Generating Diverse and Meaningful Captions

Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty. We make our source code publicly available online.Comment: Accepted for presentation at The 27th International Conference on Artificial Neural Networks (ICANN 2018

Complete Genome Sequence of \u3ci\u3eRickettsia parkeri\u3c/i\u3e Strain Black Gap

A unique genotype of Rickettsia parkeri, designated R. parkeri strain Black Gap, has thus far been associated exclusively with the North American tick, Dermacentor parumapertus. The compete genome consists of a single circular chromosome with 1,329,522 bp and a G+C content of 32.5%

Web Search of Fashion Items with Multimodal Querying

In this paper, we introduce a novel multimodal fashion search paradigm where e-commerce data is searched with a multimodal query composed of both an image and text. In this setting, the query image shows a fashion product that the user likes and the query text allows to change certain product attributes to fit the product to the user’s desire. Multimodal search gives users the means to clearly express what they are looking for. This is in contrast to current e-commerce search mechanisms, which are cumbersome and often fail to grasp the customer’s needs. Multimodal search requires intermodal representations of visual and textual fashion attributes which can be mixed and matched to form the user’s desired product, and which have a mechanism to indicate when a visual and textual fashion attribute represent the same concept. With a neural network, we induce a common, multimodal space for visual and textual fashion attributes where their inner product measures their semantic similarity. We build a multimodal retrieval model which operates on the obtained intermodal representations and which ranks images based on their relevance to a multimodal query. We demonstrate that our model is able to retrieve images that both exhibit the necessary query image attributes and satisfy the query texts. Moreover, we show that our model substantially outperforms two state-of-the-art retrieval models adapted to multimodal fashion search.status: accepte

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

Molecular typing of Rickettsia akari

Rickettsia akari, an obligately intracellular bacterium, is the causative agent of the cosmopolitan urban disease rickettsialpox. R. akari is an atypical representative of spotted fever group rickettsiae (SFG) as it is associated with rodent mites rather than ticks or fleas; however, only limited information is available about the degree of genetic variability found among isolates of R. akari. We examined 13 isolates of R. akari from humans, rodents and mites in the USA, the former Soviet Union, and the former Yugoslavia made between 1946 and 2003 for diversity in their tandem repeat regions (TR) and intergenic regions (IGR). The 1.23 Mb genome of R. akari strain Hartford CWPP was analyzed using Tandem Repeat Finder software (http://tandem.bu.edu) and 374 different TRs were identified, with size variation from 1 to 483 bp and with TR copy numbers ranging between 21 and 1.9, respectively. No size polymorphisms were detected among the 11 TR regions examined from 5 open reading frames and 6 IGR. Eighteen non-TR IGR’s were amplified and sequenced for the same isolates comprising a total of 5.995 bp (0.49%) of the Hartford CWPP strain chromosome. Three single nucleotide polymorphism (SNP) sites were detected in two IGR’s which permitted separation of the five R. akari isolates from Ukraine SSR from the other eight isolates. In conclusion, this is the first study reporting genetic heterogeneity among R. akari isolates of different geographic origins. Further exploration of this genetic diversity is needed to understand better the geographic distribution of R. akari and the epidemiology of rickettsialpox. The potential of mites as hosts for other rickettsial agents also needs further investigation

Multistate Survey of American Dog Ticks \u3ci\u3e(Dermacentor variabilis)\u3c/i\u3e for \u3ci\u3eRickettsia\u3c/i\u3e Species

Dermacentor variabilis, a common human-biting tick found throughout the eastern half and along the west coast of the United States, is a vector of multiple bacterial pathogens. Historically, D. variabilis has been considered a primary vector of Rickettsia rickettsii, the causative agent of Rocky Mountain spotted fever. A total of 883 adult D. variabilis, collected between 2012 and 2017 from various locations in 12 states across the United States, were screened for rickettsial DNA. Tick extracts were evaluated using three real-time PCR assays; an R. rickettsii-specific assay, a Rickettsia bellii-specific assay, and a Rickettsia genus-specific assay. Sequencing of ompA gene amplicons generated using a seminested PCR assay was used to determine the rickettsial species present in positive samples not already identified by species-specific real-time assays. A total of 87 (9.9%) tick extracts contained R. bellii DNA and 203 (23%) contained DNA of other rickettsial species, including 47 (5.3%) with Rickettsia montanensis, 11 (1.2%) with Rickettsia amblyommatis, 2 (0.2%) with Rickettsia rhipicephali, and 3 (0.3%) with Rickettsia parkeri. Only 1 (0.1%) tick extract contained DNA of R. rickettsii. These data support multiple other contemporary studies that indicate infrequent detection of R. rickettsii in D. variabilis in North America

Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male and female speech at various azimuths in the presence of noise produced by wind from various azimuths with a velocity of 3 m/s, using the "clean" speech as a reference. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective intelligibility and for sound quality or comfort. The conditions were unprocessed noisy speech, noisy speech processed using the RNN, and noisy speech that was high-pass filtered (which also reduced wind noise). Eighteen native English-speaking participants were tested, nine with normal hearing and nine with mild-to-moderate hearing impairment. Frequency-dependent linear amplification was provided for the latter. Processing using the RNN was significantly preferred over no processing by both subject groups for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. High-pass filtering (HPF) was not significantly preferred over no processing. Although RNN was significantly preferred over HPF only for sound quality for the hearing-impaired participants, for the results as a whole, there was a preference for RNN over HPF. Overall, the results suggest that reduction of wind noise using an RNN is possible and might have beneficial effects when used in hearing aids