Joan Basset, a poet active in the early 15th century, wrote his Vers clus around 1415-1416. This enigmatic composition presents an allegorical confrontation between two factions of monstrous figures, led respectively by a dragon and a Turk. Basset’s use of allegory can be explained by the need to disguise a politically charged message in a particularly tense socio-political context at the end of the Western Schism, as well as by an aesthetic tradition that valued poetry of formal and thematic complexity. This article offers a contextualised interpretation of the Vers clus, considering the political events of its time and the literary tradition that frames it. The poem emerges as a work in which political reality is conveyed through symbol.
China has achieved food security in the last three decades through massive use of fertilizers, pesticides, and irrigation water, resulting in negative environmental impacts to the cultivated land use system (CLUS). Hence, it is urgent to assess the green development level of cultivated land (GDL-CL). The objective of this study was to develop a new multi-dimensional framework considering environmental impacts to assess GDL-CL based on “ elements – processes – dimensions – goals – drivers ” according to the interaction between the soil-water-plant-atmosphere system (SWPAS) and CLUS. The entropy weight method, spatial autocorrelation analysis, and the Geodetector method were applied to provincial data in China from 1990 to 2018 to determine the spatiotemporal evolution, correlation, and quantitative attributes, respectively, of GDL-CL. The results indicated that the changing agricultural input-output farming patterns in China during 1990 – 2018 followed U-shaped trend in GDL-CL that reached an inflection point in 1998. In addition, GDL-CL differed significantly between the eastern and western regions in China, with the eastern areas showing an obviously high-high agglomeration and the western areas showing an apparently low-low agglomeration. The reason behind this phenomenon is that climate and socio-economic factors such as temperature, precipitation, sunshine, assets, markets, education, employment, and policies profoundly and extensively influenced GDL-CL in different regions during 1990 – 2018. However, the contribution of climate factors to GDL-CL overtook the socio-economic factors in 2010 – 2018. Therefore, this study suggests that priority should be given to optimizing production modes of cultivation, coordinating regional GDL-CL contradictions, and warning of climate change to sustainably manage cultivated land.
Abstract Purpose A 4-step lung ultrasound (LUS) score has been previously used to quantify lung density. We compared 2 versions of this scoring system for distinguishing severe from moderate loss of aeration in ARDS: coalescence-based score (cLUS) vs. quantitative-based score (qLUS – >50% pleura occupied by artefacts). Materials and Methods We compared qLUS and cLUS to lung density measured by quantitative CT scan in 12 standard thoracic regions. A simplified approach (1 scan per region) was compared to an extensive one (regional score computed as the mean of all relevant intercostal space scores). Results We examined 13 conditions in 7 ARDS patients (7 at PEEP 5, 6 at PEEP 15 cmH2O-156 regions, 398 clips). Switching from cLUS to qLUS resulted in a change in interpretation in 117 clips (29.4%, 1-point reduction) and in 41.7% of the regions (64 decreases (range 0.2–1), 1 increase (0.2 points)). Regional qLUS showed very strong correlation with lung density (rs=0.85), higher than cLUS (rs=0.79; p=0.010). The agreement with CT classification in well aerated, poorly aerated, and not aerated tissue was moderate for cLUS (agreement 65.4%; Cohen’s K coefficient 0.475 (95%CI 0.391–0.547); p<0.0001) and substantial for qLUS (agreement 81.4%; Cohen’s K coefficient 0.701 (95%CI 0.653–0.765), p<0.0001). The agreement between single spot and extensive approaches was almost perfect (cLUS: agreement 89.1%, Cohen’s kappa coefficient 0.840 (95%CI 0.811–0.911), p<0.0001; qLUS: agreement 86.5%, Cohen’s kappa coefficient 0.819 (95%CI 0.761–0.848), p<0.0001). Conclusion A LUS score based on the percentage of occupied pleura performs better than a coalescence-based approach for quantifying lung density. A simplified approach performs as well as an extensive one.
