TallyQA: Answering Complex Counting Questions

Most counting questions in visual question answering (VQA) datasets are simple and require no more than object detection. Here, we study algorithms for complex counting questions that involve relationships between objects, attribute identification, reasoning, and more. To do this, we created TallyQA, the world's largest dataset for open-ended counting. We propose a new algorithm for counting that uses relation networks with region proposals. Our method lets relation networks be efficiently used with high-resolution imagery. It yields state-of-the-art results compared to baseline and recent systems on both TallyQA and the HowMany-QA benchmark.Comment: To appear in AAAI 2019 ( To download the dataset please go to http://www.manojacharya.com/

Does relative deprivation induce migration?: evidence from Sub-Saharan Africa

This analysis revisits the decades-old relative deprivation theory of migration. In contrast to the traditional view that migration is driven by absolute income maximization, we test whether relative deprivation induces migration in the context of sub-Saharan Africa. Taking advantage of the internationally comparable longitudinal data from integrated household and agriculture surveys from Tanzania, Ethiopia, Malawi, Nigeria, and Uganda, we use panel fixed effects to estimate the effects of relative deprivation on migration decisions. Using per capita consumption expenditure and multidimensional wealth index as well-being measures, we find that a household’s migration decision is based not only on its absolute well-being level but also on the relative position of the household in the well-being distribution of the community in which it resides. We also discover that the effect of relative deprivation on migration is amplified in rural, agricultural, and male-headed households. Results are robust to alternative specifications including the use of Hausman Taylor Instrumental Variable (HTIV) estimator and pooled data across the five countries. Results confirm that the “migration-relative deprivation” relationship also holds in the context of sub-Saharan Africa. We argue that policies designed to check rural–urban migration through rural transformation and poverty reduction programs should use caution because such programs can increase economic inequality, which further increases migration flow

One-bit Compressed Sensing in the Presence of Noise

Many modern real-world systems generate large amounts of high-dimensional data stressing the available computing and signal processing systems. In resource-constrained settings, it is desirable to process, store and transmit as little amount of data as possible. It has been shown that one can obtain acceptable performance for tasks such as inference and reconstruction using fewer bits of data by exploiting low-dimensional structures on data such as sparsity. This dissertation investigates the signal acquisition paradigm known as one-bit compressed sensing (one-bit CS) for signal reconstruction and parameter estimation. We first consider the problem of joint sparse support estimation with one-bit measurements in a distributed setting. Each node observes sparse signals with the same but unknown support. The goal is to minimize the probability of error of support estimation. First, we study the performance of maximum likelihood (ML) estimation of the support set from one-bit compressed measurements when all these measurements are available at the fusion center. We provide a lower bound on the number of one-bit measurements required per node for vanishing probability of error. Though the ML estimator is optimal, its computational complexity increases exponentially with the signal dimension. So, we propose computationally tractable algorithms in a centralized setting. Further, we extend these algorithms to a decentralized setting where each node can communicate only with its one-hop neighbors. The proposed method shows excellent estimation performance even in the presence of noise. In the second part of the dissertation, we investigate the problem of sparse signal reconstruction from noisy one-bit compressed measurements using a signal that is statistically dependent on the compressed signal as an aid. We refer to this signal as side-information. We consider a generalized measurement model of one-bit CS where noise is assumed to be added at two stages of the measurement process- a) before quantizationand b) after quantization. We model the noise before quantization as additive white Gaussian noise and the noise after quantization as a sign-flip noise generated from a Bernoulli distribution. We assume that the SI at the receiver is noisy. The noise in the SI can be either in the support or in the amplitude, or both. This nature of the noise in SI suggests that the noise has a sparse structure. We use additive independent and identically distributed Laplacian noise to model such sparse nature of the noise. In this setup, we develop tractable algorithms that approximate the minimum mean square error (MMSE) estimator of the signal. We consider the following three different SI-based scenarios: 1. The side-information is assumed to be a noisy version of the signal. The noise is independent of the signal and follows the Laplacian distribution. We do not assume any temporal dependence in the signal.2. The signal exhibits temporal dependencies between signals at the current time instant and the previous time instant. The temporal dependence is modeled using the birth-death-drift (BDD) model. The side-information is a noisy version of the previous time instant signal, which is statistically dependent on the signal as defined by the BDD model. 3. The SI available at the receiver is heterogeneous. The signal and side-information are from different modalities and may not share joint sparse representation. We assume that the SI and the sparse signal are dependent and use the Copula function to model the dependence. In each of these scenarios, we develop generalized approximate message passing-based algorithms to approximate the minimum mean square error estimate. Numerical results show the effectiveness of the proposed algorithm. In the final part of the dissertation, we propose two one-bit compressed sensing reconstruction algorithms that use a deep neural network as a prior on the signal. In the first algorithm, we use a trained Generative model such as Generative Adversarial Networks and Variational Autoencoders as a prior. This trained network is used to reconstruct the compressed signal from one-bit measurements by searching over its range. We provide theoretical guarantees on the reconstruction accuracy and sample complexity of the presented algorithm. In the second algorithm, we investigate an untrained neural network architecture so that it acts as a good prior on natural signals such as images and audio. We formulate an optimization problem to reconstruct the signal from one-bit measurements using this untrained network. We demonstrate the superior performance of the proposed algorithms through numerical results. Further, in contrast to competing model-based algorithms, we demonstrate that the proposed algorithms estimate both direction and magnitude of the compressed signal from one-bit measurements

