For many applications, analyzing multiple response variables jointly is desirable because of their dependency, and valuable information about the distribution can be retrieved by estimating quantiles. In this paper, we propose a multi-task quantile re- gression method that exploits the potential factor structure of multivariate conditional quantiles through nuclear norm regularization. We jointly study the theoretical proper- ties and computational aspects of the estimating procedure. In particular, we develop an efficient iterative proximal gradient algorithm for the non-smooth and non-strictly convex optimization problem incurred in our estimating procedure, and derive oracle bounds for the estimation error in a realistic situation where the sample size and num- ber of iterative steps are both finite. The finite iteration analysis is particular useful when the matrix to be estimated is big and the computational cost is high. Merits of the proposed methodology are demonstrated through a Monte Carlo experiment and applications to climatological and financial study. Specifically, our method provides an objective foundation for spatial extreme clustering, and gives a refreshing look on the global financial systemic risk. Supplementary materials for this article are available online.
Recently, the remote sensing community also expanded their capability to learn alternative tasks together with common image classification. Marmanis et al. [ 48 ] predicted object boundaries jointly with the land cover task. This helps to sharpen classification maps, but has a high computational load, as the boundaries, separately detected from color-infrared (CIR) and height data, are added as an extra channel to each data source for further image classification task training. The work of Vakalopoulou et al. [ 49 ] introduced a model that learns jointly the registration between the images, the land use classification of each input, and a change detection map with a CRF. This is done by fusing boundary priors with the classification scores from a two-layer CNN architecture under a single energy formulation. To have a system that is able to predict reasonably accurate DSMs automatically would be very valuable. Srivastava et al. [ 50 ] proposed, to our knowledge, the first deep learning-based methodologies to predict semantic segmentation maps, as well as normalized digital surface models (nDSMs) from a single monocular image. The authors used a joint loss function for CNN training, which is a linear combination of a dense image classification loss and a regression loss responsible for DSM error minimization. The model is trained by alternating over two losses. Similar to Srivastava et al. [ 50 ], we investigate the prediction of semantic segmentation maps for a roof classification task and simultaneous generation of refined DSM with improved building forms out of a single photogrammetric depth image within an end-to-end neural network. Our contributions are: • We efficiently adapt the cGAN architecture developed by Isola et al. [ 18 ] for multi-task learning. • The proposed framework generates images with continuous values representing elevation models with enhanced building geometries and, at the same time, images with discrete values depicting the label information meaning to which class out of three (flat roof, non-flat roof, and background) every single pixel belongs.
We compare the performance of the multi-task CNN with the performance of the single-task CNNs. All the experiments start at the third-phase, i.e. the supervised phase. Since there was no predefined split in training and development set, we generated a development set by sampling 10% uniformly at random from the provided training set. The development set is needed when assess- ing the generalization power of the CNNs and the meta-classifier. For each task we compute the av- eraged F1-score (Barbieri et al., 2016). We present the results achieved on the dev-set and the test-set used for the competition. We refer to the set which was held out during a cross validation iteration as fold-set.
gene Signale zulassen, die durch Monitoring generiert werden. Laux (2001) erweitert die Be- trachtung auf mehrere Projekte mit endogen ermittelten Graden von Limited-Liability. Er lei- tet aus seinem Modell ebenfalls einen Arbeitsvertrag für den Agenten ab, der optimale Grenzwerte für den Output enthält. In dem im Hauptteil zu behandelnden Modell könnte ein solcher Grenzwertvertrag konstruiert werden, wenn nicht nur die Wahrscheinlichkeit für gute Projekte, sondern auch der Projektertrag vom Arbeitseinsatz des Agenten abhängig wäre (vgl. Kapitel 9.2.1). Lewis/Sappington (2001) analysieren ein Moral-Hazard–Problem mit „Hidden- Information“ über das Vermögen des Agenten und dessen Typ (Fähigkeit, die gestellte Aufga- be zu verrichten). Sie zeigen, dass „high-powered-incentives“ nur dann gewählt werden, wenn sowohl Vermögen als auch Fähigkeit als hoch einzuschätzen sind. Sie untersuchen weder Multi-Task noch Reputationskosten. Das im Hauptteil zu behandelnde Modell lehnt sich an Innes und Park an, verzichtet allerdings auf das Signal.
