搜索结果：Frontiers in bioscience (Elite edition)

In this study, we investigate student performance using grades and grade anomalies across periods before, during, and after COVID-19 remote instruction in courses for bioscience and health-related majors. Additionally, we explore gender equity in these courses using these measures. We define grade anomaly as the difference between a student's grade in a course of interest and their overall grade point average (GPA) across all other courses taken up to that point. If a student's grade in a course is lower than their GPA in all other courses, we refer to this as a grade penalty. Students received grade penalties in all courses studied, consisting of twelve courses taken by the majority of bioscience and health-related majors. Overall, we found that both grades and grade penalties improved during remote instruction but deteriorated after remote instruction. Additionally, we find more pronounced gender differences in grade anomalies than in grades. We hypothesize that women's decisions to pursue STEM careers may be more influenced by the grade penalties they receive in required science courses than men's, as women tend to experience larger penalties across all periods studied. Furtherm

Test anxiety is beginning to be recognized as a significant factor affecting student performance in science, technology, engineering, and mathematics (STEM) courses, potentially contributing to gender inequity within these fields. Additionally, the management of test anxiety can improve self-efficacy, which is a construct that has been well studied in the physics context. In this study, we investigated the relationship between self-efficacy, test anxiety, and gender differences in performance in a two-semester-long introductory physics course sequence for bioscience students in which women outnumber men. Using validated survey data and grade information from students in a two-semester introductory physics course sequence, we compared the predictive power of self-efficacy and test anxiety on female and male students' performance on both low- and high-stakes assessments. We found that there were gender differences disadvantaging women in self-efficacy and test anxiety in both Physics 1 and Physics 2, as well as gender differences in high-stakes outcomes in Physics 1. There were no gender differences in low-stakes assessment scores. We also found that self-efficacy and test anxiety pr

Within the framework of the ViSE (Voting in a Stochastic Environment) model, we examine the dynamics in a society, part of which can be considered an elite. The model allows us to analyze the influence of social attitudes, such as collectivism, individualism, altruism on the well-being of agents. The dynamics is determined by collective decisions and changes in the structure of society, in particular, by the formation of groups of cooperating agents. It is found that the presence of a "responsible elite", combining the support of other agents with limited concern for their own benefit, stabilizes society and eliminates the "pit of losses" paradox. The benefit to society from having a responsible elite is comparable to that from having a prosocial group of the same size. If the elite radically increases the weight of the group component in its combined voting strategy, then its incomes rise sharply, while society's incomes decline. If, in response to the selfish transformation of the elite, a new responsible elite emerges, proportionally larger than the previous one, then society will stabilize again, and the old elite will lose its dominant position. This process can be repeated as

Edit distance with moves (EDM) is a string-to-string distance measure that includes substring moves in addition to ordinal editing operations to turn one string to the other. Although optimizing EDM is intractable, it has many applications especially in error detections. Edit sensitive parsing (ESP) is an efficient parsing algorithm that guarantees an upper bound of parsing discrepancies between different appearances of the same substrings in a string. ESP can be used for computing an approximate EDM as the L1 distance between characteristic vectors built by node labels in parsing trees. However, ESP is not applicable to a streaming text data where a whole text is unknown in advance. We present an online ESP (OESP) that enables an online pattern matching for EDM. OESP builds a parse tree for a streaming text and computes the L1 distance between characteristic vectors in an online manner. For the space-efficient computation of EDM, OESP directly encodes the parse tree into a succinct representation by leveraging the idea behind recent results of a dynamic succinct tree. We experimentally test OESP on the ability to compute EDM in an online manner on benchmark datasets, and we show O

