dyfan/halluhard-seed-questions huggingface dataset:
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare B-meson decays into muon pairs constrain theories beyond the Standard Model, and can you give concrete examples of the types of new physics that are limited by these measurements?
As adjuvant therapy for high-risk stage III melanoma, 200 mg of pembrolizumab administered every 3 weeks for up to 1 year resulted in significantly longer recurrence-free survival than placebo, with no new toxic effects identified. (Funded by Merck; ClinicalTrials.gov number, NCT02362594 ; EudraCT number, 2014-004944-37 .).

Research question: What factors determine whether a patient with stage III melanoma should receive adjuvant PD‑1 inhibitor therapy, and how are the potential benefits weighed against the risks?
Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complementary or substitutable relations between products or product types, which can be utilized in recommender systems. However, relation labeling in KGs remains a challenging task due to the dynamic nature of e-commerce domains and the associated cost of human labor. Recently, breakthroughs in Large Language Models (LLMs) have shown surprising results in numerous natural language processing tasks. In this paper, we conduct an empirical study of LLMs for relation labeling in e-commerce KGs, investigating their powerful learning capabilities in natural language and effectiveness in predicting relations between product types with limited labeled data. We evaluate various LLMs, including PaLM and GPT-3.5, on benchmark datasets, demonstrating their ability to achieve competitive performance compared to humans on relation labeling tasks using just 1 to 5 labeled examples per relation. Additionally, we experiment with different prompt engineering techniques to examine their impact on model performance. Our results show that LLMs significantly outperform existing KG completion models in relation labeling for e-commerce KGs and exhibit performance strong enough to replace human labeling.

Research question: What best practices should practitioners follow when using large language models with few-shot prompts to label relations in dynamic e-commerce knowledge graphs, in order to ensure accuracy, scalability, and maintainability?
Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms' actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algorithmic management, we conducted an online experiment using four managerial decisions that required either mechanical or human skills. We manipulated the decision-maker (algorithmic or human), and measured perceived fairness, trust, and emotional response. With the mechanical tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers' fairness and trustworthiness were attributed to the manager's authority, whereas algorithms' fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms' perceived lack of intuition and subjective judgment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people's lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people's experiences with algorithmic technologies.

Research question: How can organizations design and present algorithmic decision-making systems to increase perceived fairness and trust, especially for tasks that people view as requiring human judgment or social recognition?
The previous link adaptation algorithms on ofdm based systems use equal modulation order for all sub carrier index within a block. For multimedia transmission using ofdm as the modulation technique, unequal constellation is used within one ofdm subcarrier block, a set of subcarriers for audio and another set for video transmissions. A generic model has been shown for such a transmission and link adaptation algorithm has been proposed using EESM (Effective Exponential SNR mapping) method as basic method. Mathematical model has been derived for the channel based on bivariate Gaussian distribution in which the amplitude varies two dimensionally in the same envelope. From the Moment generating function of bivariate distribution, Probability of error has been theoretically derived. Results have been shown for BER performance of an ofdm system using unequal constellation. BER performances have been shown for different values of correlation parameter and fading figure.

Research question: What are the trade-offs, benefits, and practical considerations of using different modulation orders for subcarriers within an OFDM block (e.g., separating audio and video), and how should link adaptation and error-rate prediction account for varying correlation and fading across those subcarriers?
Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.

Research question: What are the main practical challenges and limitations when applying deep learning to different stages of drug discovery (e.g., bioactivity prediction, molecular design, synthesis planning, and biological image analysis), and how can researchers address them?
The 2023-2032 Planetary Science and Astrobiology Decadal Survey Origins, Worlds, and Life recommended that "NASA develop scientific exploration strategies, as it has for Mars, in areas of broad scientific importance, e.g., Venus... that have an increasing number of U.S. missions and international collaboration opportunities" (OWL, p.22-10). In NASA's initial responses to that Decadal Survey, the agency asserted that "...specific scientific exploration strategies should be community generated by bodies such as the Analysis Groups," thus placing the onus on the planetary community to generate and support these exploration strategies. In late 2022, the Venus Exploration Analysis Group began a project to develop a new exploration strategy for Venus, reflecting the 2021 selections of the VERITAS, DAVINCI, and EnVision missions and the sweeping comparative planetology recommendations relevant to Venus in Origins, Worlds, and Life.
  This is that strategy.
  Taking a broad look at the scientific, technological, and programmatic advances required to address the key outstanding questions that Venus poses, and predicated on VERITAS, DAVINCI, and EnVision flying as planned in the early 2030s, this report outlines a set of actions available to NASA, VEXAG, and the planetary science community at large to establish a sustained program of Venus exploration in the years and decades ahead. Key to this approach is recognizing Venus as a unique setting where multiple, cross-disciplinary, Decadal-level planetary, Earth, heliophysics, and exoplanet science questions can be addressed, as well as being a worthy target of exploration in its own right.
  This report offers Assessments of the current state of Venus exploration, and Actions for the U.S. and international Venus community, as well as NASA, to consider. This strategy is a living document and should be updated as warranted.

