataset in detail to allow comparability between successive methods. To facilitate future works in building and roof plane prediction, our Roof3D dataset is accessible at <code>https://github.com/dlrPHS/GPUB</code>.

A continuous form of the Perez diffuse sky model for forward and reverse transposition

The Perez 1990 transposition model has emerged as the preferred choice for estimating global tilted irradiance, also known as plane-of-array irradiance. One notable drawback is the Perez model’s reliance on empirical coefficients assigned to discrete bins of the sky clearness parameter, resulting in discontinuities in the calculated tilted irradiance. In this study, we present a novel method to eliminate the discontinuities of the Perez model by replacing the empirical look-up table with a set of six quadratic splines. This is facilitated by transforming the unbounded sky clearness parameter (epsilon) to an equivalent bounded parameter (zeta).Transposition using the original Perez model and continuous Perez–Driesse model are compared for multiple orientations at two locations. The two models produce very similar deviation statistics, meaning the continuous version can be used as a plug-in replacement for the original. Reverse transposition is demonstrated using the Perez–Driesse model together with a new continuous version of the Erbs diffuse fraction model and a simple bisection solution search. This combination achieves a substantially higher success rate than the GTI-DIRINT algorithm in our tests.

QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking.

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions in images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning. We combine this similarity learning with multiple existing object detectors to build Quasi-Dense Tracking (QDTrack), which does not require displacement regression or motion priors. We find that the resulting distinctive feature space admits a simple nearest neighbor search at inference time for object association. In addition, we show that our similarity learning scheme is not limited to video data, but can learn effective instance similarity even from static input, enabling a competitive tracking performance without training on videos or using tracking supervision. We conduct extensive experiments on a wide variety of popular MOT benchmarks. We find that, despite its simplicity, QDTrack rivals the performance of state-of-the-art tracking methods on all benchmarks and sets a new state-of-the-art on the large-scale BDD100K MOT benchmark, while introducing negligible computational overhead to the detector.

Psychosocial interventions promoting personal recovery in people with schizophrenia: a scoping review protocol

IntroductionPersonal recovery is an important aspect for many individuals diagnosed with schizophrenia, as people can live rich, fulfilling lives despite ongoing symptoms. Prior reviews have found several factors to be...

FPGA-Based Extended Control Set Model Predictive Current Control with a Simplified Search Strategy for Permanent Magnet Synchronous Motor

The conventional finite control set model predictive current control (FCS-MPCC) suffers from suboptimal steady-state performance, primarily due to the limited selection of only eight basic voltage vectors in each control cycle. To overcome this limitation, the proposed extended control set MPCC (ECS-MPCC) utilizes an control set consisting of 818 selectable vectors, enabling a more refined voltage output and achieving a deadbeat response for current control by minimizing the cost function. To mitigate the computational burden resulting from the substantial increase in voltage vectors, a simplified search strategy is devised, which can be extended to other multi-objective cost functions. Remarkably, based on the inherent parallelism of the algorithm, the ECS-MPCC is implemented on an FPGA, further reducing the overall control time of the current loop to an impressive 0.61 μs. Through simulation and experimental tests on a laboratory PMSM driver, the effectiveness of the proposed ECS-MPCC strategy is validated. The experimental results demonstrate a significant reduction of 79% in the total harmonic distortion of phase currents compared to the conventional FCS-MPCC approach. This improvement underscores the superiority of the ECS-MPCC in enhancing the performance of PMSM drives, thereby illustrating its potential for practical implementation in real-world applications.

Long-Term Improvement of Crow's Feet Wrinkles in Combination With Cervicofacial and Temporal Lifting: Review of the Literature.

The lateral orbital crow's feet area is one for which rejuvenation is most frequently requested by patients. Moreover, lateral canthal wrinkles are a common source of dissatisfaction after rhytidoplasty. Botulinum toxin injection has emerged as a most popular, easy, and effective solution; nevertheless, repeated injections are required periodically for long-term effect. Other nonsurgical options have also been described to have some demonstrable advantages. Orbicularis oculi surgical manipulations have been described as well. A systematic PubMed literature search was conducted to identify clinical cohort studies including more than 10 patients describing surgical approaches for improvement of crows' feet wrinkles in combination with face and/or temporal lifts. The search was complemented by Embase, Medline, and Cochrane searches in addition to screening of reference lists of selected studies and simple term searches about surgical treatment of crow's feet. Fourteen studies satisfied the inclusion criteria and were included in this review describing various muscle excision techniques including vertical strip excision, lateral partial resection, wedge resection, muscle resection in "C" pattern, and enlarged myectomy of about one-third of the whole orbital extension in addition to muscle manipulation modalities, including muscle splaying, muscle division, muscle division and splaying, muscle undermining with partial denervation, and muscle suspension. Interposition of fascia between orbicularis muscle and overlying skin was also reported. View the few studies retrieved and the wide spectrum of reported techniques, it is not possible to determine from this review the most effective modality. Nevertheless, it seems that subcutaneous undermining of the lateral canthal area combined with splaying and traction of the orbicularis oculi muscle and fixation to the temporalis fascia with or without muscle division would yield the best long-term results. This review indicates also that surgical correction of crow's feet during rhytidectomy or temporal lift could be a positive complement to improve aesthetic outcome. Unfortunately, if not underreported, it is definitely neglected. We believe that this review may be an eye-opener for surgeons.

