Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data into spatial and temporal threads, and incorporates the strength of randomized tiling convolutional networks. Experimental evaluations show that it outperforms some state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance; we also achieve an order-of-magnitude speedup over equivalent networks with pretraining and finetuning. Practical suggestions and hints are summarized in the end for effectively handling the tactile data.

Entity Set Expansion (ESE) and Attribute Extraction (AE) are usually treated as two separate tasks in Information Extraction (IE). However, the two tasks are tightly coupled, and each task can benefit significantly from the other by leveraging the inherent relationship between entities and attributes. That is, 1) an attribute is important if it is shared by many typical entities of a class; 2) an entity is typical if it owns many important attributes of a class. Based on this observation, we propose a joint model for ESE and AE, which models the inherent relationship between entities and attributes as a graph. Then a graph reinforcement algorithm is proposed to jointly mine entities and attributes of a specific class. Experimental results demonstrate the superiority of our method for discovering both new entities and new attributes.

We consider a new formulation of abduction in which degrees of "plausibility" of explanations, along with the rules of the domain, are learned from concrete examples (settings of attributes). Our version of abduction thus falls in the " learning to reason " framework of Khardon and Roth. Such approaches enable us to capture a natural notion of "plausibility" in a domain while avoiding the extremely difficult problem of specifying an explicit representation of what is "plausible." We specifically consider the question of which syntactic classes of formulas have efficient algorithms for abduction. We find that the class of k -DNF explanations can be found in polynomial time for any fixed k ; but, we also find evidence that even weak versions of our abduction task are intractable for the usual class of conjunctions . This evidence is provided by a connection to the usual, inductive PAC-learning model proposed by Valiant. We also consider an exception-tolerant variant of abduction. We observe that it is possible for polynomial-time algorithms to tolerate a few adversarially chosen exceptions, again for the class of k -DNF explanations. All of the algorithms we study are particularly simple, and indeed are variants of a rule proposed by Mill.

Researchers have long converged that the evolution of a Social Networking Service (SNS) platform is driven by the interplay between users' preferences (reflected in user-item consumption behavior) and the social network structure (reflected in user-user interaction behavior), with both kinds of users' behaviors change from time to time. However, traditional approaches either modeled these two kinds of behaviors in an isolated way or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of users' historical preferences and the dynamic social network structure affect the evolution of SNSs. Furthermore, can jointly modeling users' temporal behaviors in SNSs benefit both behavior prediction tasks?In this paper, we leverage the underlying social theories(i.e., social influence and the homophily effect) to investigate the interplay and evolution of SNSs. We propose a probabilistic approach to fuse these social theories for jointly modeling users' temporal behaviors in SNSs. Thus our proposed model has both the explanatory ability and predictive power. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.

Sub-event discovery is an effective method for social event analysis in Twitter. It can discover sub-events from large amount of noisy event-related information in Twitter and semantically represent them. The task is challenging because tweets are short, informal and noisy. To solve this problem, we consider leveraging event-related hashtags that contain many locations, dates and concise sub-event related descriptions to enhance sub-event discovery. To this end, we propose a hashtag-based mutually generative Latent Dirichlet Allocation model(MGe-LDA). In MGe-LDA, hashtags and topics of a tweet are mutually generated by each other. The mutually generative process models the relationship between hashtags and topics of tweets, and highlights the role of hashtags as a semantic representation of the corresponding tweets. Experimental results show that MGe-LDA can significantly outperform state-of-the-art methods for sub-event discovery.

We introduce a model-free algorithm for learning in Markov decision processes with parameterized actions—discrete actions with continuous parameters. At each step the agent must select both which action to use and which parameters to use with that action. We introduce the Q-PAMDP algorithm for learning in these domains, show that it converges to a local optimum, and compare it to direct policy search in the goal-scoring and Platform domains.

Jacob Katuva used to get up at dawn to cycle 12 miles from his village to collect water with his uncles and cousins when he was growing up in Kenya. Now he is part of a research team at the University of Oxford using cloud computing and mobile sensors to monitor water wells and help ensure that thousands of villages in rural Africa and Asia have a safe, secure supply of water. The time spent finding and carrying water, if local wells are not reliable, steals precious time from farming, making a living or going to school. It can even force people to revert to unsanitary water sources shared with animals. Water issues are tied to a cycle of poverty.

Two images released last week from upcoming films highlight one glaring Hollywood problem. The first was the visage of Tilda Swinton in the debut trailer for Marvel's "Doctor Strange," in which the actress -- shorn and wearing the white robes of a Tibetan monk -- plays the Ancient One. In the "Doctor Strange" comics, the Ancient One is like Dumbledore meets Yoda: When the shattered former surgeon Stephen Strange makes his way deep into the Himalayas looking for enlightenment and redemption, he becomes a student of the mystically adept, absolutely Asian Ancient One. Marvel's cinematic universe just got a whole lot spookier, and a bit more magical. The world premiere of his new Marvel movie "Doctor Strange" (directed by longtime horror director Scott Derrickson) is loaded with lots of mystical charm with a serious twist.

Adrianne Haslet heard all the talk about taking back Boylston Street in the years after the Boston Marathon bombings. After losing her left leg in the 2013 finish-line explosions, Haslet decided that she would return to the course -- this time as a runner. When the race leaves Hopkinton on Monday, Haslet will be one of 31 members of the One Fund community -- survivors of the attacks, their families and supporters-- in the field. "A lot of people think about the finish line," she said. "I think about the start line."

Janicza Bravo makes short films about loneliness. In one, Michael Cera plays an abrasive paraplegic who can't get lucky. In another, Gaby Hoffmann plays a phone stalker for whom the description "comes on too strong" is not strong enough. Bravo's shorts employ the visual grammar of art-house cinema: over-the-shoulder shots representing a character's point of view, handheld tracking shots depicting urgent movement, lingering closeups to heighten intimacy or unease, carefully composed establishing shots with an actor in the center of the frame. In March, 2015, Bravo went to Venice, on the western edge of Los Angeles, to meet with a production company called Wevr. The name is pronounced "weaver," but it can also be thought of as a sentence, with "We" as the subject and "V.R." as the verb. As anyone who has read a tech blog within the past five years, or a sci-fi novel within the past five decades, knows, "V.R." stands for virtual reality--a loosely defined phrase that is now being applied to several related forms of visual media. You put your smartphone into a portable device like a Google Cardboard or a Samsung Gear--or you use a more powerful computer-based setup, such as the Oculus Rift or the HTC Vive--and the device engulfs your field of vision and tracks your head movement. The filmic world is no longer flat. Wherever you look, there's something to see. The producers at Wevr invited Bravo to write and direct a V.R. project. "I said no," she told me. "It sounded like a technical thing, and I'm not into technical. But then I talked to my husband, and he said, 'How often do people just hand you money in this business?' So I changed my mind." She thought about what kind of story might be told most effectively in the new medium. "The two words I kept hearing about V.R. were'empathy' and'immersion,' and I wasn't sure that being immersed in one of my dark comedies would be all that useful."