Interdependent Public Projects

In the interdependent values (IDV) model introduced by Milgrom and Weber [1982], agents have private signals that capture their information about different social alternatives, and the valuation of every agent is a function of all agent signals. While interdependence has been mainly studied for auctions, it is extremely relevant for a large variety of social choice settings, including the canonical setting of public projects. The IDV model is very challenging relative to standard independent private values, and welfare guarantees have been achieved through two alternative conditions known as {\em single-crossing} and {\em submodularity over signals (SOS)}. In either case, the existing theory falls short of solving the public projects setting. Our contribution is twofold: (i) We give a workable characterization of truthfulness for IDV public projects for the largest class of valuations for which such a characterization exists, and term this class \emph{decomposable valuations}; (ii) We provide possibility and impossibility results for welfare approximation in public projects with SOS valuations. Our main impossibility result is that, in contrast to auctions, no universally truthful mechanism performs better for public projects with SOS valuations than choosing a project at random. Our main positive result applies to {\em excludable} public projects with SOS, for which we establish a constant factor approximation similar to auctions. Our results suggest that exclusion may be a key tool for achieving welfare guarantees in the IDV model

Broken Heart, Broken Mind

Introduction: Takostubo Cardiomyopathy (TCM) is a unique condition of reversible cardiac dysfunction precipitated by emotional or physical stress. Studies are emerging noting an association with Amyotrophic Lateral Sclerosis (ALS). This is hypothesized to be secondary to baseline elevation of catecholamines in these individuals. Case Presentation: A 53-year-old female with background of anxiety, presented with acute onset chest pain. Initial evaluation revealed elevated troponins without ST changes on EKG. Echocardiogram showed EF 28% with apical ballooning and left heart catheterization was unremarkable. Her echocardiogram improved 2 weeks later, consistent with TCM. During this evaluation it was noted she was significantly dysarthric. On further history she revealed progressive odynophagia for the past year, generalized muscle weakness for the past few months, and dysarthria for 4-6 weeks. Neurological exam demonstrated both upper and lower motor neuron findings of diffuse muscle atrophy, fasciculations as well as brisk peripheral reflexes, jaw jerk and bilateral Hoffmans. MRI brain/spine imaging showed no significant abnormalities and EMG indicated lower motor neuron changes. CPK, anti-MUSK, anti-acetylcholine receptor antibodies were normal and extensive evaluation of paraneoplastic, vitamin and autoimmune disease were negative. An ALS diagnosis was made. Due to worsening respiratory status she required intubation with subsequent tracheostomy placement and ultimately was discharged to a long-term care facility. Conclusion: This case highlights the uncommon association of TCM in ALS with a unique scenario where it was the presenting feature of the disease. Physicians should be vigilant for neurological symptoms in patients presenting with chest discomfort or risk missing the diagnosis.https://scholarlycommons.henryford.com/merf2020caserpt/1020/thumbnail.jp

LADA presenting as hyperglycemic coma

Latent Autoimmune Diabetes in Adults (LADA), is considered a rare subset of Type 1 Diabetes Mellitus. These adults do not require insulin at diagnosis but progress to insulin dependence in a short period of time. Because of this the diagnosis is often missed and can result in potentially fatal complications such as Diabetic Ketoacidosis (DKA. Our case highlights one such example with extreme levels of hyperglycemia of 2345mg/dl and its unique therapeutic challenges. A 49-year-old male with a strong family history of diabetes and recent diagnosis of prediabetes in the outpatient setting was found unresponsive at home. He had profound hypothermia 28.4 C, hyperglycemia 2345mg/dl, corrected sodium 182mg/dl and ketoacidosis. Due to altered mental status and seizures he was intubated and fluid resuscitated. Nephrology were consulted due to high risk of cerebral edema with rapid correction, they advised lowering of glucose no more than 50mg/dl/hour. He was started on a customized DKA protocol and his mentation and all laboratory abnormalities improved over a 72 hour period. He was extubated and transferred to the floor. Further workup revealed a C-peptide level of 120 IU/ml. Patient was diagnosed with Type 1 Diabetes Mellitus (LADA).Type 1 diabetes mellitus is often unrecognized until acute decompensation. This case highlights the importance of remaining vigilant outside of the typical adolescent age group. In addition, it describes complications of extreme hyperglycemia including encephalopathy, hypothermia, seizures, acute kidney injury and its careful management in order to prevent catastrophic fluid shifts.https://scholarlycommons.henryford.com/merf2020caserpt/1114/thumbnail.jp

RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks

Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by the radar system is sent to, and used by, a peripheral consuming system, such as a missile system or a graphical user interface used by an operator. Those systems process the data stream and make real-time, operational decisions based on the data received. Given this, the reliability and availability of information provided by radar systems has grown in importance. Although the field of cyber security has been continuously evolving, no prior research has focused on anomaly detection in radar systems. In this paper, we present a deep learning-based method for detecting anomalies in radar system data streams. We propose a novel technique which learns the correlation between numerical features and an embedding representation of categorical features in an unsupervised manner. The proposed technique, which allows the detection of malicious manipulation of critical fields in the data stream, is complemented by a timing-interval anomaly detection mechanism proposed for the detection of message dropping attempts. Real radar system data is used to evaluate the proposed method. Our experiments demonstrate the method's high detection accuracy on a variety of data stream manipulation attacks (average detection rate of 88% with 1.59% false alarms) and message dropping attacks (average detection rate of 92% with 2.2% false alarms)