Emerging technology, like blockchain technology, Artificial Intelligence, virtual reality, drones, cloud computing -- are all on the table of the Narendra Modi government as it pushes ahead to realise the goal of better e-governance for citizens. As part of its vision of providing a One Government experience to citizens, the Modi government is moving forward rapidly to implement the India Enterprise Architecture (IndEA) -- a single window digitisation solution for cashless, paperless and faceless services. At an e-Governance conference held in Shillong, Meghalaya, earlier this month, representatives of all the state governments and Union Territories, as well as business houses, brainstormed on the way ahead to provide better e-governance, and exchanged success stories of various states that could be adopted at the national level. A Shillong Declaration was adopted as a roadmap of the way forward, at the conclusion of the two-day 22nd National Conference on e-Governance (NCeG) 2019, with special focus on the Northeast, held on August 8-9, in Shillong. The conference, that saw over 500 delegates attending, with representation from states at the level of Additional Secretary and Principal Secretary, and top representatives of business houses like Wipro, HP and KPMG, was organised by the Department of Administrative Reforms & Public Grievances (DARPG), in association with the Ministry of Electronics & Information Technology (MeitY), and the Meghalaya government.

According to a McKinsey report, the global payments market is estimated to grow from $1.9 trillion in 2017 at 9% CAGR to $2.9 trillion by 2022. Portsmouth-based Bottomline Technologies (Nasdaq: EPAY) is a leading player in the B2B world of digital payments. Bottomline Technologies was set up in 1999 to provide enterprise-wide payment solutions to address the need to migrate enterprises away from pre-printed, paper checks as a payment solution. Since then, the company has come a long way. Its product suite is aimed at providing organizations with access to a single platform that can control, manage, and issue all payments across an enterprise.

Reefknot Investments, a joint venture between Temasek, Singapore's sovereign fund, and global logistics company Kuehne Nagel, announced today the launch of a $50 million fund for logistics and supply chain startups. The firm is based in Singapore, but will look for companies around the world that are raising their Series A or B rounds. Managing director Marc Dragon tells TechCrunch that Reefknot will serve as a strategic investor in its portfolio companies, providing them with connections to partners that include EDBI, SGInnovate, Atlantic Bridge, Vertex Ventures, PSA unBoXed, Unilever Foundry and NUS Enterprise, in addition to Temasek and Kuehne Nagel. Dragon, a veteran of the supply chain and logistics industry, says Reefknot plans to invest in about six to eight startups. It is especially interested in companies that are using AI or deep mind tech, digital logistics and trade finance to solve problems that range from analyzing supply chain data and making forecasts to managing the risk of financing trade transactions.

Multicolored lights illuminate a rack of computer server units. "Legal confidentiality is a shield for citizens." These are the words of Shami Chakrabarti, the one-time director of the U.K.-based human rights group Liberty, speaking in 2018. Well, it seems that this shield has just been broken, because researchers at the University of Zurich in Switzerland have published a study in which they were able to identify the participants in confidential legal cases, even though such participants had been anonymized. By harnessing these technologies in tandem, the study's authors could mine over 120,000 public legal records and then use an algorithm to identify connections between them.

ZAO, a viral Chinese app that uses AI to face-swap users and famous actors, is now embroiled in a major privacy controversy. The news: On Friday, a new app released by Momo, a social-media developer, instantly went viral on Chinese social media. It allows users to upload a single portrait and, within seconds, see their face superimposed onto actors in iconic movie scenes. By Sunday, it had become the most downloaded free entertainment app in China's Apple Store. While GANs have been used for face-editing and face-swapping before (increasingly so in Hollywood films), ZAO's use of a single photo, coupled with the speed and seamlessness of its swap, demonstrates how far the state of the art in media fakery has advanced.

