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.

Spatial Competition in Quality, Demand-Induced Innovation, and Schumpeterian Growth

We develop a general equilibrium model of vertical innovation in which multiple firms compete monopolistically in the quality space. The model features many firms, each of which holds the monopoly to produce a unique quality level of an otherwise homogenous good, and consumers who are heterogeneous in their valuation of the good's quality. If the marginal cost of production is convex with respect to quality, multiple rms coexist, and their equilibrium markups are determined by the degree of convexity and the density of quality-competition. To endogenize the latter, we nest this industry setup in a Schumpeterian model of endogenous growth. Each firm enters the industry as the technology leader and successively transits through the product cycle as it is superseded by further innovations. The intrinsic reason that innovation happens in our economy is not one of displacing the incumbent; rather, innovation is a means to di-erentiate oneself from existing firms and target new consumers. Aggregate growth arises if, on the one hand, increasingly wealthy consumers are willing to pay for higher quality and, on the other hand, private firms' innovation generates income growth by enlarging the set of available technologies. Because the frequency of innovation determines the toughness of product market competition, in our framework, the relation between growth and competition is reversed compared to the standard Schumpeterian framework. Our setup does not feature business stealing in the sense that already marginal innovations grant non-negligible prots. Rather, innovators sell to a set of consumers that was served relatively poorly by pre-existing firms. Nevertheless, "creative destruction" prevails as new entrants make the set of available goods more di-erentiated, thereby exerting a pro-competitive e-ect on the entire industry.

Simulations of information processing, control, and plasticity effects in the olfactory bulb

The olfactory system processes complex and varied information in its detection, recognition, and memory of odors. The exact functions that the olfactory bulb plays in this processing is still largely unknown. Studies were performed to help reveal bulb functionality in the olfactory system while contributing to the set of computer methods available for the study of neural systems.One interesting property of bulbar neurons is an increase in primary cell firing thresholds with depth. Since increased odor concentrations generally result in higher frequency inputs to the bulb and thus higher summation levels of primary cell membrane potentials, this threshold gradation transforms the frequency-encoded concentration data into a spatial representation in the number of primary cells responding in a single olfactory bulb glomerular region.Since this transformation relies on temporal summation of post-synaptic potentials (PSPs) to reflect concentration levels, direct physiological modeling of the transformation was possible while providing the added efficiency to permit the simulation of large numbers of cells and synaptic interactions. A novel physiological modeling methodology was developed for these tests that extends the extant physiological models to include time-constant and driving-force interactive effects between post-synaptic inputs. This novel method is derived using linear superposition of inputs to a lumped-circuit cell representation, resulting in a difference-of-exponentials PSP function that is more realistic and flexible than the common empirically-chosen alpha function.Also, the effects that interneuronal dendritic spines have on bulbar inhibitions were tested using biophysical computer simulations of primary-to-granule dendrodendritic reciprocal interactions. The graded strength properties of these synapses showed that reciprocal inhibitions to primary mitral cells are facilitated by the spine structures without the need of a high gain gradation while reducing lateral inhibition to other mitral cells. Furthermore, increases in the neck axial resistance of the synapsed spine further strengthen the reciprocal response and reduce the lateral inhibition; such resistance changes could therefore result in dendrodendritic synaptic plasticities and olfactory memory operations

Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference

We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF inference problems. The core of our method is a very efficient bounding procedure, which combines scalable semidefinite programming (SDP) and a cutting-plane method for seeking violated constraints. In order to further speed up the computation, several strategies have been exploited, including model reduction, warm start and removal of inactive constraints. We analyze the performance of the proposed method under different settings, and demonstrate that our method either outperforms or performs on par with state-of-the-art approaches. Especially when the connectivities are dense or when the relative magnitudes of the unary costs are low, we achieve the best reported results. Experiments show that the proposed algorithm achieves better approximation than the state-of-the-art methods within a variety of time budgets on challenging non-submodular MAP-MRF inference problems.Comment: 21 page

A statistical perspective on biofilter performance in relation to the main process parameters and characteristics of untreated flows

