PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalization

Synthetic data offers the promise of cheap and bountiful training data for settings where lots of labeled real-world data for tasks is unavailable. However, models trained on synthetic data significantly underperform on real-world data. In this paper, we propose Proportional Amplitude Spectrum Training Augmentation (PASTA), a simple and effective augmentation strategy to improve out-of-the-box synthetic-to-real (syn-to-real) generalization performance. PASTA involves perturbing the amplitude spectrums of the synthetic images in the Fourier domain to generate augmented views. We design PASTA to perturb the amplitude spectrums in a structured manner such that high-frequency components are perturbed relatively more than the low-frequency ones. For the tasks of semantic segmentation (GTAV to Real), object detection (Sim10K to Real), and object recognition (VisDA-C Syn to Real), across a total of 5 syn-to-real shifts, we find that PASTA outperforms more complex state-of-the-art generalization methods while being complementary to the same.Comment: Code: https://github.com/prithv1/PAST

We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline

There has been abundant work in unsupervised domain adaptation for semantic segmentation (DAS) seeking to adapt a model trained on images from a labeled source domain to an unlabeled target domain. While the vast majority of prior work has studied this as a frame-level Image-DAS problem, a few Video-DAS works have sought to additionally leverage the temporal signal present in adjacent frames. However, Video-DAS works have historically studied a distinct set of benchmarks from Image-DAS, with minimal cross-benchmarking. In this work, we address this gap. Surprisingly, we find that (1) even after carefully controlling for data and model architecture, state-of-the-art Image-DAS methods (HRDA and HRDA+MIC) outperform Video-DAS methods on established Video-DAS benchmarks (+14.5 mIoU on Viper$\rightarrow$CityscapesSeq, +19.0 mIoU on Synthia$\rightarrow$CityscapesSeq), and (2) naive combinations of Image-DAS and Video-DAS techniques only lead to marginal improvements across datasets. To avoid siloed progress between Image-DAS and Video-DAS, we open-source our codebase with support for a comprehensive set of Video-DAS and Image-DAS methods on a common benchmark. Code available at https://github.com/SimarKareer/UnifiedVideoDAComment: TMLR 202

InCl3-​assisted synthesis and cytotoxic studies of some novel heteroaryl thiazoles

Heteroaryl thiazoles were synthesized by the Hantzsch reaction of various α-​bromoketones with aryl thioureas using InCl3 as a catalyst in a shorter reaction time. The synthesized compds. were characterized and screened for their in-​vitro cytotoxic activity against DAL and EAC cells. Compd. I was found to be most effective against DAL cell lines with IC50 value of 15.76 μg​/mL. Compd. II was found to be most effective against EAC cells with IC50 value of 28.73 μg​/mL

ChargeCache: Reducing DRAM Latency by Exploiting Row Access Locality

22nd IEEE International Symposium on High-Performance Computer Architecture (HPCA) (2016 : Barcelona, SPAIN)DRAM latency continues to be a critical bottleneck for system performance. In this work, we develop a low-cost mechanism, called ChargeCache, that enables faster access to recently-accessed rows in DRAM, with no modifications to DRAM chips. Our mechanism is based on the key observation that a recently-accessed row has more charge and thus the following access to the same row can be performed faster. To exploit this observation, we propose to track the addresses of recently-accessed rows in a table in the memory controller. If a later DRAM request hits in that table, the memory controller uses lower timing parameters, leading to reduced DRAM latency. Row addresses are removed from the table after a specified duration to ensure rows that have leaked too much charge are not accessed with lower latency. We evaluate ChargeCache on a wide variety of workloads and show that it provides significant performance and energy benefits for both single-core and multi-core systems

Ataxia-Telangiectasia Mutated Loss-of-Function Displays Variant and Tissue-Specific Differences across Tumor Types

PURPOSE: Mutations in the ATM gene are common in multiple cancers, but clinical studies of therapies targeting ATM-aberrant cancers have yielded mixed results. Refinement of ATM loss of function (LOF) as a predictive biomarker of response is urgently needed. EXPERIMENTAL DESIGN: We present the first disclosure and preclinical development of a novel, selective ATR inhibitor, ART0380, and test its antitumor activity in multiple preclinical cancer models. To refine ATM LOF as a predictive biomarker, we performed a comprehensive pan-cancer analysis of ATM variants in patient tumors and then assessed the ATM variant-to-protein relationship. Finally, we assessed a novel ATM LOF biomarker approach in retrospective clinical data sets of patients treated with platinum-based chemotherapy or ATR inhibition. RESULTS: ART0380 had potent, selective antitumor activity in a range of preclinical cancer models with differing degrees of ATM LOF. Pan-cancer analysis identified 10,609 ATM variants in 8,587 patient tumors. Cancer lineage-specific differences were seen in the prevalence of deleterious (Tier 1) versus unknown/benign (Tier 2) variants, selective pressure for loss of heterozygosity, and concordance between a deleterious variant and ATM loss of protein (LOP). A novel ATM LOF biomarker approach that accounts for variant classification, relationship to ATM LOP, and tissue-specific penetrance significantly enriched for patients who benefited from platinum-based chemotherapy or ATR inhibition. CONCLUSIONS: These data help to better define ATM LOF across tumor types in order to optimize patient selection and improve molecularly targeted therapeutic approaches for patients with ATM LOF cancers

Chatbot for Mental Well-being

With the world becoming more and more competitive every passing day, the number of people suffering from stress and other mental health issues is exponentially increasing. Even school children and senior citizens are becoming victims of stress and pressure in today's world. The mental health of an individual is as important as physical health is. But unfortunately, there is a lack of awareness and proper mental health facilities in today's society, which demands the individuals to fight against their odds without any support. Therefore, we propose a solution in the form of a chatbot that will act as a medium for the users to communicate with, let out their feelings and thus get relieved of the stress that is clogged up. A chatbot application will be available at all times at the user's expense and will also keep track of the user's mood over a span of time