We consider the problem of decentralized clustering and estimation over multi- task networks, where the agents infer and track different models of interest. The agents do not know beforehand which model is generating the data they sense. They also do not know which agents in their neighborhood belong to the same cluster. We propose a decentralized clustering algorithm aimed at identifying and forming clusters of agents of similar objectives, and at guiding cooperation to enhance the inference performance. While links between agents following different objectives are ignored in the clustering process, we show how to exploit these links to relay critical information across the network for enhanced performance [17–19]. Moreover, the agents do not know the index of their observed models. We propose a labeling system that depends on an electing process to elect a master agent for each cluster. This master agent provides a label for its cluster, thus ensuring that each cluster has a unique model index.
Abstract: The classical approach to non-linear regression in physics is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging are real systems, characterized by several additional influencing factors related to specific components, like electronics or optical parts. In such cases, to make the model reproduce the data, empirically determined terms are built in the models to compensate for the difficulty of modeling things that are, by construction, difficult to model. A new approach to solve this issue is to use neural networks, particularly feed-forward architectures with a sufficient number of hidden layers and an appropriate number of output neurons, each responsible for predicting the desired variables. Unfortunately, feed-forward neural networks (FFNNs) usually perform less efficiently when applied to multi-dimensional regression problems, that is when they are required to predict simultaneously multiple variables that depend from the input dataset in fundamentally different ways. To address this problem, we propose multi-task learning (MTL) architectures. These are characterized by multiple branches of task-specific layers, which have as input the output of a common set of layers. To demonstrate the power of this approach for multi-dimensional regression, the method is applied to luminescence sensing. Here, the MTL architecture allows predicting multiple parameters, the oxygen concentration and temperature, from a single set of measurements.
gloff, 2007). Some of the speakers match participants pre- viously recorded in free conversation, the audio-visual CID corpus (Bertrand et al., 2008). It means that it can be stud- ied whether the same individual participant uses different communicative strategies in free conversations versus task- oriented dialogue. Other research questions that may be addressed are for example: What interactional/sequential resources and phonetic/prosodic cues do participants use when they perform feedback? What are these when they have the visual modality available as opposed to the condi- tion without (cf. Doherty-Sneddon et al., 1997)? How does this change their feedback behaviour, e.g. do participants use more or less verbal feedback items in one or another condition; or does the choice of lexical markers change? The corpus recordings can be classified as semi- spontaneous, task-oriented dialogues – located between the extremes of artificially elicited, controlled, read speech, e.g. Astésano et al. (2007) and naturally occurring talk-in- interaction, e.g. Bertrand et al. (2008); Kurtic et al. (2012). The present paper sets out the experimental design of the corpus (Section 2.), explains how it has been processed (Section 3.) and provides some quantitative information (Section 5.). It ends with conclusions, work in progress and plans for future research applied to this corpus (Section 6.).
Clearly the core leadership task is to achieve the set organizational goals. Leadership attention needs to focus on these goals and a leader needs to reflect if s/he spends his/her time with activities that are aligned to this Goal Orientation. But a leader can only be truly committed to the organization’s goals if there is a substantial match with her/his morale values. In times where corporate goals are defined based on creating shareholder value this becomes more and more difficult. A goal like Deutsche Bank chairman Ackermann’s famous 20% profit margin or billion-dollar corporate cost cutting programs are completely abstract and not linked to any real purpose, besides the self-destructive economic idea of creating maximum profit. Therefore, it creates no real value for the employees of a company. Worse, it leads to layoffs, pay cut-downs and other actions that contradict humanistic values and the common sense of the staff and also the middle management of a company. To parallel a children’s teaching tale, it is like cutting open the goose to get all the golden eggs.
bottleneck is occupied by T1. Thus, this model accounts for dual-task related performance costs without referring to any cognitive control processes.
