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

Remote contextual bandits

We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel. This can model, for example, a personalized ad placement application, where the content owner observes the individual visitors to its website, and hence has the context information, but must convey the ads that must be shown to each visitor to a separate entity that manages the marketing content. In this remote CMAB (R-CMAB) problem, the constraint on the communication rate between the decision-maker and the agents imposes a trade-off between the number of bits sent per agent and the acquired average reward. We are particularly interested in characterizing the rate required to achieve sub-linear regret. Consequently, this can be considered as a policy compression problem, where the distortion metric is induced by the learning objectives. We first study the fundamental information theoretic limits of this problem by letting the number of agents go to infinity, and study the regret achieved when Thompson sampling strategy is adopted. In particular, we identify two distinct rate regions resulting in linear and sub-linear regret behavior, respectively. Then, we provide upper bounds for the achievable regret when the decision-maker can reliably transmit the policy without distortion

Remote Contextual Bandits

We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel. This can model, for example, a personalized ad placement application, where the content owner observes the individual visitors to its website, and hence has the context information, but must convey the ads that must be shown to each visitor to a separate entity that manages the marketing content. In this remote CMAB (R-CMAB) problem, the constraint on the communication rate between the decision-maker and the agents imposes a trade-off between the number of bits sent per agent and the acquired average reward. We are particularly interested in characterizing the rate required to achieve sub-linear regret. Consequently, this can be considered as a policy compression problem, where the distortion metric is induced by the learning objectives. We first study the fundamental information theoretic limits of this problem by letting the number of agents go to infinity, and study the regret achieved when Thompson sampling strategy is adopted. In particular, we identify two distinct rate regions resulting in linear and sub-linear regret behavior, respectively. Then, we provide upper bounds for the achievable regret when the decision-maker can reliably transmit the policy without distortion

Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel

We consider distributed image transmission over a noisy multiple access channel (MAC) using deep joint source-channel coding (DeepJSCC). It is known that Shannon's separation theorem holds when transmitting independent sources over a MAC in the asymptotic infinite block length regime. However, we are interested in the practical finite block length regime, in which case separate source and channel coding is known to be suboptimal. We introduce a novel joint image compression and transmission scheme, where the devices send their compressed image representations in a non-orthogonal manner. While non-orthogonal multiple access (NOMA) is known to achieve the capacity region, to the best of our knowledge, non-orthogonal joint source channel coding (JSCC) scheme for practical systems has not been studied before. Through extensive experiments, we show significant improvements in terms of the quality of the reconstructed images compared to orthogonal transmission employing current DeepJSCC approaches particularly for low bandwidth ratios. We publicly share source code to facilitate further research and reproducibility.Comment: To appear in IEEE International Conference on Communications (ICC) 202

Additive Manufacturing and Testing of High Metal Content High Performance Ramjet Grains

NPS NRP Project PosterFuels with high efficiency and energy densities are needed to maximize the range and speed of future air-breathing systems such as solid fuel ramjets (SFRJ). The performance of the fuel mixtures that include large amount of metal additives suffer due to the poor combustion efficiency of the metal powders as they often do not burn completely during the short residence time in the combustor. Recent research has improved the reactivity of these fuels, but introducing them into a binder at high loading densities is a challenge due to the poor rheology. In order to develop and maximize the energy density and performance of SFJR fuel grains, advancements in additive manufacturing (AM) systems will be leveraged. This study will utilize vibration-assisted printing (VAP) and liquid metal printing (LMP) with the Xerox ElemX system to print fuel grains with metal powders and aluminum alloys, and use spray dried nanocomposite mesoparticles as additives. The research will test the physical limits of these approaches and determine optimal printing parameters for producing high quality printed fuels. The fuels will be evaluated mechanically and optimized using fly out calculations and they will be characterized with small scale combustion studies.Naval Air Warfare Center Weapons Division (NAWCWD)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Additive Manufacturing and Testing of High Metal Content High Performance Ramjet Grains

