Semantic Perceptual Image Compression using Deep Convolution Networks

It has long been considered a significant problem to improve the visual quality of lossy image and video compression. Recent advances in computing power together with the availability of large training data sets has increased interest in the application of deep learning cnns to address image recognition and image processing tasks. Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression. A modest increase in complexity is incorporated to the encoder which allows a standard, off-the-shelf jpeg decoder to be used. While jpeg encoding may be optimized for generic images, the process is ultimately unaware of the specific content of the image to be compressed. Our technique makes jpeg content-aware by designing and training a model to identify multiple semantic regions in a given image. Unlike object detection techniques, our model does not require labeling of object positions and is able to identify objects in a single pass. We present a new cnn architecture directed specifically to image compression, which generates a map that highlights semantically-salient regions so that they can be encoded at higher quality as compared to background regions. By adding a complete set of features for every class, and then taking a threshold over the sum of all feature activations, we generate a map that highlights semantically-salient regions so that they can be encoded at a better quality compared to background regions. Experiments are presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset, in which our algorithm achieves higher visual quality for the same compressed size.Comment: Accepted to Data Compression Conference, 11 pages, 5 figure

Biodiversity and Conservation Study of the St. Norbert Abbey, De Pere, Wisconsin - 2018 Annual Report

Long term biological studies are rare but incredibly valuable for examining natural phenomena. Data collected over many years or decades allows for analyses of trends that would never be apparent in a single season. The research presented here is the completion of the second annual field season of a long term study to analyze biodiversity trends at the St. Norbert Abbey. Using visual searches, live trapping, and trail cameras, the biodiversity and abundance of species were examined. Special emphasis was placed on small mammals, though other groups were observed and documented. The goals of this project are to (1) collect data annually on diversity and abundance measures to allow for examination of variation and long term trends; and (2) further a research partnership and platform capable of providing stakeholders with pertinent biological data to ensure sound conservation and management decisions

Hybrid expert ensembles for identifying unreliable data in citizen science

Citizen science utilises public resources for scientific research. BirdTrack is such a project established in 2004 by the British Trust for Ornithology (BTO) for the public to log their bird observations through its web or mobile applications. It has accumulated over 40 million observations. However, the veracity of these observations needs to be checked and the current process involves time-consuming interventions by human experts. This research therefore aims to develop a more efficient system to automatically identify unreliable observations from large volume of records. This paper presents a novel approach — a Hybrid Expert Ensemble System (HEES) that combines an Expert System (ES) and machine induced models to perform the intended task. The ES is built based on human expertise and used as a base member of the ensemble. Other members are decision trees induced from county-based data. The HEES uses accuracy and diversity as criteria to select its members with an aim of improving its accuracy and reliability. The experiments were carried out using the county-based data and the results indicate that (1) the performance of the expert system is reasonable for some counties but varied considerably on others. (2) An HEES is more accurate and reliable than the Expert System and also other individual models, with Sensitivity of 85% for correctly identifying unreliable observations and Specificity of 99% for reliable observations. These results demonstrated that the proposed approach has the ability to be an alternative or additional means to validate the observations in a timely and cost-effective manner and also has a potential to be applied in other citizen science projects where the huge amount of data needs to be checked effectively and efficiently

Economic irrationality is optimal during noisy decision making

According to normative theories, reward-maximizing agents should have consistent preferences. Thus, when faced with alternatives A, B, and C, an individual preferring A to B and B to C should prefer A to C. However, it has been widely argued that humans can incur losses by violating this axiom of transitivity, despite strong evolutionary pres- sure for reward-maximizing choices. Here, adopting a biologically plausible computational framework, we show that intransitive (and thus economically irrational) choices paradoxically improve accuracy (and subsequent economic rewards) when decision formation is cor- rupted by internal neural noise. Over three experiments, we show that humans accumulate evidence over time using a “selective inte- gration” policy that discards information about alternatives with mo- mentarily lower value. This policy predicts violations of the axiom of transitivity when three equally valued alternatives differ circularly in their number of winning samples. We confirm this prediction in a fourth experiment reporting significant violations of weak stochastic transitivity in human observers. Crucially, we show that relying on selective integration protects choices against “late” noise that other- wise corrupts decision formation beyond the sensory stage. Indeed, we report that individuals with higher late noise relied more strongly on selective integration. These findings suggest that violations of ra- tional choice theory reflect adaptive computations that have evolved in response to irreducible noise during neural information processing