7903 research outputs found
Sort by
Psychotherapeutic Strategies to Cope with the Dark Side of Religious Engagement
No full textAlthough spiritual and religious engagement that includes beliefs, practices, and community involvement has been found to offer numerous physical and mental health benefits, it can also be associated with problematic and dysfunctional perspectives and behaviors. Tragically, many people suffer from obsessive scrupulosity, guilt, anxiety, and depression, as well as discrimination, racism, and violence associated with their religious traditions and engagement or those of others. Mental health professionals must be mindful of both the upsides and the downsides of religiosity and find a way to secure appropriate consultation, embrace professional ethical principles, and support health and wellness for clients. This brief clinical reflection provides examples of religion getting in the way of health and wellness and offers several strategies for therapists to use to better address these issues in their clinical work
Book Publishing in a Changing Environment
Get PDFIn 1982 Unesco held the first World Congress of Books in London. Its aim was to promote the idea of the \u27reading society\u27. As if to underline ,he problem facing Unesco in achieving its aim, 1982 was also Information Technology Year in Britain. The juxtaposition of traditional and modern forms of communication is an apt symbol of the theme of this issue.
Since the time of Gutenberg the printed book has held a prominent place in the Western cultural and intellectual system. Today book production continues to increase around the world and developing countries are ever more anxious to combat illiteracy and to encourage reading. Yet in spite ofits success, the supporters of the printed book are anxious for its future.
Their anxiety is concentrated upon changes affecting the book publishing industry and the reading public. Critics see book publishing in developed countries becoming a mere adjunct to the mass media industries and the reading public becoming ever more fragmented and smaller. Third World publishing appears to be always in crisis, still dependent upon imported paper, technologies and ideas. The future threatens the decline of the book in the face of the new information technologies.
These and other issues are explored by this report on recent research on book publishing and reading under four headings: publishing in a time of cultural change, the reading public, Third World publishing, and the impact of new technologies
Critical Reviews of Advertising
Get PDFIn the late 1950s and early 1960s a series of very influential thinkers began a strong movement of criticism of advertising. This questioning of advertising led to a deeper questioning of the \u27cult of consumerism\u27 which advertising propagates.
Vance Packard in his popular book, The Hidden Persuaders (1956) attacked advertising for using the techniques of depth psychology to raise \u27subliminal anxieties\u27 and manipulate desires for alcohol, cigarettes and other consumer items. John Kenneth Galbraith, in The Affluent Society (1958) pointed out that advertising was encouraging a wasteful consumerism which allowed a small portion of the world\u27s people to use up scarce resources with profligate abandon and destroy the environment in the process. For Herbert Marcuse in The One-Dimensional Man (1964) advertising was the means by which a dehumanising technology reached into the inner space of our consciousness and destroyed our freedom.
The criticism of advertising continued to grow during the 1970s and has become a much broader attack on the style of life and worn-out political ideals of advanced industrial societies. The worldwide \u27green movement\u27 and peace movement are but two examples. It is also expressed in the desire to protect the non-Western, traditional cultures from the domination of North Atlantic industrial societies.
Currently, there is a new wave of thinking critical of advertising. Some of it attempts a more balanced, complex analysis; some of it is even more radical. This issue reviews some of the more recent, \u27new criticism\u27 of advertising
I-280 Wildlife Culvert
No full textInterstate 280 (I-280) serves as one of two (2) main arteries between San Francisco and the peninsula and south Bay Area. Being situated through various nature reserves at the foothills of the Santa Cruz Mountains, I-280 creates a barrier between said mountains and surrounding areas, disrupting migratory patterns. The heavy usage of this road, along with natural migratory patterns of large and medium sized mammals in the area results in high amounts of wildlife vehicle collisions (WVC’s). In order to provide remedial efforts, the team explored various methods to alleviate WVC’s. Ultimately the final proposed design consisted of a cast in place concrete culvert, that was installed using a deep excavation shoring system. This system was hand calculated and checked by two (2) softwares. A full project schedule, and cost estimate was subsequently provided, giving further justification for the application of this project to the real world
Neural Ranking in Sparse Data Environments
Get PDFNeural ranking models have become central to modern information retrieval (IR) systems, powering applications such as web search, product recommendation, and question answering. However, their effectiveness often hinges on access to abundant labeled supervision, a condition that is rarely met in real-world scenarios. In many domains, including e-commerce, healthcare, and legal search, labeled user interactions (e.g., clicks, purchases, or expert annotations) are sparse, especially for long-tail queries and new content. This data sparsity challenges the generalization, adaptability, and fairness of traditional ranking approaches.
