Hasil untuk "General Works"

Hyungjin Chung, Jeongsol Kim, Michael T. McCann et al.

Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative solvers. However, most works focus on solving simple linear inverse problems in noiseless settings, which significantly under-represents the complexity of real-world problems. In this work, we extend diffusion solvers to efficiently handle general noisy (non)linear inverse problems via approximation of the posterior sampling. Interestingly, the resulting posterior sampling scheme is a blended version of diffusion sampling with the manifold constrained gradient without a strict measurement consistency projection step, yielding a more desirable generative path in noisy settings compared to the previous studies. Our method demonstrates that diffusion models can incorporate various measurement noise statistics such as Gaussian and Poisson, and also efficiently handle noisy nonlinear inverse problems such as Fourier phase retrieval and non-uniform deblurring. Code available at https://github.com/DPS2022/diffusion-posterior-sampling

N. Salari, A. Hosseinian-Far, R. Jalali et al.

Background The COVID-19 pandemic has had a significant impact on public mental health. Therefore, monitoring and oversight of the population mental health during crises such as a panedmic is an immediate priority. The aim of this study is to analyze the existing research works and findings in relation to the prevalence of stress, anxiety and depression in the general population during the COVID-19 pandemic. Method In this systematic review and meta-analysis, articles that have focused on stress and anxiety prevalence among the general population during the COVID-19 pandemic were searched in the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI) and Google Scholar databases, without a lower time limit and until May 2020. In order to perform a meta-analysis of the collected studies, the random effects model was used, and the heterogeneity of studies was investigated using the I 2 index. Moreover. data analysis was conducted using the Comprehensive Meta-Analysis (CMA) software. Results The prevalence of stress in 5 studies with a total sample size of 9074 is obtained as 29.6% (95% confidence limit: 24.3–35.4), the prevalence of anxiety in 17 studies with a sample size of 63,439 as 31.9% (95% confidence interval: 27.5–36.7), and the prevalence of depression in 14 studies with a sample size of 44,531 people as 33.7% (95% confidence interval: 27.5–40.6). Conclusion COVID-19 not only causes physical health concerns but also results in a number of psychological disorders. The spread of the new coronavirus can impact the mental health of people in different communities. Thus, it is essential to preserve the mental health of individuals and to develop psychological interventions that can improve the mental health of vulnerable groups during the COVID-19 pandemic.

Yang Liao, G. Smyth, Wei Shi

MOTIVATION Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

S. Franconeri, Lace M. K. Padilla, P. Shah et al.

Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.

Eyal Molad, Eliahu Horwitz, Dani Valevski et al.

Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing. We present the first diffusion-based method that is able to perform text-based motion and appearance editing of general videos. Our approach uses a video diffusion model to combine, at inference time, the low-resolution spatio-temporal information from the original video with new, high resolution information that it synthesized to align with the guiding text prompt. As obtaining high-fidelity to the original video requires retaining some of its high-resolution information, we add a preliminary stage of finetuning the model on the original video, significantly boosting fidelity. We propose to improve motion editability by a new, mixed objective that jointly finetunes with full temporal attention and with temporal attention masking. We further introduce a new framework for image animation. We first transform the image into a coarse video by simple image processing operations such as replication and perspective geometric projections, and then use our general video editor to animate it. As a further application, we can use our method for subject-driven video generation. Extensive qualitative and numerical experiments showcase the remarkable editing ability of our method and establish its superior performance compared to baseline methods.

Yaqi Zhang, Di Huang, B. Liu et al.

