Hasil untuk "Manners and customs (General)"

Shiyuan Yang, Ruihuang Li, Jiale Tao et al.

Visual effects (VFX) are essential for enhancing the expressiveness and creativity of video content, yet producing high-quality effects typically requires expert knowledge and costly production pipelines. Existing AIGC systems face significant challenges in VFX generation due to the scarcity of effect-specific data and the inherent difficulty of modeling supernatural or stylized effects. Moreover, these approaches often require per-effect fine-tuning, which severely limits their scalability and generalization to novel VFX. In this work, we present EffectMaker, a unified reasoning-generation framework that enables reference-based VFX customization. EffectMaker employs a multimodal large language model to interpret high-level effect semantics and reason about how they should adapt to a target subject, while a diffusion transformer leverages in-context learning to capture fine-grained visual cues from reference videos. These two components form a semantic-visual dual-path guidance mechanism that enables accurate, controllable, and effect-consistent synthesis without per-effect fine-tuning. Furthermore, we construct EffectData, the largest high-quality synthetic dataset containing 130k videos across 3k VFX categories, to improve generalization and scalability. Experiments show that EffectMaker achieves superior visual quality and effect consistency over state-of-the-art baselines, offering a scalable and flexible paradigm for customized VFX generation. Project page: https://effectmaker.github.io

Luis Maffei

A poesia de Luís de Camões se caracteriza por trabalhar, muitas vezes de modo dialético, alguns pares. Um deles, não necessariamente explorado à exaustão pelo poeta, mas aberto a uma leitura atenta, é o das duas mãos. A imagem do próprio poeta, com suas mãos em papel importante, é objeto de um dos mais famosos retratos que Camões mereceu – nele, é tematizada uma necessidade vital muito básica, que o poeta faz conviver, em sua obra, com algumas nobrezas, sobretudo a da experiência amorosa. O que está em jogo, na fome de alimento e no desejo, é o corpo. Por isso, ler imagens-chave do soneto “Bem sei, Amor, que é certo o que receio”, especialmente a imagem da mão no seio, em articulação com outros momentos da poesia camoniana, pode ser uma porta (giratória) de entrada para zona significativa dessa obra.

Fangda Chen, Shanshan Zhao, Chuanfu Xu et al.

Recent advancements in customized video generation have led to significant improvements in the simultaneous adaptation of appearance and motion. Typically, decoupling the appearance and motion training, prior methods often introduce concept interference, resulting in inaccurate rendering of appearance features or motion patterns. In addition, these methods often suffer from appearance contamination, in which background and foreground elements from reference videos distort the customized video. This paper aims to alleviate these issues by proposing JointTuner. The core motivation of our JointTuner is to enable joint optimization of both appearance and motion components, upon which two key innovations are developed, i.e., Gated Low-Rank Adaptation (GLoRA) and Appearance-independent Temporal Loss (AiT Loss). Specifically, GLoRA uses a context-aware activation layer, analogous to a gating regulator, to dynamically steer LoRA modules toward learning either appearance or motion while maintaining spatio-temporal consistency. Moreover, with the finding that channel-temporal shift noise suppresses appearance-related low-frequencies while enhancing motion-related high-frequencies, we designed the AiT Loss. This loss adds the same shift to the diffusion model's predicted noise during fine-tuning, forcing the model to prioritize learning motion patterns. JointTuner's architecture-agnostic design supports both UNet (e.g., ZeroScope) and Diffusion Transformer (e.g., CogVideoX) backbones, ensuring its customization capabilities scale with the evolution of foundational video models. Furthermore, we present a systematic evaluation framework for appearance-motion combined customization, covering 90 combinations evaluated along four critical dimensions: semantic alignment, motion dynamism, temporal consistency, and perceptual quality. Our project homepage is available online.

Jibang Wu, Chenghao Yang, Yi Wu et al.

This paper develops an agentic framework that employs large language models (LLMs) for grounded persuasive language generation in automated copywriting, with real estate marketing as a focal application. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin while maintaining the same level of factual accuracy. Our findings suggest a promising agentic approach to automate large-scale targeted copywriting while ensuring factuality of content generation.