[Abstract] A large number of olfactometric measurements of odour removal efficiency of municipal waste organic fraction and green waste composting installations were compiled and analysed graphically. The number of measurements and installations is >50 for treated gas characteristics and >15 for filter media characteristics. All installations concerned were located in the Netherlands and Belgium. All untreated and treated gas odour concentrations were measured in duplicate or triplicate, according to olfactometry standard EN13725 or its predecessor NVN2820. The data were then analysed using graphical methods to identify trends and relation between effectiveness of performance and a large number of operational parameters and characteristics, including: • Area flow loading (m3·m-2 filter area·hour-1) • Contact time • Temperature of untreated flow • Odour concentration of untreated flow • Ammonia concentration of untreated flow • Dry matter content of filter media • pH of filter medi

Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications

In computer vision, many problems such as image segmentation, pixel labelling, and scene parsing can be formulated as binary quadratic programs (BQPs). For submodular problems, cuts based methods can be employed to efficiently solve large-scale problems. However, general nonsubmodular problems are significantly more challenging to solve. Finding a solution when the problem is of large size to be of practical interest, however, typically requires relaxation. Two standard relaxation methods are widely used for solving general BQPs--spectral methods and semidefinite programming (SDP), each with their own advantages and disadvantages. Spectral relaxation is simple and easy to implement, but its bound is loose. Semidefinite relaxation has a tighter bound, but its computational complexity is high, especially for large scale problems. In this work, we present a new SDP formulation for BQPs, with two desirable properties. First, it has a similar relaxation bound to conventional SDP formulations. Second, compared with conventional SDP methods, the new SDP formulation leads to a significantly more efficient and scalable dual optimization approach, which has the same degree of complexity as spectral methods. We then propose two solvers, namely, quasi-Newton and smoothing Newton methods, for the dual problem. Both of them are significantly more efficiently than standard interior-point methods. In practice, the smoothing Newton solver is faster than the quasi-Newton solver for dense or medium-sized problems, while the quasi-Newton solver is preferable for large sparse/structured problems. Our experiments on a few computer vision applications including clustering, image segmentation, co-segmentation and registration show the potential of our SDP formulation for solving large-scale BQPs.Comment: Fixed some typos. 18 pages. Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc

The regulation of luteinizing hormone exocytosis in α-toxin permeabilized sheep anterior pituitary cells