Generating realistic human motion from given action descriptions has experienced significant advancements because of the emerging requirement of digital humans. While recent works have achieved impressive results in generating motion directly from textual action descriptions, they often support only a single modality of the control signal, which limits their application in the real digital human industry. This paper presents a Motion General-Purpose generaTor (MotionGPT) that can use multimodal control signals, e.g., text and single-frame poses, for generating consecutive human motions by treating multimodal signals as special input tokens in large language models (LLMs). Specifically, we first quantize multimodal control signals into discrete codes and then formulate them in a unified prompt instruction to ask the LLMs to generate the motion answer. Our MotionGPT demonstrates a unified human motion generation model with multimodal control signals by tuning a mere 0.4% of LLM parameters. To the best of our knowledge, MotionGPT is the first method to generate human motion by multimodal control signals, which we hope can shed light on this new direction. Visit our webpage at https://qiqiapink.github.io/MotionGPT/.

Wei Fan, Pengyang Wang, Dongkun Wang et al.

The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards distribution shift in time series are mostly limited in the quantification of distribution and, more importantly, overlook the potential shift between lookback and horizon windows. To address above challenges, we systematically summarize the distribution shift in TSF into two categories. Regarding lookback windows as input-space and horizon windows as output-space, there exist (i) intra-space shift, that the distribution within the input-space keeps shifted over time, and (ii) inter-space shift, that the distribution is shifted between input-space and output-space. Then we introduce, Dish-TS, a general neural paradigm for alleviating distribution shift in TSF. Specifically, for better distribution estimation, we propose the coefficient net (Conet), which can be any neural architectures, to map input sequences into learnable distribution coefficients. To relieve intra-space and inter-space shift, we organize Dish-TS as a Dual-Conet framework to separately learn the distribution of input- and output-space, which naturally captures the distribution difference of two spaces. In addition, we introduce a more effective training strategy for intractable Conet learning. Finally, we conduct extensive experiments on several datasets coupled with different state-of-the-art forecasting models. Experimental results show Dish-TS consistently boosts them with a more than 20% average improvement. Code is available at https://github.com/weifantt/Dish-TS.

Haocong Rao, Cyril Leung, C. Miao

Large Language Models (LLMs) especially ChatGPT have produced impressive results in various areas, but their potential human-like psychology is still largely unexplored. Existing works study the virtual personalities of LLMs but rarely explore the possibility of analyzing human personalities via LLMs. This paper presents a generic evaluation framework for LLMs to assess human personalities based on Myers Briggs Type Indicator (MBTI) tests. Specifically, we first devise unbiased prompts by randomly permuting options in MBTI questions and adopt the average testing result to encourage more impartial answer generation. Then, we propose to replace the subject in question statements to enable flexible queries and assessments on different subjects from LLMs. Finally, we re-formulate the question instructions in a manner of correctness evaluation to facilitate LLMs to generate clearer responses. The proposed framework enables LLMs to flexibly assess personalities of different groups of people. We further propose three evaluation metrics to measure the consistency, robustness, and fairness of assessment results from state-of-the-art LLMs including ChatGPT and GPT-4. Our experiments reveal ChatGPT's ability to assess human personalities, and the average results demonstrate that it can achieve more consistent and fairer assessments in spite of lower robustness against prompt biases compared with InstructGPT.

Simon Buchholz, Goutham Rajendran, Elan Rosenfeld et al.

We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general. We prove strong identifiability results given unknown single-node interventions, i.e., without having access to the intervention targets. This generalizes prior works which have focused on weaker classes, such as linear maps or paired counterfactual data. This is also the first instance of causal identifiability from non-paired interventions for deep neural network embeddings. Our proof relies on carefully uncovering the high-dimensional geometric structure present in the data distribution after a non-linear density transformation, which we capture by analyzing quadratic forms of precision matrices of the latent distributions. Finally, we propose a contrastive algorithm to identify the latent variables in practice and evaluate its performance on various tasks.

Hanqing Wang, Jiahe Chen, Wensi Huang et al.