NPS NRP Executive SummaryFuels with high efficiency and energy densities are needed to maximize the range and speed of future air-breathing systems such as solid fuel ramjets (SFRJ). The performance of the fuel mixtures that include large amount of metal additives suffer due to the poor combustion efficiency of the metal powders as they often do not burn completely during the short residence time in the combustor. Recent research has improved the reactivity of these fuels, but introducing them into a binder at high loading densities is a challenge due to the poor rheology. In order to develop and maximize the energy density and performance of SFJR fuel grains, advancements in additive manufacturing (AM) systems will be leveraged. This study will utilize vibration-assisted printing (VAP) and liquid metal printing (LMP) with the Xerox ElemX system to print fuel grains with metal powders and aluminum alloys, and use spray dried nanocomposite mesoparticles as additives. The research will test the physical limits of these approaches and determine optimal printing parameters for producing high quality printed fuels. The fuels will be evaluated mechanically and optimized using fly out calculations and they will be characterized with small scale combustion studies.Naval Air Warfare Center Weapons Division (NAWCWD)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information

We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario). In particular, we are interested in developing practical schemes using a data-driven joint source-channel coding (JSCC) approach, which has been previously shown to outperform conventional separation-based approaches in the practical finite blocklength regimes, and to provide graceful degradation with channel quality. We propose a novel neural network architecture that incorporates the decoder-only side information at multiple stages at the receiver side. Our results demonstrate that the proposed method succeeds in integrating the side information, yielding improved performance at all channel noise levels in terms of the various distortion criteria considered here, especially at low channel signal-to-noise ratios (SNRs) and small bandwidth ratios (BRs). We also provide the source code of the proposed method to enable further research and reproducibility of the results.Comment: 7 pages, 4 figure

Sparse random networks for communication-efficient federated learning

One main challenge in federated learning is the large communication cost of ex-changing weight updates from clients to the server at each round. While prior work has made great progress in compressing the weight updates through gradient compression methods, we propose a radically different approach that does not update the weights at all. Instead, our method freezes the weights at their initial random values and learns how to sparsify the random network for the best performance. To this end, the clients collaborate in training a stochastic binary mask to find the optimal sparse random network within the original one. At the end of the training, the final model is a sparse network with random weights – or a sub-network inside the dense random network. We show improvements in accuracy, communication (less than 1 bit per parameter (bpp)), convergence speed, and final model size (less than 1 bpp) over relevant baselines on MNIST, EMNIST, CIFAR- 10, and CIFAR-100 datasets, in the low bitrate regime

Additive Manufacturing of High Solids Loading Hybrid Rocket Fuel Grains

Hybrid rocket motors offer many of the benefits of both liquid and solid rocket systems. Like liquid engines, hybrid rocket motors are able to be throttled, can be stopped and restarted, and are safer than solid rocket motors since the fuel and oxidizer are in different physical states. Hybrid rocket motors are similar to solid motors in that they are relatively simple and have a high density-specific impulse. One of the major drawbacks of hybrid rocket motors is a slower burning rate than solid rocket motors. Complex port geometries provide greater burning surface area to compensate for lower burning rates but are difficult and expensive to manufacture. Additive manufacturing can reduce manufacturing costs of these complex port geometry fuel grains. It has also been shown that the addition of energetic materials, such as aluminum, can increase the burning rate and density-specific impulse of the rocket motor. Previously, additive manufacturing was restricted to plastics or fast-setting paraffin wax, both with low solids concentrations. This paper investigates the process of printing hybrid rocket fuel grains and the differences in physical characteristics between printed and conventionally cast samples. Using a proprietary printing system, we have successfully printed 85% solids loading aluminum and HTPB fuel samples. Material creep was significant and resulted in samples bulging and sagging as well as gaps between print lines being filled in more completely. The finish and cross sections of printed samples were of comparable quality to cast samples. This indicates that the manufacturing process has not significantly affected the physical characteristics of the fuel samples