Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks

We investigate a new paradigm that uses differentiable SLAM architectures in a self-supervised manner to train end-to-end deep learning models in various LiDAR based applications. To the best of our knowledge there does not exist any work that leverages SLAM as a training signal for deep learning based models. We explore new ways to improve the efficiency, robustness, and adaptability of LiDAR systems with deep learning techniques. We focus on the potential benefits of differentiable SLAM architectures for improving performance of deep learning tasks such as classification, regression as well as SLAM. Our experimental results demonstrate a non-trivial increase in the performance of two deep learning applications - Ground Level Estimation and Dynamic to Static LiDAR Translation, when used with differentiable SLAM architectures. Overall, our findings provide important insights that enhance the performance of LiDAR based navigation systems. We demonstrate that this new paradigm of using SLAM Loss signal while training LiDAR based models can be easily adopted by the community.Comment: 15 pages,6 Tables, 3 figures. Accepted at BMVC 202

The impact of transistor aging on the reliability of level shifters in nano-scale CMOS technology

On-chip level shifters are the interface between parts of an Integrated Circuit (IC) that operate in different voltage levels. For this reason, they are indispensable blocks in Multi-Vdd System-on-Chips (SoCs). In this paper, we present a comprehensive analysis of the effects of Bias Temperature Instability (BTI) aging on the delay and the power consumption of level shifters. We evaluate the standard High-to-Low/Low-to-High level shifters, as well as several recently proposed level-shifter designs, implemented using a 32 nm CMOS technology. Through SPICE simulations, we demonstrate that the delay degradation due to BTI aging varies for each level shifter design: it is 83.3% on average and it exceeds 200% after 5 years of operation for the standard Low-to-High and the NDLSs level shifters, which is 10 × higher than the BTI-induced delay degradation of standard CMOS logic cells. Similarly, we show that the examined designs can suffer from an average 38.2% additional power consumption after 5 years of operation that, however, reaches 180% for the standard level-shifter and exceeds 163% for the NDLSs design. The high susceptibility of these designs to BTI is attributed to their differential signaling structure, combined with the very low supply voltage. Moreover, we show that recently proposed level-up shifter design employing a voltage step-down technique are

A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further

IFN-γ/IL-27 axis induces PD-L1 expression in monocyte-derived dendritic cells and restores immune tolerance in CNS autoimmunity

Antigen (Ag)-specific tolerance induction by intravenous (i.v.) injection of high-dose auto-Ags has been explored for therapy of autoimmune diseases, including multiple sclerosis (MS). It is thought that the advantage of such Ag-specific therapy over non-specific immunomodulatory treatments would be selective suppression of a pathogenic immune response without impairing systemic immunity, thus avoiding adverse effects of immunosuppression. Auto-Ag i.v. tolerance induction has been extensively studied in experimental autoimmune encephalomyelitis (EAE), an animal model of MS, and limited clinical trials demonstrated that it is safe and beneficial to a subset of MS patients. Nonetheless, mechanisms of i.v. tolerance induction are incompletely understood, hampering the development of better approaches and their clinical application. Here, we describe a pathway whereby auto-Ag i.v. injected into mice with ongoing clinical EAE induces IFN-γ secretion by auto-Ag-specific CD4+ T cells, triggering IL-27 production by conventional dendritic cells type 1 (cDC1). IL-27 then, via STAT3 activation, induces PD-L1 expression by monocyte-derived DCs (moDCs) in the CNS of mice with EAE. PD-L1 interaction with PD-1 on pathogenic CD4+ T cells leads to their apoptosis/anergy, resulting in disease amelioration. These findings identify a key role of the IFN-γ/IL-27/PD-L1 axis, involving T cells/cDC1/moDCs in the induction of i.v. tolerance

A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks

Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP

An Advertiser Centered Approach to Improve Sponsored Search Effectiveness

Sponsored search is a form of advertising where advertisers pay a search engine to show their ads on the search engine results page. The ads, also known as sponsored results, are chosen and presented to the user in response to a user query alongside organic search results. Sponsored search holds the promise of allowing advertisers to precisely target their ads to the large number of users of a search engine. The rise in use of search engines and the opportunity they provide to target ads using fine- grained criteria has led to a 20% annual growth in sponsored search revenues over the last decade. The targeting criteria chosen by an advertiser for their ads allow a search engine to deliver the ads to the right users. At the same time, it also puts the onus on the advertiser to identify the right ad targeting criteria. In this dissertation, we take a two-pronged approach to improve the effectiveness of sponsored search in delivering value to advertisers and improve the quality of results shown to users. First, we improve the ability of a search engine to interpret the targeting criteria specified by the advertiser. As part of the targeting criteria advertisers submit ad keywords which specify the user queries for which they would like to advertise. We leverage the search engine itself to interpret an ad keyword by submitting the ad keyword as an independent query. Using the search results of the ad keyword associated with an ad we determine if the ad is suitable for the original user query. We then analyze the effectiveness of different targeting strategies followed by advertisers. We develop a simple metric called net acquisition benefit (NAB) that admits comparisons between the efficacy of different ad targeting strategies. Using this metric, we conduct the first large-scale measurement of different targeting strategies used by advertisers--- measured in terms of incremental conversion gains. Considering data from a month in early 2015, we employ NAB to identify cases where these targeting strategies are justifie

Modeling and minimization of PMOS NBTI effect for robust nanometer design

Negative bias temperature instability (NBTI) has become the dominant reliability concern for nanoscale PMOS transistors. In this paper, a predictive model is developed for the degradation of NBTI in both static and dynamic operations. Model scalability and generality are comprehensively verified with experimental data over a wide range of process and bias conditions. By implementing the new model into SPICE for an industrial 90nm technology, key insights are obtained for the development of robust design solutions: (1) the most effective techniques to mitigate the NBTI degradation are VDD tuning, PMOS sizing, and reducing the duty cycle; (2) an optimal V DD exists to minimize the degradation of circuit performance; (3) tuning gate length or the switching frequency has little impact on the NBTI effect; (4) a new switching scenario is identified for worst case timing analysis during NBTI stress