Globally trained scientific labor is a substantial component of U.S. universities, yet the organizational mechanisms linking foreign degree training to elite scientific output remain poorly understood. We link comprehensive U.S. faculty rosters to more than 12 million OpenAlex-indexed faculty-publication observations from 2011 to 2020. Faculty with non-U.S. degrees constitute one-tenth of the U.S. professoriate but account for larger shares of total publications and top-1% cited papers. This overrepresentation is concentrated in high-output disciplinary domains and research-intensive institutions. Within institution - domain - rank - year strata, however, differences in top-1% output, FWCI, and corresponding-author share attenuate sharply, indicating that much of the aggregate pattern reflects organizational placement rather than large within-context citation advantages. Collaboration structure further differentiates foreign- and domestically trained faculty: mixed domestic-foreign faculty teams exhibit substantially elevated elite-output rates, and the association attenuates strongly after accounting for team size, suggesting that collaboration scale is central to the pattern. Top

Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects. We propose Geometric Manifold Analysis (GeMA), a latent manifold frontier framework implemented via a productivity-manifold variational autoencoder (ProMan-VAE). Instead of specifying a frontier function in the observed space, GeMA represents the production set as the boundary of a low-dimensional manifold embedded in the joint input-output space. A split-head encoder learns latent variables that capture technological structure and operational inefficiency. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant benchmarking, and

NBA team managers and owners try to acquire high-performing players. An important consideration in these decisions is how well the new players will perform in combination with their teammates. Our objective is to identify elite five-person lineups, which we define as those having a positive plus-minus per minute (PMM). Using individual player order statistics, our model can identify an elite lineup even if the five players in the lineup have never played together, which can inform player acquisition decisions, salary negotiations, and real-time coaching decisions. We combine seven classification tools into a unanimous consent classifier (all-or-nothing classifier, or ANC) in which a lineup is predicted to be elite only if all seven classifiers predict it to be elite. In this way, we achieve high positive predictive value (i.e., precision), the likelihood that a lineup classified as elite will indeed have a positive PMM. We train and test the model on individual player and lineup data from the 2017-18 season and use the model to predict the performance of lineups drawn from all 30 NBA teams' 2018-19 regular season rosters. Although the ANC is conservative and misses some high-perfor

In year 2006 Bio-Linux with the work of Tim Booth and team gives its rising and provide an operating system that was and still specialized in providing a bioinformatic specific software environment for the working needs in this corner of bioscience. It is shown that Bio-Linux is affected by a 2 year release cycle and with this the final releases of Bio-Linux will not have the latest bioinformatic software on board. The paper shows how to get around this huge time gap and bring new software for Bio-Linux on board through a process that is called backporting. A summary of within the work to this paper just backported bioinformatic tools is given. A describtion of a workflow for continuously integration of the newest bioinformatic tools gives an outlook to further concrete planned developments and the influence of speeding up scientific progress.

In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally learn such concepts in an optimization-based manner, yet bringing excessive computation or memory burden. In this paper, we instead propose a learning-based encoder, which consists of a global and a local mapping networks for fast and accurate customized text-to-image generation. In specific, the global mapping network projects the hierarchical features of a given image into multiple new words in the textual word embedding space, i.e., one primary word for well-editable concept and other auxiliary words to exclude irrelevant disturbances (e.g., background). In the meantime, a local mapping network injects the encoded patch features into cross attention layers to provide omitted details, without sacrificing the editability of primary concepts. We compare our method with existing optimization-based approaches on a variety of user-defined concepts, and demonstrate that our method enables high-fidelity inversion and more robust editability with a significantly faster encoding process. Our code is publicly avail

How do socioeconomically unequal screening practices impact access to elite firms and what policies might reduce inequality? Using personnel data from elite U.S. and European multinational corporations recruiting from an elite Indian college, I show that caste disparities in hiring do not arise in many job search stages, including: applications, application reading, written aptitude tests, large group debates that assess socio-emotional skills, and job choices. Rather, disparities arise in the final round, comprising non-technical personal interviews that screen on family background, neighborhood, and "cultural fit." These characteristics are plausibly weakly correlated with productivity (at the interview round) but strongly correlated with caste. Employer willingness to pay for an advantaged caste is as large as that for a full standard deviation increase in college GPA. A hiring subsidy that eliminates the caste penalty would be more cost-effective in diversifying elite hiring than equalizing the caste distribution of pre-college test scores or enforcing hiring quotas.

