Bayesian Decision Making to Localize Visual Queries in 2D

This report describes our approach for the EGO4D 2023 Visual Query 2D Localization Challenge. Our method aims to reduce the number of False Positives (FP) that occur because of high similarity between the visual crop and the proposed bounding boxes from the baseline's Region Proposal Network (RPN). Our method uses a transformer to determine similarity in higher dimensions which is used as our prior belief. The results are then combined together with the similarity in lower dimensions from the Siamese Head, acting as our measurement, to generate a posterior which is then used to determine the final similarity of the visual crop with the proposed bounding box. Our code is publicly available $\href{https://github.com/s-m-asjad/EGO4D_VQ2D}{here}$.Comment: Report for the EGO4D 2023 Visual Query 2D Localization Challeng

Direct Superpoints Matching for Fast and Robust Point Cloud Registration

Although deep neural networks endow the downsampled superpoints with discriminative feature representations, directly matching them is usually not used alone in state-of-the-art methods, mainly for two reasons. First, the correspondences are inevitably noisy, so RANSAC-like refinement is usually adopted. Such ad hoc postprocessing, however, is slow and not differentiable, which can not be jointly optimized with feature learning. Second, superpoints are sparse and thus more RANSAC iterations are needed. Existing approaches use the coarse-to-fine strategy to propagate the superpoints correspondences to the point level, which are not discriminative enough and further necessitates the postprocessing refinement. In this paper, we present a simple yet effective approach to extract correspondences by directly matching superpoints using a global softmax layer in an end-to-end manner, which are used to determine the rigid transformation between the source and target point cloud. Compared with methods that directly predict corresponding points, by leveraging the rich information from the superpoints matchings, we can obtain more accurate estimation of the transformation and effectively filter out outliers without any postprocessing refinement. As a result, our approach is not only fast, but also achieves state-of-the-art results on the challenging ModelNet and 3DMatch benchmarks. Our code and model weights will be publicly released

On the Critical Role of Ferroelectric Thickness for Negative Capacitance Device-Circuit Interaction

This paper demonstrates the critical role that Ferroelectric (FE) layer thickness (tFE) plays in Negative Capacitance (NC) transistors connecting device and circuit levels together. The study is done through fully-calibrated TCAD simulations for a 14nm FDSOI technology node, exploring the impact of tFE on the figures of merit of n-type and p-type devices, voltage transfer characteristic (VTC) and noise margin of inverter as well as the speed of buffer circuits. First, we analyze the device electrical parameters (e.g., ION, SS, ION/IOFF and Cgg) by varying tFE up to the maximum level at which hysteresis in the I-V characteristic starts. Then, we analyze the deleterious impact of Negative Differential Resistance (NDR), due to the drain to gate coupling, demonstrating how it imposes an additional constraint limiting the maximum tFE. We show the consequences of NDR effects on the VTC and noise margin of inverter, which are essential components for constructing robust clock trees in any chip. We demonstrate how the considerable increase in the gate’s capacitance due to FE seriously degrades the circuit’s performance imposing further constraints limiting the maximum tFE. Further, we analyze the impact of tFE on the SRAM cell static performance metrics such hold noise margin (HNM), read noise margin (RNM) and write noise margin (WNM) at supply voltages of 0.7V and 0.4V. We demonstrate that the HNM and RNM in a NC-FDSOI FET based SRAM cell are higher then those of the baseline FDSOI FET based SRAM cell noise margin and further increase with tFE. However, the WNM in general follows a non monotonic trend w.r.t tFE, and the trend also depends on the supply voltage. Finally, we optimize the design of the SRAM cell considering overall performance metrics. All in all, our analysis provides guidance for device and circuit designers to select the optimal FE thickness for NCFETs in which hysteresis-free operations, reliability, and performance are optimized

AffectEcho: Speaker Independent and Language-Agnostic Emotion and Affect Transfer for Speech Synthesis

Affect is an emotional characteristic encompassing valence, arousal, and intensity, and is a crucial attribute for enabling authentic conversations. While existing text-to-speech (TTS) and speech-to-speech systems rely on strength embedding vectors and global style tokens to capture emotions, these models represent emotions as a component of style or represent them in discrete categories. We propose AffectEcho, an emotion translation model, that uses a Vector Quantized codebook to model emotions within a quantized space featuring five levels of affect intensity to capture complex nuances and subtle differences in the same emotion. The quantized emotional embeddings are implicitly derived from spoken speech samples, eliminating the need for one-hot vectors or explicit strength embeddings. Experimental results demonstrate the effectiveness of our approach in controlling the emotions of generated speech while preserving identity, style, and emotional cadence unique to each speaker. We showcase the language-independent emotion modeling capability of the quantized emotional embeddings learned from a bilingual (English and Chinese) speech corpus with an emotion transfer task from a reference speech to a target speech. We achieve state-of-art results on both qualitative and quantitative metrics

Estimating Emotion Contagion on Social Media via Localized Diffusion in Dynamic Graphs

