Metsän erirakenteisuutta edistävien hakkuiden kannattavuustarkastelu metsäaluetasolla

Tutkimuksessa vertailtiin metsän erirakenteisuutta edistävien poimintahakkuiden ja pienaukkohakkuiden kannattavuutta metsänhoitosuositusten mukaiseen metsänkasvatukseen Keski-Suomessa. Poimintahakkuut ja pienaukkohakkuut ovat menetelmiä, joilla voidaan lisätä luonnonmetsän häiriödynamiikan mukaista pienipiirteistä elinympäristöjen vaihtelua ja siksi ne sopivat etenkin erityiskohteisiin monimuotoisuuden, maiseman tai metsien monikäytön vuoksi. Ne johtavat yleensä vähitellen eri-ikäisrakenteiseen metsään, jossa puuston läpimittaluokkajakauma muistuttaa käänteistä J-kirjainta. Eri-ikäisrakenteisen metsänkäsittelyn taloudellista kannattavuutta puoltavat uudistumiskustannusten poisjäänti ja tukkipuihin painottuvat säännöllisin väliajoin toteutuvat hakkuut. Menetelmän soveltumista Suomen olosuhteisiin pidetään kuitenkin epävarmana. Tässä tutkimuksessa tarkasteltiin tasaikäisrakenteisen metsän muuttamista eri-ikäisrakenteiseksi 40 vuoden siirtymäaikana Metsähallituksen hallinnoimassa Isojäven ympäristöarvometsässä Kuhmoisissa. Tutkimusaineisto koostui 405 kuusivaltaisesta tasaikäisestä kuviosta, joiden pinta-alasta metsämaata on 636 hehtaaria. Metsän kehitystä simuloitiin puutason kasvumalleja käyttäen ja käsittelytoimenpiteet simuloitiin viisivuotiskausittain SIMO-metsäsuunnitteluohjelmistolla. Simulointien avulla selvitettiin jokaisen käsittelyskenaarion hakkuumäärät puutavaralajeittain, diskontatut kassavirrat ja puustopääoman muutos tarkasteluajanjakson aikana. Puunkorjuun yksikkökustannusten laskennan apuna käytettiin automatisoitua seurantajärjestelmää, jossa metsäkoneisiin asennettuilla matkapuhelimilla kerättiin MobiDoc2-sovelluksella metsäkoneiden käytöstä kiihtyvyystiedot, GPS-paikkatiedot ja syötetiedot. Lopulta jokaiselle käsittelyskenaariolle laskettiin metsän puuntuotannollista arvoa kuvaavalla tuottoarvon yhtälöllä nettonykyarvot, josta vähennettiin diskontatut puunkorjuun kustannukset. Tutkimuksen tulosten mukaan poimintahakkuun NPV oli 3 prosentin korkokannalla noin 91 % (7420 €/ha) ja pienaukkohakkuiden noin 99 % (8076 €/ha) metsänhoitosuositusten mukaisesta käsittelystä (8176 €/ha). Komparatiivinen statiikka osoitti, että korkokannan kasvattaminen 5 prosenttiin ei olennaisesti lisännyt nettonykyarvojen eroja. Poimintahakkuiden puunkorjuun yksikkökustannukset olivat 0,8 €/m3 harvennushakkuita pienemmät ja 7,2 €/m3 uudistushakkuita suuremmat. Pienaukkohakkuiden yksikkökustannukset olivat 0,7 €/m3 uudistushakkuita suuremmat.Tulosten perusteella on väistämätöntä että siirtymävaihe tasaikäisrakenteisesta eri-ikäisrakenteiseksi metsäksi aiheuttaa taloudellisia tappioita siitäkin huolimatta, että hakkuut ovat voimakkaita ja tehdään varttuneeseen kasvatusmetsään. Tappion määrä on metsän peitteisyyden ylläpidosta aiheutuva vaihtoehtoiskustannus.

Surface resistance and residual losses of Ag?doped YBa2Cu3O7?? thin films on sapphire

High?quality Ag?doped YBa2Cu3O7?? thin films have been grown by laser ablation on R?plane ?1102? sapphire without any buffer layer. Thin films have been found to be highly c?axis oriented with Tc=90 K, transition width ?T?1 K, and transport Jc=1.2×106 A?cm?2 at 77 K in self?field conditions. The microwave surface resistance of these films measured on patterned microstrip resonators has been found to be 530 ?? at 10 GHz at 77 K which is the lowest reported on unbuffered sapphire. Improved in?plane epitaxy and reduced reaction rate between the substrate and the film caused due to Ag in the film are believed to be responsible for this greatly improved microwave surface resistance. © 1995 American Institute of Physics.

Structured Learning for Non-Smooth Ranking losses

Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP(mean average precision). We propose new, almost-lineartime algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain)in the max-margin structured learning framework. We also demonstrate that, for different ranking criteria, one may need to use different feature maps. Search applications should not be optimized in favor of a single criterion, because they need to cater to a variety of queries. E.g., MRR is best for navigational queries, while NDCG is best for informational queries. A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization.The result is a single, robust ranking model that is close to the best accuracy of learners trained on individual criteria. In fact, experiments over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.

Pricing credit derivatives

The financial crisis set off by the default of Lehman Brothers in 2008 leading to disastrous consequences for the global economy has focused attention on regulation and pricing issues related to credit derivatives. Credit risk refers to the potential losses that can arise due to the changes in the credit quality of financial instruments. These changes could be due to changes in the ratings, market price (spread) or default on contractual obligations. Credit derivatives are financial instruments designed to mitigate the adverse impact that may arise due to credit risks. However, they also allow the investors to take up purely speculative positions. In this article we provide a succinct introduction to the notions of credit risk, the credit derivatives market and describe some of the important credit derivative products. There are two approaches to pricing credit derivatives, namely the structural and the reduced form or intensity-based models. A crucial aspect of the modelling that we touch upon briefly in this article is the problem of calibration of these models. We hope to convey through this article the challenges that are inherent in credit risk modelling, the elegant mathematics and concepts that underlie some of the models and the importance of understanding the limitations of the models.

Evaluation of mechanical losses in a linear motor pressure wave generator

A moving magnet linear motor compressor or pressure wave generator (PWG) of 2 cc swept volume with dual opposed piston configuration has been developed to operate miniature pulse tube coolers. Prelimnary experiments yielded only a no-load cold end temperature of 180 K. Auxiliary tests and the interpretation of detailed modeling of a PWG suggest that much of the PV power has been lost in the form of blow-by at piston seals due to large and non-optimum clearance seal gap between piston and cylinder. The results of experimental parameters simulated using Sage provide the optimum seal gap value for maximizing the delivered PV power.

Classification calibration dimension for general multiclass losses

We study consistency properties of surrogate loss functions for general multiclass classification problems, defined by a general loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be classification calibrated with respect to a loss matrix in this setting. We then introduce the notion of \emph{classification calibration dimension} of a multiclass loss matrix, which measures the smallest `size' of a prediction space for which it is possible to design a convex surrogate that is classification calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, as one application, we provide a different route from the recent result of Duchi et al.\ (2010) for analyzing the difficulty of designing `low-dimensional' convex surrogates that are consistent with respect to pairwise subset ranking losses. We anticipate the classification calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).