Recent progress in 4D representations, such as Dynamic NeRF and 4D Gaussian Splatting (4DGS), has enabled dynamic 4D scene reconstruction. However, text-driven 4D scene editing remains under-explored due to the challenge of ensuring both multi-view and temporal consistency across space and time during editing. Existing studies rely on 2D diffusion models that edit frames independently, often causing motion distortion, geometric drift, and incomplete editing. We introduce Dynamic-eDiTor, a training-free text-driven 4D editing framework leveraging Multimodal Diffusion Transformer (MM-DiT) and 4DGS. This mechanism consists of Spatio-Temporal Sub-Grid Attention (STGA) for locally consistent cross-view and temporal fusion, and Context Token Propagation (CTP) for global propagation via token inheritance and optical-flow-guided token replacement. Together, these components allow Dynamic-eDiTor to perform seamless, globally consistent multi-view video without additional training and directly optimize pre-trained source 4DGS. Extensive experiments on multi-view video dataset DyNeRF demonstrate that our method achieves superior editing fidelity and both multi-view and temporal consistency pr

We introduce ELITE, an Efficient Gaussian head avatar synthesis from a monocular video via Learned Initialization and TEst-time generative adaptation. Prior works rely either on a 3D data prior or a 2D generative prior to compensate for missing visual cues in monocular videos. However, 3D data prior methods often struggle to generalize in-the-wild, while 2D generative prior methods are computationally heavy and prone to identity hallucination. We identify a complementary synergy between these two priors and design an efficient system that achieves high-fidelity animatable avatar synthesis with strong in-the-wild generalization. Specifically, we introduce a feed-forward Mesh2Gaussian Prior Model (MGPM) that enables fast initialization of a Gaussian avatar. To further bridge the domain gap at test time, we design a test-time generative adaptation stage, leveraging both real and synthetic images as supervision. Unlike previous full diffusion denoising strategies that are slow and hallucination-prone, we propose a rendering-guided single-step diffusion enhancer that restores missing visual details, grounded on Gaussian avatar renderings. Our experiments demonstrate that ELITE produces

Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group identity of hyperedges, thereby resulting in limited attack performance and detectable attacks. In this manuscript, we present a novel framework, i.e., Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges (IE-Attack), to tackle these challenges. Initially, utilizing the node spanning in the hypergraph, we propose the elite hyperedges sampler to identify hyperedges to be injected. Subsequently, a node generator utilizing Kernel Density Estimation (KDE) is proposed to generate the homogeneous node with the group identity of hyperedges. Finally, by injecting the homogeneous node into elite hyperedges, IE-Attack improves the attack performance and enhances the imperceptibility of attacks. Extensive experiments are conducted on five authentic datasets to validate the effectiveness of IE-Attack and the corresponding superiority to state-of-the-art methods.

Vision-language models (VLMs) have shown remarkable general capabilities, yet embodied agents built on them fail at complex tasks, often skipping critical steps, proposing invalid actions, and repeating mistakes. These failures arise from a fundamental gap between the static training data of VLMs and the physical interaction for embodied tasks. VLMs can learn rich semantic knowledge from static data but lack the ability to interact with the world. To address this issue, we introduce ELITE, an embodied agent framework with {E}xperiential {L}earning and {I}ntent-aware {T}ransfer that enables agents to continuously learn from their own environment interaction experiences, and transfer acquired knowledge to procedurally similar tasks. ELITE operates through two synergistic mechanisms, \textit{i.e.,} self-reflective knowledge construction and intent-aware retrieval. Specifically, self-reflective knowledge construction extracts reusable strategies from execution trajectories and maintains an evolving strategy pool through structured refinement operations. Then, intent-aware retrieval identifies relevant strategies from the pool and applies them to current tasks. Experiments on the EB-ALF