Of late, this model has gone through various modiﬁcations, all of which maintain the idea of a processing bottleneck at the stage of re- sponse-selection. For example, Hommel ( 1998 ; see also Lien & Proctor, 2002 ; Schubert, Fischer, & Stelzel, 2008 ) proposed to subdivide the response-selection stage into an automatic response-activation stage occurring immediately after stimulus identiﬁcation and activating task- relevant stimulus-response mappings, and a ﬁnal response-selection stage which can be occupied by one task only. With suﬃciently short SOAs, the response activation stages overlap for T1 and T2, enabling T2 characteristics to aﬀect the duration of the response-activation stage of T1. This modiﬁcation enables the response-selection bottleneck model to account for a variety of ﬁndings observed in dual-task research (e.g., response-response compatibility eﬀects; e.g., Fischer & Dreisbach, 2015 ; Hommel, 1998 ; Schuch & Koch, 2004 ).
bottleneck is occupied by T1. Thus, this model accounts for dual-task related performance costs without referring to any cognitive control processes.
Of late, this model has gone through various modifications, all of which maintain the idea of a processing bottleneck at the stage of re- sponse-selection. For example, Hommel ( 1998 ; see also Lien & Proctor, 2002 ; Schubert, Fischer, & Stelzel, 2008 ) proposed to subdivide the response-selection stage into an automatic response-activation stage occurring immediately after stimulus identification and activating task- relevant stimulus-response mappings, and a final response-selection stage which can be occupied by one task only. With sufficiently short SOAs, the response activation stages overlap for T1 and T2, enabling T2 characteristics to affect the duration of the response-activation stage of T1. This modification enables the response-selection bottleneck model to account for a variety of findings observed in dual-task research (e.g.,
In emergency locations, such as in disaster sites where an earth quake or a volcanic eruption just happened, the communication infrastructures, both wired and wireless networks, may not function properly or may even be totally down. However, if there are wireless communication devices which are able to exchange messages in an ad- hoc manner, a conferencing wireless multi-way communication between several parties may be performed. For example, a wireless multi-way communication between several emergency staff members who are on the road or a wireless multi-way communication between several red-cross members who are working in different emergency stations will enable good coordination to provide first aid to the victims. Figure 2.1(a) shows an illustration where three red-cross emergency stations which are equipped with wireless communication devices exchange messages in an emergency location. The emergency staff members in three different emergency stations may perform voice, video or web conference to communicate with each other. They may work cooperatively from dis- tance, for example, to help the emergency staff members in one emergency station to perform emergency operations to the victims.
To optimize loop nests, in particular for mathematical and scientific applications mostly based on the usage of regular data structures and control flow, so-called polyhedral loop nest optimization has been proposed by Feautrier  in his seminal work on scheduling in the polyhedral model for one-dimensional  and multi-dimensional  time. The focus of that work has been on efficient scheduling, while parallelization has been described as one possible use-case. Later work by Lengauer  and Feautrier  specifically dealt with parallelization based on polyhedral scheduling. Pluto by Bondhugula et al.  is a C source- to-source compiler which uses polyhedral scheduling to produce a parallelized OpenMP [6, 7] program. Pluto is able to achieve extreme performance by far outperforming state-of-the- art productive compilers, if, and only if, the polyhedral model is applicable at all, which still is a drawback of polyhedral optimization. The cost of using this very clean and elegant mathematical model is a limited applicability with respect to irregular applications. A loop, or more precisely a static control part (SCoP), represented in the model typically needs to fulfill certain criteria: loop bounds as well as the predicates of conditionals used in the loop body have to be representable by affine functions in the surrounding loop indices as well as (provably) loop invariant parameters. Dependences between individual statements are only allowed via accesses to indexed variables (arrays), whose access functions are affine, also in the above mentioned parameters. Furthermore, called functions need to be statically known and provably pure 1 . These are severe restrictions whose mitigation has been the goal of excessive research work [65–70], conducted also by ourselves [16, 18] and Doerfert et al. . Parallelization in the polyhedral domain is related but not addressed by the work described in this thesis. Its mathematically clean representation and optimization-based scheduling however have had a strong influence on our work.
