In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the sensing phase, the user is able to sense a subset of channels sequentially before it decides to use one of them in the following transmission phase. We assume the channel states alternate between busy and idle according to independent Bernoulli random processes from frame to frame. To capture the inherent uncertainty in channel sensing, we assume the reward of each transmission when the channel is idle is a random variable. We also associate random costs with sensing and transmission actions. Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i.e., reward-minus-cost). We start with an offline setting where the statistics of the channel status, costs and reward are known beforehand. We show that the the optimal policy exhibits a recursive double threshold structure, and the user needs to compare the channel statistics with those thresholds sequentially in order to decide its actions. With such insights, we then study the online setting, where the statistical information of the channels, costs and reward are unknown a priori. We judiciously balance exploration and exploitation, and show that the cumulative regret scales in O(log T). We also establish a matched lower bound, which implies that our online algorithm is order-optimal. Simulation results corroborate our theoretical analysis.

We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems. The objective of the challenge was to perform speaker conversion (i.e. transform the vocal identity) of a source speaker to a target speaker while maintaining linguistic information. As an update to the previous challenge, we considered both parallel and non-parallel data to form the Hub and Spoke tasks, respectively. A total of 23 teams from around the world submitted their systems, 11 of them additionally participated in the optional Spoke task. A large-scale crowdsourced perceptual evaluation was then carried out to rate the submitted converted speech in terms of naturalness and similarity to the target speaker identity. In this paper, we present a brief summary of the state-of-the-art techniques for VC, followed by a detailed explanation of the challenge tasks and the results that were obtained.

Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits exhibit stronger expressibility compared to classical neural networks. One can efficiently draw samples from the quantum circuits via projective measurements on qubits. However, similar to the leading implicit generative models in deep learning, such as the generative adversarial networks, the quantum circuits cannot provide the likelihood of the generated samples, which poses a challenge to the training. We devise an efficient gradient-based learning algorithm for the quantum circuit Born machine by minimizing the kerneled maximum mean discrepancy loss. We simulated generative modeling of the Bars-and-Stripes dataset and Gaussian mixture distributions using deep quantum circuits. Our experiments show the importance of circuit depth and gradient-based optimization algorithm. The proposed learning algorithm is runnable on near-term quantum device and can exhibit quantum advantages for generative modeling.

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods have a strong bias towards low- or high-order interactions, or rely on expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed framework, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared raw feature input to both its "wide" and "deep" components, with no need of feature engineering besides raw features. DeepFM, as a general learning framework, can incorporate various network architectures in its deep component. In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data. We conduct online A/B test in Huawei App Market, which reveals that DeepFM-D leads to more than 10% improvement of click-through rate in the production environment, compared to a well-engineered LR model. We also covered related practice in deploying our framework in Huawei App Market.

Chirality describes the quality of possessing a mirror image to something else, but without the ability to superimpose it. Your left foot, for example, is a mirror of your right. They look similar, but they are not the same. This is why you cannot wear a left shoe on your right foot. The idea is similar in chemistry.

With IBM, Google, and Microsoft pouring funding into the research of quantum computing, it's really starting to look like we are going to see the benefits in the next 5 - 10 years. Google may be just weeks from announcing they reached the quantum supremacy milestone and IBM may not be far behind either. Today, I wanted to share my thoughts on how quantum computing may affect cryptography as we know it. Effects on cryptography When we talk about the basic cryptography used for things like TLS when you access your bank's website, the premise behind securing your data is surprisingly simple. The certificate uses a public key which is really just a large number that's the result of multiplying two prime numbers together.

A NEW commodity spawns a lucrative, fast-growing industry, prompting antitrust regulators to step in to restrain those who control its flow. A century ago, the resource in question was oil. Now similar concerns are being raised by the giants that deal in data, the oil of the digital era. These titans--Alphabet (Google's parent company), Amazon, Apple, Facebook and Microsoft--look unstoppable. They are the five most valuable listed firms in the world.

Artificial intelligence, or AI: what exactly is it? How much should we trust it? There are numerous questions we could ask regarding this issue, and many of these concerns were brought into focus several weeks ago when an Uber self-driving car struck and killed a pedestrian in Tempe, Arizona. The accident resulted in the first fatality from a self-driving car accident in history, and as a result, Uber and other developers of self-driving vehicles temporarily halted their testing. Self-driving cars are just one of thousands of different ways that AI and machine learning are used, and the way that we view this incident has implications for the ways in which we think about AI in the future.

Next month, Dubai will begin testing smart license plates, the BBC reports, and they'll be able to contact emergency services in the event of a crash, communicate with other cars about traffic conditions and display an alert if it or the car it's on are stolen. A trial will begin next month to try out the system, test for any technological issues arising because of the city's hot, arid climate and figure out how best to roll out the devices in the future. Along with its communication features, the digital plate will also allow for users' fines, parking fees and plate registration costs to be deducted automatically from their accounts. However, because the plates will be outfitted with a GPS and transmitters, they could trigger concerns over privacy and data security. Dubai explores the use of new technology quite a bit. In the past, it has tested robot police officers, autonomous patrol cars and a flying taxi service.

Facebook began alerting some users that their personal information was accessed during the Cambridge Analytica data breach, and suffice to say, Facebook users aren't happy. Some 87 million people are estimated to have possibly been affected by the Facebook Cambridge Analytica data breach, a higher number than the social media giant originally anticipated. Last week, Facebook announced that users who may have had their data misused by Cambridge Analytica would get a detailed message via their News Feed on Monday. Facebook has said most of the affected users are in the U.S., though there are over a million each in the Philippines, Indonesia and the U.K. Now users are taking to social media to reveal whether or not their personal information was obtained during the data breach, screen-shotting their Facebook notification. "Facebook sold me out to Cambridge Analytica too," one user wrote on Twitter.