New record set for the world's fastest BED with motorised mattress clocking 84mph on a race track Engineers were commissioned by a hotel booking site to build a motorised bed British racing diver Tom Onslow-Cole, 29, took the piece of furniture for a spin He broke the Guinness World Record for the World's Fastest Bed at 83.8mph He broke the Guinness World Record for the World's Fastest Bed at 83.8mph British racing diver Tom Onslow-Cole, 29, took the piece of furniture for a spin and broke the Guinness World Record for the World's Fastest Bed, clocking 84mph The do's and don'ts of après-ski revealed (including why... Aviation expert reveals how to travel in luxury on a... The do's and don'ts of après-ski revealed (including why... Aviation expert reveals how to travel in luxury on a... Crossing the finish line: Adjudicators clocked it whooshing forwards at 83.8 mph A wheely great sleep: Onslow-Cole said his speedy snooze was an'unforgettable experience'. He added: 'I hope it'll stand the test of time – it'll take some beating!' Woman goes on racist rant while waiting in line at J.C. Penney Black blues musician explores racism by befriending the KKK A young thug is filmed fly kicking a lady in the back Dramatic moment man removed from flight for'speaking Arabic' GRAPHIC: Robber is left writhing on the pavement after shot out Syrian police injured after girl blows herself up inside station Male guests in a Chinese wedding flock to harass a bridesmaid Angela Rye shares video of her invasive ordeal with TSA agent Body cam footage shows moments before two Georgia cops are shot Boeing cargo plane overshoots runway before crashing in Colombia Shocking video shows a Texas mother hitting her daughter Adorable moment puppy excitedly unwraps Christmas present Woman goes on racist rant while waiting in line at J.C. Penney Dramatic moment man removed from flight for'speaking Arabic' Is resting your head on a BOX the best way to sleep on a... Shocking pictures reveal how some of the most picturesque... Choose the right seat, alter your watch and drink alcohol:... Fascinating images capture the... Should you be worried about flying in the snow? When photographers were asked to submit their best holiday... 'Is this a real picture?

We present a general theoretical analysis of structured prediction with a series of new results. We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition. These are the tightest margin bounds known for both standard multi-class and general structured prediction problems. Our guarantees are expressed in terms of a data-dependent complexity measure, factor graph complexity, which we show can be estimated from data and bounded in terms of familiar quantities. We further extend our theory by leveraging the principle of Voted Risk Minimization (VRM) and show that learning is possible even with complex factor graphs. We present new learning bounds for this advanced setting, which we use to design two new algorithms, Voted Conditional Random Field (VCRF) and Voted Structured Boosting (StructBoost). These algorithms can make use of complex features and factor graphs and yet benefit from favorable learning guarantees. We also report the results of experiments with VCRF on several datasets to validate our theory.

Hundreds of the nation's top female soccer players are expected to gather in Norco on Friday for the first day of a three-day college showcase. More than 140 registered teams from all over the western U.S. are scheduled to compete before more than 100 coaches from 16 conferences and more than three dozen states. Among the elite clubs who have confirmed their participation are Slammers FC, Legends FC, Eagles SC, Sereno Soccer Club of Arizona, LA Premier FC and Pateadores SC. The event kicks off at 8 a.m. For information, go to the tournament's website at silverlakestournaments.com.

Many machine learning problems, especially multi-modal learning problems, have two sets of distinct features (e.g., image and text features in news story classification, or neuroimaging data and neurocognitive data in cognitive science research). This paper addresses the joint dimensionality reduction of two feature vectors in supervised learning problems. In particular, we assume a discriminative model where low-dimensional linear embeddings of the two feature vectors are sufficient statistics for predicting a dependent variable. We show that a simple algorithm involving singular value decomposition can accurately estimate the embeddings provided that certain sample complexities are satisfied, without specifying the nonlinear link function (regressor or classifier). The main results establish sample complexities under multiple settings. Sample complexities for different link functions only differ by constant factors.