It is essential to delve into the strategy of multimodal model pre-training, which is an obvious impact on downstream tasks. Currently, clustering learning has achieved noteworthy benefits in multiple methods. However, due to the availability of open image-text pairs, it is challenging for multimodal with clustering learning. In this paper, we propose an approach that utilizes clustering swap prediction strategy to learn image-text clustering embedding space by interaction prediction between image and text features. Unlike existing models with clustering learning, our method (Clus) allows for an open number of clusters for web-scale alt-text data. Furthermore, in order to train the image and text encoders efficiently, we introduce distillation learning approach and evaluate the performance of the image-encoder in downstream visual tasks. In addition, Clus is pre-trained end-to-end by using large-scale image-text pairs. Specifically, both text and image serve as ground truth for swap prediction, enabling effective representation learning. Concurrently, extensive experiments demonstrate that Clus achieves state-of-the-art performance on multiple downstream fine-tuning and zero-shot tasks (i.e., Image-Text Retrieval, VQA, NLVR2, Image Captioning, Object Detection, and Semantic Segmentation).
In this paper we introduce the clus model, which has been newly implemented in the X-ray spectral fitting software package SPEX. Based on 3D radial profiles of the gas density, temperature, and metal abundance as well as the turbulent, inflow, and outflow velocities, the clus model creates spectra for a chosen projected region on the sky. Additionally, it can also take into account the resonant scattering. We show a few applications of the clus model on simulated spectra of the massive elliptical galaxy NGC 4636; galaxy clusters A383, A2029, A1795, and A262; and the Perseus cluster. We quantify the effect of projection as well as the resonant scattering on inferred profiles of the iron abundance and temperature, assuming a resolution similar to Chandra ACIS-S and XRISM Resolve. Our results show that depending on the mass of the object as well as the projected distance from its core, neither a single-temperature or double-temperature model nor the Gaussian-shaped differential emission measure model can accurately describe the input emission measure distribution of these massive objects. The largest effect of projection as well as resonant scattering was observed for projected profiles of iron abundance of NGC 4636, which is where we could reproduce the observed iron abundance drop in its innermost few kiloparsecs. Furthermore, we find that projection effects also influence the best-fit temperature, and the magnitude of this effect varies depending on the underlying hydrodynamical profiles of individual objects. In the core, the projection effects are the largest for A1795 and NGC 4636, while in the outskirts, the largest difference between 2D and 3D temperature profiles is for Perseus and A1795, regardless of the instrumental resolution. These findings might potentially have an impact on cross-calibration studies between different instruments as well as on the precision cosmology.
Human learning thrives on the ability to learn from mistakes, adapt through feedback, and refine understanding-processes often missing in static machine learning models. In this work, we introduce Composite Learning Units (CLUs) designed to transform reasoners, such as Large Language Models (LLMs), into learners capable of generalized, continuous learning without conventional parameter updates while enhancing their reasoning abilities through continual interaction and feedback. CLUs are built on an architecture that allows a reasoning model to maintain and evolve a dynamic knowledge repository: a General Knowledge Space for broad, reusable insights and a Prompt-Specific Knowledge Space for task-specific learning. Through goal-driven interactions, CLUs iteratively refine these knowledge spaces, enabling the system to adapt dynamically to complex tasks, extract nuanced insights, and build upon past experiences autonomously. We demonstrate CLUs' effectiveness through a cryptographic reasoning task, where they continuously evolve their understanding through feedback to uncover hidden transformation rules. While conventional models struggle to grasp underlying logic, CLUs excel by engaging in an iterative, goal-oriented process. Specialized components-handling knowledge retrieval, prompt generation, and feedback analysis-work together within a reinforcing feedback loop. This approach allows CLUs to retain the memory of past failures and successes, adapt autonomously, and apply sophisticated reasoning effectively, continually learning from mistakes while also building on breakthroughs.
Background In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. Results The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. Conclusions The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository ( https://github.com/yshen25/HLA-Clus ).