Effects of Search Strategies on Collective Problem-Solving

In today’s dynamic and complex social environments, collaborative human groups play a critical role in addressing a wide range of real-world challenges. Collective problem-solving, the process of finding solutions through the collaboration of individuals, has become imperative in addressing scientific and technical problems. This paper develops an agent-based model to investigate the influence of different search strategies (simple local search, random search, and adaptive search) on the performance of collective problem-solving under various conditions. The research involves simulations on various problem spaces and considers distinct search errors. Results show that random search initially outperforms other strategies when the search errors are relatively small, yet it is surpassed by adaptive search in the long term when the search errors increase. A simple local search consistently performs the worst among the three strategies. Furthermore, the findings regarding adaptive search reveal that the speed of adaptation in adaptive search varies across problem spaces and search error levels, emphasizing the importance of context-specific parameterization in adaptive search strategies. Lastly, the values of Ps=0.9 and Pf=0.2 obtained through human subject experiments in adaptive search appear to be a favorable choice across various scenarios in this simulation work, particularly for complex problems entailing substantial search errors. This research contributes to a deeper understanding of the effectiveness of search strategies in complex environments, providing insights for improving collaborative problem-solving processes in real-world applications.

DSLS: a simple and efficient local search algorithm for the maximum bisection problem

DSLS: a simple and efficient local search algorithm for the maximum bisection problem

88 Determining the Eye-Tracking Strategies Used in the Game “Spot the Missing Object (SMO)” by Simulator Malingerers, ADHD, and Non-ADHD

Objective:Typical evaluations of adult ADHD consist of behavior self-report rating scales, cognitive or intellectual functioning measures, and specific measures designed to measure attention. Boone (2009) suggested monitoring continuous effort is essential throughout psychological assessments. However, very few research studies have contributed to malingering literature on the ADHD population. Many studies have reported the adequate use of symptom validity tests, which assess effortful performance in ADHD evaluations (Jasinski et al., 2011; Sollman et al., 2010; Schneider et al., 2014). Because of the length of ADHD assessments, individuals are likely to become weary and tired, thus impacting their performance. This study investigates the eye movement strategies used by a clinical ADHD population, non-ADHD subjects, and malingering simulators when playing a common simple visual search task.Participants and Methods:A total of 153 college students participated in this study. To be placed in the ADHD group, a participant must endorse four or more symptoms on the ASRS (N = 37). To be placed in the non-ADHD, participants should have endorsed no ADHD symptoms (N = 43). Participants that did not meet the above criteria for ADHD and not-ADHD were placed in an Indeterminate group and were not included in the analysis. A total of 20 participants were instructed to fake symptoms related to ADHD during the session. A total of twelve Spot the Difference images were used as the visual picture stimuli. Sticky by Tobii Pro (2020) was used for the collection of eye-movement data was utilized. Sticky by Tobii Pro is an online self-service platform that combines online survey questions with an eye-tracking webcam, allowing participants to see images from their home computers.Results:Results indicated on the participants classified as Malingering had a significantly Visit Count (M = 17.16; SD= 4.99) compared to the ADHD(M = 12.53; SD= 43.92) and not-ADHD groups (M =11.51; SD=3.23). Results also indicated a statistically significant Area Under the Curve (AUC) = .784; SE = .067; p -.003; 95% CI = .652-.916. Optimal cutoffs suggest a Sensitivity of 50% with a False Positive Rate of 10%.Conclusions:Results indicated that eye-tracking technology could help differentiate simulator malingerers from non-malingerers with ADHD. Eye-tracking research’ relates to a patchwork of fields more diverse than the study of perceptual systems. Due to their close relation to attentional mechanisms, the study’s results can provide an insight into cognitive processes related to malingering performance.

