There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Satellite imagery provides an efficient means of assessing and effectively planning search and rescue efforts in the aftermath of disasters such as earthquakes, flooding, tsunamis, wildfires, and conflicts. It enables timely visualization of buildings and the human population affected by these disasters and provides humanitarian organizations with crucial information needed to strategize and deliver the much need aid effectively. Recent research on remote sensing combines machine learning methodologies with satellite imagery to automate information extraction, thus reducing turn-around time and manual labor. The existing state-of-the-art approach for building damage assessment relies on an ensemble of different models to obtain independent predictions that are then aggregated to one final output. Other methods rely on a multi-stage model that involves a building localization module and a damage classification module. These methods are either not end-to-end trainable or are impractical for real-time applications. This paper proposes an Attention-based Two-Stream High-Resolution Network (ATS-HRNet), which unifies the building localization and classification problem in an end-to-end trainable manner. The basic residual blocks in HRNet are replaced with attention-based residual blocks to improve the model's performance. Furthermore, a modified cutmix data augmentation technique is introduced for handling class imbalance in satellite imagery. Experiments show that our approach significantly performs better than the baseline and other state-of-the-art methods for building damage classification.

Attention-based two-stream high-resolution networks for building damage assessment from satellite imagery

Augmented Reality (AR) can seamlessly combine a real scene viewed by a user and a virtual component generated by a computer. This work introduces a system architecture integrating augmented reality technology with state-of-art computer vison techniques such as image semantic segmentation and style colorization. The proposed production system, ARNature, is able to superimpose a virtual scene, audio, and other enhancements in real time over a realworld environment for enhancing tourism experience. ARNature: A lot times, tourists have limited money and time to experience a tourist site during different seasons or weather conditions. The visitors are able to go on an augmented reality journey using an AR device, such as HoloLens, tablet or cellphones, and interact with real objects in a natural scene. Different enhancements of a tourism site are digitally overlaid onto visitors’ direct field of vision in real time. In addition, a voice module can be used to play music and provide additional information. Related algorithms, system design, and simulation results for a prototype ARNature system are presented. Furthermore, a no-reference image quality measure, Naturalness Image Quality Evaluator (NIQE), was utilized to evaluate the immersiveness and naturalness of ARNature. The results demonstrate that ARNature has the ability to enhance tourist experience in a truly immersive manner.

ARNature: augmented reality style colorization for enhancing tourism experience

Cellular communications constitute a significant portion of the global telecommunications market. Therefore, the
need for secured communication over a mobile platform has increased exponentially. Steganography is an art of hiding
critical data into an innocuous signal, which provide answers to the above needs. The JPEG is one of commonly used
format for storing and transmitting images on the web. In addition, the pictures captured using mobile cameras are in
mostly in JPEG format.
In this article, we introduce a switching theory based steganographic system for JPEG images which is applicable
for mobile and computer platforms. The proposed algorithm uses the fact that energy distribution among the quantized
AC coefficients varies from block to block and coefficient to coefficient. Existing approaches are effective with a part
of these coefficients but when employed over all the coefficients they show there ineffectiveness. Therefore, we propose
an approach that works each set of AC coefficients with different frame work thus enhancing the performance of the
approach. The proposed system offers a high capacity and embedding efficiency simultaneously withstanding to simple
statistical attacks. In addition, the embedded information could be retrieved without prior knowledge of the cover
image. Based on simulation results, the proposed method demonstrates an improved embedding capacity over existing
algorithms while maintaining a high embedding efficiency and preserving the statistics of the JPEG image after hiding
information.

Switching theory-based steganographic system for JPEG images

Aerial border surveillance is a crucial activity, which can assure the security of the country boarder and aid in search and rescue missions. This paper offers a novel “hands-free” tool for aerial border surveillance, search and rescue missions using head-mounted eye tracking technology. The contributions of this work are: i) a gaze based aerial boarder surveillance object classification and recognition framework; ii) real-time object detection and identification system in non-scanned regions; iii) investigating the scan-path (fixation and non-scanned) provided by mobile eye tracker can help improve training professional search and rescue organizations or even artificial intelligence robots for searching and rescuing missions. The proposed system architecture is further demonstrated using a dataset of large-scale real-life head-mounted eye tracking data.

Aerial Border Surveillance for Search and Rescue Missions Using Eye Tracking Techniques

While bandwidth and storage capabilities are increasing with advances in technology, so are the demands of users as
quantities of data to transmit and store grow simultaneously. The need for effective signal compression is always
present, as reduction in data size is required while maintaining minimal signal destruction. This paper presents a new
signal compression scheme that uses coordinate logic transforms in combination with Boolean minimized
representations. The processing of the coordinate logic transforms on signal data helps reduce unnecessary signal
complexity allowing more effective data reduction during the Boolean minimized form encoding. The coordinate logic
transforms are an alternate method for calculating coordinate logic filters which simplify the computational complexity
through algorithmic parallelism. In this work, the combination of coordinate logic transforms with a Boolean minimized
form encoding scheme allows for a new compression technique that is applicable to both binary and grayscale input images.

Sos S. Agaian

Papers

Attention-based two-stream high-resolution networks for building damage assessment from satellite imagery

ARNature: augmented reality style colorization for enhancing tourism experience

Switching theory-based steganographic system for JPEG images

Aerial Border Surveillance for Search and Rescue Missions Using Eye Tracking Techniques

Signal compression via coordinate logic transforms