SEMPART: Self-supervised Multi-resolution Partitioning of Image Semantics

Accurately determining salient regions of an image is challenging when labeled data is scarce. DINO-based self-supervised approaches have recently leveraged meaningful image semantics captured by patch-wise features for locating foreground objects. Recent methods have also incorporated intuitive priors and demonstrated value in unsupervised methods for object partitioning. In this paper, we propose SEMPART, which jointly infers coarse and fine bi-partitions over an image's DINO-based semantic graph. Furthermore, SEMPART preserves fine boundary details using graph-driven regularization and successfully distills the coarse mask semantics into the fine mask. Our salient object detection and single object localization findings suggest that SEMPART produces high-quality masks rapidly without additional post-processing and benefits from co-optimizing the coarse and fine branches

Effect of Air Pollution on the Occurrences and Death of COVID-19

Air contamination continues to be the leading environmental risk factor for all causes of death, leading to substantial years of lives and economic decline adapted to incapacity increased deaths in air pollution in past pandemics, in 1918, Spanish Flu and in 2003 with SARS-CoV-1. The host susceptibility and respiratory virulence are increased and viral clearance is decreased. Therefore, there is a question about the effect of air contamination on the current 2019 coronavirus pandemic (COVID-19). History and research have until now been concerned with the huge potential consequences of the COVID-19 air pollution pandemic. In order to validate this correlation, more epidemiological and environmental research is necessary. Moreover, countries must leverage air emissions reduction funds to benefit their wellbeing and enhance their possible impact on future pandemics

Performance Comparison of A New Non-RSSI Based Wireless Transmission Power Control Protocol with RSSI Based Methods:Experimentation with Real World Data

In this paper, simulations with MATLAB are used to compare the performance of a RSSI-based output power control with non-RSSI based adaptive power in terms of saving energy and extending the lifetime of battery powered wireless sensor nodes. This non-RSSI (received signal strength indicator) based adaptive power control algorithm does not use RSSI side information to estimate the link quality. The non-RSSI based approach has a unique methodology to choose the appropriate power level. It has drop-off algorithm that enables it to come back from a higher to a lower power level when deemed necessary. The performance parameters are compared with the RSSI-based adaptive power control algorithm and fixed power transmission. In order to evaluate the protocols in the real world scenarios, RSSI data from different indoor radio environments are collected. In simulation, these RSSI values are used as an input to the RSSI based power control algorithm to calculate the packet success rates and the energy expenditures. In this paper we present extensive analysis of the simulation results to find out the advantages and limitations of the non-RSSI based adaptive power control algorithm under different channel conditions

Qsparse-local-SGD: Communication Efficient Distributed SGD with Quantization, Sparsification, and Local Computations

Large scale distributed optimization has become increasingly important with the emergence of edge computation architectures such as in the federated learning setup, where large amounts of data, possibly of a secure nature and generated in an online manner can be massively distributed across personal devices. A key bottleneck for many such large-scale problems is in the communication overhead of exchanging information between devices over bandwidth limited networks as well as in the unreliability of communication for distributed optimization. The existing approaches propose to mitigate these bottlenecks either by using different forms of compression or by computing local models and mixing them iteratively. In this thesis we first propose a novel class of highly communication efficient operators that employ stochastic and deterministic quantization with aggressive sparsification such as Top-k in the form of a composed operator. Furthermore, in federated learning one can use local computations to reduce communication. Using such a framework, we incorporate local iterations into our algorithm which allows the communication to be infrequent and possibly asynchronous thereby enabling significantly reduced communication. Putting them together we have distributed Qsparse-local-SGD for federated learning for which our analysis demonstrates convergence rates matching vanilla distributed SGD where we observe that quantization and sparsification are almost for free for smooth functions, both non-convex and convex. We characterize the asymptotic allowable limits of local iterations for synchronous and asynchronous implementations of Qsparse-local-SGD, so as to harness both the distributed processing gains as well as the benefits of quantization, sparsification and local computations. Our numerics demonstrate that Qsparse-local-SGD combines the bit savings of our composed operators, as well as local computations, thereby outperforming the cases where these techniques are individually used. We use it to train ResNet-50 on ImageNet, as well as a softmax multi-class classifier on MNIST, resulting in significant savings over the state-of-the-art, in the number of bits transmitted to reach target accuracy