Learning Prices for Repeated Auctions with Strategic Buyers

Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism. We model the buyer as a strategic agent, whose goal is to maximize her long-term surplus, and we are interested in mechanisms that maximize the seller's long-term revenue. We define the natural notion of strategic regret --- the lost revenue as measured against a truthful (non-strategic) buyer. We present seller algorithms that are no-(strategic)-regret when the buyer discounts her future surplus --- i.e. the buyer prefers showing advertisements to users sooner rather than later. We also give a lower bound on strategic regret that increases as the buyer's discounting weakens and shows, in particular, that any seller algorithm will suffer linear strategic regret if there is no discounting.Comment: Neural Information Processing Systems (NIPS 2013

New Models qnd Algorithms for Bandits and Markets

Inspired by advertising markets, we consider large-scale sequential decision making problems in which a learner must deploy an algorithm to behave optimally under uncertainty. Although many of these problems can be modeled as contextual bandit problems, we argue that the tools and techniques for analyzing bandit problems with large numbers of actions and contexts can be greatly expanded. While convexity and metric-similarity assumptions on the process generating rewards have yielded some algorithms in existing literature, certain types of assumptions that have been fruitful in offline supervised learning settings have yet to even be considered. Notably missing, for example, is any kind of graphical model approach to assuming structured rewards, despite the success such assumptions have achieved in inducing scalable learning and inference with high-dimensional distributions. Similarly, we observe that there are countless tools for understanding the relationship between a choice of model class in supervised learning, and the generalization error of the best fit from that class, such as the celebrated VC-theory. However, an analogous notion of dimensionality, which relates a generic structural assumption on rewards to regret rates in an online optimization problem, is not fully developed. The primary goal of this dissertation, therefore, will be to fill out the space of models, algorithms, and assumptions used in sequential decision making problems. Toward this end, we will develop a theory for bandit problems with structured rewards that permit a graphical model representation. We will give an efficient algorithm for regret-minimization in such a setting, and along the way will develop a deeper connection between online supervised learning and regret-minimization. This dissertation will also introduce a complexity measure for generic structural assumptions on reward functions, which we call the Haystack Dimension. We will prove that the Haystack Dimension characterizes the optimal rates achievable up to log factors. Finally, we will describe more application-oriented techniques for solving problems in advertising markets, which again demonstrate how methods from traditional disciplines, such as statistical survival analysis, can be leveraged to design novel algorithms for optimization in markets

Online Learning and Profit Maximization from Revealed Preferences

We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant according to her (linear) utility function and current prices, subject to a budget constraint. The merchant observes only the purchased goods, and seeks to adapt prices to optimize his profits. We give an efficient algorithm for the merchant's problem that consists of a learning phase in which the consumer's utility function is (perhaps partially) inferred, followed by a price optimization step. We also consider an alternative online learning algorithm for the setting where prices are set exogenously, but the merchant would still like to predict the bundle that will be bought by the consumer for purposes of inventory or supply chain management. In contrast with most prior work on the revealed preferences problem, we demonstrate that by making stronger assumptions on the form of utility functions, efficient algorithms for both learning and profit maximization are possible, even in adaptive, online settings

Influence of low grade exercise on skeletal scintigraphy using Tc-99m methylene diphosphonate

BACKGROUND: Tc-99m methylene diphosphonate [MDP] bone scan is the basis of the skeletal imaging in nuclear medicine being a highly sensitive tool for detecting bone diseases. Mechanical stimulation induced by low grade exercise or whole-body vibration appears to be advantageous regarding the maintenance and/or improvement of skeletal mass in humans. We aimed to assess the physiological influence of low grade exercise on the quality of skeletal scintigraphy using Tc-99m MDP. MATERIAL AND METHODS: Tc-99m MDP bone scan was done for 92 volunteers [Group 1; G1]. Five days later, the same subjects were re-scanned [Group 2; G2] after an exercise on treadmill for 5 minutes. Image quality was assessed using quantitative measures whereby equal regions of interest (ROI) were drawn over the femoral diaphysis, and the contralateral adductor area. The total number of counts from the bone [B] ROI and soft tissue [ST] ROI was expressed as a ratio [B:ST ratio] and a mean value for each was established. RESULTS: Statistically significant difference was found between the B:ST ratio means [p = 0.001] in G1 and G2. CONCLUSION: This study raised a physiological influence of low grade exercise on the image quality of tc-99m MDP skeletal scintigraphy by increasing MDP osseous uptake

