Deep MDP: A Modular Framework for Multi-Object Tracking

This paper presents a fast and modular framework for Multi-Object Tracking (MOT) based on the Markov descision process (MDP) tracking-by-detection paradigm. It is designed to allow its various functional components to be replaced by custom-designed alternatives to suit a given application. An interactive GUI with integrated object detection, segmentation, MOT and semi-automated labeling is also provided to help make it easier to get started with this framework. Though not breaking new ground in terms of performance, Deep MDP has a large code-base that should be useful for the community to try out new ideas or simply to have an easy-to-use and easy-to-adapt system for any MOT application. Deep MDP is available at https://github.com/abhineet123/deep_mdp

Weak Lensing Effect on CMB in the Presence of a Dipole Anisotropy

We investigate weak lensing effect on cosmic microwave background (CMB) in the presence of dipole anisotropy. The approach of flat-sky approximation is considered. We determine the functions $\sigma_0^2$ and $\sigma_2^2$ that appear in expressions of the lensed CMB power spectrum in the presence of a dipole anisotropy. We determine the correction to B-mode power spectrum which is found to be appreciable at low multipoles ($l$). However, the temperature and E-mode power spectrum are not altered significantly.Comment: 9 page

Quintessential Inflation in a thawing realization

We study quintessential inflation with an inverse hyperbolic type potential $V(\phi) = {V_0}/{\cosh \left( {\phi^n}/{\lambda^n} \right)}$, where $V_0$, $\lambda$ and "n" are parameters of the theory. We obtain a bound on $\lambda$ for different values of the parameter n. The spectral index and the tensor-to-scalar-ratio fall in the $1 \sigma$ bound given by the Planck 2015 data for $n \geq 5$ for certain values of $\lambda$. However for $3 \leq n < 5$ there exist values of $\lambda$ for which the spectral index and the tensor-to-scalar-ratio fall only within the $2 \sigma$ bound of the Planck data. Furthermore, we show that the scalar field with the given potential can also give rise to late time acceleration if we invoke the coupling to massive neutrino matter. We also consider the instant preheating mechanism with Yukawa interaction and put bounds on the coupling constants for our model using the nucleosynthesis constraint on relic gravity waves produced during inflation.Comment: 11 page

Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping

This paper presents a novel real-time method for tracking salient closed boundaries from video image sequences. This method operates on a set of straight line segments that are produced by line detection. The tracking scheme is coherently integrated into a perceptual grouping framework in which the visual tracking problem is tackled by identifying a subset of these line segments and connecting them sequentially to form a closed boundary with the largest saliency and a certain similarity to the previous one. Specifically, we define a new tracking criterion which combines a grouping cost and an area similarity constraint. The proposed criterion makes the resulting boundary tracking more robust to local minima. To achieve real-time tracking performance, we use Delaunay Triangulation to build a graph model with the detected line segments and then reduce the tracking problem to finding the optimal cycle in this graph. This is solved by our newly proposed closed boundary candidates searching algorithm called "Bidirectional Shortest Path (BDSP)". The efficiency and robustness of the proposed method are tested on real video sequences as well as during a robot arm pouring experiment.Comment: 7 pages, 8 figures, The 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) submission ID 103

Philanthropy for Impact in West Bengal

The paper highlights West Bengal's development performance vis-a-vis other Indian states in the following focus areas: Education, Health, Nutrition, WASH, Livelihood, Environment and Gender. Apart from examining trends, gaps, assets and intra-state disparities, the paper also provides a glimpse of the solution ecosystem in the state as well as philanthropic funding flows from various quarters including government and CSR

Towards Early Prediction of Human iPSC Reprogramming Success

This paper presents advancements in automated early-stage prediction of the success of reprogramming human induced pluripotent stem cells (iPSCs) as a potential source for regenerative cell therapies.The minuscule success rate of iPSC-reprogramming of around $0.01$ to $0.1$ makes it labor-intensive, time-consuming, and exorbitantly expensive to generate a stable iPSC line. Since that requires culturing of millions of cells and intense biological scrutiny of multiple clones to identify a single optimal clone. The ability to reliably predict which cells are likely to establish as an optimal iPSC line at an early stage of pluripotency would therefore be ground-breaking in rendering this a practical and cost-effective approach to personalized medicine. Temporal information about changes in cellular appearance over time is crucial for predicting its future growth outcomes. In order to generate this data, we first performed continuous time-lapse imaging of iPSCs in culture using an ultra-high resolution microscope. We then annotated the locations and identities of cells in late-stage images where reliable manual identification is possible. Next, we propagated these labels backwards in time using a semi-automated tracking system to obtain labels for early stages of growth. Finally, we used this data to train deep neural networks to perform automatic cell segmentation and classification. Our code and data are available at https://github.com/abhineet123/ipsc_prediction.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:01