Being tone deaf could affect more than just your karaoke ability. A new study shows that being unable to recognise musical pitch could also have a serious affect on social skills. People with'congenital amusia' - the scientific term for tone deafness - may find it more difficult to accurately read facial expressions or tell if a laugh is fake, according to a new study. As part of the study by researchers from Goldsmiths, University of London and University College London (UCL), 24 participants were asked to judge the emotion in extracts of speech, based on patterns hear in the tone of voice. Some people claim they're born to sing, while others openly admit they're tone deaf, but what if you could take a pill and become pitch perfect?

Elon Musk named his electric-car company after the engineering genius Nikola Tesla, but the sweeping nature of his vision to replace fossil fuels is reminiscent of Thomas Edison, Tesla's arch-rival. After creating the incandescent bulb, the home electric meter, and one of the first alkaline batteries, Edison spent much of his personal fortune building factories to produce them--all in the service of a grand plan to electrify society using his direct-current transmission technology. Eighty years before Musk was born, Edison was urging U.S. cities to set up networks of charging stations so those newfangled horseless carriages could run on electricity rather than gasoline. For Musk's fans and investors, the comparison should not be entirely comforting. In the course of a few short years, the Wizard of Menlo Park was unceremoniously forced out of the electricity game. After he stubbornly refused to embrace the transmission technology that became the foundation of the U.S. grid and focused increasingly on developing inventions such as the phonograph and the motion picture, his board of directors merged his Edison General Lighting with a rival to create today's General Electric--leaving the 46-year-old Edison with no management role.

While some view videogames as a waste of time, researchers have found that spending time in these virtual worlds can actually enhance your perception and attention skills. People who play between one and five hours a week are able to process visual information five percent more accurately than those who don't play at all, finds a new study. These findings suggest that those who play video games are faster and more efficient at processing rapidly-presented stimuli. People who play between 1 and 5 hours a week are able to process visual information 5% more accurately than those who don't play at all, finds a new study from psychologists at Nottingham Trent University Nottingham Trent University asked 43 participants to perform observation tasks presented on a screen. The first asked participants to identify a white letter within the stream of black letters, and the other the letter'T' in one of four orientations, rotated by 0, 90, 180, and 270 and observers attempted to discriminate between these orientations The team found that video gamers were able to perform this dual task on average 5% more accurately than non-gamers, suggesting faster and more efficient processing of rapidly-presented stimuli.

We introduce a Maximum Entropy model able to capture the statistics of melodies in music. The model can be used to generate new melodies that emulate the style of the musical corpus which was used to train it. Instead of using the $n-$body interactions of $(n-1)-$order Markov models, traditionally used in automatic music generation, we use a $k-$nearest neighbour model with pairwise interactions only. In that way, we keep the number of parameters low and avoid over-fitting problems typical of Markov models. We show that long-range musical phrases don't need to be explicitly enforced using high-order Markov interactions, but can instead emerge from multiple, competing, pairwise interactions. We validate our Maximum Entropy model by contrasting how much the generated sequences capture the style of the original corpus without plagiarizing it. To this end we use a data-compression approach to discriminate the levels of borrowing and innovation featured by the artificial sequences. The results show that our modelling scheme outperforms both fixed-order and variable-order Markov models. This shows that, despite being based only on pairwise interactions, this Maximum Entropy scheme opens the possibility to generate musically sensible alterations of the original phrases, providing a way to generate innovation.

Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too defensive so that normal traffic flow is maintained. In this paper we apply deep reinforcement learning to the problem of forming long term driving strategies. We note that there are two major challenges that make autonomous driving different from other robotic tasks. First, is the necessity for ensuring functional safety - something that machine learning has difficulty with given that performance is optimized at the level of an expectation over many instances. Second, the Markov Decision Process model often used in robotics is problematic in our case because of unpredictable behavior of other agents in this multi-agent scenario. We make three contributions in our work. First, we show how policy gradient iterations can be used without Markovian assumptions. Second, we decompose the problem into a composition of a Policy for Desires (which is to be learned) and trajectory planning with hard constraints (which is not learned). The goal of Desires is to enable comfort of driving, while hard constraints guarantees the safety of driving. Third, we introduce a hierarchical temporal abstraction we call an "Option Graph" with a gating mechanism that significantly reduces the effective horizon and thereby reducing the variance of the gradient estimation even further.