Research question: What technological advancements and mission capabilities are most critical to enable a sustained, long-term program of surface and atmospheric exploration of Venus?
The objective of this book is to give a concise account of the analysis of survival data. The book is intended both for the applied statistician and for a wider statistical audience wanting an introduction to this field. Particular attention is paid to new theory on the relationship between survival factors and identified explanatory variables. Each chapter concludes with bibliographic notes and outline statements of further results that can be used for student exercises. (ANNOTATION)

Research question: How do survival analysis methods handle censored observations and incorporate explanatory variables to assess their relationship with survival outcomes?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare B meson decays into muon pairs constrain or inform models of physics beyond the Standard Model?
Lignin is one of the main components of plant cell wall and it is a natural phenolic polymer with high molecular weight, complex composition and structure. Lignin biosynthesis extensively contributes to plant growth, tissue/organ development, lodging resistance and the responses to a variety of biotic and abiotic stresses. In the present review, we systematically introduce the biosynthesis of lignin and its regulation by genetic modification and summarize the main biological functions of lignin in plants and their applications. We hope this review will give an in-depth understanding of the important roles of lignin biosynthesis in various plants’ biological processes and provide a theoretical basis for the genetic improvement of lignin content and composition in energy plants and crops.

Research question: What are the potential trade-offs and unintended consequences of genetically modifying lignin content or composition in crops and energy plants, especially regarding plant growth, stress resistance, and suitability for industrial uses?
During the past twelve years, NASA's Fermi Gamma-ray Space Telescope has supported a wide range of Education and Public Outreach (E/PO) activities, targeting K-14 students and the general public. The purpose of the Fermi E/PO program is to increase student and public understanding of the science of the high-energy Universe, through inspiring, engaging and educational activities linked to the mission's science objectives. The E/PO program has additional more general goals, including increasing the diversity of students in the Science, Technology, Engineering and Mathematics (STEM) pipeline, and increasing public awareness and understanding of Fermi science and technology. Fermi's multi-faceted E/PO program includes elements in each major outcome category: Higher Education; Elementary and Secondary Education; Informal Education and Public Outreach.

Research question: What are effective methods for evaluating the impact of space-science education and public-outreach programs on student interest, learning outcomes, and diversity in the STEM pipeline?
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image superresolution, and classification. The aim of this review article is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

Research question: What are the main practical challenges in training generative adversarial networks, and which techniques are most effective for improving their stability and convergence?
Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics.
  This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.

Research question: What practical strategies can statistical agencies use to detect, monitor, and mitigate the effects of changes in external data sources on machine-learning models and published statistics?
Circular dichroism (CD) spectroscopy is a widely used method to study the protein secondary structure. However, for decades, the general opinion was that the correct estimation of β-sheet content is challenging because of the large spectral and structural diversity of β-sheets. Recently, we showed that the orientation and twisting of β-sheets account for the observed spectral diversity, and developed a new method to estimate accurately the secondary structure (PNAS, 112, E3095). BeStSel web server provides the Beta Structure Selection method to analyze the CD spectra recorded by conventional or synchrotron radiation CD equipment. Both normalized and measured data can be uploaded to the server either as a single spectrum or series of spectra. The originality of BeStSel is that it carries out a detailed secondary structure analysis providing information on eight secondary structure components including parallel-β structure and antiparallel β-sheets with three different groups of twist. Based on these, it predicts the protein fold down to the topology/homology level of the CATH protein fold classification. The server also provides a module to analyze the structures deposited in the PDB for BeStSel secondary structure contents in relation to Dictionary of Secondary Structure of Proteins data. The BeStSel server is freely accessible at http://bestsel.elte.hu.