Association of the Arginase Ι with Bronchial Asthma

الهدف: الهدف من هذه الدراسة هو الكشف عن إنزيم ارجينز I ودوره في مرضى المصابين بالربو و دراسة علاقته بالربو القصبي. المواد والطرق: تم جمع عينات الدم من 100 مرضى مصابين بمرض الربو اللذين ادخلوا قسم الباطنية في مستشفى رزكاري التعليمي و 100 شخصاً من الاصحاء الذين ليس لديهم علامات سريرية سابقة لأي مرض حاد أو مزمن كمجموعة المقارنة. حيث تم ملء الاستبيان على أساس مرض الربو الناجم عن نوعه ومدة المرض وعمر المرضى والجنس والحالة السريرية للعائلة وظرف الحساسية. في مركز البحث الطبي تم فصل مصل الدم لإجراء فحص ARGΙ وايجاد علاقته مع الربو القصبي باستخدام تقنية مناعي مرتبط بالإنزيم (ELISA). تم تحديد نشاط ARGΙ وقياس تحويل الأرجينين إلى اورثنين واليوريا باستخدام مقياس قياس الألوان الكمية علي طول موجي490 نانومتر. النتائج: أظهرت النتائج الي وجود علاقة ايجابية بين ARGΙ والربو القصبي. لوحظ ارتفاع ملحوظ في مستوى الارجينيز في مصل الدم للمرضى الذين تتجاوز اعمارهم عن 81 سنة و قيمة متوسط و قيمة p (0.000); (100.16±19.77c),). كما لوحظ علاقة ملحوظ في مستوى الارجينيز مع مدة الاصابة بالمرض. حيث كان مستوى الارجينيز عالية في المرضى المصابة بالربو لأكثر من 20 سنة (82.48±38.81c), و قيمة p (0.01). كذلك لوحظ علاقة ملحوظ بين مستوى الارجينيز وأولئك الذين يعانون من أنواع الربو المستحث ومع ظرف الحساسية أيضا. ولكن لوحظ اختلاف غير معنوي في مستوى الارجينيز للمصل مع الحالة السريرية للعائلة لمرض الربو وجنس المريض. وأظهرت النتائج وجود ارتباط ملحوظ ل ARG Ι في تطويرمرض الربو في p ˂ 0.05.Objective The aim of this study was to detect the arginase Ι (ARG I) enzyme in asthma patients, clarify its role, in addition to examining the relationship of this enzyme with bronchial asthma. Methods: Blood samples were collected from 100 patients from the Department of Medicine in Rizgary Hospital in Erbil City, in addition to intact 100 volunteers; the introduced questionnaire was filled out on the basis of type-induced asthma, duration of the disease, age of the patients, gender, family history, and allergy condition, Serum was separated to perform Enzyme-linked immunosorbent assay (ELISA) in Medical Research Center to examine the association of ARGΙ with bronchial asthma. By ARG activity we can measure the conversion of arginine to ornithine and urea. By using a quantitative colorimetric assay at 490 nm, employing a QuantiChrom arginase assay kit (Bioassay Systems). Results: Our results depicted the association between ARGΙ and bronchial asthma: based on their age, significant elevation of serum arginase level was observed in the patients with ≥81 years old, which mean value (100.16±19.77c), p-value (0.000); also the duration of asthma ≥20 years (82.48±38.81c) , p-value (0.01) were remarkably affected; this sign was found in those with types of induced asthma and with allergy condition.  But the non- significant difference in the frequency of abnormal serum arginase level was observed in those patients that have a family history of asthma disease and gender of the patients. This finding demonstrated a remarkable association of ARG Ι in the development of asthma at p ˂ 0.05

Preface: case-based reasoning and deep learning.

Recent advances in deep learning (DL) have helped to usher in a new wave of confidence in the capability of artificial intelligence. Increasingly, we are seeing DL architectures out perform long established state-of-the-art algorithms in a number of diverse tasks. In fact, DL has reached a point where it currently rivals or has surpassed human performance in a number of challenges e.g. image classification, speech recognition and game play