This thesis presents a unified investigation of neural ranking under sparse supervision, introducing novel methods and analytical frameworks across four key dimensions: query, label, model, and corpus. First, we propose Meta-Learning to Rank (MLTR), a metalearning- based framework that enables fast adaptation to weakly supervised or unseen queries, enhancing query-level generalization. Second, we introduce a Multi-Task Learning (MTL) framework for product ranking in e-commerce, which jointly models diverse engagement signals, such as clicks, add-to-cart actions, and purchases, to improve supervision in imbalanced data settings. Third, we develop Passage-Specific Prompt Tuning (PSPT), a parameter-efficient method for adapting large language models (LLMs) to open-domain question answering tasks, where both task specificity and training data are limited. Finally, we conduct the first systematic fairness evaluation of Retrieval- Augmented Generation (RAG) systems, identifying how demographic biases emerge across retriever, refiner, and generator components under sparse or skewed retrieval conditions.
Together, these contributions form a comprehensive approach to improving the performance, adaptability, and trustworthiness of neural ranking systems in data-limited environments. By integrating meta-learning, multi-task optimization, LLM adaptation, and fairness-aware evaluation, this research offers both theoretical advances and practical insights toward building effective and responsible ranking models under sparse supervision
Dynamic Workload Management for Highly Concurrent Database Management System
No full textModern database management systems must balance high throughput with low-latency query execution across diverse and dynamic workloads, where queries vary significantly in complexity, resource demands, and latency sensitivity. This challenge is further compounded in multi-tenant environments, where the system must concurrently serve a wide range of query types while adapting to workload fluctuations and ensuring stable performance. Achieving this balance requires intelligent scheduling mechanisms that can effectively classify, prioritize, and allocate resources to queries in a manner that aligns with both user expectations and system-level performance goals.
This thesis surveys both academic and industrial advancements in workload and resource-aware query scheduling techniques designed to meet varying performance requirements. Drawing inspiration from heuristic scheduling strategies and recent developments in industrial systems, the thesis presents the design and implementation of a dynamic workload management module for AsterixDB. The proposed scheduler accommodates both user- and system-level requirements by enabling semantic query classification, user-defined prioritization, and fairness-aware scheduling policies tailored to different workload scenarios. To evaluate its effectiveness, we simulate three representative workload patterns under high query concurrency and conduct a comprehensive experimental analysis. The results demonstrate the importance of fine-grained query characterization, memory-aware scheduling, and feedback-driven control in supporting responsive and multi-tenant workload management in highly concurrent database systems
GBOTuner: Autotuning of OpenMP Parallel Codes with Bayesian Optimization and Code Representation Transfer Learning
No full textEmpirical autotuning methods such as Bayesian optimization (BO) are a powerful approach that allows us to optimize tuning parameters of parallel codes as black-boxes. However, BO is an expensive approach because it relies on empirical samples from true evaluations for varying parameter configurations. In this thesis, we present GBOTuner, an autotuning framework for optimizing the performance of OpenMP parallel codes, where OpenMP is a widely used API that enables shared-memory parallelism in C, C++, and Fortran using simple compiler directives. GBOTuner improves sample efficiency of BO by combining code representation learning from a Graph Neural Network (GNN) into a BO autotuning pipeline. Compared to typical BO, GBOTuner uses a hybrid approach that exploits not only a Gaussian Process (GP)-based surrogate model learned from the empirical samples for the given target code but also a GNN-based performance prediction model learned from other codes. We evaluate GBOTuner using 78 OpenMP parallel code kernels obtained from five benchmark suites. GBOTuner significantly and consistently improves the tuning cost and quality over state-of-the-art BO tools across most cases, especially with a small tuning budget, resulting in up to 1.4x/1.3x higher tuned results on an Intel and an AMD platform, respectively