Although exocytosis is the major mechanism by which cells secrete products into their environment, little is known about the mechanism of this fundamental process. Previous studies on the regulation of luteinizing hormone (LH) exocytosis have used intact cells exclusively. It is not possible, however, to determine the precise requirements for exocytosis in intact cells since the cytosol is not directly accessible. Permeabilization of the plasma membrane allows experimental manipulation of the intracellular milieu while preserving the exocytic apparatus. The diameter of the atoxin pores (2-3 nm) allowed the exchange of small molecules such as ATP while larger cytosolic proteins such as lactate dehydrogenase were retained. Because of the slow exchange of small molecules through a-toxin pores a protocol was developed which combines prolonged pre-equilibration of the permeabilized cells at 0°C before stimulation with strong Ca²⁺ buffering. Under these conditions an increase in the [Ca²⁺]free stimulated a 15-20 fold increase in LH exocytosis (EC₅₀ pCa 5.5). After 12-15 minutes the rate of exocytosis declined and the cells became refractory to Ca²⁺. At resting [Ca²⁺]free (pea 7), cAMP stimulated a rapid, 2 - 3 fold, increase in LH exocytosis. cAMP caused a modest enhancement of Ca²⁺-stimulated LH exocytosis by causing a left shift in the EC₅₀ for Ca²⁺ from pCa 5.6 to pCa 5.9. Activation of protein kinase C (PKC) with phorbol 12-myristate 13-acetate (PMA) synergistically enhanced cAMP-stimulated LH exocytosis, an effect which was further augmented by increasing the [Ca²⁺]free· Gonadotrophin-releasing hormone (GnRH) was found to stimulate cAMP production in intact pituitary cells. Since previous studies have shown that GnRH activates PKC and stimulates a rise in cytosolic [Ca²⁺]free, these results suggest that a synergistic interaction of the cAMP, PKC and Ca²⁺ second messenger systems is of importance in the mechanism of GnRH-stimulated LH exocytosis. When permeabilized cells were equilibrated for prolonged periods in the absence of MgATP, Ca²⁺-stimulated LH exocytosis declined. The time course of the decline closely followed the leakage of intracellular ¹⁴C-ATP. Addition of MgATP rapidly restored full Ca²⁺-stimulated LH exocytosis. Ca²⁺-, cAMP-, and PMA-stimulated LH exocytosis were all dependent on millimolar MgATP concentrations (EC₅₀ 1 .5-3 mM). It has been postulated that PKC is a mediator of Ca²⁺- stimulated exocytosis. Several findings in the present study argue against this hypothesis. Firstly, PMA and Ca²⁺ had additive effects on LH exocytosis at all [Ca²⁺]free· Secondly, PMA was able to stimulate further LH release from cells made refractory to high [Ca²⁺]free· Thirdly, the PKC inhibitor staurosporine did not inhibit Ca²⁺-stimulated LH exocytosis under conditions in which it inhibited PMAstimulated exocytosis. Fourthly, in cells desensitized to PMA by prolonged exposure to a high PMA concentrations, Ca²⁺-stimulated LH exocytosis was not inhibited. And finally, Ba²⁺+ was able to stimulate LH exocytosis to a maximal extent similar to Ca²⁺ despite the fact that Ba²⁺+ is an extremely poor activator of PKC. Since Ba²⁺+ is also a poor activator of calmodulin, this latter result implies that calmodulin does not mediate the effect of Ca²⁺. In agreement with this, the calmodulin inhibitor calmidazolium did not inhibit Ca²⁺-stimulated LH exocytosis. Since GTP-binding proteins have been implicated in regulated exocytosis in other cell systems, the effects of guanine nucleotides on LH exocytosis were examined. At resting cytosolic [Ca²⁺]free (pea 7), the GTP analogues GTPyS and GMPPNP stimulated LH exocytosis with similar potencies (EC₅₀ 20-50 μM). Additional experiments indicated that the effects of these GTP analogues could not be explained by activation of either PKC alone or cAMP-dependent protein kinase alone. In the presence of both PMA and cAMP, GMPPNP did not stimulate a further increase in the rate of LH exocytosis, suggesting that the stimulatory actions of guanine nucleotides may be mediated by the combined activation of PKC and generation of cAMP, as a result of activation of signal-transducing G proteins. In contrast, pretreatment of cells with GTPyS at low [Ca²⁺]free markedly inhibited subsequent responses to Ca²⁺, cAMP, PMA, and cAMP plus PMA. This inhibitory effect required lower GTPyS concentrations than the stimulatory effect (IC₅₀ 1-10 μM), and was not observed with GMPPNP. These findings indicate the involvement of a distinct guanine nucleotide-binding protein in exocytosis at a site distal to second messenger generation

Peak dictatorship : Ceaușescu’s state visit to Great Britain, June 1978

The Ceauşescu couple's State Visit to Great Britain in June 1978 has been considered by academic and media commentators as a dark hour for British diplomacy. However, this article aims to place this event in its context, drawing mainly upon newly-declassified British and Romanian documents. It investigates the background and the results of the Visit and the interests of both parties. If, on his return to Bucharest, Ceauşescu boasted of diplomatic and economic success, we argue that there can be detected weaknesses in the regime that anticipate its demise in 1989.Publisher PDFPeer reviewe

Realization of a photonic CNOT gate sufficient for quantum computation

We report the first experimental demonstration of a quantum controlled-NOT gate for different photons, which is classically feed-forwardable. In the experiment, we achieved this goal with the use only of linear optics, an entangled ancillary pair of photons and post-selection. The techniques developed in our experiment will be of significant importance for quantum information processing with linear optics.Comment: 4 pages 4 figures, sumbitted to PR