(United States Fourth Circuit) - In a class action complaint against the developer of a mobile video game entitled 'Game of War: Fire Age', pursuant to Federal Rule of Civil Procedure 23(b)(3), asserting a claim under Maryland's gambling loss recovery statute (Loss Recovery Statute), Md. Code Ann., Crim. Law section 12-110, alleging plaintiffs lost money participating in an unlawful 'gaming device,' a component of Game of War that allows players to 'spin' a virtual wheel to win virtual prizes for use within that video game, and seeking recovery of gambling losses that players incurred as a result of 'spinning' the virtual wheel, the district court's dismissal of the complaint is affirmed where the district court correctly concluded that plaintiff did not 'lose money' within the meaning of the Loss Recovery Statute as a result of her participation in the Game of War casino, and thus she failed to state a claim under Maryland's Loss Recovery Statute.

With a lot of celebrities!

The post RAGE AGAINST THE MACHINE's Tom Morello Has Been Playing Weekly Dungeons & Dragons Games appeared first on Metal Injection.

Legendary Roland 808 Drum Machine Inspires New PUMA Sneaker Style

Recent advancements in Machine Learning (ML) have taught us two main lessons: a large proportion of things that humans do can actually be automated, and that a substantial part of this automation can be done with minimal human supervision. One no longer needs to select features for models to use; in many cases people are … Continue reading Three Paper Thursday: Adversarial Machine Learning, Humans and everything in between →

В рамках интервью для Rolling Stone барабанщик METALLICA Lars Ulrich ответил на вопрос о том, какая музыка его зацепила за последнее время:

«Я бы назвал Fiona Apple — её пластинка ["Fetch the Bolt Cutters"] просто сногсшибательна. В тот день, когда альбом только вышел, я его слушал часа три-четыре подряд и читал тексты. Честно говоря, я был поражён тем, насколько это необычно и гениально, потому что это так нестандартно. Каждые несколько лет выходит пластинка с другим звучанием. Я думаю, что последние ARCTIC MONKEYS, выпущенные года два назад ["Tranquility Base Hotel & Casino"], также имели схожий эффект, когда ты получаешь совсем не то, что ожидаешь, потом слушаешь это и просто говоришь: "Вау, это всё ещё возможно — сделать что-то музыкальное и не так, как у всех, ты ощущаешь уникальность, свежесть и неожиданность". Так что я бы сказал, что альбом Fiona Apple определённо из этой категории. Мне также нравится альбом Ed'a O'Brien'а. И его выступление на "Kimmel" — мы смотрели его достаточно часто.

В остальном, я думаю, вы заметили, что я больше обращаюсь ко многим своим старым друзьям [в музыкальном плане]. Пожалуй, музыка, которую я слушал больше всего за последние два-три месяца, — это RAGE AGAINST THE MACHINE. Неужели я единственный, кто думает, что их музыка с каждым днём становится всё более и более актуальной? Она всё больше и больше ассоциируется с тем, что происходит в мире. Просто такое ощущение, что все четыре этих альбома были записаны на прошлой неделе. И когда я делаю свои жалкие слабенькие упражнения, надеваю наушники и начинаю заниматься, я выдаю что-то типа: "Охренеть", когда просто слышу что-то в "Calm Like A Bomb", "Sleep Now In The Fire" или "Bombtrack", и думаю: "Не, ну правда, что ли? Какого хрена? Откуда это вообще взялось?" Так что RAGE AGAINST THE MACHINE никогда не подведут!»

Ulrich также рассказал, что слушают члены его семьи:

«В доме довольно часто можно услышать Radiohead. Radiohead начали делать что-то вроде того, что делает Metallica, — они транслируют концерты, так что у нас дом фанатов Radiohead».

На вопрос о том, чем он заполняет своё время в период самоизоляции, Lars ответил:

«Я пытаюсь оставаться здоровым и здравомыслящим и быть как можно более продуктивным. Я пытаюсь быть более физически активным, чем несколько месяцев назад, занимаясь тренировками и играя, вместо того, чтобы валяться на диване. Это помогает чувствовать себя лучше в психологическом плане». #Metallica #ModernRock #Modern_Rock #AvantgardeRock #Avantgarde_Rock #HeavyMetal #Heavy_Metal #SymphonicMetal #Symphonic_Metal #ThrashMetal #Thrash_Metal

The IBM Research findings achieve the highest recorded accuracy of 86 percent by using deep learning and pathology insights to identify the severity of diabetic retinopathy.