Chinese face-swap app Zao rocketed to the top of app store charts over the weekend, but user delight at the prospect of becoming instant superstars quickly turned sour as privacy implications began to sink in. Launched recently, Zao is currently topping the free download chart on China's iOS store. Its popularity has also pushed another face-swap app, Yanji, to fifth place on the list. Behind Zao is a company fully owned by Chinese hookup and live-streaming service Momo Inc. Users of the app upload a photo of themselves to drop their likeness into popular scenes from hundreds of movies or TV shows. It's a chance to be the star and swap places with the likes of Marilyn Monroe, Leonardo DiCaprio or Sheldon Cooper from The Big Bang Theory in a matter of moments.

The fixed parameter tractable (FPT) approach is a powerful tool in tackling computationally hard problems.  In this paper, we link FPT results to classic artificial intelligence (AI) techniques to show how they complement each other.  Specifically, we consider the workflow satisfiability problem (WSP) which asks whether there exists an assignment of authorised users to the steps in a workflow specification, subject to certain constraints on the assignment.  It was shown by Cohen et al. (JAIR 2014) that WSP restricted to the class of user-independent constraints (UI), covering many practical cases, admits FPT algorithms, i.e. can be solved in time exponential only in the number of steps k and polynomial in the number of users n.  Since usually k << n in WSP, such FPT algorithms are of great practical interest.We present a new interpretation of the FPT nature of the WSP with UI constraints giving a decomposition of the problem into two levels.  Exploiting this two-level split, we develop a new FPT algorithm that is by many orders of magnitude faster than the previous state-of-the-art WSP algorithm and also has only polynomial-space complexity.  We also introduce new pseudo-Boolean (PB) and Constraint Satisfaction (CSP) formulations of the WSP with UI constraints which efficiently exploit this new decomposition of the problem and raise the novel issue of how to use general-purpose solvers to tackle FPT problems in a fashion that meets FPT efficiency expectations.  In our computational study, we investigate, for the first time, the phase transition (PT) properties of the WSP, under a model for generation of random instances.  We show how PT studies can be extended, in a novel fashion, to support empirical evaluation of scaling of FPT algorithms.

Recently there has been an increased interest in unsupervised learning of disentangled representations using the Variational Autoencoder (VAE) framework. Most of the existing work has focused largely on modifying the variational cost function to achieve this goal. We first show that these modifications, e.g. beta-VAE, simplify the tendency of variational inference to underfit causing pathological over-pruning and over-orthogonalization of learned components. Second we propose a complementary approach: to modify the probabilistic model with a structured latent prior. This prior allows to discover latent variable representations that are structured into a hierarchy of independent vector spaces. The proposed prior has three major advantages: First, in contrast to the standard VAE normal prior the proposed prior is not rotationally invariant. This resolves the problem of unidentifiability of the standard VAE normal prior. Second, we demonstrate that the proposed prior encourages a disentangled latent representation which facilitates learning of disentangled representations. Third, extensive quantitative experiments demonstrate that the prior significantly mitigates the trade-off between reconstruction loss and disentanglement over the state of the art.

The segmentation of the breast from the chest wall is an important first step in the analysis of breast magnetic resonance images. 3D U-nets have been shown to obtain high segmentation accuracy and appear to generalize well when trained on one scanner type and tested on another scanner, provided that a very similar T1-weighted MR protocol is used. There has, however, been little work addressing the problem of domain adaptation when image intensities or patient orientation differ markedly between the training set and an unseen test set. To overcome the domain shift we propose to apply extensive intensity augmentation in addition to geometric augmentation during training. We explored both style transfer and a novel intensity remapping approach as intensity augmentation strategies. For our experiments, we trained a 3D U-net on T1-weighted scans and tested on T2-weighted scans. By applying intensity augmentation we increased segmentation performance from a DSC of 0.71 to 0.90. This performance is very close to the baseline performance of training and testing on T2-weighted scans (0.92). Furthermore, we applied our network to an independent test set made up of publicly available scans acquired using a T1-weighted TWIST sequence and a different coil configuration. On this dataset we obtained a performance of 0.89, close to the inter-observer variability of the ground truth segmentations (0.92). Our results show that using intensity augmentation in addition to geometric augmentation is a suitable method to overcome the intensity domain shift and we expect it to be useful for a wide range of segmentation tasks.

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date Jezero delta or groundwater alteration of portions of the Jezero sedimentary sequence.