ABSTRACT Extracting information from functional magnetic resonance (fMRI) images has been a major area of research for more than two decades. The goal of this work is to present a new method for the analysis of fMRI data sets, that is capable to incorporate a priori available information, via an efficient optimization framework. Tests on synthetic data sets demonstrate significant performance gains over existing methods of this kind. Index Terms -- fMRI Data Analysis, Dictionary Learning, Blind Source Separation 1. INTRODUCTION Functional magnetic resonance imaging (fMRI) is a powerful noninvasive technique suitable to providing important information concerning the brain activity. Studying the different areas in the brain that correspond to important tasks such as vision, perception, recognition, etc., constitutes a major open area of research, demanding robust and high precision techniques for the analysis of fMRI data analysis [1], [2], [3], [4].

Th is study presents a rapid multiple incremental and decremental mechanism ba sed on Weight - Error Curves (WECs) fo r support - vector a nalysi s . To ha ndle rapidly increas ing amounts of data, recursion - free computation is proposed for predicting the Lagrangian multipliers of new samples . This study examines the characteristics of Ridge S upport V ector M odels, including Ridge S upport V ector Machines and Regression, subsequently devis ing a recursion - free function derived from WECs . With this proposed function, a ll of the new Lagrang ian multipliers can be computed at once without using any gradual step sizes. Moreover, such a function can relax a constraint, where the increment of new multiple Lagrang ian multipliers should be the same in the previous work, thereby easily satisfying the requirement of Karush - Kuhn - Tucker (KKT) conditions . The proposed mechanism no longer requires t ypical time - consuming bookkeeping strategies, which compute the step size by checking all the training samples in each incremental round. Experiments were carried out on open datasets for evaluating our work. The results showed that the computation al speed was successfully enhanced, better than the baselines. Besides, the accuracy still remained. These findings revealed that the proposed method was appropriate for incremental/decremental learning, thereby demonstrating the effectiveness of the propose d idea.

Low-rank matrix completion (LRMC) problems arise in a wide variety of applications. Previous theory mainly provides conditions for completion under missing-at-random samplings. This paper studies deterministic conditions for completion. An incomplete $d \times N$ matrix is finitely rank-$r$ completable if there are at most finitely many rank-$r$ matrices that agree with all its observed entries. Finite completability is the tipping point in LRMC, as a few additional samples of a finitely completable matrix guarantee its unique completability. The main contribution of this paper is a deterministic sampling condition for finite completability. We use this to also derive deterministic sampling conditions for unique completability that can be efficiently verified. We also show that under uniform random sampling schemes, these conditions are satisfied with high probability if $O(\max\{r,\log d\})$ entries per column are observed. These findings have several implications on LRMC regarding lower bounds, sample and computational complexity, the role of coherence, adaptive settings and the validation of any completion algorithm. We complement our theoretical results with experiments that support our findings and motivate future analysis of uncharted sampling regimes.

Due to the stochastic SASE operating principles and other technical issues the output pulses are subject to large fluctuations, making it necessary to characterize the x-ray pulses on every shot for data sorting purposes. We present a technique that applies machine learning tools to predict x-ray pulse properties using simple electron beam and x-ray parameters as input. Using this technique at the Linac Coherent Light Source (LCLS), we report mean errors below 0.3 eV for the prediction of the photon energy at 530 eV and below 1.6 fs for the prediction of the delay between two x-ray pulses. We also demonstrate spectral shape prediction with a mean agreement of 97%. This approach could potentially be used at the next generation of high-repetition-rate XFELs to provide accurate knowledge of complex x-ray pulses at the full repetition rate. I. INTRODUCTION X-ray free-electron lasers (XFELs) 1-3 are emerging as one of the most versatile tools in x-ray research, becoming widely used by the scientific community, as well as industry, in many fields including physics, chemistry, biology, and material science. Their brightness, coherence, tun-ability, and ability to generate pairs of few-fs multicolor pulses for pump-probe experiments 4-7 make them ideal sources to perform diffract-before-destroy imaging 8, resonant x-ray spectroscopy 9, and a range of time resolved measurements of picosecond to few-femtosecond dynamics in molecules and atoms 10-16 . A drawback to XFELs is their current poor stability. XFELs are driven by single-pass electron linear accelerators (LINAC) typically several hundred meters in length.

If you're feeling sick and you want to know what's wrong with you, there's an app for that. But the diagnosis won't be as accurate as the one you'd get from a doctor -- not by a long shot. In a head-to-head comparison, real human physicians outperformed a collection of 23 symptom-checker apps and websites by a margin of more than 2 to 1, according to a report published Monday in the journal JAMA Internal Medicine. Even when the contestants got three chances to figure out what ailed a hypothetical patient, the diagnostic software lagged far behind actual doctors. Indeed, the apps and websites suggested the right diagnosis only slightly more than half of the time, the report says.