Recent works have been exploring the scaling laws in the field of Embodied AI. Given the prohibitive costs of collecting real-world data, we believe the Simulation-to-Real (Sim2Real) paradigm is a crucial step for scaling the learning of embodied models. This paper introduces project GRUtopia, the first simulated interactive 3D society designed for various robots. It features several advancements: (a) The scene dataset, GRScenes, includes 100k interactive, finely annotated scenes, which can be freely combined into city-scale environments. In contrast to previous works mainly focusing on home, GRScenes covers 89 diverse scene categories, bridging the gap of service-oriented environments where general robots would be initially deployed. (b) GRResidents, a Large Language Model (LLM) driven Non-Player Character (NPC) system that is responsible for social interaction, task generation, and task assignment, thus simulating social scenarios for embodied AI applications. (c) The benchmark, GRBench, supports various robots but focuses on legged robots as primary agents and poses moderately challenging tasks involving Object Loco-Navigation, Social Loco-Navigation, and Loco-Manipulation. We hope that this work can alleviate the scarcity of high-quality data in this field and provide a more comprehensive assessment of Embodied AI research. The project is available at https://github.com/OpenRobotLab/GRUtopia.

Hind Waleed Farhan, Khalid Akbar Abdullah

Aims: This current study aimed to analyze the demographic characteristics of ‎the agricultural workforce and demonstrate the impact of these characteristics ‎on agricultural activity in terms of production and diversity. It also aimed to ‎clarify the population distribution of the workforce according to demographic ‎and economic indicators, and finally to evaluate its relationship with ‎agricultural activities. Methodology: A descriptive and analytical approach was ‎adopted, relying on statistical data available from official and field sources. The ‎study also tried to determine the size of the workforce and its geographical, age, ‎educational distribution, and analyze the extent of its contribution to ‎agricultural activity. The correlation coefficient was used to measure the ‎relationship between labor force characteristics and agricultural production ‎indicators in the studied areas. Results: After analyzing all the gathered data, ‎the results showed a positive correlation between the density of the agricultural ‎workforce and the level of agricultural production in most of the studied areas, ‎especially in areas with adequate infrastructure and agricultural services. It also ‎showed that the productive age group of the workforce represents the highest ‎percentage of those employed in this sector, in addition to the concentration of ‎workers with limited education in traditional agricultural activities, which limits ‎the expansion of modern methods. Conclusions: It can be concluded that ‎workforce represents one of the fundamental pillars of agricultural sector ‎development, and that improving its demographic and educational characteristics ‎would contribute to increasing agricultural production efficiency significantly. The ‎study emphasized the importance of supporting agricultural training programs and ‎developing workers' skills in order to achieve optimal use of human resources in this ‎vital sector.‎

Zhi Zhang

As smart city development deepens, its impact on the urban-rural income gap has become a key concern for both the government and society. This article uses panel data from Chinese prefecture-level cities between 2010 and 2022, treating the pilot smart city policy as a quasi-natural experiment, and applying a multi-period difference-in-differences (DID) method to empirically examine how smart city construction affects the urban-rural income gap. It also analyzes the role of economic agglomeration in this process. The research indicates that smart city development has significantly increased the incomes of urban and rural residents and has positively contributed to sharing development benefits between these areas. Mechanism analysis shows that economic agglomeration plays an important mediating and threshold role—smart cities indirectly influence the urban-rural income distribution by fostering economic agglomeration, with this effect showing nonlinear characteristics at different levels of agglomeration. Based on these findings, the article proposes policy recommendations aimed at optimizing economic agglomeration models and advancing urban-rural integrated development, offering theoretical insights and practical strategies for narrowing the income gap and promoting common prosperity.

