Expert system can process large amounts of known information and apply reasoning capabilities to provide conclusions. An expert system is a system that employs human knowledge captured in an automated system to solve problems that typically require human expertise. In this paper we propose the design and development of a medical knowledge based system (MKBS) for disease diagnosis from symptoms. It provides rich features for searching properties like symptoms, treatments, hierarchical clusters of particular diseases. The system supports a knowledge construction module and an inference engine module. The knowledge construction was built on a concept of rules, which was represented in a tree structure, and properties of a particular disease were stored as a semantic net.

We propose a three-stage framework for forecasting high-dimensional time-series data. Our method first estimates parameters for each univariate time series. Next, we use these parameters to cluster the time series. These clusters can be viewed as multivariate time series, for which we then compute parameters. The forecasted values of a single time series can depend on the history of other time series in the same cluster, accounting for intra-cluster similarity while minimizing potential noise in predictions by ignoring inter-cluster effects. Our framework -- which we refer to as "cluster-and-conquer" -- is highly general, allowing for any time-series forecasting and clustering method to be used in each step. It is computationally efficient and embarrassingly parallel. We motivate our framework with a theoretical analysis in an idealized mixed linear regression setting, where we provide guarantees on the quality of the estimates. We accompany these guarantees with experimental results that demonstrate the advantages of our framework: when instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets, sometimes outperforming deep-learning-based approaches.

Recently, deep reinforcement learning (DRL) frameworks have shown potential for solving NP-hard routing problems such as the traveling salesman problem (TSP) without problem-specific expert knowledge. Although DRL can be used to solve complex problems, DRL frameworks still struggle to compete with state-of-the-art heuristics showing a substantial performance gap. This paper proposes a novel hierarchical problem-solving strategy, termed learning collaborative policies (LCP), which can effectively find the near-optimum solution using two iterative DRL policies: the seeder and reviser. The seeder generates as diversified candidate solutions as possible (seeds) while being dedicated to exploring over the full combinatorial action space (i.e., sequence of assignment action). To this end, we train the seeder's policy using a simple yet effective entropy regularization reward to encourage the seeder to find diverse solutions. On the other hand, the reviser modifies each candidate solution generated by the seeder; it partitions the full trajectory into sub-tours and simultaneously revises each sub-tour to minimize its traveling distance. Thus, the reviser is trained to improve the candidate solution's quality, focusing on the reduced solution space (which is beneficial for exploitation). Extensive experiments demonstrate that the proposed two-policies collaboration scheme improves over single-policy DRL framework on various NP-hard routing problems, including TSP, prize collecting TSP (PCTSP), and capacitated vehicle routing problem (CVRP).

Random projection (RP) have recently emerged as popular techniques in themachine learning community for their ability in reducing the dimension of veryhigh-dimensional tensors. Following the work in [29], we consider a tensorizedrandom projection relying on Tensor Train (TT) decomposition where each elementof the core tensors is drawn from a Rademacher distribution. Our theoreticalresults reveal that the Gaussian low-rank tensor represented in compressed formin TT format in [29] can be replaced by a TT tensor with core elements drawnfrom a Rademacher distribution with the same embedding size. Experiments onsynthetic data demonstrate that tensorized Rademacher RP can outperform thetensorized Gaussian RP studied in [29]. In addition, we show both theoreticallyand experimentally, that the tensorized RP in the Matrix Product Operator (MPO)format proposed in [5] for performing SVD on large matrices is not a Johnson-Lindenstrauss transform (JLT) and therefore not a well-suited random projectionmap

Bias correction techniques are used by most of the high-performing methods for off-policy reinforcement learning. However, these techniques rely on a pre-defined bias correction policy that is either not flexible enough or requires environment-specific tuning of hyperparameters. In this work, we present a simple data-driven approach for guiding bias correction. We demonstrate its effectiveness on the Truncated Quantile Critics -- a state-of-the-art continuous control algorithm. The proposed technique can adjust the bias correction across environments automatically. As a result, it eliminates the need for an extensive hyperparameter search, significantly reducing the actual number of interactions and computation.

