by jsendak | Jan 17, 2024 | Computer Science
Analysis of the LDBC SNB and its Workloads
The Linked Data Benchmark Council’s Social Network Benchmark (LDBC SNB) is a benchmarking effort designed to evaluate the performance and functionality of systems used for managing graph-like data. It achieves this by simulating the operations and data characteristics of a social network, which is known for its graph-shaped data structure.
The LDBC SNB consists of two distinct workloads that test different aspects of graph data management systems: the Interactive workload and the Business Intelligence workload. These workloads are carefully designed to cover a wide range of functionalities and use cases, allowing for a comprehensive evaluation of system performance.
Interactive Workload
The Interactive workload of the LDBC SNB focuses on interactive transactional queries. These queries simulate the typical actions and interactions performed by users in a social network, such as posting messages, sending friend requests, or creating events. The goal of this workload is to assess how well a system can handle real-time user interactions and maintain responsiveness even under high load.
The Interactive workload consists of a set of predefined queries that cover various aspects of social network usage. These queries include actions such as creating and deleting entities, adding and removing relationships between entities, retrieving user profiles, and searching for specific content. By executing these queries in a controlled environment, the benchmark can measure the system’s performance in terms of execution time, response rate, and scalability.
Business Intelligence Workload
The Business Intelligence workload, on the other hand, focuses on analytical queries that involve complex aggregations and data mining operations. These queries aim to assess the system’s ability to process large volumes of data and extract meaningful insights from the social network dataset. Examples of analytical queries in this workload include retrieving top-k results, calculating statistics on user behavior, identifying influential users, and analyzing social network dynamics.
Similar to the Interactive workload, the Business Intelligence workload consists of a set of predefined queries that cover different analytical scenarios. These queries leverage advanced graph algorithms and aggregation functions to generate valuable insights from the social network data. By executing these queries on a benchmark system, it becomes possible to evaluate the system’s performance in terms of query execution time, scalability, and accuracy of results.
Data Generation and Benchmark Execution
In order to facilitate the execution of the LDBC SNB, the benchmark provides detailed instructions on how to generate the required social network dataset. The dataset contains a diverse set of entities such as users, messages, events, and relationships between them. The instructions also cover the generation of various data distributions and parameters to ensure the realism and diversity of the dataset.
Once the dataset is generated, the LDBC SNB provides software tools and scripts to load the dataset into the benchmark system and execute the predefined queries. These tools enable the benchmark to measure the system’s performance and compare it against other systems in a standardized and reproducible manner.
Expert Insights
The LDBC SNB is an essential benchmark to evaluate the performance and capabilities of graph data management systems in the context of social networks. By simulating real-world scenarios and covering a wide range of functionalities, it provides valuable insights into the strengths and weaknesses of different systems.
From an analysis perspective, the Interactive workload is particularly relevant as it tests a system’s ability to handle real-time user interactions. This is crucial for applications that require high responsiveness, such as social media platforms or collaborative environments. On the other hand, the Business Intelligence workload allows us to assess a system’s analytical capabilities, which are key for extracting valuable insights from large graph datasets.
Furthermore, the LDBC SNB’s focus on generating realistic and diverse datasets ensures that benchmark results reflect real-world challenges. This realism helps in identifying potential bottlenecks and performance issues that may arise in production scenarios.
Overall, the LDBC SNB is a valuable resource for developers, researchers, and practitioners in the field of graph data management. Its defined workloads, data generation instructions, and benchmark execution tools make it a standardized and reliable benchmark for evaluating graph database systems.
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by jsendak | Jan 17, 2024 | Computer Science
A large body of research has focused on understanding how online content and
disordered eating behaviors are associated. However, there is a lack of
comprehensive studies investigating digital food content’s influence on
individuals with eating disorders. We conducted two rounds of studies (N=23 and
22, respectively) with individuals with binge eating disorder (BED) or bulimia
nervosa (BN) to understand their motivations and practices of consuming digital
food content. Our study reveals that individuals with BED and BN anticipate
positive effects from food media to overcome their condition, but in practice,
it often exacerbates their disorder. We also discovered that many individuals
have experienced a cycle of quitting and returning to digital food content
consumption. Based on these findings, we articulate design implications for
digital food content and multimedia platforms to support vulnerable individuals
in everyday online platform interactions.
The Influence of Digital Food Content on Individuals with Eating Disorders
A growing body of research has explored the relationship between online content and disordered eating behaviors. However, there is a dearth of comprehensive studies specifically examining the impact of digital food content on individuals with eating disorders. In an effort to fill this gap, we conducted two rounds of studies with individuals diagnosed with binge eating disorder (BED) or bulimia nervosa (BN) to gain insights into their motivations and practices surrounding digital food content consumption.
