Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems

One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and hence domain generalization error. To overcome, we propose to learn a meta future gradient generator that forecasts the gradient information of the future data distribution for training so that the recommendation model can be trained as if we were able to look ahead at the future of its deployment. Compared with Batch Update, a widely used paradigm, our theory suggests that the proposed algorithm achieves smaller temporal domain generalization error measured by a gradient variation term in a local regret. We demonstrate the empirical advantage by comparing with various representative baselines

RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure

We present RecD (Recommendation Deduplication), a suite of end-to-end infrastructure optimizations across the Deep Learning Recommendation Model (DLRM) training pipeline. RecD addresses immense storage, preprocessing, and training overheads caused by feature duplication inherent in industry-scale DLRM training datasets. Feature duplication arises because DLRM datasets are generated from interactions. While each user session can generate multiple training samples, many features' values do not change across these samples. We demonstrate how RecD exploits this property, end-to-end, across a deployed training pipeline. RecD optimizes data generation pipelines to decrease dataset storage and preprocessing resource demands and to maximize duplication within a training batch. RecD introduces a new tensor format, InverseKeyedJaggedTensors (IKJTs), to deduplicate feature values in each batch. We show how DLRM model architectures can leverage IKJTs to drastically increase training throughput. RecD improves the training and preprocessing throughput and storage efficiency by up to 2.48x, 1.79x, and 3.71x, respectively, in an industry-scale DLRM training system.Comment: Published in the Proceedings of the Sixth Conference on Machine Learning and Systems (MLSys 2023

High Precision Measurements of Interstellar Dispersion Measure with the upgraded GMRT

Pulsar radio emission undergoes dispersion due to the presence of free electrons in the interstellar medium (ISM). The dispersive delay in the arrival time of pulsar signal changes over time due to the varying ISM electron column density along the line of sight. Correcting for this delay accurately is crucial for the detection of nanohertz gravitational waves using Pulsar Timing Arrays. In this work, we present in-band and inter-band DM estimates of four pulsars observed with uGMRT over the timescale of a year using two different template alignment methods. The DMs obtained using both these methods show only subtle differences for PSR 1713+0747 and J1909$-$3744. A considerable offset is seen in the DM of PSR J1939+2134 and J2145$-$0750 between the two methods. This could be due to the presence of scattering in the former and profile evolution in the latter. We find that both methods are useful but could have a systematic offset between the DMs obtained. Irrespective of the template alignment methods followed, the precision on the DMs obtained is about $10^{-3}$ pc cm$^{-3}$ using only BAND3 and $10^{-4}$ pc cm$^{-3}$ after combining data from BAND3 and BAND5 of the uGMRT. In a particular result, we have detected a DM excess of about $5\times10^{-3}$ pc cm$^{-3}$ on 24 February 2019 for PSR J2145$-$0750. This excess appears to be due to the interaction region created by fast solar wind from a coronal hole and a coronal mass ejection (CME) observed from the Sun on that epoch. A detailed analysis of this interesting event is presented.Comment: 11 pages, 6 figures, 2 tables. Accepted by A&

Microscaling Data Formats for Deep Learning

Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and integer types for individual elements. MX formats balance the competing needs of hardware efficiency, model accuracy, and user friction. Empirical results on over two dozen benchmarks demonstrate practicality of MX data formats as a drop-in replacement for baseline FP32 for AI inference and training with low user friction. We also show the first instance of training generative language models at sub-8-bit weights, activations, and gradients with minimal accuracy loss and no modifications to the training recipe

Micro-scheduling and its interaction with cache partitioning

The thesis explores the sources of energy inefficiency in asymmetric multi- core architectures where energy efficiency is measured by the energy-delay squared product. The insights gathered from this study drive the development of optimized thread scheduling and coordinated cache management strategies in an important class of asymmetric shared memory architectures. The proposed techniques are founded on well known mathematical optimization techniques yet are lightweight enough to be implemented in practical systems.M.S.Committee Chair: Yalamanchili, Sudhakar; Committee Member: George Riley; Committee Member: Kim, Hyesoo

Importance and Calculation of Pharyngeal Spaces and Hyoid Position Before and After Orthognathic Surgery

Aim: The purpose of the study was to examine the changes in dimension of pharyngeal airway spaces (PAS) and hyoid bone position after surgery in class II and class III skeletal malocclusion. Methods: This study included 50 patients of class II and class III skeletal and dental malocclusion who had undergone orthognathic surgery. The pre and post treatment lateral cephalograms were taken to calculate upper and lower air way spaces and position of hyoid bone was also measured before and after the surgery. Results: Changes in upper and lower airway spaces and alteration in the location of hyoid bone was seen in Class II cases only. Conclusion: Maintaining of lower air way spaces in surgical class III cases of mandibular set back and position of hyoid bone is very important

Comparative Efficacy of different Funnel Diameters in Light Traps against Major Phototactic Insect Pests of rabi Season

Light traps have been long used to reduce and manage insect populations. Although there are numerous types and designs of insect light traps. Four distinct light traps were employed for the study and positioned within the BSP Unit Adhartal, JNKVV Jabalpur from mid-November 2022 to mid-April 2023 for the study of the efficiency test of different funnel diameters of light traps based on performance. Comparative studies of light trap catches revealed that 50 cm funnel diameter light traps have given higher response in following species for Helicoverpa armigera (13.91%), Agrotis ipsilon (14.36%), Creatonotus gengis (13.97%), Gryllus bamaculatus (22.26%), Gryllotalpa orientalis (45.30%), Nezara viridula (9.82%), Amata cyssea (13.35%), Asota ficus (7.01%), Perina nuda (21.78%), and Thysanoplusia orichalcea (22.75%). There was a statistically significant increase in trap catches in the 50 cm funnel diameter light traps compared to the 40 cm funnel diameter traps. However, there was a statistically non-significant difference between the 40 cm and 50 cm funnel diameter traps for Spodoptera litura and Theretra oldenlandiae species, although the trap catches were numerically higher in the 50 cm funnel diameter traps because 50 cm funnel diameter provide large catchment area for insects

Anti-Inflammatory and Anticancer Properties of Birch Bark-Derived Betulin: Recent Developments

Birch tree bark-derived betulin has attracted scientific interest already for several centuries, being one of the first natural products identified from plants. However, the cellular events regulated by betulin and precise molecular mechanisms under these processes have been begun to be understood only recently. Today, we know that betulin can exert important anticancer activities through modulation of diverse cellular pathways. In this review article, betulin-regulated molecular signaling is unraveled and presented with a special focus on its participation in anti-inflammatory processes, especially by modulating nuclear factor-kappa B (NF-kappa B), prostaglandin/COX, and nuclear factor erythroid2-related factor 2 (Nrf2)-mediated cascades. By regulating these diverse pathways, betulin can not only affect the development and progression of different cancers, but also enhance the antitumor action of traditional therapeutic modalities. It is expected that by overcoming the low bioavailability of betulin by encapsulating it into nanocarriers, this promising natural compound may provide novel possibilities for targeting inflammation-related cancers