We present a computational approach for estimating emotion contagion on social media networks. Built on a foundation of psychology literature, our approach estimates the degree to which the perceivers' emotional states (positive or negative) start to match those of the expressors, based on the latter's content. We use a combination of deep learning and social network analysis to model emotion contagion as a diffusion process in dynamic social network graphs, taking into consideration key aspects like causality, homophily, and interference. We evaluate our approach on user behavior data obtained from a popular social media platform for sharing short videos. We analyze the behavior of 48 users over a span of 8 weeks (over 200k audio-visual short posts analyzed) and estimate how contagious the users with whom they engage with are on social media. As per the theory of diffusion, we account for the videos a user watches during this time (inflow) and the daily engagements; liking, sharing, downloading or creating new videos (outflow) to estimate contagion. To validate our approach and analysis, we obtain human feedback on these 48 social media platform users with an online study by collecting responses of about 150 participants. We report users who interact with more number of creators on the platform are 12% less prone to contagion, and those who consume more content of `negative' sentiment are 23% more prone to contagion. We will publicly release our code upon acceptance

FeFET-based Binarized Neural Networks Under Temperature-dependent Bit Errors

Ferroelectric FET (FeFET) is a highly promising emerging non-volatile memory (NVM) technology, especially for binarized neural network (BNN) inference on the low-power edge. The reliability of such devices, however, inherently depends on temperature. Hence, changes in temperature during run time manifest themselves as changes in bit error rates. In this work, we reveal the temperature-dependent bit error model of FeFET memories, evaluate its effect on BNN accuracy, and propose countermeasures. We begin on the transistor level and accurately model the impact of temperature on bit error rates of FeFET. This analysis reveals temperature-dependent asymmetric bit error rates. Afterwards, on the application level, we evaluate the impact of the temperature-dependent bit errors on the accuracy of BNNs. Under such bit errors, the BNN accuracy drops to unacceptable levels when no countermeasures are employed. We propose two countermeasures: (1) Training BNNs for bit error tolerance by injecting bit flips into the BNN data, and (2) applying a bit error rate assignment algorithm (BERA) which operates in a layer-wise manner and does not inject bit flips during training. In experiments, the BNNs, to which the countermeasures are applied to, effectively tolerate temperature-dependent bit errors for the entire range of operating temperature

Socio-economic patterning of cardiometabolic risk factors in rural and peri-urban India: Andhra Pradesh children and parents study (APCAPS).

AIM: To assess the prevalence of cardiometabolic risk factors by socio-economic position (SEP) in rural and peri-urban Indian population. SUBJECTS AND METHODS: Cross-sectional survey of 3,948 adults (1,154 households) from Telangana (2010-2012) was conducted to collect questionnaire-based data, physical measurements and fasting blood samples. We compared the prevalence of risk factors and their clustering by SEP adjusting for age using the Mantel Hansel test. RESULTS: Men and women with no education had higher prevalence of increased waist circumference (men: 8 vs. 6.4 %, P < 0.001; women: 20.9 vs. 12.0 %, P = 0.01), waist-hip ratio (men: 46.5 vs. 25.8 %, P = 0.003; women: 58.8 vs. 29.2 %, P = 0.04) and regular alcohol intake (61.7 vs. 32.5 %, P < 0.001; women: 25.7 vs. 3.8 %, P < 0.001) than educated participants. Unskilled participants had higher prevalence of regular alcohol intake (men: 57.7 vs. 38.7 %, P = 0.001; women: 28.3 vs. 7.3 %, P < 0.001). In contrast, participants with a higher standard of living index had higher prevalence of diabetes (top third vs. bottom third: men 5.2 vs. 3.5 %, P = 0.004; women 5.5 vs. 2.4 %, P = 0.003), hyperinsulinemia (men 29.5 vs. 16.3 %, P = 0.002; women 31.1 vs. 14.3 %, P < 0.001), obesity (men 23.3 vs. 10.6 %, P < 0.001; women 25.9 vs. 12.8 %, P < 0.001), and raised LDL (men 16.8 vs. 11.4 %, P = 0.001; women 21.3 vs. 14.0 %, P < 0.001). CONCLUSIONS: Cardiometabolic risk factors are common in rural India but do not show a consistent association with SEP except for higher prevalence of smoking and regular alcohol intake in lower SEP group. Strategies to address the growing burden of cardiometabolic diseases in urbanizing rural India should be assessed for their potential impact on social inequalities in health

Cohort profile: Andhra Pradesh Children and Parents Study (APCAPS).

The Andhra Pradesh Children and Parents Study (APCAPS) was originally established to study the long-term effects of early-life undernutrition on risk of cardiovascular disease. Its aims were subsequently expanded to include trans-generational influences of other environmental and genetic factors on chronic diseases in rural India. It builds on the Hyderabad Nutrition Trial (HNT) conducted in 1987-90 to compare the effects on birthweight of a protein-calorie supplement for pregnant women and children. The index children of HNT and their mothers were retraced and examined in 2003-05, and the children re-examined as young adults aged 18-21 years in 2009-10. The cohort was expanded to include both parents and siblings of the index children in a recently completed follow-up conducted in 2010-12 (N=∼6225 out of 10,213 participants). Recruitment of the remaining residents of these 29 villages (N=∼55,000) in Ranga Reddy district of Andhra Pradesh is now under way. Extensive data on socio-demographic, lifestyle, medical, anthropometric, physiological, vascular and body composition measures, DNA, stored plasma, and assays of lipids and inflammatory markers on APCAPS participants are available. Details of how to access these data are available from the corresponding author