In the era of Big Data, cluster analysis of high-dimensional data sets often suffers from the Curse of dimensionality. To overcome this problem, the dimensionality reduction through feature selection becomes inevitable. Co-clustering or two-way clustering is considered to be a more sophisticated tool than conventional one-way clustering. Moreover, the advent of multi-view learning shows that the subjects of a data set can be interpreted in many ways. Interestingly, a minimal number of existing feature selection algorithms take advantage of the co-clustering method and are designed to consider multi-view data. Motivated by this, in the current article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a multi-objective optimization based multi-view Co-Clustering algorithm (named MMCo-Clus). A popular evolutionary technique – Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been utilized as the proposed method's underlying optimization strategy. First, we construct two views of a chosen data set, utilizing knowledge from two different biological data sources. Next, we develop the MMCo-Clus algorithm considering the constructed views to identify a set of “good” co-clustering solutions. Finally, based on a concept of consensus operation on the co-clustering outcome, a small number of most relevant and non-redundant features are extracted from the original feature-space. The reduced dimension formed by new feature-space causes to decrease the computational burden and noise level of original data. For experimental analysis, we have chosen three benchmark GE data sets. Our feature selection method's effectiveness is evaluated through sample-classification accuracy, accompanied by the cluster profile plot/Eisen plot/t-SNE plot, and biological/statistical significance test. A thorough comparative analysis with existing feature selection algorithms using external and internal evaluation metrics supports our proposed method's potency.
Abtin Ijadi Maghsoodi, Dara Riahi, E. Herrera-Viedma
et al.
Abstract One of the most primary issues that organizations have to deal with is incorporating massive structured data problems, simultaneously. Additionally, a vital division in any organization is the department of human resources (HR), which is in charge of the recruitment and personnel selection procedures. Due to the nature of the personnel assessment problems, which include multiple candidates as alternatives along with various complex evaluating criteria, these types of problems can be tackled by the aid of multi-attribute decision making (MADM) techniques. Moreover, in mega-structured organizations, the procedure of personnel selection contains massive structures of data due to the number of potential candidates for job positions in various sub-divisions and departments. Therefore, the personnel selection problem in such environments can be subjected as a big data problem which should be handled prudently to save time and cost. The main objective of the current study is to extend the CLUS-MCDA approach (CLUSter analysis for improving Multiple Criteria Decision Analysis) and integrate it with the Best–Worst Method (BWM) and a specific structure to solve multi-scenario big data decision-making problems. In this study, to validate the practicality and reliability of the W-CLUS-MCDA approach, multiple personnel selection and risk assessment problems have been investigated with various scenarios within several departments, simultaneously. This study has also introduced the concept of multi-scenario parallel decision making (PDM) within the context of MADM methodology using a data-driven decision-making approach solving various big data problems.
Multiplication is an important function of logic operation, and all-optical high-speed multiplication logic operation will lay the foundation for future high-speed optical computing and optical logic processing chip. In this article, by introducing the structure of canonical logic units-based programmable logic array (CLUs-PLA), we propose a scheme to realize all-optical 2 × 2-bit multiplier. In our scheme, different types of CLUs are generated using bidirectional multichannel four-wave mixing (FWM), then the results of multiplier at the operation of 40 Gb/s can be obtained by simple power coupling of corresponding CLUs. Eye diagrams of logic results are widely open, and the extinction ratios are more than 9.4 dB. Comparing with multiplier based on traditional hierarchical computing, multiplier based on parallel computing in our scheme can reduce the number of AND gate by 4, and avoid further deterioration of signal quality due to three-order cascade of AND gate. Moreover, the scheme has the potential to realize m × n-bit ($m + n \leq 9$, m and n are positive integers) multiplier at higher operation rate in the integrated platform, paving the way towards multi-bit high-speed compact complex logic devices for future high-performance optical computing and optical logic processing chip.