This paper introduces a newly constructed individual-level dataset of prewar Japanese elites using the ``Who's Who'' directories published in 1903--1939. Covering approximately the top 0.1\% of the population, the dataset contains rich information on social group, education, occupation, and family structure. By reconstructing intergenerational links and family networks, we provide descriptive evidence on elite formation and persistence across geography, social groups, and education during transitions from a feudal system to a modern system. We also use family records to document elite marriage patterns and family-based mobility, showing stable age assortative matching, widening husband--wife age gaps, and associations between marriage-age structure, adoption, and elite persistence. The dataset provides a foundational empirical resource for studying intergenerational and intergroup mobility, and institutional development during Japan's transition to a modern society.

We present the second edition of the book `CosmoAmautas: Astrophysics in the classroom' which is a text and activity book on selected topics in astrophysics. The content is written in Spanish and intended as the introductory literature and material for high school teachers who want to integrate astronomy to their science and math classes. The book includes an introduction to topics such as planetary science, stellar evolution, exoplanets, galaxies, black holes, and cosmology. In addition to the astronomy content, the book includes didactic notes on general physics of gravity and light, unit conversion, history of science, and tools for the digital classroom (ICT tools), which are compatible with the requirements of the Peruvian high school curriculum. This book is constructed based on the astrophysics curriculum and education model introduced in the online teacher-training workshop CosmoAmautas, which took place in 2021 and 2022. CosmoAmautas (amauta = teacher in the Quechua language) is an astronomy education project in Peru, funded by the IAU Office of Astronomy for Development. For more information, please visit www.cosmoamautas.org.

Small-scale automation services in Software Engineering, known as SE Bots, have gradually infiltrated every aspect of daily software development with the goal of enhancing productivity and well-being. While leading the OSS development, elite developers have often burned out from holistic responsibilities in projects and looked for automation support. Building on prior research in BotSE and our interviews with elite developers, this paper discusses how to design and implement SE bots that integrate into the workflows of elite developers and meet their expectations. We present six main design guidelines for implementing SE bots for elite developers, based on their concerns about noise, security, simplicity, and other factors. Additionally, we discuss the future directions of SE bots, especially in supporting elite developers' increasing workload due to rising demands.

Elites are subgroups of individuals within a society that have the ability and means to influence, lead, govern, and shape societies. Members of elites are often well connected individuals, which enables them to impose their influence to many and to quickly gather, process, and spread information. Here we argue that elites are not only composed of highly connected individuals, but also of intermediaries connecting hubs to form a cohesive and structured elite-subgroup at the core of a social network. For this purpose we present a generalization of the $K$-core algorithm that allows to identify a social core that is composed of well-connected hubs together with their `connectors'. We show the validity of the idea in the framework of a virtual world defined by a massive multiplayer online game, on which we have complete information of various social networks. Exploiting this multiplex structure, we find that the hubs of the generalized $K$-core identify those individuals that are high social performers in terms of a series of indicators that are available in the game. In addition, using a combined strategy which involves the generalized $K$-core and the recently introduced $M$-core, t

Training of elite athletes requires regular physiological and medical monitoring to plan the schedule, intensity and volume of training, and subsequent recovery. In sports medicine, ECG-based analyses are well established. However, they rarely consider the correspondence of respiratory and cardiac activity. Given such mutual influence, we hypothesize that athlete monitoring might be developed with causal inference and that detailed, time-related techniques should be preceded by a more general, time-independent approach that considers the whole group of participants and parameters describing whole signals. The aim of this study was to discover general causal paths among cardiac and respiratory variables in elite athletes in two body positions (supine and standing), at rest. ECG and impedance pneumography signals were obtained from 100 elite athletes. The mean HR, the RMSSD, its natural logarithm, the mean respiratory rate, the breathing activity coefficients, and the resulting breathing regularity were estimated. Several causal discovery frameworks were applied: generalized correlations, CAM, FGES, GFCI, and two Bayesian network learning algorithms: Hill-Climbing and Tabu. The main,