A Reference Finding Rarely Seen in Primary Hyperparathyroidism: Brown Tumor

Primary hyperparathyroidism is an endocrinopathy which is characterized with the hypersecretion of parathormone. During the progress of the disease bone loss takes place due to resorption on the subperiosteal and endosteal surfaces. Brown tumor is a localized form of osteitis fibrosa cystica, being part of the hyperparathyroid bone disease. It is rarely the first symptom of hyperparathyroidism. Nowadays, the diagnosis is made at an asymptomatic or minimally symptomatic stage. We present a male patient presented with a massive painless swelling in the left maxilla as the first manifestation of primary hyperparathyroidism due to a parathyroid adenoma. Parathyroidectomy was performed, and there was a regression of the bone lesion, without the need of performing other local surgical procedures

Compound Multiple Access Channels with Partial Cooperation

A two-user discrete memoryless compound multiple access channel with a common message and conferencing decoders is considered. The capacity region is characterized in the special cases of physically degraded channels and unidirectional cooperation, and achievable rate regions are provided for the general case. The results are then extended to the corresponding Gaussian model. In the Gaussian setup, the provided achievable rates are shown to lie within some constant number of bits from the boundary of the capacity region in several special cases. An alternative model, in which the encoders are connected by conferencing links rather than having a common message, is studied as well, and the capacity region for this model is also determined for the cases of physically degraded channels and unidirectional cooperation. Numerical results are also provided to obtain insights about the potential gains of conferencing at the decoders and encoders.Comment: Submitted to IEEE Transactions on Information Theor

Relaying Simultaneous Multicast Messages

The problem of multicasting multiple messages with the help of a relay, which may also have an independent message of its own to multicast, is considered. As a first step to address this general model, referred to as the compound multiple access channel with a relay (cMACr), the capacity region of the multiple access channel with a "cognitive" relay is characterized, including the cases of partial and rate-limited cognition. Achievable rate regions for the cMACr model are then presented based on decode-and-forward (DF) and compress-and-forward (CF) relaying strategies. Moreover, an outer bound is derived for the special case in which each transmitter has a direct link to one of the receivers while the connection to the other receiver is enabled only through the relay terminal. Numerical results for the Gaussian channel are also provided.Comment: This paper was presented at the IEEE Information Theory Workshop, Volos, Greece, June 200

Distributed hypothesis testing over discrete memoryless channels

A distributed binary hypothesis testing (HT) problem involving two parties, one referred to as the observer and the other as the detector is studied. The observer observes a discrete memoryless source (DMS) and communicates its observations to the detector over a discrete memoryless channel (DMC). The detector observes another DMS correlated with that at the observer, and performs a binary HT on the joint distribution of the two DMS’s using its own observed data and the information received from the observer. The trade-off between the type I error probability and the type II error-exponent of the HT is explored. Single-letter lower bounds on the optimal type II errorexponent are obtained by using two different coding schemes, a separate HT and channel coding scheme and a joint HT and channel coding scheme based on hybrid coding for the matched bandwidth case. Exact single-letter characterization of the same is established for the special case of testing against conditional independence, and it is shown to be achieved by the separate HT and channel coding scheme. An example is provided where the joint scheme achieves a strictly better performance than the separation based scheme

Improved policy representation and policy search for proactive content caching in wireless networks

We study the problem of proactively pushing contents into a finite capacity cache memory of a user equipment in order to reduce the long-term average energy consumption in a wireless network. We consider an online social network (OSN) framework, in which new contents are generated over time and each content remains relevant to the user for a random time period, called the lifetime of the content. The user accesses the OSN through a wireless network at random time instants to download and consume all the relevant contents. Downloading contents has an energy cost that depends on the channel state and the number of downloaded contents. Our aim is to reduce the long-term average energy consumption by proactively caching contents at favorable channel conditions. In previous work, it was shown that the optimal caching policy is infeasible to compute (even with the complete knowledge of a stochastic model describing the system), and a simple family of threshold policies was introduced and optimised using the finite difference method. In this paper we improve upon both components of this approach: we use linear function approximation (LFA) to better approximate the considered family of caching policies, and apply the REINFORCE algorithm to optimise its parameters. Numerical simulations show that the new approach provides reduction in both the average energy cost and the running time for policy optimisation

A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images

Cataloged from PDF version of article.Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. (C) 2014 International Society for Advancement of Cytometr

Regional Differences in Economic Impacts of Power Outages in Finland

Estimating the worth of continuity of electricity supply is of interest to industry, governments, regulators and the research community. There are numerous methods to calculate the Customer Interruption Costs (CICs). Each method has its advantages and disadvantages. We approach the problem from the Distribution System Operator (DSO) point of view and employ two existing analytical models. One model is used by the Finnish Energy Market Authority and the second one was proposed by some of the authors in a previous study. Our model offers a simple and straightforward methodology which can provide credible and objective estimations utilizing only publicly available analytical data. We made use of cost and reliability indices data of 78 DSOs in Finland from the 2016. In addition to cost estimations, we highlight regional differences in CIC estimations in different parts of Finland and provide a critical overview of the existing standard customer compensation scheme in Finland

Shadow Pricing of Electric Power Interruptions for Distribution System Operators in Finland

Increasing distributed generation and intermittency, along with the increasing frequency of extreme weather events, pose a serious challenge supply security in the electric power sector. Understanding the costs of interruption is vital for enhancing power system infrastructure and planning the distribution grid. Customer rights and demand response are additional reasons to study the value of power reliability. We make use of the directional distance function and shadow pricing method for a case study from Finland with the aim of calculating the cost of one minute of power interruption from the perspective of the distribution network operator. The sample consists of 78 distribution network operators from Finland based on cost and network information between 2013 and 2015

Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning

Federated edge learning (FEEL) is a promising distributed machine learning (ML) framework to drive edge intelligence applications. However, due to the dynamic wireless environments and the resource limitations of edge devices, communication becomes a major bottleneck. In this work, we propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL, which exploits jointly the power of model compression and over-the-air computation. By exploiting the temporal correlations among model parameters, we construct a global sparsification mask, which is identical across devices, and thus enables efficient model aggregation over-the-air. Each device further constructs a local sparse vector to explore its own important parameters, which are aggregated via digital communication with orthogonal multiple access. We further design device scheduling and power allocation algorithms for TCS-H. Experiment results show that, under limited communication resources, TCS-H can achieve significantly higher accuracy compared to the conventional top-K sparsification with orthogonal model aggregation, with both i.i.d. and non-i.i.d. data distributions

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy Images

Cataloged from PDF version of article.More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms