Optimal Stopping of a Random Sequence with Unknown Distribution

The subject of this paper is the problem of optimal stopping of a sequence of independent and identically distributed random variables with unknown distribution. We propose a stopping rule that is based on relative ranks and study its performance as measured by the maximal relative regret over suitable nonparametric classes of distributions. It is shown that the proposed rule is first-order asymptotically optimal and nearly rate optimal in terms of the rate at which the relative regret converges to zero. We also develop a general method for numerical solution of sequential stopping problems with no distributional information and use it in order to implement the proposed stopping rule. Some numerical experiments illustrating performance of the rule are presented as well.

Category-specific representational patterns in left inferior frontal and temporal cortex reflect similarities and differences in the sensorimotor and distributional properties of concepts

Neuronal populations code similar concepts by similar activity patterns across the human brain’s networks supporting language comprehension. However, it is unclear to what extent such meaning-to-symbol mapping reflects statistical distributions of symbol meanings in language use, as quantified by word co-occurrence frequencies, or, rather, experiential information thought to be necessary for grounding symbols in sensorimotor knowledge. Here we asked whether integrating distributional semantics with human judgments of grounded sensorimotor semantics better approximates the representational similarity of conceptual categories in the brain, as compared with each of these methods used separately. We examined the similarity structure of activation patterns elicited by action- and object-related concepts using multivariate representational similarity analysis (RSA) of fMRI data. The results suggested that a semantic vector integrating both sensorimotor and distributional information yields best category discrimination on the cognitive-linguistic level, and explains the corresponding activation patterns in left posterior inferior temporal cortex. In turn, semantic vectors based on detailed visual and motor information uncovered category-specific similarity patterns in fusiform and angular gyrus for object-related concepts, and in motor cortex, left inferior frontal cortex (BA 44), and supramarginal gyrus for action-related concepts.

New species and records of Anthomyzidae (Diptera) from the East Palaearctic, with a checklist of taxa occurring in the area

The knowledge of Anthomyzidae (Diptera: Acalyptratae) in the East Palaearctic area is extended by new taxonomic, biological and distributional information. Two new species of the genus Anthomyza Fallén, 1810, viz. A. aspina sp. nov. (Russia: Far East) and A. breviclavus sp. nov. (North Korea), and a species tentatively affi liated with the genus Zealantha Roháček, 2007, viz. Z. fasciolata sp. nov. (Japan) are described. Although all are based only on females, they are distinctive species unmistakably recognized by external characters and structures of the female postabdomen. Relationships of all new species are discussed. Records of 24 species are presented; 3 species are recorded from the E. Palaearctic, 1 species from continental Asia, 3 species from North Korea and a number of species from particular parts of E. Palaearctic Russia for the fi rst time. An updated checklist of East Palaearctic Anthomyzidae is presented, now comprising 13 genera and 44 species. Taxonomic notes for Anthomyza clara Roháček, 2006, Fungomyza cercata Roháček, 2009 and some other species, and new biological information for Anthomyza trifurca Sueyoshi & Roháček, 2003 are given. Longitudinal dark patterning of the wing found in A. breviclavus sp. nov., represents the fi fth lineage of Anthomyzidae in which this type of pattern has evolved independently. Species richness of Anthomyzidae in the E. Palaearctic is discussed with an estimate of about 60 species occurring in the area.

Deals or No Deals: Contract Design for Online Advertising

We present a formal study of first-look and preferred deals that are a recently introduced generation of contracts for selling online advertisements, which generalize traditional reservation contracts and are suitable for advertisers with advanced targeting capabilities. Under these deals, one or more advertisers gain priority access to an inventory of impressions before others and can choose to purchase in real time. In particular, we propose constant-factor approximation algorithms for maximizing the revenue that can be obtained from these deals when offered to all or a subset of the advertisers, whose valuation distributions can be independent or correlated through a common value component. We evaluate our algorithms using data from Google’s ad exchange platform and show they perform better than the approximation guarantees and obtain significantly higher revenue than auctions; in certain cases, the observed revenue is 85%–96% of the optimal revenue achievable. We also prove the NP-hardness of designing deals when advertisers’ valuations are arbitrarily correlated and the optimality of menus of deals among a certain class of selling mechanisms in an incomplete distributional information setting.

Adults adapt to child speech in semantic use

Children acquire their first language while interacting with adults in a highly adaptive manner. While adaptation occurs at many linguistic levels such as syntax and speech complexity, semantic adaptation remains unclear due to the difficulty of efficient meaning extraction. In this study, we examine the adaptation of semantics with a computational approach based on distributional information. We show that adults, in their speech addressed to children, adapt their distributional semantics to that in the speech children produce. By analyzing semantic representations modeled from the Manchester corpus, a large longitudinal acquisition corpus of English, we find striking similarity of semantic development between child and child-directed speech, with a slight time lag in the latter. These findings provide strong evidence for the semantic adaptation in first language acquisition and suggest the important role of child-directed speech in semantic learning.