Tripartite Interactions of Legumes with Arbuscular Mycorrhizal Fungi and Rhizobial Bacteria: Insight into Plant Growth, Seed Yield, and Resource Exchange

Under natural conditions, legumes, such as alfalfa (Medicago) and soybean (Glycine max) are colonized with arbuscular mycorrhizal (AM) fungi and rhizobial bacteria forming tripartite interactions. Legumes are important crop species due to their high nutritional and economic values. Most of the previous literatures focused on experiments with an individual symbiont: either AM fungi or rhizobial bacteria, but not with both symbionts at the same time, thus our current understanding of resource exchange in tripartite interactions is limited. It has been reported that AM fungi primarily provide phosphate (P), nitrogen (N), and other nutritional and non-nutritional benefits while rhizobial bacteria solely supply N to their host plant. In return for the nutritional benefits conferred by root symbionts, the host plant reciprocally allocates a significant proportion of its photosynthetic carbon (C) resources to its root symbionts. In tripartite interactions, AM fungi and rhizobial bacteria facilitate synergistically for plant growth and nutrient acquisition. However, how the host plant allocates its C resources to both symbionts in tripartite interactions is still poorly studied. More attention has been paid to AM fungal benefits in terms of nutrient acquisition and growth response for soybean plants under the controlled conditions using laboratory produced AM fungal inoculum. Due to technical difficulties to produce in a large quantity of AM inoculum, it is not pragmatic to apply this lab-based AM fungi for agronomic purpose in a larger area in the field conditions. However, effects of commercially available AM fungal additives on soybean cultivars in the greenhouse and field conditions have not well reported before despite importance of AM fungi on soybean. To address these questions, we conducted different experiments in a pot system, split root system with and without fungal access to exogenous N in a hyphal compartment. Medicago truncatula was kept either non-inoculated as control (none), or with only AM fungi, or with only rhizobial bacteria, or with dual symbionts (both with AM fungi and rhizobial bacteria) with different nutrient supply conditions. To tract the C allocation to different symbiotic partners, we labelled/exposed the host shoot with 13CO2. Similarly, to test how does host plant change its strategy for C allocation to symbionts if AM fungus has an exogenous source of N, we provided 15NH4Cl in the hyphal compartment to which only AM fungus had access not to host root. Moreover, in association with C allocation to symbiotic root halves, we examined gene expression of several plant transporters of Sucrose Uptake Transporter (SUT) and Sugars Will Eventually be Exported Transporter (SWEET) family. We also analysed P and N acquisition of host tissues in association with plant growth response. We used four different soybean cultivars in separate experiments that usually use by farmers for the seed production in this region of Upper Midwest. These soybean cultivars were either non-inoculated control (none), or inoculated with only commercially available AM additives, or with only rhizobial bacteria, or with both AM fungi and rhizobial bacteria (dual inoculation). Soybean plant growth response in association with plant nutrient uptake, and seed yield was compared between control and AM plants of greenhouse and field condition experiments. Tripartite interactions favor the growth response in association with higher P and N uptake of the host plant in nutrient limited soil conditions. We found that the nutrient demand of the host, and the fungal access to nutrients are important factors that control the carbon allocation to individual root symbionts in tripartite interactions. Plant allocated more carbon to rhizobia under nitrogen demand, but more carbon to the fungal partner when exogenous nitrogen was available. The expression of genes for several SUTs and SWEETs transporters was consistent with the observed changes in carbon allocation. Exploring the full yield potential of legumes will require insights in how host plants regulate the substantial carbon costs of these interactions as host plant invest substantial amount of energy and resources to produce carbon during photosynthetic process. We observed soybean plant growth and seed yield was significantly higher with only AM inoculation than either control or only rhizobial alone inoculation. Moreover, the difference in seed yield of AM additives plants was notably higher in limited supply of P and N both in greenhouse and field conditions. Interestingly, seed yield of AM inoculated soybean was similar with or without fertilizer application in the field conditions. Different soybean cultivars had different response to AM fungal inocula for plant growth and seed yield. Among commercial AM fungal additives, MycoApply outperformed other two commercial inocula for plant growth and seed yield. Taken together, tripartite interactions of legumes with AM fungi and rhizobial bacteria facilitate for the plant growth and seed yield in limited soil nutrient conditions indicating tripartite interactions may have a bigger potential role to maintain sustainable agriculture