Andrzej Kansy

W artykule przedstawiono wyniki badań na temat strategii komunikowania buddyjskich związków wyznaniowych zarejestrowanych w Polsce. Badanie przeprowadzono metodami analizy treści oraz analizy danych zastanych. Wykorzystano założenia etnografii wirtualnej, która ma na celu poznanie i opisanie zachowań ludzi i zjawisk dostępnych w internecie oraz ich efektów. Stwierdzono m.in., że buddyjskie związki wyznaniowe stosują nowe media do realizacji strategii komunikacji. Są one ukierunkowane na: autoprezentację, budowanie relacji między członkami wspólnoty, propagowanie buddyzmu, aksjologizację. Strony internetowe realizują wymienione strategie, zwłaszcza w zakresie autoprezentacji. Profile w mediach społecznościowych służą głównie budowaniu relacji oraz aksjologizacji. Buddyzm w Polsce nie jest zjawiskiem masowym, ale istotnym dla kultury polskiej. Badania w tym zakresie służą więc budowaniu wzajemnego szacunku w warunkach wielokulturowych społeczeństw.

В. В. Леушкина, Л. А. Полонская

В статье представлен всесторонний анализ трудового потенциала Омской области с акцентом на демографические, экономические и социальные факторы. Рассматриваются общая численность и национальный состав населения, распределение трудовых ресурсов по видам экономической деятельности, динамика естественного прироста и уровня образования занятого населения, уровень безработицы и преступности, а также дифференциация доходов. В рамках проведенного анализа наблюдаются стабильные тенденции улучшения уровня образования занятого населения в Омской области, что свидетельствует о повышении квалификации рабочей силы. Уровень безработицы снижается, что указывает на восстановление экономики и увеличение потребности в рабочей силе. Однако растущая дифференциация доходов населения вызывает беспокойство, поскольку увеличивается социальное неравенство. В результате проведенный анализ трудового потенциала Омской области показывает, как положительные, так и отрицательные тенденции в развитии региона, подчеркивая необходимость комплексного подхода к решению существующих проблем.

Fabio Rincón Cardona

Este artículo expone los avances del proyecto “Pacto del País por el Río Grande de la Magdalena”, el cual resalta la importancia de abordar el río como Itinerario Cultural, al considerar su valor simbólico, ambiental, histórico y cultural; brinda apoyo y propone acciones previas para la futura declaración como patrimonio mundial de la unesco en la categoría de itinerarios culturales, fundamentado principalmente en la apropiación social, cultural, la participación interinstitucional, conservación, protección, desarrollo sostenible del río y las comunidades. Analiza la problemática del río Magdalena, y evidencia cómo desde el proyecto se ha trabajado entendiendo el río como sujeto de derechos, el reconocimiento que ha tenido y sus declaratorias, tomando en cuenta el ods 6; además, expone y analiza la matriz dofa del Plan Maestro Fluvial realizado por arcadis el cual contiene información fundamental sobre el río. El proyecto busca no solo proteger y valorar el río como Itinerario Cultural, sino también promover el desarrollo sostenible en la región; abordar los desafíos del río mediante un enfoque colaborativo e interdisciplinario, al promover su sostenibilidad y valor cultural mientras fortalece la investigación y la cooperación entre entidades.

محمد المهداوي , صادق محمد

تعد الرموز من التقانات المهمة  التي تثير فضول المتلقي وتزيد رغبته في الوصول الى قصد الشاعر ، والكشف عن دلالاتها القارة داخل نتاجه الشعري ، فديناميكية الرمز تكفل ولوج القارئ الى عمق النص ، فعمل القارئ يصبح فضاء لقراءات متعددة ، ذات الدلالة البعيدة عن الواقع المذكور ، إذ ان الرمز المكاني في النص الأدبي أداة فاعلة في كشف الخبايا النصية المفروزة من دلالاته في بعده المعبر عن البوح المكنون في أهواء الشاعر المكبوتة في دواخلها والمنطوية على ذاتها ، لذا فان اختيار هذا الموضوع يعد محاولة لتسليط الضوء على تلك الرموز وبخاصة الطللية منها ، وإظهار أهميتها في شعر حسام الدين الحاجري ، وما دامت الرموز متعلقة بالقارئ أكثر من ارتباطها بالنص ، لذا وجب على القارئ تفكيك تلك الرموز ، فهذا البحث قائم على رصدها والكشف عن مكنوناتها وبخاصة الرموز (الطللية) بوصفها مكانا محملا بأبعاد وايحاءات كثيرة .