TüEyeQ, a rich IQ test performance data set with eye movement, educational and socio-demographic information

We present the TüEyeQ data set - to the best of our knowledge - the most comprehensive data set generated on a culture fair intelligence test (CFT 20-R), i.e., an IQ Test, consisting of 56 single tasks, taken by 315 individuals aged between 18 and 30 years. In addition to socio-demographic and educational information, the data set also includes the eye movements of the individuals while taking the IQ test. Along with distributional information we also highlight the potential for predictive analysis on the TüEyeQ data set and report the most important covariates for predicting the performance of a participant on a given task along with their influence on the prediction.

Distributional learning of recursive structures

Languages differ regarding the depth, structure, and syntactic domains of recursive structures. Even within a single language, some structures allow infinite self-embedding while others are more restricted. For example, English allows infinite free embedding of the prenominal genitive -s, whereas the postnominal genitive of is largely restricted to only one level and to a limited set of items. Therefore, while the ability for recursion is considered as a crucial part of the language faculty, speakers need to learn from experience which specific structures allow free embedding and which do not. One effort to account for the mechanism that underlies this learning process, the distributional learning proposal, suggests that the recursion of a structure (e.g. X1’s-X2) is licensed if the X1 position and the X2 position are productively substitutable in the input. A series of corpus studies have confirmed the availability of such distributional cues in child directed speech. The present study further tests the distributional learning proposal with an artificial language learning experiment. We found that, as predicted, participants exposed to productive input were more likely to accept unattested strings at both one and two-embedding levels than participants exposed to unproductive input. Therefore, our results suggest that speakers can indeed use distributional information at one level to learn whether or not a structure is freely recursive.

Synopsis of Cnidoscolus (Euphorbiaceae) in midwestern Brazil, including taxonomic updates, a new species, and a reestablishment of C. neglectus

A synopsis of the genus Cnidoscolus is presented for the midwestern region of Brazil, which resulted from the analysis of about 1,200 specimens from 62 national and foreign herbaria, including type collections. Observations of populations in field were also made. Nine species are recognized, one of which, C. mcvaughii, is new to science. It is described and illustrated, and comments about its geographic distribution, morphological relationships, systematic position, phenology, and conservation status are provided, as well as images and a map. The other species are contrasted by a dichotomous key. Also, distributional information, maps, conservation evaluations, images and morphologically diagnoses are included. Eight synonymizations, one lectotypification, a neotypifcation, and the re-establishment of C. neglectus are proposed.

Freshwater molluscs of the Zrmanja and Krupa Rivers (Croatia) – the role of these rivers as a refuge of threatened and endemic species

The Balkan region is inhabited by hundreds of endemic species of freshwater molluscs but the information on their distribution, as well as that on the distribution of more common and widespread species, is still insufficient; most areas have no complete inventories of their mollusc faunas. The Zrmanja is one of the largest rivers in the Adriatic part of Croatia. Its freshwater molluscs were studied in the last decade, resulting in a substantial body of distributional data. The main objective of this study was to summarise and analyse the recent distributional information on its freshwater molluscs, with special reference to endemic or threatened species. In total 33 freshwater mollusc species were found in the Zrmanja River and its tributaries at 73 sites. The middle section of the Zrmanja River and its largest tributary, the Krupa River, were the richest studied parts. Dalmatinella fluviatilis Radoman, Tanousia zrmanjae (Brusina) and Islamia zermanica Radoman, endemic to the Zrmanja catchment area, were found in the middle and lower sections of the river. Their occurrence is scattered and limited in area; T. zrmanjae is probably on the verge of extinction. The occurrence of the endemic Belgrandiella kusceri (Wagner), Hadziella sketi Bole and Kerkia sp. in springs or underground waters and an abundant population of Unio elongatulus C. Pfeiffer in the Zrmanja and Krupa Rivers is also noteworthy. The area can be regarded as an important part of the Balkan region, one of world’s hotspots of global biodiversity. Although it is protected, there are still anthropogenic factors that are negatively affecting or may affect the mollusc assemblages.

Linking the neural basis of hierarchical prediction with statistical learning: The paradox of attention

Statistical learning, the process of tracking distributional information and discovering embedded patterns, is traditionally regarded as a form of implicit learning. However, recent studies proposed that both implicit (attention-independent) and explicit (attention-dependent) learning systems are involved in statistical learning. To understand the role of attention in statistical learning, the current study investigates the cortical processing of prediction errors in speech based on either local or global distributional information. We then ask how these cortical responses relate to statistical learning behavior in a word segmentation task. We found ERP evidence of pre-attentive processing of both the local (mismatching negativity) and global distributional information (late discriminative negativity). However, as speech elements became less frequent and more surprising, some participants showed an involuntary attentional shift, reflected in a P3a response. Individuals who displayed attentive neural tracking of distributional information showed faster learning in a speech statistical learning task. These results provide important neural evidence elucidating the facilitatory role of attention in statistical learning.