SVD Factorization for Tall-and-Fat Matrices on Map/Reduce Architectures

We demonstrate an implementation for an approximate rank-k SVD factorization, combining well-known randomized projection techniques with previously implemented map/reduce solutions in order to compute steps of the random projection based SVD procedure, such QR and SVD. We structure the problem in a way that it reduces to Cholesky and SVD factorizations on $k \times k$ matrices computed on a single machine, greatly easing the computability of the problem.Comment: There are mistakes in the approac

Temporal Continuity Based Unsupervised Learning for Person Re-Identification

Person re-identification (re-id) aims to match the same person from images taken across multiple cameras. Most existing person re-id methods generally require a large amount of identity labeled data to act as discriminative guideline for representation learning. Difficulty in manually collecting identity labeled data leads to poor adaptability in practical scenarios. To overcome this problem, we propose an unsupervised center-based clustering approach capable of progressively learning and exploiting the underlying re-id discriminative information from temporal continuity within a camera. We call our framework Temporal Continuity based Unsupervised Learning (TCUL). Specifically, TCUL simultaneously does center based clustering of unlabeled (target) dataset and fine-tunes a convolutional neural network (CNN) pre-trained on irrelevant labeled (source) dataset to enhance discriminative capability of the CNN for the target dataset. Furthermore, it exploits temporally continuous nature of images within-camera jointly with spatial similarity of feature maps across-cameras to generate reliable pseudo-labels for training a re-identification model. As the training progresses, number of reliable samples keep on growing adaptively which in turn boosts representation ability of the CNN. Extensive experiments on three large-scale person re-id benchmark datasets are conducted to compare our framework with state-of-the-art techniques, which demonstrate superiority of TCUL over existing methods

Task Indicating Transformer for Task-conditional Dense Predictions

The task-conditional model is a distinctive stream for efficient multi-task learning. Existing works encounter a critical limitation in learning task-agnostic and task-specific representations, primarily due to shortcomings in global context modeling arising from CNN-based architectures, as well as a deficiency in multi-scale feature interaction within the decoder. In this paper, we introduce a novel task-conditional framework called Task Indicating Transformer (TIT) to tackle this challenge. Our approach designs a Mix Task Adapter module within the transformer block, which incorporates a Task Indicating Matrix through matrix decomposition, thereby enhancing long-range dependency modeling and parameter-efficient feature adaptation by capturing intra- and inter-task features. Moreover, we propose a Task Gate Decoder module that harnesses a Task Indicating Vector and gating mechanism to facilitate adaptive multi-scale feature refinement guided by task embeddings. Experiments on two public multi-task dense prediction benchmarks, NYUD-v2 and PASCAL-Context, demonstrate that our approach surpasses state-of-the-art task-conditional methods.Comment: Accepted by ICASSP 202

Conducting polymer composites: Polypyrrole and poly (vinyl chloride-vinyl acetate) copolymer

Composites of a polypyrrole (PPy) and poly (vinyl chloride-vinyl acetate) copolymer (PVC-PVA) were prepared both chemically and electrochemically. An insulating polymer was retained in the blend and the thermal stability of the polymer was enhanced by polymerizing pyrrole into the host matrix in both cases. The composites prepared electrochemically gave the best results in terms of conductivity and air stability. © 1997 John Wiley * Sons, Inc

Which index is more affected by CDS premium and VIX index : BIST-30 or Participation-30?

PURPOSE: This study aims at determining the existence and, if any, the extent of comparative effects of the CDS premium and the VIX index on the BIST-30 and the Participation-30 indices before and during the pandemic.METHODOLOGY: The study explores the relationships of the CDS premium and VIX index to the BIST-30 index and the Participation-30 index for two time periods, as pre-pandemic and pandemic. The date range is set as 02.01.2018-10.03.2020 for the pre-pandemic period and as 11.03.2020-31.12.2021 for the pandemic period. Following the Johansen cointegration and ARDL tests employed to detect the long run relationships between the variables, FMOLS regression tests were used to determine the effect sizes.RESULTS: As a result of the cointegration tests, long-term cointegration relationships of both the BIST-30 and the Participation-30 indices with the variables of the CDS premiums and the VIX index were determined before and during the pandemic period. FMOLS regression results posited that the VIX index had greater effect on the Participation 30 index in both periods.ORIGINALITY AND PRACTICAL IMPLICATIONS: The fact that the literature review does not reveal the existence of any study providing the comparative effects of the CDS premiums and the VIX index on both the BIST-30 and Participation-30 indices contributes to the originality of this paper.peer-reviewe

G.R. Little Library Newsletter 2020

G.R. Little Library Newsletter 2019. Editor: Nurhak Tuncer Bayraml