ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation

In the absence of parallax cues, a learning-based single image depth estimation (SIDE) model relies heavily on shading and contextual cues in the image. While this simplicity is attractive, it is necessary to train such models on large and varied datasets, which are difficult to capture. It has been shown that using embeddings from pre-trained foundational models, such as CLIP, improves zero shot transfer in several applications. Taking inspiration from this, in our paper we explore the use of global image priors generated from a pre-trained ViT model to provide more detailed contextual information. We argue that the embedding vector from a ViT model, pre-trained on a large dataset, captures greater relevant information for SIDE than the usual route of generating pseudo image captions, followed by CLIP based text embeddings. Based on this idea, we propose a new SIDE model using a diffusion backbone which is conditioned on ViT embeddings. Our proposed design establishes a new state-of-the-art (SOTA) for SIDE on NYUv2 dataset, achieving Abs Rel error of 0.059 (14% improvement) compared to 0.069 by the current SOTA (VPD). And on KITTI dataset, achieving Sq Rel error of 0.139 (2% improvement) compared to 0.142 by the current SOTA (GEDepth). For zero-shot transfer with a model trained on NYUv2, we report mean relative improvement of (20%, 23%, 81%, 25%) over NeWCRFs on (Sun-RGBD, iBims1, DIODE, HyperSim) datasets, compared to (16%, 18%, 45%, 9%) by ZoeDepth. The project page is available at https://ecodepth-iitd.github.ioComment: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 202

Composite Lognormal-T regression models with varying threshold and its insurance application

Composite probability models have shown very promising results for modeling claim severity data comprised of small, moderate, and large losses. In this paper, we introduce three classes of parametric composite regression models with a varying threshold. We consider the Lognormal distribution for the head and the Burr, the Stoppa and the generalized log-Moyal (GlogM) distributions for the tail part of the composite family. Further, the Mode-Matching procedure has been utilized for the composition of the two densities. To capture the heterogeneous behavior of the policyholder's characteristics, covariates are introduced into the scale parameter of the tail distribution. Finally, the applicability of the proposed models has been shown using a real-world insurance data set.Comment: 22 pages, 4 figure

AUTOMATIC RECOGNITION OF TRAFFIC SIGNS USING FANN AND OPEN CV

Automation Recognition of Traffic Signs is integrated and automation software for Traffic Symbol Recognition. The proposed system detects candidate regions as Maximally Stable Extremely Region (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on Artificial Neural Network (ANN) classifiers. The training data are generated from real footage road signs which will be fetched using camera board and by applying threshold values we get proper training data for each frame. By applying thinning mechanism like erode and corrode and segmentation we can recognize proper shape and symbol. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 10 frames per second, and recognizes all classes of ideogram-based (non-text) traffic symbols from real footage road signs. Comprehensive comparative results to illustrate the performance of the system are presented. https://journalnx.com/journal-article/2015023

The emerging role of FTY720 (Fingolimod) in cancer treatment

FTY720 (Fingolimod) is a clinically approved immunomodulating therapy for multiple sclerosis that sequesters T-cells to lymph nodes through functional antagonism of sphingosine-1-phosphate 1 receptor. FTY720 also demonstrates a proven efficacy in multiple in vitro and in vivo cancer models, suggesting a potential therapeutic role in cancer patients. A potential anticancer mechanism of FTY720 is through the inhibition of sphingosine kinase 1, a proto-oncogene with in vitro and clinical cancer association. In addition, FTY720's anticancer properties may be attributable to actions on several other molecular targets. This study focuses on reviewing the emerging evidence regarding the anticancer properties and molecular targets of FTY720. While the clinical transition of FTY720 is currently limited by its immune suppression effects, studies aiming at FTY720 delivery and release together with identifying its key synergetic combinations and relevant patient subsets may lead to its rapid introduction into the clinic

МОДЕЛІ ЕКОНОМІЧНОЇ ДІЯЛЬНОСТІ ЛЮДИНИ В СУЧАСНИХ ЕКОНОМІЧНИХ ТЕОРІЯХ

We consider human's economical activity models in neoclassical, institutional, and evolutional theories. Development of the interdisciplinary synthesis of ideas of human's activity in economics is analyzed. В статье рассматриваются модели экономической деятельности человека в неоклассической, институциональной и эволюционной теориях. Раскрываются поиски междисциплинарного синтеза представлений о деятельности человека в экономике. У статті розглядаються моделі економічної діяльності людини в неокласичній, інституціональній та еволюційній теоріях. Розкриваються пошуки міждисциплінарного синтезу уявлень про діяльність людини в економіці

Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models

Fine-tuning large language models (LLMs) on downstream tasks requires substantial computational resources. A class of parameter-efficient fine-tuning (PEFT) aims to mitigate these computational challenges by selectively fine-tuning only a small fraction of the model parameters. Although computationally efficient, these techniques often fail to match the performance of fully fine-tuned models, primarily due to inherent biases introduced during parameter selection. Traditional selective PEFT techniques use a fixed set of parameters based on a predefined budget (a process also known as unmasking), failing to capture parameter importance dynamically and often ending up exceeding the budget. We introduce $\text{ID}^3$, a novel selective PEFT method that calculates parameter importance continually and dynamically unmasks parameters by balancing exploration and exploitation in parameter selection. Our empirical study on 15 tasks spanning natural language understanding and generative tasks demonstrates the effectiveness of our method compared to fixed-masking-based PEFT techniques. We analytically show that $\text{ID}^3$ reduces the number of gradient updates by a factor of two, enhancing computational efficiency. $\text{ID}^3$ is robust to random initialization of neurons and, therefore, can be seamlessly integrated into existing additive and reparametrization-based PEFT modules such as adapters and LoRA for dynamic sparsification.Comment: 15 pages, 7 tables, 9 figure

Robust kernel distance multivariate control chart using support vector principles

It is important to monitor manufacturing processes in order to improve product quality and reduce production cost. Statistical Process Control (SPC) is the most commonly used method for process monitoring, in particular making distinctions between variations attributed to normal process variability to those caused by ‘special causes’. Most SPC and multivariate SPC (MSPC) methods are parametric in that they make assumptions about the distributional properties and autocorrelation structure of in-control process parameters, and, if satisfied, are effective in managing false alarms/-positives and false- negatives. However, when processes do not satisfy these assumptions, the effectiveness of SPC methods is compromised. Several non-parametric control charts based on sequential ranks of data depth measures have been proposed in the literature, but their development and implementation have been rather slow in industrial process control. Several non-parametric control charts based on machine learning principles have also been proposed in the literature to overcome some of these limitations. However, unlike conventional SPC methods, these non-parametric methods require event data from each out-of-control process state for effective model building. The paper presents a new non-parametric multivariate control chart based on kernel distance that overcomes these limitations by employing the notion of one-class classification based on support vector principles. The chart is non-parametric in that it makes no assumptions regarding the data probability density and only requires ‘normal’ or in-control data for effective representation of an in-control process. It does, however, make an explicit provision to incorporate any available data from out-of-control process states. Experimental evaluation on a variety of benchmarking datasets suggests that the proposed chart is effective for process mo