Solange Damião

Este artigo tem o objetivo de analisar a antropofágica festa do corpo na poesia de Herberto Helder e, para isso, foram selecionados alguns poemas do autor. O suporte teórico do artigo se concentra na presença do pensamento sobre a desfiguração do corpo humano (Moraes, 2017), nas noções de metamorfose do corpo (Gil, 1997), no corpo e suas diferentes significações (Nancy, 2000) e, especificamente, no corpo como expressão e linguagem (Merleau- Ponty, 2018).

Matteo Gigante

No presente artigo refletimos acerca de alguns aspetos da obra-vida de Herbert Eustáquio de Carvalho (1946-1992), mais conhecido pelo pseudónimo Herbert Daniel, autor brasileiro, ainda pouco reconhecido, que está recentemente a despertar o interesse do mundo académico brasileiro e internacional. Fugindo da ditadura militar brasileira, em 1975 Daniel residiu em Portugal trabalhando na revista feminina lisboeta Modas e Bordados (suplemento de O Século), sob a direção da jornalista feminista Maria Antónia Palla. A partir desta colaboração, Daniel contribuiu à introdução de instâncias sociais progressistas atualizando, ao longo do Processo Revolucionário em Curso (PREC), a linha editorial da revista. No mesmo período, o namorado Cláudio Mesquita, que o acompanhou na fuga da ditadura militar brasileira, trabalhou como responsável pela realização de panfletos e campanhas pedagógicas junto do Movimento das Forças Armadas (MFA). Em vista dos seus rudimentos de medicina, Daniel assinou uma coluna chamada “Haja Saúde” abordando questões como o racismo, o direito a condições de trabalho dignas, ao descanso e ao lazer, assistindo à implementação do Serviço Nacional de Saúde (SNS). Além disso, Daniel refletiu sobre a importância do planejamento familiar e da liberdade de escolha das mulheres, reivindicando o direito ao aborto. Por outro lado, Daniel observou as conturbadas vivências de homossexuais e de refugiados brasileiros em Portugal, numa altura em que o autor reconheceu, pela primeira vez, conscientemente, a sua condição de homossexual e exilado. Portanto, analisando as crónicas publicadas em 1975 ao lado de excertos das suas obras posteriores, observamos o diálogo entre a vida, o pensamento do autor e o contexto histórico-cultural da Revolução dos Cravos, ressaltando a relevância das suas reivindicações que se apresentam, ainda hoje, vanguardistas.

Héctor Laria, Alex Gomez-Villa, Kai Wang et al.

Recent advances in diffusion models have significantly enhanced image generation capabilities. However, customizing these models with new classes often leads to unintended consequences that compromise their reliability. We introduce the concept of open-world forgetting to characterize the vast scope of these unintended alterations. Our work presents the first systematic investigation into open-world forgetting in diffusion models, focusing on semantic and appearance drift of representations. Using zero-shot classification, we demonstrate that even minor model adaptations can lead to significant semantic drift affecting areas far beyond newly introduced concepts, with accuracy drops of up to 60% on previously learned concepts. Our analysis of appearance drift reveals substantial changes in texture and color distributions of generated content. To address these issues, we propose a functional regularization strategy that effectively preserves original capabilities while accommodating new concepts. Through extensive experiments across multiple datasets and evaluation metrics, we demonstrate that our approach significantly reduces both semantic and appearance drift. Our study highlights the importance of considering open-world forgetting in future research on model customization and finetuning methods.

Jingzhe Shi, Jialuo Li, Qinwei Ma et al.