Our findings shed light on the complex relationship between individuals with BED or BN and digital food content. Surprisingly, our study revealed that these individuals often anticipate positive outcomes from engaging with food media, seeing it as a potential tool to overcome their condition. However, the reality is quite different. In practice, most individuals found that consuming digital food content actually intensified their disorder, exacerbating their symptoms and triggering disordered eating behaviors.
A striking discovery was that many individuals experienced a cycle of quitting and returning to digital food content consumption. This finding suggests that the allure of food media is difficult to resist, despite its negative impact on their well-being. The cyclical nature of their engagement with digital food content highlights the persistent struggle faced by those with eating disorders when attempting to navigate online platforms.
These results have significant implications for the design and development of digital food content and multimedia platforms. Specifically, they call for the implementation of strategies aimed at supporting vulnerable individuals in their everyday interactions on online platforms.
Given the multi-disciplinary nature of this research, its implications extend beyond the scope of eating disorders and shed light on broader concepts within multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Understanding how individuals interact with digital food content can inform the development of more holistic and inclusive multimedia platforms that cater to a diverse user base.
Implications for Multimedia Information Systems
The findings of this study emphasize the importance of considering the potential negative impact of digital food content on individuals with eating disorders within the field of multimedia information systems. Researchers and developers must be mindful of the potential harm that certain types of content can cause, especially when targeting vulnerable populations. By implementing safeguards and guidelines that promote the creation and consumption of healthier food media, multimedia information systems can contribute to better user experiences and foster a more positive online environment.
Significance for Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The study’s insights also have implications for the fields of animations, artificial reality, augmented reality, and virtual realities. These mediums offer unique opportunities to engage users and provide immersive experiences. However, they also possess the potential to influence individuals’ perceptions of food and body image. By incorporating responsible design principles and considering the impact of animations, artificial reality, augmented reality, and virtual realities on individuals with eating disorders, developers can create more inclusive and empowering experiences that promote positive mental health and well-being.
In conclusion, our study highlights the detrimental effects of digital food content on individuals with binge eating disorder or bulimia nervosa. It underscores the importance of understanding the motivations and practices surrounding their consumption of food media. Moreover, it emphasizes the need for designers, developers, and researchers in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities to collaborate to create platforms that support vulnerable individuals in their online interactions.
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by jsendak | Jan 17, 2024 | Computer Science
The Role of Tit-for-Tat Dynamics in Production Markets
Tit-for-tat dynamics have long been studied in various fields, and in this article, we explore their implications in production markets. In a production market setting, we have a group of players connected through a weighted graph. Each player has the capability to produce goods using their linear production function, which takes into account the amounts of goods available in the system.
The key idea in the tit-for-tat dynamic is that players share their produced goods with their neighbors in fractions proportional to how much help they received from those neighbors in the previous round. This concept mimics the idea of reciprocation and cooperation often observed in real-world scenarios.
Characterizing Asymptotic Behavior
An important contribution of this study is the characterization of the asymptotic behavior of the dynamic based on the underlying graph structure. The results show that a player’s fortune, measured by their production growth in the long term, is influenced by two key factors: having a good self-loop and having effective relationships with other players.
A player with a good self-loop is one who can work well independently, regardless of the contributions from other players. These self-sufficient players are able to consistently produce goods without relying heavily on others. On the other hand, players who work well with at least one other player can also experience growth in the long term. This highlights the importance of forming efficient partnerships or collaborations in production markets.
Generalized Damped Update
In addition to the basic tit-for-tat dynamic, the study also considers a generalized damped update scenario. In this case, players can update their strategies or production behaviors at different speeds, adding an element of diversity to the dynamics of the system.
An interesting finding is that the rate of growth of the players can be bounded by a function that provides insights into the behavior of the dynamical system. This lower bound on growth rate helps us understand the limits and potential of the system, showing that even with variations in update speeds, some level of growth can still be guaranteed.
Practical Applications: Circular Economies and Organizational Partnerships
The model presented in this study has practical implications for circular economies and organizational partnerships. Circular economies are characterized by players using each other’s products, creating a mutually beneficial network. The tit-for-tat dynamic allows us to analyze the long-term growth and stability of such circular economies, shedding light on the dynamics and potential outcomes.
In organizational partnerships, fostering long-term growth and success relies on establishing reciprocal exchanges between the agents in the organization. By understanding the conditions under which players experience fortune growth, organizations can strategically form partnerships that enhance production and sustainability.
To summarize, this study provides valuable insights into the behavior of tit-for-tat dynamics in production markets. By characterizing the asymptotic behavior and considering a generalized damped update scenario, the study highlights the importance of self-sufficiency and effective partnerships for long-term growth. The findings have practical applications in circular economies and organizational partnerships, offering a framework for analyzing and optimizing these systems.