M. Schweizer, N. Straumann, A. Wipf

We adapt the post-Newtonian gravitational-radiation methods developed within general relativity by Epstein and Wagoner to the gravitation theory with torsion, recently proposed by Hehl et al., and show that the two theories predict in this approximation the same gravitational radiation losses. Since they agree also on the first post-Newtonian level, they are at the present time - observationally - indistinguishable.

Erik Brynjolfsson, Danielle Li, Lindsey Raymond

We study the staggered introduction of a generative AI-based conversational assistant using data from 5,172 customer support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15\% on average, with substantial heterogeneity across workers. Less experienced and lower-skilled workers improve both the speed and quality of their output while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for relatively rare problems, where human agents have less baseline training and experience. Finally, we provide evidence that AI assistance improves the experience of work along two key dimensions: customers are more polite and less likely to ask to speak to a manager.

Beatriz Garcia-Ortega, Javier Galan-Cubillo, Blanca de-Miguel-Molina

This paper assesses whether and to which extent the COVID-19 pandemic, which represents a scenario of high moral intensity, is influencing the moral reasoning of top CEOs (chief executive officers) in the paradigmatic case of the automotive industry and how this moral reasoning relates to their CSR response to the crisis and their CSR plans in the long run. To this end, we took the CEO letters before and after the pandemic outbreak of the top 15 automotive companies, and applied Weber’s method to conduct a moral reasoning categorization, along with an examination of their CSR approach and initiatives. The results show a predominant moral paralysis among these CEOs, where positive reactions addressed are philanthropic in nature and more likely to be a transient response to the crisis, rather than a sustained long-term improvement of their CSR rooted in a significant moral approach enhancement. Furthermore, CEOs at the lowest stages of moral reasoning, primarily focused on their own business and immediate stakeholders, are less likely to highlight these philanthropic initiatives. The outcome evidences the convenience of addressing CSR from the lens of moral reasoning, and it further draws the attention of the scientific community, companies and their top management, stakeholders, and society to the relevance of investigating and considering the moral reasoning of top management in large corporations and its implications.

Monib Ahmad, Abraiz Khattak, Abdul Kashif Janjua et al.

The globally soaring energy prices and electricity shortfall are major hurdles in the economic development of Pakistan. To cope with periodic power outages, small and medium enterprise (SME) business owners have to fall back on alternate power sources such as backup generators and uninterruptible power supplies (UPS), which further increase the per kWh cost of electricity, power quality issues, and greenhouse gas (GHG) emissions. On the contrary, grid-tied solar photovoltaic (PV) systems are not only economical and sustainable but support the national power grid to mitigate environmental emissions. This study aims to investigate and compare the techno-economic viability of grid-connected solar photovoltaic power plants for the manufacturing SME sector in four different districts of Punjab, Pakistan. Based on the technical, financial, and environmental indicators, a detailed techno-economic, sensitivity, and GHG emission analysis is conducted using RETScreen Expert software. The research findings clearly show that the proposed solar PV projects for all four locations are technically, financially, and environmentally viable, however, Sargodha as compared to other sites is the most feasible location with the highest capacity factor of 17.8 %, highest internal rate of return 14.9 %, lowest payback period 7.7 years, and least levelized cost of electricity 8.5 ¢/kWh. For validation, the simulation results are compared with performance metrics from PV plants erected in various parts of the world. Applying the same research approach to the whole industrial sector of Punjab recommends adding 13,469 MW of PV capacity to satisfy the industry’s 20446.21 GWh annual energy consumption and to cut emissions by 90,17,581 t CO2 per year. This research work presents guidelines for researchers to evaluate the feasibility of suitable PV technologies for the SME sector thereby helping investors to have a holistic view of potential investment zones.