Businesses and software platforms are increasingly turning to Large Language Models (LLMs) such as GPT-3.5, GPT-4, GLM-3, and LLaMa-2 for chat assistance with file access or as reasoning agents for customer service. However, current LLM-based customer service models have limited integration with customer profiles and lack the operational capabilities necessary for effective service. Moreover, existing API integrations emphasize diversity over the precision and error avoidance essential in real-world customer service scenarios. To address these issues, we propose an LLM agent named CHOPS (CHat with custOmer Profile in existing System), designed to: (1) efficiently utilize existing databases or systems for accessing user information or interacting with these systems following existing guidelines; (2) provide accurate and reasonable responses or carry out required operations in the system while avoiding harmful operations; and (3) leverage a combination of small and large LLMs to achieve satisfying performance at a reasonable inference cost. We introduce a practical dataset, the CPHOS-dataset, which includes a database, guiding files, and QA pairs collected from CPHOS, an online platform that facilitates the organization of simulated Physics Olympiads for high school teachers and students. We have conducted extensive experiments to validate the performance of our proposed CHOPS architecture using the CPHOS-dataset, with the aim of demonstrating how LLMs can enhance or serve as alternatives to human customer service. Code for our proposed architecture and dataset can be found at {https://github.com/JingzheShi/CHOPS}.

Edward Jay Wang

Peer-customer is a mechanism to pair student teams with customers in hands-on curriculum courses. Each student pitches a problem they want someone else in the class to solve for them. The use of peer-customers provides practical and scalable access for students to work with a customer on a real-world need for their final project. The peer-customer, despite being a student in the class, do not work on the project with the team. This dissociation forces a student team to practice customer needs assessment, testing, and surveying that can often be lacking in self-ideated final projects that do not have resources to curate external customers like in capstone courses. We prototyped the use of peer-customers in an introductory physical prototyping course focused on basic embedded systems design and python programming. In this paper, we present a practical guide on how best to use peer-customers, supported by key observations made during two separate offerings of the course with a total of N=64 students (N=29 Y1 and N=35 Y2).

Bang Yang, Asif Raza, Yuexian Zou et al.

Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given medical images. However, the scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks capable of harnessing the potential artificial general intelligence power like large language models (LLMs). In this work, we propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs), in computer vision and natural language processing with a specific focus on medical report generation. Specifically, following BLIP-2, a state-of-the-art vision-language pre-training approach, we introduce our encoder-decoder-based MRG model. This model utilizes a lightweight query Transformer to connect two FMs: the giant vision Transformer EVA-ViT-g and a bilingual LLM trained to align with human intentions (referred to as ChatGLM-6B). Furthermore, we conduct ablative experiments on the trainable components of the model to identify the crucial factors for effective transfer learning. Our findings demonstrate that unfreezing EVA-ViT-g to learn medical image representations, followed by parameter-efficient training of ChatGLM-6B to capture the writing styles of medical reports, is essential for achieving optimal results. Our best attempt (PCLmed Team) achieved the 4th and the 2nd, respectively, out of 13 participating teams, based on the BERTScore and ROUGE-1 metrics, in the ImageCLEFmedical Caption 2023 Caption Prediction Task competition.

Jiashuo Jiang

The Network Revenue Management (NRM) problem is a well-known challenge in dynamic decision-making under uncertainty. In this problem, fixed resources must be allocated to serve customers over a finite horizon, while customers arrive according to a stochastic process. The typical NRM model assumes that customer arrivals are independent over time. However, in this paper, we explore a more general setting where customer arrivals over different periods can be correlated. We propose a model that assumes the existence of a system state, which determines customer arrivals for the current period. This system state evolves over time according to a time-inhomogeneous Markov chain. We show our model can be used to represent correlation in various settings. To solve the NRM problem under our correlated model, we derive a new linear programming (LP) approximation of the optimal policy. Our approximation provides an upper bound on the total expected value collected by the optimal policy. We use our LP to develop a new bid price policy, which computes bid prices for each system state and time period in a backward induction manner. The decision is then made by comparing the reward of the customer against the associated bid prices. Our policy guarantees to collect at least $1/(1+L)$ fraction of the total reward collected by the optimal policy, where $L$ denotes the maximum number of resources required by a customer. In summary, our work presents a Markovian model for correlated customer arrivals in the NRM problem and provides a new LP approximation for solving the problem under this model. We derive a new bid price policy and provides a theoretical guarantee of the performance of the policy.