Based on a meta-analysis, Redick and Lindsey (2013) found that complex span and
n-back tasks show an average correlation of r = 0.20, and concluded that “complex
span and n-back tasks cannot be used interchangeably as working memory measures in research applications” (p. 1102). Here, we comment on this conclusion from a psychometric perspective. In addition to construct variance, performance on a test contains measurement error, task-specific variance, and paradigm-specific variance. Hence, low correlations among dissimilar indicators do not provide strong evidence for the existence, or absence, of a construct common to both indicators. One way to arrive at such evidence is to fit hierarchical latent factors that model task-specific, paradigm-specific, and construct variance. We report analyses for 101 younger and 103 older adults who worked on nine different working memory tasks. The data are consistent with a hierarchical model of working memory, according to which both complex span and n-back tasks are valid indicators of working memory. The working memory factor predicts 71% of the variance in a factor of reasoning among younger adults (83% for among older adults). When the working memory factor was restricted to any possible triplet of working memory tasks, the correlation between working memory and reasoning was inversely related to the average magnitude of the correlations among the indicators, indicating that more highly intercorrelated indicators may provide poorer coverage of the construct space. We stress the need to go beyond specific tasks and paradigms when studying higher-order cognitive constructs, such as working memory.
Since the scheduling and mapping decisions are made at runtime, complex heuris- tics such as simulated annealing or genetic algorithms are not suitable for dynamic management due to their large latency. Rather, relative simpler heuristics are pre- ferred to enable the system to react to changing scenarios in time. Typical scheduling algorithms are, e.g., first-come first-served (FCFS), priority-based and fair queuing. These algorithms define the policies, which task should be executed as the next. For map- ping, round-robin (RR) and priority-based are widely used algorithms. They decide which PE should execute the next task. Different PEs can be assigned with different priorities depending on the PE types and design optimization goals. A fast PE like ASIP will most probably be assigned with a higher priority than a slow PE based on RISC if the design targets performance optimization. Due to the limitations in apply- ing highly complex heuristic optimization algorithms, dynamic task management is unlikely able to achieve the same optimization level as static/semi-static management, if the applications are known at design time. Also, compared to static management, dynamic management introduces additional runtime computational overhead. But it can efficiently meet the inherent challenges faced by static/semi-static management in handling unpredictable system, application and user behavior.
continuous space translation models in a post-processing step. The main novelties of this year’s participation are the follow- ing: for Russian-English, we investigate a tailored normalization of Russian to trans- late into English, and a two-step process to translate first into simplified Russian, fol- lowed by a conversion into inflected Rus- sian. For French-English, the challenge is domain adaptation, for which only mono- lingual corpora are available. Finally, for the Finnish-to-English task, we explore unsupervised morphological segmentation to reduce the sparsity of data induced by the rich morphology on the Finnish side.
