Information overload in structured data

Information overload refers to the difficulty of making decisions caused by too much information. In this dissertation, we address information overload problem in two separate structured domains, namely, graphs and text. Graph kernels have been proposed as an efficient and theoretically sound approach to compute graph similarity. They decompose graphs into certain sub-structures, such as subtrees, or subgraphs. However, existing graph kernels suffer from a few drawbacks. First, the dimension of the feature space associated with the kernel often grows exponentially as the complexity of sub-structures increase. One immediate consequence of this behavior is that small, non-informative, sub-structures occur more frequently and cause information overload. Second, as the number of features increase, we encounter sparsity: only a few informative sub-structures will co-occur in multiple graphs. In the first part of this dissertation, we propose to tackle the above problems by exploiting the dependency relationship among sub-structures. First, we propose a novel framework that learns the latent representations of sub-structures by leveraging recent advancements in deep learning. Second, we propose a general smoothing framework that takes structural similarity into account, inspired by state-of-the-art smoothing techniques used in natural language processing. Both the proposed frameworks are applicable to popular graph kernel families, and achieve significant performance improvements over state-of-the-art graph kernels. In the second part of this dissertation, we tackle information overload in text. We first focus on a popular social news aggregation website, Reddit, and design a submodular recommender system that tailors a personalized frontpage for individual users. Second, we propose a novel submodular framework to summarize videos, where both transcript and comments are available. Third, we demonstrate how to apply filtering techniques to select a small subset of informative features from virtual machine logs in order to predict resource usage

MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models

Diffusion-based text-to-image models have rapidly gained popularity for their ability to generate detailed and realistic images from textual descriptions. However, these models often reflect the biases present in their training data, especially impacting marginalized groups. While prior efforts to debias language models have focused on addressing specific biases, such as racial or gender biases, efforts to tackle intersectional bias have been limited. Intersectional bias refers to the unique form of bias experienced by individuals at the intersection of multiple social identities. Addressing intersectional bias is crucial because it amplifies the negative effects of discrimination based on race, gender, and other identities. In this paper, we introduce a method that addresses intersectional bias in diffusion-based text-to-image models by modifying cross-attention maps in a disentangled manner. Our approach utilizes a pre-trained Stable Diffusion model, eliminates the need for an additional set of reference images, and preserves the original quality for unaltered concepts. Comprehensive experiments demonstrate that our method surpasses existing approaches in mitigating both single and intersectional biases across various attributes. We make our source code and debiased models for various attributes available to encourage fairness in generative models and to support further research

WordRank: Learning Word Embeddings via Robust Ranking

Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation is often left unclear. In this paper, we argue that word embedding can be naturally viewed as a ranking problem due to the ranking nature of the evaluation metrics. Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses. The performance of WordRank is measured in word similarity and word analogy benchmarks, and the results are compared to the state-of-the-art word embedding techniques. Our algorithm is very competitive to the state-of-the- arts on large corpora, while outperforms them by a significant margin when the training set is limited (i.e., sparse and noisy). With 17 million tokens, WordRank performs almost as well as existing methods using 7.2 billion tokens on a popular word similarity benchmark. Our multi-node distributed implementation of WordRank is publicly available for general usage.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP), November 1-5, 2016, Austin, Texas, US

CLoRA: A Contrastive Approach to Compose Multiple LoRA Models

Low-Rank Adaptations (LoRAs) have emerged as a powerful and popular technique in the field of image generation, offering a highly effective way to adapt and refine pre-trained deep learning models for specific tasks without the need for comprehensive retraining. By employing pre-trained LoRA models, such as those representing a specific cat and a particular dog, the objective is to generate an image that faithfully embodies both animals as defined by the LoRAs. However, the task of seamlessly blending multiple concept LoRAs to capture a variety of concepts in one image proves to be a significant challenge. Common approaches often fall short, primarily because the attention mechanisms within different LoRA models overlap, leading to scenarios where one concept may be completely ignored (e.g., omitting the dog) or where concepts are incorrectly combined (e.g., producing an image of two cats instead of one cat and one dog). To overcome these issues, CLoRA addresses them by updating the attention maps of multiple LoRA models and leveraging them to create semantic masks that facilitate the fusion of latent representations. Our method enables the creation of composite images that truly reflect the characteristics of each LoRA, successfully merging multiple concepts or styles. Our comprehensive evaluations, both qualitative and quantitative, demonstrate that our approach outperforms existing methodologies, marking a significant advancement in the field of image generation with LoRAs. Furthermore, we share our source code, benchmark dataset, and trained LoRA models to promote further research on this topic

RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models

Recent advancements in diffusion-based models have demonstrated significant success in generating images from text. However, video editing models have not yet reached the same level of visual quality and user control. To address this, we introduce RAVE, a zero-shot video editing method that leverages pre-trained text-to-image diffusion models without additional training. RAVE takes an input video and a text prompt to produce high-quality videos while preserving the original motion and semantic structure. It employs a novel noise shuffling strategy, leveraging spatio-temporal interactions between frames, to produce temporally consistent videos faster than existing methods. It is also efficient in terms of memory requirements, allowing it to handle longer videos. RAVE is capable of a wide range of edits, from local attribute modifications to shape transformations. In order to demonstrate the versatility of RAVE, we create a comprehensive video evaluation dataset ranging from object-focused scenes to complex human activities like dancing and typing, and dynamic scenes featuring swimming fish and boats. Our qualitative and quantitative experiments highlight the effectiveness of RAVE in diverse video editing scenarios compared to existing methods. Our code, dataset and videos can be found in https://rave-video.github.io.Comment: Project webpage: https://rave-video.github.io , Github: http://github.com/rehg-lab/RAV

GANTASTIC: GAN-based Transfer of Interpretable Directions for Disentangled Image Editing in Text-to-Image Diffusion Models

The rapid advancement in image generation models has predominantly been driven by diffusion models, which have demonstrated unparalleled success in generating high-fidelity, diverse images from textual prompts. Despite their success, diffusion models encounter substantial challenges in the domain of image editing, particularly in executing disentangled edits-changes that target specific attributes of an image while leaving irrelevant parts untouched. In contrast, Generative Adversarial Networks (GANs) have been recognized for their success in disentangled edits through their interpretable latent spaces. We introduce GANTASTIC, a novel framework that takes existing directions from pre-trained GAN models-representative of specific, controllable attributes-and transfers these directions into diffusion-based models. This novel approach not only maintains the generative quality and diversity that diffusion models are known for but also significantly enhances their capability to perform precise, targeted image edits, thereby leveraging the best of both worlds.Comment: Project page: https://gantastic.github.i

The protective effect of metformin against testicular damage in diabetes and prostate cancer model

Individuals with diabetes have an increased risk of breast, colorectal, pancreatic and prostate cancer. Metformin, an oral biguanide used to treat diabetes, has anti-hyperglycaemic, anti-hyperinsulinemic and antioxidant activities. The effects of metformin on testicular tissue damage in cancer and diabetic + cancer rat models were evaluated histologically, immunohistochemically and biochemically. The diabetic model was produced in Copenhagen rats using a single dose of streptozotocin (65 mg/kg), while prostate cancer was induced through subcutaneous inoculation of 2 x 10(4) Mat-LyLu cells into the animals. At the end of the experimental period, testicular tissues with a close functional relationship to the prostate were collected. Histological evaluation found moderate to severe damage to testes following the diabetes and cancer process. Histopathological and biochemical impairments were observed in the early stage of prostate cancer, which were increased in the diabetic animals. Metformin administration reversed these injuries and provided substantial protection of the testes. In particular, metformin had protective effects on tissue damage, apoptosis, oxidative stress and antioxidant capacity. This suggests that metformin should be further investigated as a targeted protective drug against prostate cancer-related damage to the testes

Brain Boron Level, DNA Content, and Myeloperoxidase Activity of Metformin-Treated Rats in Diabetes and Prostate Cancer Model

In this study, the effect of metformin on boron levels and oxidative brain damage in rats due to diabetes and prostate cancer was investigated for the first time. Myeloperoxidase (MPO) activity and the amount of DNA were investigated as tissue oxidative and toxic damage parameters. In Copenhagen rats, Dunning prostate cancer was induced using high metastatic MAT-Lylu cells and diabetes was induced by single dose of streptozotocin (STZ) injection. Metformin was administered for 14 days after diabetes and prostate cancer induced. The rats were divided into six groups as follows: control group, diabetic group (D), cancer group (C), diabetic + cancer (DC) group, cancer + metformin (CM) group, diabetic + cancer + metformin (DCM) group. At the end of the experiment, brains were removed. Significant decrease of brain boron levels and significant elevation of MPO activity and DNA levels were observed in D, C, and DC groups as compared to control group. The effect of diabetes induction on the brain boron levels was much more than prostate cancer induction. The administration of metformin with CM and DCM obviously declined MPO activity and increased brain boron levels almost near to control group level. In conclusion, this study shows that the protective effect of metformin against brain damage in STZ-induced diabetic rats with Dunning prostate cancer may also be related to increased boron levels. The boron levels may be a novel indicator of reduced toxic and oxidative stress. Furthermore, the distribution and mechanism of action of boron should be clarified