Obergefell’s Missed Opportunity

The stock market plays a big role in our current nancial system and the uctuations onit are believed to depend on many dierent factors. One of the factors that are believedto be correlated to the stock market are macroeconomic variables, that is, variables thatindicate the status of the economical situation. Examples of such macroeconomic variablesare unemployment rate, loan interests and ination. Earlier attempts to predictthe stock market have been made by using process demanding methods such as arti-cial neural network. A multilayer perceptron is a self learning system that goes underthe category of an articial neural network. Such a network learns by being trainedon old data sets and has the capacity to identify relationships between dierent data.This method has been used in earlier studies to predict the stock market with goodresults. The problem statement of this report is to nd the optimal training interval fora multilayer perceptron on a day to day estimation of the Swedish OMXS30 index. Theinput to the algorithm consisted of 38 parameters, which in this case was a collectionof individual companies stock prices, foreign stock indexes, macroeconomic variables,previous and current values of the OMXS30 index. The results from the simulationsthat were executed on old stock data shows that 180 to 200 days of training yielded thebest results, where eight of nine periods over seven years (2007-2014) yielded prot. Theresults from the simulations during the periods with increasing index were sometimesbelow the index gain, but always with a prot. During periods of index decrease theresults were sometimes with a prot and sometimes non-prot. In the case of indexdecrease the result was always above the total index decrease. The conclusion is as theresults shows, that the optimal training interval is 180 to 200 days for the simulationsrun in the study of this report.1Aktiemarknaden spelar en stor roll i dagens finansiella system och fluktutionerna på börsen tros bero pa många orsaker. En av de saker som tros ha en koppling till börsen är makroekonomiska variabler, dvs sådana variabler som indikerar hur ekonomin mår. Exempel på makroekonomiska variabler ar arbetslöshet,       räntenivåer och i nation. Andra kopplingar som tros finnas till börsens utveckling är hur individuella aktier och utlandskabörser utvecklas. Tidigare försök har gjorts att forsöka forutsäga aktiemarknaden med hjalp av beräkningskrävande metoder, t. ex. Articiella neuron nät. En flerlagers perceptronar ett självlärande system som räknas som en typ av articiellt neuron nät. Nätverket lär sig genom att tränas pa gammal data och har formåagan att hitta samband mellan olika data. I tidigare studier har denna metod använts for att förutsäga aktiemarknaden med goda resultat. Problemformulering i denna rapport ar att ta reda på vilket det optimala träningsintervallet ar för en flerlagers perceptron för att, från en dag till en annan, förutsäga indexet på Stockholmsbörsen, OMXS30. Algoritmens indata bestod av totalt 38 parametrar som i detta fall var en samling av enskilda företagsaktievärden, utländska börsers index, makroekonomiska variabler, tidigare värden på OMXS30 samt det nuvarande värdet pa börsen. Resultaten från simulationerna som kördes pa gammal aktiedata visar att 180-200 dagar är det basta träningsintervallet daatta av nio stycken perioder över sju år (2007-2014) gick med vinst. Resultaten fransimulationerna under de perioder med stigande index blev i vissa fall under index, men alltid med vinst. I perioder med avtagande index sa blev resultaten i vissa fall vinstgivande och i andra fall inte vinstgivande, men i dessa fall alltid battre an den totalaindex nedgangen. Slutsatsen ar som resultaten visar att 180-200 dagar ar det optimala träningsintervallet for de simulationer som gjordes i undersökningen i denna rapport.

Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned

The Ugly Truth About Appearance Discrimination and the Beauty of Our Employment Discrimination Law

The keynote speaker for the conference begins by reminding the audience that a mere quarter of a century earlier there was no federal law that expressly prohibited discrimination in employment based on physical appearance. Considering the difficulty of crafting and enacting an appearance-based employment discrimination law should lead to a fuller appreciation of not only our employment discrimination laws generally, but also the Americans with Disabilities Act specifically

Verifying UML/OCL operation contracts

In current model-driven development approaches, software models are the primary artifacts of the development process. Therefore, assessment of their correctness is a key issue to ensure the quality of the final application. Research on model consistency has focused mostly on the models' static aspects. Instead, this paper addresses the verification of their dynamic aspects, expressed as a set of operations defined by means of pre/postcondition contracts. This paper presents an automatic method based on Constraint Programming to verify UML models extended with OCL constraints and operation contracts. In our approach, both static and dynamic aspects are translated into a Constraint Satisfaction Problem. Then, compliance of the operations with respect to several correctness properties such as operation executability or determinism are formally verified

Socially-Tolerable Discrimination

History is replete with overt discrimination on the basis of race, gender, age, citizenship, ethnicity, marital status, academic performance, health status, volume of market transactions, religion, sexual orientation, etc. However, these forms of discrimination are not equally tolerable. For example, discrimination based on immutable or prohibitively unalterable characteristics such as race, gender, or ethnicity is much less acceptable. Why? I develop a simple rent-seeking model of conflict which is driven by either racial (gender or ethnic) discrimination or generational discrimination (i.e., young versus old). When the conflicts are mutually exclusive, I find that racial discrimination is socially intolerable for a much wider range of parameter values relative to generational discrimination. When they are not mutually exclusive, I find that racial discrimination can be socially intolerable while generational discrimination is socially tolerable. The converse is not true. My results are not driven by a stronger intrinsic aversion to discrimination on the basis of immutable characteristics. I am able to explain why some forms of discrimination (e.g., racism) are much less tolerable than other forms of discrimination (e.g., age discrimination) without making any value judgements about either form of discrimination.conflict, contest, discrimination, race, generation, rent-seeking

Socially-Tolerable Discrimination

History is replete with overt discrimination on the basis of race, gender, age, citizenship, ethnicity, marital status, academic performance, health status, volume of market transactions, religion, sexual orientation, etc. However, these forms of discrimination are not equally tolerable. For example, discrimination based on immutable or prohibitively unalterable characteristics such as race, gender, or ethnicity is much less acceptable. Why? I develop a simple rent-seeking model of conflict which is driven by either racial (gender or ethnic) discrimination or generational discrimination (i.e., young versus old). When the conflicts are mutually exclusive, I find that racial discrimination is socially intolerable for a much wider range of parameter values relative to generational discrimination. When they are not mutually exclusive, I find that racial discrimination can be socially intolerable while generational discrimination is socially tolerable. The converse is not true. My results are not driven by a stronger intrinsic aversion to discrimination on the basis of immutable characteristics. I am able to explain why some forms of discrimination (e.g., racism) are much less tolerable than other forms of discrimination (e.g., age discrimination) without making any value judgements about either form of discrimination.conflict, contest, discrimination, race, generation, rent-seeking