Bei der avisierten Task Force geht es nicht nur um die Verhinderung von Steuerbetrug, sondern auch um die zwingende Umsetzung der am 25.6.2018 in Kraft getre- tenen EU-Richtlinie 2018/522/EU, die eine Meldepﬂ icht für grenzüberschreitende Steuergestaltungen vorsieht. Damit soll Steuervermeidung und aggressive Steuerge- staltung eingedämmt werden, um eine Erosion des nati- onalen Steuersubstrats der EU-Mitgliedstaaten zu verhin- dern. Die Richtlinie ist bis Ende 2019 in nationales Recht umzusetzen. Das BMF nennt im Zusammenhang mit dem nationalen Umsetzungsgesetz zwar beispielhaft Cum-Ex- Transaktionen, darum geht es aber gar nicht ausschließ- lich. Es geht vielmehr zentral um die Anzeige von Steu- ergestaltungen. Wer würde denn bitte schön Steuerhin- terziehung im Voraus anzeigen?! Liegt eine Steuergestal- tung vor, ist eine Mitteilung an das zuständige Finanzamt abzugeben, das die Mitteilung automatisch an das BZSt weiterleitet. Deswegen sind die meisten Personen der Task Force auch beim BZSt angesiedelt. Das BZSt stellt die gemeldeten Daten anschließend in ein von der EU- Kommission eingerichtetes Zentralverzeichnis ein. Auf diese Weise können die Meldungen im Idealfall EU-weit ausgewertet werden. Warten wir das alles ab.
In the mainstream commercial devices, RISC architectures are also the most popu- lar choice for the implementation of the task manager. For example, the master of the OMAP processors is based on the ARM architectures. While on the OMAP 1 platform, only one ARM926EJ-S processor controls one C55x DSP, in the latest OMAP systems, a MPCore technology based master (ARM Cortex-A15 with four parallel cores) is used to control a bunch of slave components including two ARM Cortex-M4 microcon- trollers, a mini-C64x DSP, a PowerVR GPU and several other hardware accelerators for image, video and audio processing. Similar to the OMAP processors, the Qual- comm Snapdragon processors apply a single ARM11 processor as the master in its first generation, and later on a quad-core Krait processor (ARM instruction-set based) in the latest generation. The Samsung Exynos application processor family  also follows the same strategy. In its early generation, only one ARM processor is used to control the system. In the later generations multi-core processors based on the MPCore technology or even eight cores (a quad-core A15 and a quad-core A7), con- figured as the ARM big.LITTLE architecture , are used. In the big.LITTLE archi- tecture, the quad-core A15 is larger and has higher performance, while the quad-core A7 is smaller but more power-efficient. Depending on the system load, the task man- agement can be internally switched between both quad-cores. Another commercial example is made by the Cell Broadband Engine, which was jointly developed by IBM, Sony and Toshiba. In this system, one PowerPC is used as the master of the sys- tem, which runs the operating system and coordinates eight specialized coprocessors known as Synergistic Processing Elements (SPEs).
Die Delegation von Aufgabenbereichen von medizinischem Personal an andere Gesundheitsfachpersonen(GF) ist aus einer direkten betriebswirtschaftlichen Kostenbetrachtung als eher kostenneutral zu bewerten, da die erweiter- ten Rollenfunktionen der Gesundheitsfachpersonen mit entsprechenden höheren Gehaltseinstufungen verbunden sind, wodurch sich keine Personalkosten als Betrieb einsparen lassen. Indirekt sollten sich positive Effekte auf die Kosteneffizienz ergeben, aufgrund der zu erwartenden positiven Effekte des Task Shiftings auf die Versorgungs- qualität. Andererseits verursachen höhere Koordinationsaufwände und eine gelingende IPZ zusätzliche Koordina- tionskosten, die zu höheren betriebswirtschaftlichen Kosten führen. Aus einer systemischen Perspektive kann dem- nach nur aufgrund der Qualitätseffekte (geringere Verweildauern, weniger Komplikationen) von positiven Effekten auf die Kosteneffizienz der Gesundheitsversorgung ausgegangen werden. Die höheren Koordinationskosten nach der Implementierung von Task Shifting werden bislang unzureichend im Tarifsystem abgegolten. Im Falle neuer Tarifpositionen oder der Erweiterung bestehender Tarifpositionen wären sogar Kostensteigerungen im System durch TS denkbar.