I will meet you there this weekend!

I'll be at TABLE F2 with the lovely keshii! There's going to be a lot of amazing and beautiful people there!

I will have Starfighter: Chapter 01, t-shirts, hot shorts, AND ALSO, some sweet extras. This will be my first con with a table.. and it is all because of you guys! Thank you so, so much! I can only hope I can meet you guys so I can thank you in person; you have my humble love and affection.

I'll see you there, sweethearts! -HamletMachine

TCAF site!

If you're attending, stop by and say hello!

(I don't have a table number yet but I'll be sure to announce this as soon as I know! If not, I'm sure you can find where my table is in the directory!)

EDIT: OH MAN! I almost forgot— At last Yaoi-con, there was a sweet person who had lost her voice from a cold (ah, I'm not sure if I got your name) and had printed out some TCAF info so I could attend— SWEETHEART, IF YOU READ THIS, THANK YOU! I hope to see you there!) -Hamlet

BabyWasher, the automatic dirty-diaper-changing invention, honored by the 2019 Ig Nobel Prize for engineering, now has a name, and is now undergoing intense development. You can follow the progress by visiting the inventor’s new web site, BabyWashers.com.

We have highlighted the issue of door switches causing fire in different white goods to Whirlpool, Government and in our evidence to the Business, Energy and Industrial Strategy Select Committee, so we are pleased to hear that Whirlpool have decided to take the step to get these potentially lethal washing machines out of people’s homes.

Thousands more consumers have learned they are at risk in their own homes from faulty washing machines which have been added to Whirlpool’s expanding list of recalled models

If you’re looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.You’ll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes.

If you’re training a machine learning model but aren’t sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model’s lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.

William Bao Bean, managing director of Chinaccelerator, explains how investments in artificial intelligence and machine learning are helping marketers improve user targeting and return on investment.

12 March 2015 , Volume 91, Number 2

A father is questioning the circumstances that led to the death of his 20-year-old son, Romell Forbes, at a Manchester Avenue-based hardware in May Pen, Clarendon, on Wednesday. Superintendent Christopher Philips, in charge of operations for the...

The Trump administration has significantly cranked up the immigration enforcement machinery in the U.S. interior. Yet even as arrests and deportations are up in the early Trump months, they remain less than half their peaks. This report demonstrates how pushback from California and other "sanctuary" locations makes it quite unlikely that ICE will be able to match record enforcement levels.

OBJECTIVE

We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.

MPI has released a major study that describes and analyzes today’s immigration enforcement programs, as they have developed and grown in the 25 years since IRCA launched the current enforcement era.

Release of a major report that describes and analyzes the immigration enforcement system in the United States as it has developed and grown in the quarter century since the Immigration Reform and Control Act of 1986 launched the current era of enforcement.

OBJECTIVE

To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery.

Internet -- Social aspects.

Edinburgh : printed for William Creech, 1787.

In this paper, we extend the methodology developed for Support Vector Machines (SVM) using the $ell_2$-norm ($ell_2$-SVM) to the more general case of $ell_p$-norms with $p>1$ ($ell_p$-SVM). We derive second order cone formulations for the resulting dual and primal problems. The concept of kernel function, widely applied in $ell_2$-SVM, is extended to the more general case of $ell_p$-norms with $p>1$ by defining a new operator called multidimensional kernel. This object gives rise to reformulations of dual problems, in a transformed space of the original data, where the dependence on the original data always appear as homogeneous polynomials. We adapt known solution algorithms to efficiently solve the primal and dual resulting problems and some computational experiments on real-world datasets are presented showing rather good behavior in terms of the accuracy of $ell_p$-SVM with $p>1$.

Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solution have cubic complexity in the number of datapoints, large datasets require to resort to approximations. In this work, the computation of the least-squares prediction is itself treated as a probabilistic inference problem. We propose a structured Gaussian regression model on the kernel function that uses projections of the kernel matrix to obtain a low-rank approximation of the kernel and the matrix. A central result is an enhanced way to use the method of conjugate gradients for the specific setting of least-squares regression as encountered in machine learning.

