搜索结果：Survey

The recent development and wider accessibility of LLMs have spurred discussions about how they can be used in survey research, including classifying open-ended survey responses. Due to their linguistic capacities, it is possible that LLMs are an efficient alternative to time-consuming manual coding and the pre-training of supervised machine learning models. As most existing research on this topic has focused on English-language responses relating to non-complex topics or on single LLMs, it is unclear whether its findings generalize and how the quality of these classifications compares to established methods. In this study, we investigate to what extent different LLMs can be used to code open-ended survey responses in other contexts, using German data on reasons for survey participation as an example. We compare several state-of-the-art LLMs and several prompting approaches, and evaluate the LLMs' performance by using human expert codings. Overall performance differs greatly between LLMs, and only a fine-tuned LLM achieves satisfactory levels of predictive performance. Performance differences between prompting approaches are conditional on the LLM used. Finally, LLMs' unequal classi

The Roman Galactic Plane Survey (RGPS) is a 700-hour program approved for early definition as a community-designed General Astrophysics Survey. It was selected following a proposal call for science programs that would benefit from an early community-based definition (Sanderson et al 2024). The community was invited to submit white papers and science pitches with a deadline of May 20, 2024; the Roman Galactic Plane Survey Definition Committee (RGPS-DC) first met on Sep 11, 2024. Based on the input provided, the RGPS-DC recommends a survey consisting of three elements: (1) a wide-field science element (691 sq deg, 541 hrs) covering the Galactic plane, Galactic latitude |b|<2 deg and Galactic longitude l=+50.1 deg to -79 deg (281 deg), in four filters (F129, F159, F184, and F213) with higher latitude extensions for the bulge, the Serpens South/W40 star formation region, and Carina, (2) a time-domain science element (19 sq deg , 130 hrs) of six fields, including the full Nuclear Stellar Disk (NSD) and Central Molecular Zone (CMZ), with coverage in seven filters and repeat observations in one or more filters with cadences from 11 minutes to weeks, and (3) a deep-field/spectroscopic s

Recommender systems (RecSys) have been widely applied to various applications, including E-commerce, finance, healthcare, social media and have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. However, recent studies have shown that RecSys are vulnerable to membership inference attacks (MIAs), which aim to infer whether user interaction record was used to train a target model or not. MIAs on RecSys models can directly lead to a privacy breach. For example, via identifying the fact that a purchase record that has been used to train a RecSys associated with a specific user, an attacker can infer that user's special quirks. In recent years, MIAs have been shown to be effective on other ML tasks, e.g., classification models and natural language processing. However, traditional MIAs are ill-suited for RecSys due to the unseen posterior probability. Although MIAs on RecSys form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this article, we conduct the first comprehensive survey on RecSys MIAs. This survey offers a comprehensive review of the l

We present a joint cosmic shear analysis of the Dark Energy Survey (DES Y3) and the Kilo-Degree Survey (KiDS-1000) in a collaborative effort between the two survey teams. We find consistent cosmological parameter constraints between DES Y3 and KiDS-1000 which, when combined in a joint-survey analysis, constrain the parameter $S_8 = σ_8 \sqrt{Ω_{\rm m}/0.3}$ with a mean value of $0.790^{+0.018}_{-0.014}$. The mean marginal is lower than the maximum a posteriori estimate, $S_8=0.801$, owing to skewness in the marginal distribution and projection effects in the multi-dimensional parameter space. Our results are consistent with $S_8$ constraints from observations of the cosmic microwave background by Planck, with agreement at the $1.7σ$ level. We use a Hybrid analysis pipeline, defined from a mock survey study quantifying the impact of the different analysis choices originally adopted by each survey team. We review intrinsic alignment models, baryon feedback mitigation strategies, priors, samplers and models of the non-linear matter power spectrum.

The rise of large language models (LLMs), such as ChatGPT, Gemini, and Grok, has reshaped the AI landscape. As prominent instances of foundational models (FMs), they exhibit remarkable capabilities in generating human-like content, pushing the boundaries towards artificial general intelligence (AGI). However, their large-scale nature, privacy sensitivity, and substantial computational demands pose significant challenges for personalized customization for end users. To bridge this gap, we present the vision of artificial personalized intelligence (API), which focuses on adapting FMs to individual users while ensuring privacy. As a central enabler of API, we propose personalized federated intelligence (PFI), a new paradigm that not only integrates the privacy benefits of federated learning (FL) with the generalization capabilities of FMs but also places personalization at its core. To this end, we first survey recent advances in FL and FMs that lay the foundation for PFI. We then explore core stages of the PFI pipeline: efficient personalization at the edge, trustworthy adaptation, and adaptive refinement via retrieval-augmented generation. Finally, we highlight future directions for

