We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds without extensive knowledge of signal processing. We use a feedforward convolutional neural network-based architecture, which is able to map input parameters to the corresponding waveform. We propose two datasets to evaluate our approach on both a restrictive context, and in one covering a broader spectrum of sounds. The timbral features used as parameters are taken from recent literature in signal processing. We also use these features for evaluation and validation of the presented model, to ensure that changing the input parameters produces a congruent waveform with the desired characteristics. Finally, we evaluate the quality of the output sound using a subjective listening test. We provide sound examples and the system's source code for reproducibility.

The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bias values. Similar approaches use independent direction vectors and biases. To combine the binary predictions, most of them adopt a simple counting heuristics. Here, we introduce a novel cumulative sum scoring function to combine the binary predictions. The proposed score value aggregates the strength of each one of the relevant binary classifications on how large is the item's rank. We show that our modeling casts ordinal regression as a Structured Perceptron problem. As a consequence, we simplify its formulation and description, which results in two simple online learning algorithms. The second algorithm is a Passive-Aggressive version of the first algorithm. We show that under some rank separability condition both algorithms converge. Furthermore, we provide mistake bounds for each one of the two online algorithms. For the Passive-Aggressive version, we assume the knowledge of a separation margin, what significantly improves the corresponding mistake bound. Additionally, we show that Ranking by Projecting is a special case of our prediction algorithm. From a neural network architecture point of view, our empirical findings suggest a layer of cusum units for ordinal regression, instead of the usual softmax layer of multiclass problems.

--This research proposes a new (old) metric for evaluating goodness of fit in topic models, the coefficient of determination, or R 2 . Within the context of topic modeling, R 2 has the same interpretation that it does when used in a broader class of statistical models. Reporting R 2 with topic models addresses two current problems in topic modeling: a lack of standard cross-contextual evaluation metrics for topic modeling and ease of communication with lay audiences. The author proposes that R 2 should be reported as a standard metric when constructing topic models. I NTRODUCTION According to an often-quoted but never cited definition, "the goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question." 1 Goodness of fit measures vary with the goals of those constructing the statistical model. Inferential goals may emphasize in-sample fit while predictive goals may emphasize out-of-sample fit. Prior information may be included in the goodness of fit measure for Bayesian models, or it may not. Goodness of fit measures may include methods to correct for model overfitting. In short, goodness of fit measures the performance of a statistical model against the ground truth of observed data. Fitting the data well is generally a necessary--though not sufficient--condition for trust in a statistical model, whatever its goals. Of course, goodness of fit is only one concern in statistical modeling.

--In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the gap in terms of a classical architecture for multimedia processing and discuss a structured approach to bridge it. This approach combines machine learning (for mapping signals to objects) and symbolic AI (for linking objects to meanings). Our main goal is to raise awareness and discuss the challenges involved in this structured approach to multimedia understanding, especially in the view of the latest developments in machine learning and symbolic AI. A classic problem in multimedia representation and understanding is the semantic gap problem [1].

We consider a multi-agent model for fair division of mixed manna (i.e. items for which agents can have positive, zero or negative utilities), in which agents have additive utilities for bundles of items. For this model, we give several general impossibility results and special possibility results for three common fairness concepts (i.e. EF1, EFX, EFX3) and one popular efficiency concept (i.e. PO). We also study how these interact with common welfare objectives such as the Nash, disutility Nash and egalitarian welfares. For example, we show that maximizing the Nash welfare with mixed manna (or minimizing the disutility Nash welfare) does not ensure an EF1 allocation whereas with goods and the Nash welfare it does. We also prove that an EFX3 allocation may not exist even with identical utilities. By comparison, with tertiary utilities, EFX and PO allocations, or EFX3 and PO allocations always exist. Also, with identical utilities, EFX and PO allocations always exist. For these cases, we give polynomial-time algorithms, returning such allocations and approximating further the Nash, disutility Nash and egalitarian welfares in special cases.

Archaeologists in Yamagata, Japan said they have discovered a cluster of enormous, ancient geoglyphs in Southern Peru. The discovery was made with the help of cutting-edge artificial intelligence (AI) technology. A research group at Yamagata University identified 143 new geoglyphs etched into the desert terrains of Nazca in southern Peru. The giant land art pieces, known as the Nazca Lines, depict human-like figures and a variety of animals including birds, fish, snakes, foxes, felines, and camelids. Many can only be identified from the air due to their large size.

The Internet & Television Association (formerly the National Cable & Telecommunications Association, and commonly known as the NCTA) estimates that 80% of residences in the United States have access to gigabit speeds from cable companies via HFC and FTTH. Cable operators seek to solidify their position within consumer markets for broadband services as wireless carriers seek to leverage the enhanced mobile broadband (eMBB) component of 5G to gain a foothold for indoor residential and small business services. With little competition in the consumer in-home segment, certain wireless carriers see fixed wireless as a pathway to early revenue as their vendors work diligently to ensure eMBB services may be provided on a mobility basis rather than simply portable or fixed wireless solutions, which shall be predominate initially. A battleground is emerging for consumer broadband between cable companies espousing 10G (meaning symmetrical 10 Gbps speeds delivered over hybrid fiber-coaxial networks and not tenth generation) versus wireless carriers such as Verizon Wireless who will pursue the residential and small business market with fixed wireless 5G. Earlier this year, AT&T likewise stated that 5G will be a substitution for fixed-line broadband within the next three to five years. However, we see the consumer segment as a major challenge area for mobile communications service providers due to a few key factors including market inertia and deployment of WiFi6 devices.

The Research Insights recently published a new comprehensive report titled the Artificial Intelligence in Digital Marketing Market. This report uses investigative primary and secondary research techniques to arrive at inferences. Researchers of the Artificial Intelligence in Digital Marketing Market report throw light on economic factors that affect the progress of the market. It includes some online and offline activities suggestions for branding the businesses strategized by our team of expert analysts. Artificial intelligence (AI) is science that deals with building intelligent machines that can think and respond like a human.

Before the year 2014, there were many people using Google, Facebook, and Amazon. Today, there are still many people using services from those three tech giants (respectively, GOOG, FB, AMZN). Not much has changed, and quite literally the user interface and features on those sites has remained mostly untouched. However, the underlying dynamics of power on the Web have drastically changed, and those three companies are at the center of a fundamental transformation of the Web. It looks like nothing changed since 2014, but GOOG and FB now have direct influence over 70% of internet traffic. Internet activity itself hasn't slowed down. What has changed over the last 4 years is market share of traffic on the Web.