When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper. In particular, we propose a fast, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem. The proposed algorithm, Group Alternating Direction Method of Multipliers (GADMM) is based on the Alternating Direction Method of Multipliers (ADMM) framework. The key novelty in GADMM is that it solves the problem in a decentralized topology where at most half of the workers are competing for the limited communication resources at any given time. Moreover, each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with a lower amount of communication overhead in each exchange. We prove that GADMM converges to the optimal solution for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets. Furthermore, we propose Dynamic GADMM (D-GADMM), a variant of GADMM, and prove its convergence under the time-varying network topology of the workers.

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.

In the variational relevance vector machine, the gamma distribution is representative as a hyperprior over the noise precision of automatic relevance determination prior. Instead of the gamma hyperprior, we propose to use the inverse gamma hyperprior with a shape parameter close to zero and a scale parameter not necessary close to zero. This hyperprior is associated with the concept of a weakly informative prior. The effect of this hyperprior is investigated through regression to non-homogeneous data. Because it is difficult to capture the structure of such data with a single kernel function, we apply the multiple kernel method, in which multiple kernel functions with different widths are arranged for input data. We confirm that the degrees of freedom in a model is controlled by adjusting the scale parameter and keeping the shape parameter close to zero. A candidate for selecting the scale parameter is the predictive information criterion. However the estimated model using this criterion seems to cause over-fitting. This is because the multiple kernel method makes the model a situation where the dimension of the model is larger than the data size. To select an appropriate scale parameter even in such a situation, we also propose an extended prediction information criterion. It is confirmed that a multiple kernel relevance vector regression model with good predictive accuracy can be obtained by selecting the scale parameter minimizing extended prediction information criterion.

Restricted Boltzmann Machine (RBM) is an energy based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate exact gradient of log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), which is much faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results are compared with CD. The classification accuracy results indicate similar performance of both methods. Image reconstruction as well as log-likelihood calculations are used to compare the performance of quantum and classical algorithms for RBM training. It is shown that the samples obtained from quantum annealer can be used to train a RBM on a 64-bit `bars and stripes' data set with classification performance similar to a RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer eliminates computationally expensive MCMC steps of CD.

Aryeh Kontorovich, Iosif Pinelis.

Source: The Annals of Statistics, Volume 47, Number 5, 2822--2854.

Abstract:
We provide an exact nonasymptotic lower bound on the minimax expected excess risk (EER) in the agnostic probably-approximately-correct (PAC) machine learning classification model and identify minimax learning algorithms as certain maximally symmetric and minimally randomized “voting” procedures. Based on this result, an exact asymptotic lower bound on the minimax EER is provided. This bound is of the simple form $c_{infty}/sqrt{ u}$ as $ u oinfty$, where $c_{infty}=0.16997dots$ is a universal constant, $ u=m/d$, $m$ is the size of the training sample and $d$ is the Vapnik–Chervonenkis dimension of the hypothesis class. It is shown that the differences between these asymptotic and nonasymptotic bounds, as well as the differences between these two bounds and the maximum EER of any learning algorithms that minimize the empirical risk, are asymptotically negligible, and all these differences are due to ties in the mentioned “voting” procedures. A few easy to compute nonasymptotic lower bounds on the minimax EER are also obtained, which are shown to be close to the exact asymptotic lower bound $c_{infty}/sqrt{ u}$ even for rather small values of the ratio $ u=m/d$. As an application of these results, we substantially improve existing lower bounds on the tail probability of the excess risk. Among the tools used are Bayes estimation and apparently new identities and inequalities for binomial distributions.

T. Tony Cai.

Source: Statistical Science, Volume 34, Number 4, 635--656.

Abstract:
A wide range of nonparametric function estimation models have been studied individually in the literature. Among them the homoscedastic nonparametric Gaussian regression is arguably the best known and understood. Inspired by the asymptotic equivalence theory, Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046) and Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433) developed a unified approach to turn a collection of non-Gaussian function estimation models into a standard Gaussian regression and any good Gaussian nonparametric regression method can then be used. These Gaussianization Machines have two key components, binning and transformation. When combined with BlockJS, a wavelet thresholding procedure for Gaussian regression, the procedures are computationally efficient with strong theoretical guarantees. Technical analysis given in Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046) and Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433) shows that the estimators attain the optimal rate of convergence adaptively over a large set of Besov spaces and across a collection of non-Gaussian function estimation models, including robust nonparametric regression, density estimation, and nonparametric regression in exponential families. The estimators are also spatially adaptive. The Gaussianization Machines significantly extend the flexibility and scope of the theories and methodologies originally developed for the conventional nonparametric Gaussian regression. This article aims to provide a concise account of the Gaussianization Machines developed in Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046), Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433).