In reinforcement learning (RL), agents continually interact with the environment and use the feedback to refine their behavior. To guide policy optimization, reward models are introduced as proxies of the desired objectives, such that when the agent maximizes the accumulated reward, it also fulfills the task designer's intentions. Recently, significant attention from both academic and industrial researchers has focused on developing reward models that not only align closely with the true objectives but also facilitate policy optimization. In this survey, we provide a comprehensive review of reward modeling techniques within the deep RL literature. We begin by outlining the background and preliminaries in reward modeling. Next, we present an overview of recent reward modeling approaches, categorizing them based on the source, the mechanism, and the learning paradigm. Building on this understanding, we discuss various applications of these reward modeling techniques and review methods for evaluating reward models. Finally, we conclude by highlighting promising research directions in reward modeling. Altogether, this survey includes both established and emerging methods, filling the v

Nancy Grace Roman Space Telescope will revolutionize our understanding of the Galactic Bulge with its Galactic Bulge Time Domain survey. At the same time, Rubin Observatories's Legacy Survey of Space and Time (LSST) will monitor billions of stars in the Milky Way. The proposed Roman survey of the Galactic Plane, with its NIR passbands and exquisite spacial resolution, promises groundbreaking insights for a wide range of time-domain galactic astrophysics. In this white paper, we describe the scientific returns possible from the combination of the Roman Galactic Plane Survey with the data from LSST.

The Snowmass Community Survey was designed by the Snowmass Early Career (SEC) Survey Core Initiative team between April 2020 and June 2021, and released to the community on June 28, 2021. It aims to be a comprehensive assessment of the state of the high-energy particle and astrophysics (HEPA) community, if not the field, though the Snowmass process is largely based within the United States. Among other topics, some of the central foci of the Survey were to gather demographic, career, physics outlook, and workplace culture data on a large segment of the Snowmass community. With nearly $1500$ total interactions with the Survey, the SEC Survey team hopes the findings and discussions within this report will be of service to the community over the next decade. Some conclusions should reinforce the aspects of HEPA which are already functional and productive, while others should strengthen arguments for cultural and policy changes within the field.

General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual environments to decision-making systems. Recently, the emergence of the Sora model has attained significant attention due to its remarkable simulation capabilities, which exhibits an incipient comprehension of physical laws. In this survey, we embark on a comprehensive exploration of the latest advancements in world models. Our analysis navigates through the forefront of generative methodologies in video generation, where world models stand as pivotal constructs facilitating the synthesis of highly realistic visual content. Additionally, we scrutinize the burgeoning field of autonomous-driving world models, meticulously delineating their indispensable role in reshaping transportation and urban mobility. Furthermore, we delve into the intricacies inherent in world models deployed within autonomous agents, shedding light on their profound significance in enabling intelligent interactions within dynamic environmental contexts. At last, we examine challenges and limitations of world models, and discuss their po

Wide-angle surveys have been an engine for new discoveries throughout the modern history of astronomy, and have been among the most highly cited and scientifically productive observing facilities in recent years. This trend is likely to continue over the next decade, as many of the most important questions in astrophysics are best tackled with massive surveys, often in synergy with each other and in tandem with the more traditional observatories. We argue that these surveys are most productive and have the greatest impact when the data from the surveys are made public in a timely manner. The rise of the "survey astronomer" is a substantial change in the demographics of our field; one of the most important challenges of the next decade is to find ways to recognize the intellectual contributions of those who work on the infrastructure of surveys (hardware, software, survey planning and operations, and databases/data distribution), and to make career paths to allow them to thrive.

The study of word maps on groups has been of deep interest in recent years. This survey focuses on the case of power maps on groups; $viz.$ the map $x\mapsto x^M$ for a group $G$, and an integer $M\geq 2$. Here, we accumulate various results on the subject and pose some questions.

Here, we present the angular diameter distance measurement obtained from the measurement of the Baryonic Acoustic Oscillation (BAO) feature using the completed Dark Energy Survey (DES) data, summarizing the main results of [Phys. Rev. D 110, 063514] and [Phys. Rev. D 110, 063515]. We use a galaxy sample optimized for BAO science in the redshift range 0.6 < z < 1.2, with an effective redshift of $z_{\rm eff}$ = 0.85. Our consensus measurement constrains the ratio of the angular distance to the sound horizon scale to $D_M(z_{\rm eff})/r_d$ = 19.51 $\pm$ 0.41. This measurement is found to be 2.13$σ$ below the angular BAO scale predicted by Planck. To date, it represents the most precise measurement from purely photometric data, and the most precise from any Stage-III experiment at such high redshift. The analysis was performed blinded to the BAO position and is shown to be robust against analysis choices, data removal, redshift calibrations and observational systematics.

Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain). In this survey, we first define the CDSR problem using a four-dimensional tensor and then analyze its multi-type input representations under multidirectional dimensionality reductions. Following that, we provide a systematic overview from both macro and micro views. From a macro view, we abstract the multi-level fusion structures of various models across domains and discuss their bridges for fusion. From a micro view, focusing on the existing models, we first discuss the basic technologies and then explain the auxiliary learning technologies. Finally, we exhibit the available public datasets and the representative experimental results as well as provide some insights into future directions for research in CDSR.

We survey recent developments in the theory and applications of the broken ray transforms. Furthermore, we discuss some open problems.

Personalized prediction of responses for individual entities caused by external drivers is vital across many disciplines. Recent machine learning (ML) advances have led to new state-of-the-art response prediction models. Models built at a population level often lead to sub-optimal performance in many personalized prediction settings due to heterogeneity in data across entities (tasks). In personalized prediction, the goal is to incorporate inherent characteristics of different entities to improve prediction performance. In this survey, we focus on the recent developments in the ML community for such entity-aware modeling approaches. ML algorithms often modulate the network using these entity characteristics when they are readily available. However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data. In this survey, we have organized the current literature on entity-aware modeling based on the availability of these characteristics as well as the amount of training data. We highlight how recent innovations in other disciplines, such as uncertainty quantification,

We propose an extension of the LSST survey to cover the northern sky to DEC < +30 (accessible at airmass <1.8). This survey will increase the LSST sky coverage by ~9,600 square degrees from 18,900 to 28,500 square degrees (a 50% increase) but use only 0.6-2.5% of the time depending on the synergies with other surveys. This increased area addresses a wide range of science cases that enhance all of the primary LSST science goals by significant amounts. The science enabled includes: increasing the area of the sky accessible for follow-up of multi-messenger transients including gravitational waves, mapping the milky way halo and halo dwarfs including discovery of RR Lyrae stars in the outer galactic halo, discovery of z>7 quasars in combination Euclid, enabling a second generation DESI and other spectroscopic surveys, and enhancing all areas of science by improving synergies with Euclid, WFIRST, and unique northern survey facilities. This white paper is the result of the Tri-Agency Working Group (TAG) appointed to develop synergies between missions and presents a unified plan for northern coverage. The range of time estimates reflects synergies with other surveys. If the modif

Anti-unification (AU) is a fundamental operation for generalization computation used for inductive inference. It is the dual operation to unification, an operation at the foundation of automated theorem proving. Interest in AU from the AI and related communities is growing, but without a systematic study of the concept nor surveys of existing work, investigations often resort to developing application-specific methods that existing approaches may cover. We provide the first survey of AU research and its applications and a general framework for categorizing existing and future developments.

UKIDSS is the next generation near-infrared sky survey. The survey will commence in early 2004, and over 7 years will collect 100 times as many photons as 2MASS. UKIDSS will use the UKIRT Wide Field Camera to survey 7500 square degrees of the northern sky, extending over both high and low Galactic latitudes, in JHK to K=18.5 (over three magnitudes deeper than 2MASS). UKIDSS will be the true near-infrared counterpart to the Sloan survey, and will produce as well a panoramic clear atlas of the Galactic plane. In fact UKIDSS is made up of five surveys and includes two deep extra-Galactic elements, one covering 35 square degrees to K=21, and the other reaching K=23 over 0.77 square degrees. This paper provides the details of the five UKIDSS surveys and describes the main science goals.

Deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis. This survey has been conducted with a different perspective compared to existing survey papers, that mostly focus on just video and image deepfakes. This survey not only evaluates generation and detection methods in the different deepfake categories, but mainly focuses on audio deepfakes that are overlooked in most of the existing surveys. This paper critically analyzes and provides a unique source of audio deepfake research, mostly ranging from 2016 to 2020. To the best of our knowledge, this is the first survey focusing on audio deepfakes in English. This survey provides readers with a summary of 1) different deepfake categories 2) how they could be created and detected 3) the most recent trends in this domain and shortcomings in detection methods 4) audio deepfakes, how they are created and detected in more detail which is the main focus of this paper. We found that Generative Adversarial Networks(GAN), Convolutional Neural Networks (CNN), and Deep Neural Networks (DNN) are com