While several datasets for autonomous navigation have become available in recent years, they tend to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in object or background appearance and strict adherence to traffic rules. We propose IDD, a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes. It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity. Consistent with real driving behaviors, it also identifies new classes such as drivable areas besides the road. We propose a new four-level label hierarchy, which allows varying degrees of complexity and opens up possibilities for new training methods. Our empirical study provides an in-depth analysis of the label characteristics. State-of-theart methods for semantic segmentation achieve much lower accuracies on our dataset, demonstrating its distinction compared to Cityscapes. Finally, we propose that our dataset is an ideal opportunity for new problems such as domain adaptation, few-shot learning and behavior prediction in road scenes.

Physarum polycephalum is a slime mold that is apparently able to solve shortest path problems. A mathematical model has been proposed by Tero et al. (Journal of Theoretical Biology, 244, 2007, pp. 553-564) to describe the feedback mechanism used by the slime mold to adapt its tubular channels while foraging two food sources s(0) and s(1). We prove that, under this model, the mass of the mold will eventually converge to the shortest s(0)-s(1) path of the network that the mold lies on, independently of the structure of the network or of the initial mass distribution. This matches the experimental observations by Tero et al. and can be seen as an example of a "natural algorithm", that is, an algorithm developed by evolution over millions of years.

In recent years, the need for semantic segmentation has arisen across several different applications and environments. However, the expense and redundancy of annotation often limits the quantity of labels available for training in any domain, while deployment is easier if a single model works well across domains. In this paper, we pose the novel problem of universal semi-supervised semantic segmentation and propose a solution framework, to meet the dual needs of lower annotation and deployment costs. In contrast to counterpoints such as fine tuning, joint training or unsupervised domain adaptation, universal semi-supervised segmentation ensures that across all domains: (i) a single model is deployed, (ii) unlabeled data is used, (iii) performance is improved, (iv) only a few labels are needed and (v) label spaces may differ. To address this, we minimize supervised as well as within and cross-domain unsupervised losses, introducing a novel feature alignment objective based on pixel-aware entropy regularization for the latter. We demonstrate quantitative advantages over other approaches on several combinations of segmentation datasets across different geographies (Germany, England, India) and environments (outdoors, indoors), as well as qualitative insights on the aligned representations 1 .

Physarum Polycephalum is a slime mold that is apparently able to solve shortest path problems. A mathematical model has been proposed by biologists to describe the feedback mechanism used by the slime mold to adapt its tubular channels while foraging two food sources s 0 and s 1 . We prove that, under this model, the mass of the mold will eventually converge to the shortest s 0 -s 1 path of the network that the mold lies on, independently of the structure of the network or of the initial mass distribution.This matches the experimental observations by the biologists and can be seen as an example of a "natural algorithm", that is, an algorithm developed by evolution over millions of years.

Recent years have witnessed utilization of modern machine learning approaches for predicting properties of material using available datasets. However, to identify potential candidates for material discovery, one has to systematically...

Efficient CNN designs like ResNets and DenseNet were proposed to improve accuracy vs efficiency trade-offs. They essentially increased the connectivity, allowing efficient information flow across layers. Inspired by these techniques, we propose to model connections between filters of a CNN using graphs which are simultaneously sparse and well connected. Sparsity results in efficiency while well connectedness can preserve the expressive power of the CNNs. We use a well-studied class of graphs from theoretical computer science that satisfies these properties known as Expander graphs. Expander graphs are used to model connections between filters in CNNs to design networks called X-Nets. We present two guarantees on the connectivity of X-Nets: Each node influences every node in a layer in logarithmic steps, and the number of paths between two sets of nodes is proportional to the product of their sizes. We also propose efficient training and inference algorithms, making it possible to train deeper and wider X-Nets effectively.Expander based models give a 4% improvement in accuracy on Mo-bileNet over grouped convolutions, a popular technique, which has the same sparsity but worse connectivity. X-Nets give better performance trade-offs than the original ResNet and DenseNet-BC architectures. We achieve model sizes comparable to state-of-the-art pruning techniques using our simple architecture design, without any pruning. We hope that this work motivates other approaches to utilize results from graph theory to develop efficient network architectures.

We study the complexity of the following problems in the streaming model.Membership testing for DLIN. We show that every language in DLIN can be recognised by a randomized one-pass O(log n) space algorithm with inverse polynomial one-sided error, and by a deterministic p-pass O(n/p) space algorithm. We show that these algorithms are optimal.Membership testing for LL(k). For languages generated by LL(k) grammars with a bound of r on the number of nonterminals at any stage in the left-most derivation, we show that membership can be tested by a randomized one-pass O(r log n) space algorithm with inverse polynomial (in n) one-sided error.Membership testing for DCFL. We show that randomized algorithms as efficient as the ones described above for DLIN and LL(k) (which are subclasses of DCFL) cannot exist for all of DCFL: there is a language in VPL (a subclass of DCFL) for which any randomized p-pass algorithm with error bounded by ǫ < 1/2 must use Ω(n/p) space.Degree sequence problem. We study the problem of determining, given a sequence d 1 , d 2 , . . . , d n and a graph G, whether the degree sequence of G is precisely d 1 , d 2 , . . . , d n . We give a randomized one-pass O(log n) space algorithm with inverse polynomial one-sided error probability. We show that our algorithms are optimal.Our randomized algorithms are based on the recent work of Magniez et al.[1]; our lower bounds are obtained by considering related communication complexity problems.

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