PU-ResInterp: Point Cloud Upsampling via Residual Interpolation
No full textPoint cloud upsampling is a critical task in 3D vision, aiming to reconstruct dense and uniform point sets from sparse and irregular inputs. Existing interpolation-based methods offer high efficiency but suffer from limited reconstruction fidelity, while learningbased approaches improve accuracy at the cost of increased complexity. To address this tradeoff, we propose PU-ResInterp, a lightweight and interpretable framework for point cloud upsampling that integrates residual interpolation with a dual-branch architecture. The main branch employs a DGCNN-based encoder enhanced with CBAM attention modules to capture fine-grained geometric features, while the condition branch uses a PointNetstyle lightweight encoder to extract structural priors. These two branches are fused via FiLM modulation to enable semantic alignment. A two-stage decoder is then applied: a coarse decoder generates a preliminary upsampled point cloud, followed by a residual refinement module that predicts high-quality offsets to restore detailed geometry. Extensive experiments on the PU1K dataset demonstrate that PU-ResInterp achieves competitive performance with significantly fewer parameters and faster inference compared to existing methods. Our approach strikes a compelling balance between accuracy, interpretability, and computational efficiency, making it suitable for practical 3D applications
Deepfake Detection
No full textFrom elections, to wars, to people’s personal lives, deepfakes have become ever more present, blurring the distinction between reality and fiction. Many deep learning-based deepfake detection methods lack generalizability. They often overfit to deepfake generation techniques in their training distribution, and fail to detect deepfake generation techniques outside of their training distribution. One approach to solve the generalization problem has been to use Vision Language Models, such as the pretrained CLIP model, to extract features that generalize across different deepfake generation techniques, including diffusion and GAN images. However, most CLIP approaches only consider image level features for detection, such as artifacts from blending or generative models, rather than video level features such as flicking or temporal inconsistencies. The importance of capturing both spatial and temporal information is demonstrated by two state-of-the-art detection methods: AltFreezing, which uses a 3D CNN and an alternating weight freezing strategy to train both spatial and temporal weights; and TALL, which uses a Swin-Transformer and a 2D thumbnail layout to capture spatial information within frames and temporal information across consecutive frames. To compete with the state-of-the-art in video deepfake detection, we propose two methods to integrate temporal information with the spatial information that CLIP is already able to capture. The first is to combine CLIP with a transformer trained from scratch. The second is to combine CLIP with TimeSformer. We furthermore investigate X-CLIP, a variant of CLIP with video understanding capabilities, and the addition of its multiframe integration transformer for deepfake detection
Communication Assistance Using A Large Language Model
No full textThe number of people diagnosed with Autism Spectrum Disorder is growing annually at a rapid pace. Autistic people are known to have multiple problems with communication, requiring assistance and coaching which is already limited in availability due to the amount of training required. At the same time, there has been a recent exponential rise in usage of LLMs like ChatGPT which is widely available. This project aims to prototype a solution using ChatGPT to provide communication assistance to those diagnosed with autism. The project would record voice input through a microphone and transcribe it into text. The text would be inserted into an annotated prompt and sent to ChatGPT, which would then generate a response as text and displayed to guide conversation in real time. The prototype would also manage pace, detect silence, and re-generate advice when needed. These features would facilitate a conversation between an autistic person and non-autistic person. Analysis shows that selecting the appropriate models helps improve usability by reducing latency, and fine-tuning prompts helps improve advice quality. Further refinements in hardware, software, and cloud components can be explored to enhance performance for communication assistance