A key barrier to making phonetic studies scalable and replicable is the need to rely on subjective, manual annotation. To help meet this challenge, a machine learning algorithm was developed for automatic measurement of a widely used phonetic measure: vowel duration. Manually-annotated data were used to train a model that takes as input an arbitrary length segment of the acoustic signal containing a single vowel that is preceded and followed by consonants and outputs the duration of the vowel. The model is based on the structured prediction framework. The input signal and a hypothesized set of a vowel's onset and offset are mapped to an abstract vector space by a set of acoustic feature functions. The learning algorithm is trained in this space to minimize the difference in expectations between predicted and manually-measured vowel durations. The trained model can then automatically estimate vowel durations without phonetic or orthographic transcription. Results comparing the model to three sets of manually annotated data suggest it out-performed the current gold standard for duration measurement, an HMM-based forced aligner (which requires orthographic or phonetic transcription as an input).

A former solicitor general has said she is concerned the Ched Evans rape case could discourage victims of sexual offences from coming forward. The 27-year-old footballer was cleared on Friday of raping a 19-year-old woman in a hotel room. Vera Baird told the BBC that details of the woman's sexual past should not have been heard in court. Mr Evans was found guilty of rape in 2012, but that conviction was quashed in April. The Chesterfield striker was accused of attacking the woman at a Premier Inn in Rhuddlan, Denbighshire, on 30 May 2011.

A key pretrial hearing to determine what evidence prosecutors can use in Bill Cosby's Pennsylvania sex assault case has been scheduled for early November. Prosecutors hope to call 13 other accusers to show the comedian had a pattern of drugging and molesting women. The criminal charges involve an encounter with Andrea Constand in 2004. Prosecutors also want to use Cosby's deposition from Constand's 2005 lawsuit. Cosby acknowledges under oath that he had sexual encounters with a series of women after giving them drugs or alcohol.

Back in 1970, the social activist Irina Dunn scribbled a slogan on the back of a toilet cubicle door at the University of Sydney. It said: "A woman needs a man like a fish needs a bicycle." The phrase went viral and eventually became a famous refrain for the growing feminist movement of the time. The phrase is also an example of sarcasm. The humor comes from the fact that a fish doesn't need a bicycle.

Multilingual Text Classification (MLTC) is a text classification task in which documents are written each in one among a set L of natural languages, and in which all documents must be classified under the same classification scheme, irrespective of language. There are two main variants of MLTC, namely Cross-Lingual Text Classification (CLTC) and Polylingual Text Classification (PLTC). In PLTC, which is the focus of this paper, we assume (differently from CLTC) that for each language in L there is a representative set of training documents; PLTC consists of improving the accuracy of each of the |L| monolingual classifiers by also leveraging the training documents written in the other (|L| − 1) languages. The obvious solution, consisting of generating a single polylingual classifier from the juxtaposed monolingual vector spaces, is usually infeasible, since the dimensionality of the resulting vector space is roughly |L| times that of a monolingual one, and is thus often unmanageable. As a response, the use of machine translation tools or multilingual dictionaries has been proposed. However, these resources are not always available, or are not always free to use. One machine-translation-free and dictionary-free method that, to the best of our knowledge, has never been applied to PLTC before, is Random Indexing (RI). We analyse RI in terms of space and time efficiency, and propose a particular configuration of it (that we dub Lightweight Random Indexing LRI). By running experiments on two well known public benchmarks, Reuters RCV1/RCV2 (a comparable corpus) and JRC-Acquis (a parallel one), we show LRI to outperform (both in terms of effectiveness and efficiency) a number of previously proposed machine-translation-free and dictionary-free PLTC methods that we use as baselines.

Last time we investigated the k-nearest-neighbors algorithm and the underlying idea that one can learn a classification rule by copying the known classification of nearby data points. This required that we view our data as sitting inside a metric space; that is, we imposed a kind of geometric structure on our data. One glaring problem is that there may be no reasonable way to do this. While we mentioned scaling issues and provided a number of possible metrics in our primer, a more common problem is that the data simply isn't numeric. For instance, a poll of US citizens might ask the respondent to select which of a number of issues he cares most about. There could be 50 choices, and there is no reasonable way to assign these numerical values so that all are equidistant in the resulting metric space. Another issue is that the quality of the data could be bad. For instance, there may be missing values for some attributes (e.g., a respondent may neglect to answer one or more questions).