Keivalya Pandya, Mehfuza Holia

In the digital age, the dynamics of customer service are evolving, driven by technological advancements and the integration of Large Language Models (LLMs). This research paper introduces a groundbreaking approach to automating customer service using LangChain, a custom LLM tailored for organizations. The paper explores the obsolescence of traditional customer support techniques, particularly Frequently Asked Questions (FAQs), and proposes a paradigm shift towards responsive, context-aware, and personalized customer interactions. The heart of this innovation lies in the fusion of open-source methodologies, web scraping, fine-tuning, and the seamless integration of LangChain into customer service platforms. This open-source state-of-the-art framework, presented as "Sahaay," demonstrates the ability to scale across industries and organizations, offering real-time support and query resolution. Key elements of this research encompass data collection via web scraping, the role of embeddings, the utilization of Google's Flan T5 XXL, Base and Small language models for knowledge retrieval, and the integration of the chatbot into customer service platforms. The results section provides insights into their performance and use cases, here particularly within an educational institution. This research heralds a new era in customer service, where technology is harnessed to create efficient, personalized, and responsive interactions. Sahaay, powered by LangChain, redefines the customer-company relationship, elevating customer retention, value extraction, and brand image. As organizations embrace LLMs, customer service becomes a dynamic and customer-centric ecosystem.

Janhavi Prabhu, Myron Hlynka

We propose a new queueing model, motivated by the phenomenon of pseudoprogression in cancer, in which the length of a queue appears to increase initially, before reducing to a steady state. We assume that servers arrive to the queue alongside the customers, i.e. `disguised' as customers. We derive the general equations for this model using matrix analytic methods, and demonstrate its behaviour with numerical simulations.

Hyun Joon Park, Byung Ha Kang, Wooseok Shin et al.

In the field of speech enhancement, time domain methods have difficulties in achieving both high performance and efficiency. Recently, dual-path models have been adopted to represent long sequential features, but they still have limited representations and poor memory efficiency. In this study, we propose Multi-view Attention Network for Noise ERasure (MANNER) consisting of a convolutional encoder-decoder with a multi-view attention block, applied to the time-domain signals. MANNER efficiently extracts three different representations from noisy speech and estimates high-quality clean speech. We evaluated MANNER on the VoiceBank-DEMAND dataset in terms of five objective speech quality metrics. Experimental results show that MANNER achieves state-of-the-art performance while efficiently processing noisy speech.

Chengyuan Wang, Yun Chen, Jinwen Wang et al.

We experimentally demonstrate the generation of customized perfect Laguerre-Gaussian (PLG) beams whose intensity maxima localized around any desired curves. The principle is to act appropriate algebraic functions on the angular spectra of PLG beams. We characterize the propagation properties of these beams and compare them with non-diffraction caustic beams possessing the same intensity profiles. The results manifest that the customized-PLG beams can maintain their profiles during propagation and suffer less energy loss than the non-diffraction caustic beams, and hence are able to propagate a longer distance. This new structure beam would have potential applications in areas such as optical communication, soliton routing and steering, optical tweezing and trapping, atom optics, etc.

Samuel Lima da Silva

Estudo acerca da configuração estilístico-formal da figura da absolvição, na realidade diegética do romance brasileiro Em nome do desejo (1985), de João Silvério Trevisan. Na trama, o leitor é posto frente a um discurso homoerótico potencializado pela consternação do narrador Tiquinho, personagem esse que se exaspera emocionalmente devido a separação amorosa, bem como pelo descobrimento do corpo, da alma e das encruzilhadas do desejo sexual.

Samara Lima, Luciene Azevedo

ensaio tem como principal objetivo discutir a noção de autorrepresentação em produções da literatura brasileira recente. Tomando como fundamento teórico os estudos propostos por bell hooks em seu livro Olhares negros: raça e representação (2019) e as discussões empreendidas por Souza (2010), Rankine (2019), Gonçalves (2018), pretendemos discutir e analisar o posicionamento afirmativo cada vez mais recorrente na literatura brasileira por parte dos escritores afrodescendentes que buscam marcar um lugar de presença, por meio de diferentes recursos literários, para questionar os estereótipos que, por muito tempo, estiveram presentes no discurso literário brasileiro. Assim, pretende-se refletir sobre o elo entre o autor, sua etnia e seu projeto literário, caracterizando melhor diferentes estratégias de "autorrepresentação" do negro em três contos de autores negros da literatura contemporânea brasileira: "Metal-Metal" de Cidinha da Silva (2019), "Espiral" de Geovani Martins (2018) e "Elevador a serviço" de Cristiane Sobral (2016).}

