Talent and skills shortage continues to plague the information technology industry that companies are looking at the various use cases of emerging technologies such as artificial intelligence (AI). In an email interview with Back End News, Mark Weaser, regional VP for the Asia Pacific, OutSystems, explained how AI has the potential to transform the entire software lifecycle in Asia-Pacific enterprises by enabling them to model new applications, analyze their impact on the organization. OutSystems is a global enterprise software company with headquarters in Atlanta, Georgia, United States. "AI and ML (machine learning) will soon become an integral part of software development in the Asia-Pacific and the world," Weaser said. "For example, AI will be a core component in increasingly intuitive development platforms will allow developers to apply cognitive learning and analytics to various application development and testing processes."

Google's DeepMind AI division will likely end up making the next generation of military killbots, but before then, at least they'll provide new challenges for the esports crowd. To make sure it wasn't a fluke, they've unleashed AlphaStar on the European public. According to this official blog post, AlphaStar is limited to Europe for now. StarCraft II players can opt for a chance to have their next 1v1 partner partner swapped out for an unfeeling machine that's less likely to insult your mother. The good news is that AlphaStar isn't going to be learning bad habits and worse language from StarCraft 2's player population.

This article develops a deep reinforcement learning (Deep-RL) framework for dynamic pricing on managed lanes with multiple access locations and heterogeneity in travelers' value of time, origin, and destination. This framework relaxes assumptions in the literature by considering multiple origins and destinations, multiple access locations to the managed lane, en route diversion of travelers, partial observability of the sensor readings, and stochastic demand and observations. The problem is formulated as a partially observable Markov decision process (POMDP) and policy gradient methods are used to determine tolls as a function of real-time observations. Tolls are modeled as continuous and stochastic variables, and are determined using a feedforward neural network. The method is compared against a feedback control method used for dynamic pricing. We show that Deep-RL is effective in learning toll policies for maximizing revenue, minimizing total system travel time, and other joint weighted objectives, when tested on real-world transportation networks. The Deep-RL toll policies outperform the feedback control heuristic for the revenue maximization objective by generating revenues up to 9.5% higher than the heuristic and for the objective minimizing total system travel time (TSTT) by generating TSTT up to 10.4% lower than the heuristic. We also propose reward shaping methods for the POMDP to overcome the undesired behavior of toll policies, like the jam-and-harvest behavior of revenue-maximizing policies. Additionally, we test transferability of the algorithm trained on one set of inputs for new input distributions and offer recommendations on real-time implementations of Deep-RL algorithms. The source code for our experiments is available online at https://github.com/venktesh22/ExpressLanes_Deep-RL

The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide us with a procedure to identify important structural features in materials that could be missed by standard techniques and give us a unique insight into how these neural networks process data. I. INTRODUCTION Classifying material structures and predicting their properties are important tasks in materials science. The behavior of materials often depends strongly on their underlying structure, and understanding these structure-property relationships relies on accurately describing the structural features of a material. However, quantifying structure-property relationships and identifying structural features in complex materials are difficult tasks. A variety of standard techniques have been developed to analyze material structures. Some of the most common techniques include the Steinhardt bond order parameters, 1 Bond Angle Analysis (BAA), 2 and Common Neighbor Analysis (CNA), 3 which are useful for detecting order-disorder transitions and differentiating between crystal structures in ordered samples. As discussed in Reinhardt et al., 4 the Steinhardt bond order parameters can be stymied by thermal fluctuations or am-a) Electronic mail: swansonk1@uchicago.edu BAA relies on a small set of crystalline reference structures that may not be present in amorphous samples. CNA is more flexible than BAA, but it cannot provide accurate information about particles that do not exhibit known symmetries, making analysis of irregular structures challenging.

Combinatorial linear semi-bandits (CLS) are widely applicable frameworks of sequential decision-making, in which a learner chooses a subset of arms from a given set of arms associated with feature vectors. Existing algorithms work poorly for the clustered case, in which the feature vectors form several large clusters. This shortcoming is critical in practice because it can be found in many applications, including recommender systems. In this paper, we clarify why such a shortcoming occurs, and we introduce a key technique of arm-wise randomization to overcome it. We propose two algorithms with this technique: the perturbed C${}^2$UCB (PC${}^2$UCB) and the Thompson sampling (TS). Our empirical evaluation with artificial and real-world datasets demonstrates that the proposed algorithms with the arm-wise randomization technique outperform the existing algorithms without this technique, especially for the clustered case. Our contributions also include theoretical analyses that provide high probability asymptotic regret bounds for our algorithms.

