Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting the direction of the company value over the longer period of time has been provided from the machines. In this paper we present a machine learning aided approach to evaluate the equity's future price over the long time. Our method is able to correctly predict whether some company's value will be 10% higher or not over the period of one year in 76.5% of cases.

Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks (EONs). In the past, they were normally developed and investigated separately. Moreover, the accuracy of the previously proposed methods still needs to be improved for heterogenous dynamic optical networks. In this paper, we present the application of machine learning (ML) in NLI modeling and monitoring. In particular, we first propose to use ML approaches to calibrate the errors of current fiber nonlinearity models. The Gaussian-noise (GN) model is used as an illustrative example, and significant improvement is demonstrated with the aid of an artificial neural network (ANN). Further, we propose to use ML to combine the modeling and monitoring schemes for a better estimation of NLI variance. Extensive simulations with 1603 links are conducted to evaluate and analyze the performance of various schemes, and the superior performance of the ML-aided combination of modeling and monitoring is demonstrated.

Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees has been proposed to identify such subgroups, but most of them focus on the two-arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package \textbf{psica} available at CRAN. In addition to numerical simulations based on artificial data, we present an analysis of a community-based nutrition intervention trial that justifies the validity of our method.

Random projections are able to perform dimension reduction efficiently for datasets with nonlinear low-dimensional structures. One well-known example is that random matrices embed sparse vectors into a low-dimensional subspace nearly isometrically, known as the restricted isometric property in compressed sensing. In this paper, we explore some applications of random projections in deep neural networks. We provide the expressive power of fully connected neural networks when the input data are sparse vectors or form a low-dimensional smooth manifold. We prove that the number of neurons required for approximating a Lipschitz function with a prescribed precision depends on the sparsity or the dimension of the manifold and weakly on the dimension of the input vector. The key in our proof is that random projections embed stably the set of sparse vectors or a low-dimensional smooth manifold into a low-dimensional subspace. Based on this fact, we also propose some new neural network models, where at each layer the input is first projected onto a low-dimensional subspace by a random projection and then the standard linear connection and non-linear activation are applied. In this way, the number of parameters in neural networks is significantly reduced, and therefore the training of neural networks can be accelerated without too much performance loss.

In one perspective, the main theme of this research revolves around the inverse problem in the context of general rough sets that concerns the existence of rough basis for given approximations in a context. Granular operator spaces and variants were recently introduced by the present author as an optimal framework for anti-chain based algebraic semantics of general rough sets and the inverse problem. In the framework, various sub-types of crisp and non-crisp objects are identifiable that may be missed in more restrictive formalism. This is also because in the latter cases concepts of complementation and negation are taken for granted - while in reality they have a complicated dialectical basis. This motivates a general approach to dialectical rough sets building on previous work of the present author and figures of opposition. In this paper dialectical rough logics are invented from a semantic perspective, a concept of dialectical predicates is formalised, connection with dialetheias and glutty negation are established, parthood analyzed and studied from the viewpoint of classical and dialectical figures of opposition by the present author. Her methods become more geometrical and encompass parthood as a primary relation (as opposed to roughly equivalent objects) for algebraic semantics.

After you finish stuffing yourself full of (hopefully) perfectly cooked turkey this Thanksgiving, you may want to snag something nice for yourself (or save some money on a gift for a family member) during Black Friday, the day after Thanksgiving when deals and discounts take center stage at every retailer. Not every price drop is created equal, however. Where some retailers may offer incentives like gift cards or reduced activation costs for items like smartphones, others might just slash a few hundred bucks off the sticker price. Here are just a few of the best deals you can expect to find on your most desired electronics on Black Friday. Of course, be sure to go for what you want before it's out of stock, and remember you can probably get the same deal on some of the most popular gadgets at a competing retailer if one store's stock runs dry.

The world has been through multiple'AI winters' (a time when the perception of artificial intelligence as a solution collapses and funding is withdrawn from major projects) since the tech's early days in the mid-20th century. However, the last such period, in the 1990s, is long since over, and AI is back in vogue once again, with ever-rising numbers of people working to prove that machines can simulate human learning. One of AI′s early movers and shakers was Marvin Minsky, whose work includes the first randomly wired neural network learning machine, which he built in 1951. In 1967 Minsky predicted that "within a generation... the problem of creating'artificial intelligence' will substantially be solved." He was wrong about that.

Mars is about to get its first U.S. visitor in years: a three-legged, one-armed geologist to dig deep and listen for quakes. NASA's InSight makes its grand entrance through the rose-tinted Martian skies on Monday, after a six-month, 300 million-mile (480 million-kilometer) journey. It will be the first American spacecraft to land since the Curiosity rover in 2012 and the first dedicated to exploring underground. The illustration shows the InSight lander drilling into the surface of Mars. InSight, short for Interior Exploration using Seismic Investigations, Geodesy and Heat Transport, is scheduled to arrive at the planet on Monday, Nov. 26 NASA is going with a tried-and-true method to get this mechanical miner to the surface of the red planet. Engine firings will slow its final descent and the spacecraft will plop down on its rigid legs, mimicking the landings of earlier successful missions.

Shenzhen, China - The video screen shows a constant procession of Chinese citizens moving past a camera at what appears to be a public festival, park or central city plaza. Their faces are bracketed by yellow squares with various numbers hovering below, including each person's estimated age and gender. If the person is known to the system, their name appears too. The facial recognition technology was just one of dozens of such systems at the China Hi-Tech Fair in the southern city of Shenzhen this month. But while other companies tried to bring some levity to the dystopian feel by making cute dog heads appear on the faces of passers-by, others had no qualms about showcasing the Orwellian nature of their products.

NEW YORK – Stocks are skidding Tuesday as weak results from retailers and mounting losses for big technology companies push the market back into the red for the year. Energy companies are slumping because of a 7 percent plunge in the price of oil. Crude is on track for its biggest loss in three years. Industrial companies are also dropping as the downward momentum in stocks builds after steep losses Monday. The S&P 500 index lost 38 points, or 1.4 percent, to 2,652 as of 1:15 p.m. Eastern time.