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by jsendak | Jan 17, 2024 | Computer Science
Language Models (LMs) have demonstrated impressive molecule understanding
ability on various 1D text-related tasks. However, they inherently lack 2D
graph perception – a critical ability of human professionals in comprehending
molecules’ topological structures. To bridge this gap, we propose MolCA:
Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal
Adapter. MolCA enables an LM (e.g., Galactica) to understand both text- and
graph-based molecular contents via the cross-modal projector. Specifically, the
cross-modal projector is implemented as a Q-Former to connect a graph encoder’s
representation space and an LM’s text space. Further, MolCA employs a uni-modal
adapter (i.e., LoRA) for the LM’s efficient adaptation to downstream tasks.
Unlike previous studies that couple an LM with a graph encoder via cross-modal
contrastive learning, MolCA retains the LM’s ability of open-ended text
generation and augments it with 2D graph information. To showcase its
effectiveness, we extensively benchmark MolCA on tasks of molecule captioning,
IUPAC name prediction, and molecule-text retrieval, on which MolCA
significantly outperforms the baselines. Our codes and checkpoints can be found
at https://github.com/acharkq/MolCA.
Expert Commentary: Bridging the Gap Between Language Models and Molecule Understanding
Language Models (LMs) have made significant strides in understanding molecular information in text-based tasks. However, they lack the crucial ability to comprehend and interpret the topological structures of molecules represented in 2D graphs. This gap between text and graph perception has limited the potential of LMs in delivering comprehensive insights into molecular content.
In order to address this limitation, the authors propose MolCA (Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter), a novel approach that enables LMs to understand both text-based and graph-based molecular content. MolCA integrates a cross-modal projector, implemented as a Q-Former, to connect the representation spaces of a graph encoder and an LM. By doing so, MolCA establishes a bridge between the visual representations captured by the graph encoder and the language representations processed by the LM.
Additionally, MolCA incorporates a uni-modal adapter called LoRA, which aids the LM in efficiently adapting to downstream tasks. Unlike previous studies that focus on coupling LMs with graph encoders using cross-modal contrastive learning, MolCA preserves the LM’s ability to generate open-ended text and enhances it with 2D graph information.
To evaluate the effectiveness of MolCA, the authors conducted extensive benchmarking on tasks such as molecule captioning, IUPAC name prediction, and molecule-text retrieval. The results demonstrate that MolCA outperforms the baselines significantly, showcasing its potential to bridge the gap between LMs and molecule understanding.
The concepts presented in this research demonstrate the multi-disciplinary nature of multimedia information systems, encompassing various domains such as chemistry, computer science, and artificial intelligence. By integrating graph-based molecular structures with textual data, researchers can leverage the power of LMs to extract valuable insights from complex molecular information.
Moreover, this work aligns with the broader field of multimedia information systems, as it leverages the potential of animations, artificial reality, augmented reality, and virtual realities to enhance the understanding and analysis of molecular structures. By incorporating 2D graph information into LMs, researchers can explore the possibilities of creating immersive visualizations and interactive experiences for studying molecular content.
In conclusion, MolCA presents a promising approach to bridge the gap between language models and molecule understanding. By enabling LMs to comprehend both textual and graph-based molecular content, researchers can unlock new avenues for analyzing, interpreting, and visualizing complex molecular structures. This research highlights the importance of integration between different disciplines and sets the stage for future advancements in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 17, 2024 | Computer Science
Analysis of Momentum-based Accelerated Variants of Stochastic Gradient Descent
In this article, the authors discuss the theoretical understanding and generalization error of momentum-based accelerated variants of stochastic gradient descent (SGD) when training machine learning models. They present several key findings and propose improvements to enhance the generalization error of these methods.
Stability Gap in SGD with Standard Heavy-Ball Momentum (SGDM)
The authors first demonstrate that there exists a convex loss function for which the stability gap for multiple epochs of SGD with standard heavy-ball momentum (SGDM) becomes unbounded. This highlights a limitation of using SGDM for training machine learning models, especially in cases where stability is crucial.
Improved Generalization with SGD and Early Momentum (SGDEM)
To address the generalization issue, the authors introduce a modified momentum-based update rule called SGD with early momentum (SGDEM). They evaluate SGDEM under a broad range of step-sizes for smooth Lipschitz loss functions and show that it can train machine learning models for multiple epochs while guaranteeing generalization. This improvement is significant as it provides a practical solution that can be applied to various scenarios.
Generalization of Standard SGDM for Strongly Convex Loss Functions
In the case of strongly convex loss functions, the authors find that there exists a range of momentum values for which multiple epochs of standard SGDM can also generalize. This discovery presents a specific condition where SGDM is effective, reinforcing its applicability in certain contexts.