Paulo Jorge Martins Nunes, Maíra Oliveira Maia, Thainá Oliveira Chemelo

A partir do conceito cultural e linguístico de lusografia, de Jean-Michel Massa, que reitera a diversidade de enunciados em uma única – embora diversificada – língua portuguesa, e das ideias de memória pessoal e memória histórica da região amazônica, o presente estudo pretende desvendar, através da análise dos romances Belém do Grão-Pará (1960) e Passagem dos Inocentes (1936), ambos de Dalcídio Jurandir, a sociedade paraense da primeira metade do século XX, já que o citado autor testemunhou esse mundo e construiu nos seus romances uma interpretação da sua experiência testemunhal, vista a partir das margens, das periferias, com os pés bem firmes no chão. Para demonstrar essa preocupação do autor em retratar as minorias, nos debruçamos sobre a análise da personagem "Mãe Ciana", de Belém do Grão-Pará, que, embora aparentemente secundária na trama, constitui uma perfeita alegoria étnicocultural da Amazônia – fruto da mistura envolvente entre nativos ameríndios e negros filhos da diáspora africana no Norte do Brasil. Dalcídio, autor etnógrafo, tem como perspectiva principal a de romancista, embora também chame a atenção a sua prática jornalística, que brilhantemente faz par  com a outra, a de criador de romances. Nesse estudo, ressaltamos que o autor utiliza, além de sua memória pessoal, a memória histórica da região amazônica baseada em pesquisas e em testemunhos de amigos e parentes, fatos e pessoas que viveram em Belém na primeira metade do século XX, transformando os seus romances em verdadeiros "lugares de memória", emaranhados à memória coletiva dos sujeitos amazônicos, priorizando os mais pobres, que ganham o leitor na mesma forma de identificação e significados. Dalcídio percebia a história como um campo de lutas e sua narrativa compromissada com a maioria explorada demonstra isso, escrevendo um romance politico capaz de contribuir com a transformação do mundo.

Jiahui Liang, Junwei Bao, Yifan Wang et al.

Product summarization aims to automatically generate product descriptions, which is of great commercial potential. Considering the customer preferences on different product aspects, it would benefit from generating aspect-oriented customized summaries. However, conventional systems typically focus on providing general product summaries, which may miss the opportunity to match products with customer interests. To address the problem, we propose CUSTOM, aspect-oriented product summarization for e-commerce, which generates diverse and controllable summaries towards different product aspects. To support the study of CUSTOM and further this line of research, we construct two Chinese datasets, i.e., SMARTPHONE and COMPUTER, including 76,279 / 49,280 short summaries for 12,118 / 11,497 real-world commercial products, respectively. Furthermore, we introduce EXT, an extraction-enhanced generation framework for CUSTOM, where two famous sequence-to-sequence models are implemented in this paper. We conduct extensive experiments on the two proposed datasets for CUSTOM and show results of two famous baseline models and EXT, which indicates that EXT can generate diverse, high-quality, and consistent summaries.

Priya Gupta, Cuize Han

E-commerce websites use machine learned ranking models to serve shopping results to customers. Typically, the websites log the customer search events, which include the query entered and the resulting engagement with the shopping results, such as clicks and purchases. Each customer search event serves as input training data for the models, and the individual customer engagement serves as a signal for customer preference. So a purchased shopping result, for example, is perceived to be more important than one that is not. However, new or under-impressed products do not have enough customer engagement signals and end up at a disadvantage when being ranked alongside popular products. In this paper, we propose a novel method for data curation that aggregates all customer engagements within a day for the same query to use as input training data. This aggregated customer engagement gives the models a complete picture of the relative importance of shopping results. Training models on this aggregated data leads to less reliance on behavioral features. This helps mitigate the cold start problem and boosted relevant new products to top search results. In this paper, we present the offline and online analysis and results comparing the individual and aggregated customer engagement models trained on e-commerce data.