Israel is holding its largest-ever air force exercise this week with the participation of several countries including the United Arab Emirates, with whom it normalised ties last year. Amir Lazar, chief of Israeli air force operations, told reporters at the southern Ovda airbase the drills "don't focus on Iran", but army officials have said Iran remains Israel's top strategic threat and at the centre of much of its military planning. Israel has held the so-called "Blue Flag" exercises every two years since 2013 in the Negev desert to synchronise different types of aircraft, piloted by different countries to counter armed drones and other threats. With more than 70 fighter jets and some 1,500 personnel participating, this year's drills are the largest-ever held in Israel, Lazar said. Among the nations taking part are France, the United States and Germany, as well as the United Kingdom, whose aircraft flew over Israeli territory for the first time since the Jewish state's creation in 1948.

Madagascar's critically endangered'singing' lemurs -- Indri indri -- have a natural ability to keep a beat, just like us humans do, a study has concluded. Researchers from the Max Planck Institute for Psycholinguistics and the University of Turin studied the songs of indri in the rainforests of the island country. They found that the lemurs' strange, wailing songs have the same kinds of universal, categorical rhythms found across human musical cultures. Outside of humans, having rhythm is a rare trait in mammals -- although it can be found elsewhere in the animal kingdom, perhaps most notably in songbirds. Madagascar's critically endangered'singing' lemurs -- Indri indri -- have a natural ability to keep a beat, just like us humans do, a study has concluded.

The quest for national AI success has electrified the world--at last count, 44 countries have entered the race by creating their own national AI strategic plan. While the inclusion of countries like China, India, and the U.S. are expected, unexpected countries, including Uganda, Armenia, and Latvia, have also drafted national plans in hopes of realizing the promise. Our earlier posts, entitled "How different countries view artificial intelligence" and "Analyzing artificial intelligence plans in 34 countries" detailed how countries are approaching national AI plans, as well as how to interpret those plans. In this piece, we go a step further by examining indicators of future AI needs. Clearly, having a national AI plan is a necessary but not sufficient condition to achieve the goals of the various AI plans circulating around the world; 44 countries currently have such plans. In previous posts, we noted how AI plans were largely aspirational, and that moving from this aspiration to successful implementation required substantial public-private investments and efforts.

Python is one of the most widely used programming languages in the world, and for good reason. Because of its vast libraries and flexible structure, it's simple to learn, has consistent and easy-to-parse syntax, and is utilized for artificial intelligence applications. The platform's spectacular ascent has sparked a devoted community, fueled in no little part by its adoption by big companies such as DropBox, Reddit, and Instagram, to name a few. Check out this list of Python developers to follow if you're seeking Python programmers who are leading the charge. The people on this list have solid technical credentials, are constantly adding new and interesting features to the platform, and have a strong social media presence.

'Gutfeld!' panel weighs in on China's response to the statement This is a rush transcript of "Gutfeld" on October 22, 2021. This copy may not be in its final form and may be updated. Bad things are happening, but it's OK because we're all in this together. What did we get from Joe? An incoherent jumble of memories and confused looks. What the hell was that? JOE BIDEN, PRESIDENT OF THE UNITED STATES: Forty percent of all products coming into the United States of America on the West Coast go through Los Angeles and -- what am I doing here? COOPER: Do you have plans to visit the southern border? BIDEN: I've been there before and I haven't -- I mean, I know it well. I guess I should go down. But what you see is wages are actually up. I have the freedom to kill you. My guess is you'll start to see gas prices come down as we get by -- and going into the winter. I mean, excuse me, and then next year in 2022. I must tell you, I don't have a near-term answer. Well, that was the opposite of comforting. It seems his only strategy is to deflect from our current misery to promising more misery. Angelo Negri was from memory ranch. And she came up to me one day when I was -- when they just had announced that I had flown one million some X number of miles on Air Force aircraft. And asked, she comes up and I'm getting in the car and he goes, Joey baby, what do you do?