MIT Lincoln Laboratory, the Medical Device Realization Center (MEDRC) at MIT, and the Massachusetts General Hospital (MGH) are collaboratively developing a novel optical system that acquires ultrasound images within the human body without physical contact to the patient. The system is termed, non-contact laser ultrasound (N-CLUS) and yields anatomical images in tissue and bone and can also measure elastographic properties, in-vivo, all from an operational standoff of a few inches to several meters as desired. N-CLUS employs a pulsed laser that converts optical energy into ultrasonic waves at the skin surface via photoacoustic mechanisms, while, a laser Doppler vibrometer measures reflected-emerging ultrasonic waves from tissue at depth at the skin surface. The key of the N-CLUS approach is driven by shallow optical absorptivity that creates an acoustic source that enables ultrasound propagation deeper into the tissue.We discuss the motivation of the non-contact laser concept, its development path involving signal generation, skin and eye safe laser measurement, and system design perspectives. Elastogrphic measurements are then demonstrated with determination of bone elastic moduli for beef rib within tissue. N-CLUS images from soft tissue specimens are also compared with commercial ultrasound, showing that the noncontact optical approach may have potential as a viable method in medical ultrasound.
The spread of real-time applications has led to a huge amount of data shared between users. This vast volume of data rapidly evolving over time is referred to as data stream. Clustering and processing such data poses many challenges to the data mining community. Indeed, traditional data mining techniques become unfeasible to mine such a continuous flow of data where characteristics, features, and concepts are rapidly changing over time. This paper presents a novel method for data stream clustering. In this context, major challenges of data stream processing are addressed, namely, infinite length, concept drift, novelty detection, and feature evolution. To handle these issues, the proposed method uses the Artificial Immune System (AIS) meta-heuristic. The latter has been widely used for data mining tasks and it owns the property of adaptability required by data stream clustering algorithms. Our method, called AIS-Clus, is able to detect novel concepts using the performance of the learning process of the AIS meta-heuristic. Furthermore, AIS-Clus has the ability to adapt its model to handle concept drift and feature evolution for textual data streams. Experimental results have been performed on textual datasets where efficient and promising results are obtained.
Sophie V. Pageon, P. Nicovich, Mahdie Mollazade
et al.
Despite the increasingly widespread use of single-molecule localization microscopy (SMLM) in biology, the extent to which the spatial organization of proteins influences signaling is not easy to quantify. Clus-DoC is a novel analysis method that combines cluster detection and colocalization analysis for SMLM data.
H. Bahcivan, J. W. Cutler, J. C. Springmann
et al.
The second Radio Aurora Explorer (RAX‐2) satellite has completed more than 30 conjunction experiments with the Advanced Modular Incoherent Scatter Radar chain of incoherent scatter radars in Alaska and Resolute Bay, Canada. Coherent radar echoing occurred during four of the passes: three when E region electron drifts exceeded the ion acoustic speed threshold and one during HF heating of the ionosphere by the High Frequency Active Auroral Research Program heater. In this paper, we present the results for the first three passes associated with backscatter from natural irregularities. We analyze, in detail, the largest drift case because the plasma turbulence was the most intense and because the corresponding ground‐to‐space bistatic scattering geometry was the most favorable for magnetic aspect sensitivity analysis. A set of data analysis procedures including interference removal, autocorrelation analysis, and the application of a radar beam deconvolution algorithm mapped the distribution of E region backscatter with 3 km resolution in altitude and ∼0.1° in magnetic aspect angle. To our knowledge, these are the highest resolution altitude‐resolved magnetic aspect sensitivity measurements made at UHF frequencies in the auroral region. In this paper, we show that despite the large electron drift speed of ∼1500 m/s, the magnetic aspect sensitivity of submeter scale irregularities is much higher than previously reported. The root‐mean‐square of the aspect angle distribution varied monotonically between 0.5° and 0.1° for the altitude range 100–110 km. Findings from this single but compelling event suggest that submeter scale waves propagating at larger angles from the main E×B flow direction (secondary waves) have parallel electric fields that are too small to contribute to E region electron heating. It is possible that anomalous electron heating in the auroral electrojet can be explained by (a) the dynamics of those submeter scale waves propagating in the E×B direction (primary waves) or (b) the dynamics of longer wavelengths.