Department of Computer Science and Statistics Trinity College Dublin Ireland September 2019 Abstract Recently Jaouad Mourtada and St ephane Ga ıffas showed the anytime hedge algorithm has pseudo-regret O (log( d)/) if the cost vectors are generated by an i.i.d sequence in the cube [0, 1] d . This is remarkable because the Hedge algorithm was designed for the antagonistic setting. We prove a similar result for the anytime subgradient algorithm on the simplex. Given i.i.d cost vectors in the unit ball our pseudo-regret bound is O (1 /) and does not depend on the dimension of the problem. 1 Introduction and Related Work Online convex optimisation comes from Zinkevich [2003] who gave the following toy example: Suppose at the start of the year we must choose which of two crops to plant. Bananas grow best in warm weather and potatoes grow best in cold weather. We may plant all bananas or all potatoes or mix the two in any proportion. The average temperature for the year determines which of the two crops grows better and thus the profit for that year. Suppose also we have two neighbors, one who plants only potatoes every year and one who plants only bananas. Zincevich gave algorithms whereby after N seasons the more succesful of the two neighborhours will be only O ( N) more succesful than us. Taking averages the difference is O (1/ N) which decays to zero. Formally suppose a 1,a 2,... R d are cost vectors. On turn n we know only a 1,...,a n 1 and must select an action x n in the d-simplex S with a mind to minimising the sum null N i 1a i · x i. There exist algorithms whereby the regretnull N i 1a i · x i null N i 1a i · x has order O ( N) for all x S simultaneously. Remarkably these algorithms work when the only assumption is a uniform bound for the cost vectors. To extend the metaphor, there is a planting strategy that is effective even if the weather is selected antagonistically by nature. 1 arXiv:1909.05007v1 One popular model for predictable weather is to assume a 1,a 2,... are generated from some i.i.d sequence 1 of random variables with expectation a . In this case a 1,a 2,...,a n 1 provide estimates for a n and there are strategies where for E[a i] a the pseudo-regret null N i 1a · x i null N i 1a · x has expectation O (log N). Such algorithms exist even in the bandit setting, where on turn n we know only the sequence of costs a 1 · x 1,a 2 · x 2,...,a n 1 · x n 1 rather than the full information setting where we know the cost vectors a 1,a 2,...,a n 1 themselves. The bandit setting comes from Flaxman et al. [2004].

Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILEs. In this study, we introduce a new robust architecture, Generative Examination Networks GEN, based on bidirectional RNNs with concatenated sub-models to learn and generate molecular SMILES with a trained target space. GENs autonomously learn the target space in a few epochs while being subjected to an independent online examination mechanism to measure the quality of the generated set. Here we have used online statistical quality control (SQC) on the percentage of valid molecules SMILES as an examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our architecture combines an excellent novelty rate (85-90%) while generating SMILES with a strong conservation of the property space (95-99%). Our flexible examination mechanism is open to other quality criteria.

Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex semantics of reviews. Although recent works apply deep learning methods to learn review-level semantic features, their models ignore the impact of the user-level and product-level information on learning review semantics and the inherent user-review-product relationship information. In this paper, we propose a Hierarchical Fusion Attention Network (HFAN) to automatically learn the semantics of reviews from the user and product level. Specifically, we design a multi-attention unit to extract user(product)-related review information. Then, we use orthogonal decomposition and fusion attention to learn a user, review, and product representation from the review information. Finally, we take the review as a relation between user and product entity and apply TransH to jointly encode this relationship into review representation. Experimental results obtained more than 10\% absolute precision improvement over the state-of-the-art performances on four real-world datasets, which show the effectiveness and versatility of the model.

The state of each component is determined by a Boolean function of the state of (a subset of) the components of the network. This paper addresses the synthesis of these Boolean functions from constraints on their domain and emerging dynamical properties of the resulting network. The dynamical properties relate to the existence and absence of trajectories between partially observed configurations, and to the stable behaviours (fixpoints and cyclic attractors). The synthesis is expressed as a Boolean satisfiability problem relying on Answer-Set Programming with a parametrized complexity, and leads to a complete non-redundant characterization of the set of solutions. Considered constraints are particularly suited to address the synthesis of models of cellular differentiation processes, as illustrated on a case study. The scalability of the approach is demonstrated on random networks with scale-free structures up to 100 to 1,000 nodes depending on the type of constraints.

Artificial neural networks have been successfully used for many different classification tasks including malware detection and distinguishing between malicious and non-malicious programs. Although artificial neural networks perform very well on these tasks, they are also vulnerable to adversarial examples. An adversarial example is a sample that has minor modifications made to it so that the neural network misclassifies it. Many techniques have been proposed, both for crafting adversarial examples and for hardening neural networks against them. Most previous work has been done in the image domain. Some of the attacks have been adopted to work in the malware domain which typically deals with binary feature vectors. In order to better understand the space of adversarial examples in malware classification, we study different approaches of crafting adversarial examples and defense techniques in the malware domain and compare their effectiveness on multiple datasets.