AN ENERGY-HARNESSING wave machine is set to begin sea trials later this year.

Many parenting Web sites encourage use of infant "sleep machines" to play ambient noise while infants sleep. Noise recommendations for hospital nurseries suggest a limit of 50 A-weighted dB, whereas occupational standards limit exposure times for noise >85 A-weighted dB.

We measured the maximum sound level outputs of infant sleep machines and found that several devices are capable of producing levels that may be damaging to infant hearing and may be detrimental to auditory development. (Read the full article)

Microbial exposure during early life may prevent, or reduce, the risk of allergy development.

Allergic diseases are less common in children whose parents use hand dishwashing instead of machine dishwashing, and we hypothesize that this allergy-preventive effect is mediated via an increased microbial exposure. (Read the full article)

Despite the worldwide re-emergence of the chikungunya virus (CHIKV) and the high morbidity associated with CHIKV infections, there is no approved vaccine or antiviral treatment available. We here aim to identify the target of a novel class of CHIKV inhibitors i.e. CHVB series. CHVB compounds inhibit the in vitro replication of CHIKV isolates with 50% effective concentrations in the low micromolar range. A CHVB-resistant variant (CHVB^res) was selected that carried two mutations in the gene encoding nsP1 (responsible for viral RNA capping), one mutation in nsP2 and one mutation in nsP3. Reverse genetics studies demonstrated that both nsP1 mutations were necessary and sufficient to achieve ~18-fold resistance, suggesting that CHVB targets viral mRNA capping. Interestingly, CHVB^res was cross-resistant to the previously described CHIKV capping inhibitors from the MADTP series, suggesting they share a similar mechanism of action. In enzymatic assays, CHVB inhibited the methyltransferase and guanylyltransferase activities of alphavirus nsP1 proteins. To conclude, we identified a class of CHIKV inhibitors that targets the viral capping machinery. The potent anti-CHIKV activity makes this chemical scaffold a potential candidate for CHIKV drug development.

HP Smart Tank 530 delivers hassle-free, reliable printing at an affordable price. It is designed to provide good ink tank experience and print quality for home users.

Microsoft announced it has acquired Semantic Machines, a conversational AI startup providing chatbots and AI chat apps founded in 2014 having $20.9 million in funding from investors. The acquisition will help Microsoft catch up with Amazon Alexa, though the latter is more focused on enabling consumer applications of conversational AI.

Microsoft will use Semantic Machine’s acquisition to establish a conversational AI center of excellence in Berkeley to help it innovate in natural language interfaces.

Microsoft has been stepping up its products in conversational AI. It launched the digital assistant Cortana in 2015, as well as social chatbots like XiaoIce. The latest acquisition can help Microsoft beef up its ‘enterprise AI’ offerings.

As the use of NLP (natural language processing) increases in IoT products and services, more startups are getting traction from investors and established players. In June last year, Josh.ai, avoice-controlled home automation software has raised $8M.

Followed by it was SparkCognition that raised $32.5M Series B for its NLP-based threat intelligence platform.

It appears Microsoft’s acquisition of Semantic Machines was motivated by the latter’s strong AI team. The team includes technology entrepreneur Daniel Roth who sold his previous startups Voice Signal Technologies and Shaser BioScience for $300M and $100M respectively. Other team members include Stanford AI Professor Percy Liang, developer of Google Assistant Core AI technology and former Apple chief speech scientist Larry Gillick.

As the stakes for winning server segment market share grow ever higher an increasing number of companies are seeking to grasp the latest Holy Grail of multi-chip coherence. The approach promises to better enable applications such as machine learning...(read more)

BIOVIA Direct 2020

This week Sempra Energy subsidiary PXiSE Energy Solutions announced that Sempra-owned development company Infraestructura Energetica Nova (IEnova) would be using its software at the 110-MW Pima Solar facility located in Mexico to help manage the integration of renewable power to the electric grid.