Upper Bound on Expected True Risk
In addition to the findings on generalization, the authors develop an upper bound on the expected true risk. This bound takes into account the number of training steps, sample size, and momentum. By providing an analytical estimation of the true risk, this bound offers insights into the reliability and performance of momentum-based variants of SGD.
Experimental Evaluations and Consistency with Theoretical Bounds
The authors conclude their work by showcasing experimental evaluations that verify the consistency between their theoretical bounds and numerical results. They specifically focus on the application of SGDEM to improve the generalization error of SGDM when training ResNet-18 on the ImageNet dataset in practical distributed settings. This practical validation further strengthens the significance and relevance of their findings.
In summary, this article sheds light on the theoretical understanding and generalization error of momentum-based accelerated variants of stochastic gradient descent. It introduces an improved momentum-based update rule (SGDEM) to enhance generalization, identifies a condition where standard SGDM can also generalize, and provides an upper bound on the expected true risk. The experimental evaluations validate the theoretical findings and highlight the potential practical applications. This work contributes to advancing the understanding and optimization of machine learning model training algorithms.
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by jsendak | Jan 17, 2024 | Computer Science
In this study, we address the emerging necessity of converting Standard
Dynamic Range Television (SDRTV) content into High Dynamic Range Television
(HDRTV) in light of the limited number of native HDRTV content. A principal
technical challenge in this conversion is the exacerbation of coding artifacts
inherent in SDRTV, which detrimentally impacts the quality of the resulting
HDRTV. To address this issue, our method introduces a novel approach that
conceptualizes the SDRTV-to-HDRTV conversion as a composite task involving dual
degradation restoration. This encompasses inverse tone mapping in conjunction
with video restoration. We propose Dual Inversion Downgraded SDRTV to HDRTV
Network (DIDNet), which can accurately perform inverse tone mapping while
preventing encoding artifacts from being amplified, thereby significantly
improving visual quality. DIDNet integrates an intermediate auxiliary loss
function to effectively separate the dual degradation restoration tasks and
efficient learning of both artifact reduction and inverse tone mapping during
end-to-end training. Additionally, DIDNet introduces a spatio-temporal feature
alignment module for video frame fusion, which augments texture quality and
reduces artifacts. The architecture further includes a dual-modulation
convolution mechanism for optimized inverse tone mapping. Recognizing the
richer texture and high-frequency information in HDRTV compared to SDRTV, we
further introduce a wavelet attention module to enhance frequency features. Our
approach demonstrates marked superiority over existing state-of-the-art
techniques in terms of quantitative performance and visual quality.
In this study, the authors address the challenge of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) due to the limited availability of native HDRTV content. The conversion process poses technical challenges, particularly in relation to coding artifacts that are inherent in SDRTV and negatively impact the quality of the resulting HDRTV.
The authors propose a novel approach called Dual Inversion Downgraded SDRTV to HDRTV Network (DIDNet) to address this issue. DIDNet conceptualizes the SDRTV-to-HDRTV conversion as a composite task involving dual degradation restoration, combining inverse tone mapping and video restoration.
To achieve accurate inverse tone mapping while preventing the amplification of encoding artifacts, DIDNet incorporates an intermediate auxiliary loss function. This helps effectively separate the tasks of artifact reduction and inverse tone mapping, allowing for efficient learning during end-to-end training.
DIDNet also includes a spatio-temporal feature alignment module for video frame fusion, which enhances texture quality and reduces artifacts. Furthermore, a dual-modulation convolution mechanism is introduced for optimized inverse tone mapping.
Recognizing the richer texture and high-frequency information present in HDRTV compared to SDRTV, the authors introduce a wavelet attention module to enhance frequency features.
The authors demonstrate the superiority of their approach over existing state-of-the-art techniques in terms of both quantitative performance and visual quality.
Multi-Disciplinary Nature of the Concepts
This study involves the integration of concepts from various disciplines, highlighting its multi-disciplinary nature. The authors combine techniques from image processing, computer vision, and machine learning to tackle the challenges of converting SDRTV to HDRTV. The use of neural networks, loss functions, convolution mechanisms, and attention modules showcases the convergence of these disciplines in the field of multimedia information systems.
Relation to Multimedia Information Systems
Multimedia information systems encompass the management, organization, and retrieval of multimedia content. The conversion of SDRTV to HDRTV is crucial in enhancing the visual quality and user experience of multimedia content. By addressing the technical challenges of this conversion and improving the visual quality of HDRTV, this study contributes to the wider field of multimedia information systems.
Relation to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The conversion of SDRTV to HDRTV has implications for various fields that utilize animations, artificial reality, augmented reality, and virtual realities. High-quality visual content is essential in these domains to provide immersive and realistic experiences. By improving the visual quality of HDRTV, this study contributes to enhancing the realism and immersion in animations, artificial reality, augmented reality, and virtual realities.
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