Research question: How do the orientation and twist of β-sheets affect circular dichroism spectra, and what practical approaches can be used during CD experiments to distinguish different β‑sheet arrangements?
We present a mass lumping approach based on an isogeometric Petrov-Galerkin method that preserves higher-order spatial accuracy in explicit dynamics calculations irrespective of the polynomial degree of the spline approximation. To discretize the test function space, our method uses an approximate dual basis, whose functions are smooth, have local support and satisfy approximate bi-orthogonality with respect to a trial space of B-splines. The resulting mass matrix is ``close'' to the identity matrix. Specifically, a lumped version of this mass matrix preserves all relevant polynomials when utilized in a Galerkin projection. Consequently, the mass matrix can be lumped (via row-sum lumping) without compromising spatial accuracy in explicit dynamics calculations. We address the imposition of Dirichlet boundary conditions and the preservation of approximate bi-orthogonality under geometric mappings. In addition, we establish a link between the exact dual and approximate dual basis functions via an iterative algorithm that improves the approximate dual basis towards exact bi-orthogonality. We demonstrate the performance of our higher-order accurate mass lumping approach via convergence studies and spectral analyses of discretized beam, plate and shell models.

Research question: How does using lumped mass matrices with high-order spline-based discretizations affect the stability limits and allowable time step size in explicit dynamic simulations compared to consistent mass matrices?
Taking CRISPR technology further CRISPR techniques are allowing the development of technologies for nucleic acid detection (see the Perspective by Chertow). Taking advantages of the distinctive enzymatic properties of CRISPR enzymes, Gootenberg et al. developed an improved nucleic acid detection technology for multiplexed quantitative and highly sensitive detection, combined with lateral flow for visual readout. Myhrvold et al. added a sample preparation protocol to create a field-deployable viral diagnostic platform for rapid detection of specific strains of pathogens in clinical samples. Cas12a (also known as Cpf1), a type V CRISPR protein, cleaves double-stranded DNA and has been adapted for genome editing. Chen et al. discovered that Cas12a also processes single-stranded DNA threading activity. A technology platform based on this activity detected human papillomavirus in patient samples with high sensitivity. Science , this issue p. 439 , p. 444 , p. 436 ; see also p. 381

Research question: How do CRISPR-based diagnostic tests work, and what are their main advantages and limitations compared with traditional PCR-based diagnostics?
The characterisation of CMB polarisation is one of the next challenge in observationnal cosmology. This is especially true for the so-called B-modes that are at least 3 order of magnitude lower than CMB temperature fluctuations. A precise measurement of the angular power spectrum of these B-modes will give important constraints on inflation parameters. In this talk, I will describe two complementary experiments, BRAIN and CLOVER, dedicated to CMB polarisation measurement. These experiments are proposed to be installed in Dome-C, Antarctica, to take advantage of the extreme dryness of the atmosphere and to allow long integration time.

Research question: What makes Antarctic sites like Dome C particularly well-suited for long-duration CMB polarization observations, and what practical logistical or environmental challenges do they present?
Context. We present the second Gaia data release, Gaia DR2, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21. In addition epoch astrometry and photometry are provided for a modest sample of minor planets in the solar system. Aims. A summary of the contents of Gaia DR2 is presented, accompanied by a discussion on the differences with respect to Gaia DR1 and an overview of the main limitations which are still present in the survey. Recommendations are made on the responsible use of Gaia DR2 results. Methods. The raw data collected with the Gaia instruments during the first 22 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into this second data release, which represents a major advance with respect to Gaia DR1 in terms of completeness, performance, and richness of the data products. Results. Gaia DR2 contains celestial positions and the apparent brightness in G for approximately 1.7 billion sources. For 1.3 billion of those sources, parallaxes and proper motions are in addition available. The sample of sources for which variability information is provided is expanded to 0.5 million stars. This data release contains four new elements: broad-band colour information in the form of the apparent brightness in the G BP (330–680 nm) and G RP (630–1050 nm) bands is available for 1.4 billion sources; median radial velocities for some 7 million sources are presented; for between 77 and 161 million sources estimates are provided of the stellar effective temperature, extinction, reddening, and radius and luminosity; and for a pre-selected list of 14 000 minor planets in the solar system epoch astrometry and photometry are presented. Finally, Gaia DR2 also represents a new materialisation of the celestial reference frame in the optical, the Gaia -CRF2, which is the first optical reference frame based solely on extragalactic sources. There are notable changes in the photometric system and the catalogue source list with respect to Gaia DR1, and we stress the need to consider the two data releases as independent. Conclusions. Gaia DR2 represents a major achievement for the Gaia mission, delivering on the long standing promise to provide parallaxes and proper motions for over 1 billion stars, and representing a first step in the availability of complementary radial velocity and source astrophysical information for a sample of stars in the Gaia survey which covers a very substantial fraction of the volume of our galaxy.

Research question: How should I account for parallax zero-point offsets and systematic uncertainties when using parallaxes from a large space-based astrometric catalogue to estimate distances to stars?