A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.

Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.

Developing anti-microbial peptide database version 1 to provide comprehensive and exhaustive resource of manually curated AMPs

Anti-Microbial Peptide Database version 1 (AMPDB v1) is a meticulously curated resource that aims to address the limitations of existing databases in the field of antimicrobial research. We have utilized the latest technology and put our best efforts into adding all relevant tools to cater to the needs of our users. AMPDB v1 is a derived database, built upon information gathered from the available resources and boasts a significant size of 59,122 entries which are classified into 88 classes. All the information in this resource was curated manually. Sequence alignment and protein feature calculation tools were integrated into the database in the form of web applications, to make them easy to use, quick, and responsive in real-time. We have included multiple types of browsing and searching options to enhance the user experience, from simple text search to a completely customizable advanced search page with intuitive options that let the user combine multiple options together to make a powerful search query. The database is accessible by a web browser at https://bblserver.org.in/ampdb/.

A progressive damage model for carbon fiber-reinforced polymer laminates subjected to fastener pull-through failure

Most studies on composite joints focus on in-plane bearing mode, while little attention to fastener pull-through failure mode from out-of-plane loads. This paper presents a 3D continuum damage mechanics (CDM) model to investigate and assess the mechanical behaviors of carbon fiber-reinforced polymer (CFRP) laminates subjected to pull-through failure. The proposed model based on the use of intralaminar and interlaminar damage models is established and implemented by means of a user-defined subroutine VUMAT for Abaqus/Explicit. The novelty lies in the material model integrating the thresholds preset by the semianalytical approximation approach, which largely eliminates unnecessary fracture angle enumerations in Puck’s IFF theory, coupled with the simple parabolic interpolation search (SPIS) technique for optimal fracture angle search, considerably reducing the computational burden. Moreover, the numerical results compare well with experiments, effectively replicating the load–displacement response, damage modes, failure behaviors and damaged areas. This study provides an accurate and efficient computational methodology to analyze pull-through failures, enhancing the damage modeling and predictive capabilities for composite joints under out-of-plane loads.

Solving travelling thief problems using coordination based methods

A travelling thief problem (TTP) is a proxy to real-life problems such as postal collection. TTP comprises an entanglement of a travelling salesman problem (TSP) and a knapsack problem (KP) since items of KP are scattered over cities of TSP, and a thief has to visit cities to collect items. In TTP, city selection and item selection decisions need close coordination since the thief’s travelling speed depends on the knapsack’s weight and the order of visiting cities affects the order of item collection. Existing TTP solvers deal with city selection and item selection separately, keeping decisions for one type unchanged while dealing with the other type. This separation essentially means very poor coordination between two types of decision. In this paper, we first show that a simple local search based coordination approach does not work in TTP. Then, to address the aforementioned problems, we propose a human designed coordination heuristic that makes changes to collection plans during exploration of cyclic tours. We further propose another human designed coordination heuristic that explicitly exploits the cyclic tours in item selections during collection plan exploration. Lastly, we propose a machine learning based coordination heuristic that captures characteristics of the two human designed coordination heuristics. Our proposed coordination based approaches help our TTP solver significantly outperform existing state-of-the-art TTP solvers on a set of benchmark problems. Our solver is named Cooperation Coordination (CoCo) and its source code is available from https://github.com/majid75/CoCo.

Cyberchondria and Information Seeking Behavior: A Bibliometric Analysis

The pervasive online availability of health information has revolutionized how individuals seek medical advice. With a simple search, individuals can access vast amounts of information about their symptoms, medical conditions, and treatments. This has led to the emergence of a phenomenon known as “cyberchondria,” which refers to excessive searching for health information online. While seeking health information can improve health literacy, cyberchondria can have negative consequences, such as increased distress and healthcare utilization. This bibliometric study explores the literature of cyberchondria and online health information seeking behavior in order to inform professional practice in libraries.

A local search approximation algorithm for the multiway cut problem

In the multiway cut problem we are given a weighted undirected graph G=(V,E) and a set T⊆V of k terminals. The goal is to find a minimum weight set of edges E′⊆E with the property that by removing E′ from G all the terminals become disconnected. In this paper we present a simple local search approximation algorithm for the multiway cut problem with approximation ratio 2−2k. We present an experimental evaluation of the performance of our local search algorithm and show that it greatly outperforms the isolation heuristic of Dalhaus et al. and it has similar performance as the much more complex algorithms of Calinescu et al., Sharma and Vondrak, and Buchbinder et al. which have the currently best known approximation ratios for this problem.