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Two-particle angular correlations are measured in high-multiplicity proton-proton collisions at s√=13 TeV by the ALICE Collaboration. The yields of particle pairs at short-(Δη ∼ 0) and long-range (1.6<|Δη|<1.8) in pseudorapidity are extracted on the near-side (Δφ ∼ 0). They are reported as a function of transverse momentum (pT) in the range 1<pT<4 GeV/c. Furthermore, the event-scale dependence is studied for the first time by requiring the presence of high-pT leading particles and jets for varying pT thresholds. The results demonstrate that the long-range "ridge" yield, possibly related to the collective behavior of the system, is present in events with high-pT processes. The magnitudes of the short- and long-range yields are found to grow with the event scale. The results are compared to EPOS LHC and PYTHIA 8 calculations, with and without string-shoving interactions. It is found that while both models describe the qualitative trends in the data, calculations from EPOS LHC show a better quantitative agreement, in particular for the pT and event-scale dependencies.
We call a distribution on n bit strings (", e) locally random, if for every choice of e · n positions the induced distribution on e bit strings is in the L1 norm at most " away from the uniform distribution on e bit strings. We establish local randomness in polynomial random number generators (RNG) that are candidate one way functions. Let N be a squarefree integer and let f1, . . . , f be polynomials with coe±- cients in ZZN = ZZ/NZZ. We study the RNG that stretches a random x 2 ZZN into the sequence of least significant bits of f1(x), . . . , f(x). We show that this RNG provides local randomness if for every prime divisor p of N the polynomials f1, . . . , f are linearly independent modulo the subspace of polynomials of degree · 1 in ZZp[x]. We also establish local randomness in polynomial random function generators. This yields candidates for cryptographic hash functions. The concept of local randomness in families of functions extends the concept of universal families of hash functions by Carter and Wegman (1979). The proofs of our results rely on upper bounds for exponential sums.
The second and the third order anisotropic flow, V2 and V3, are mostly determined by the corresponding initial spatial anisotropy coefficients, ε2 and ε3, in the initial density distribution. In addition to their dependence on the same order initial anisotropy coefficient, higher order anisotropic flow, Vn (n>3), can also have a significant contribution from lower order initial anisotropy coefficients, which leads to mode-coupling effects. In this Letter we investigate the linear and non-linear modes in higher order anisotropic flow Vn for n=4, 5, and 6 in Pb-Pb collisions at sNN−−−√=2.76 TeV with the ALICE detector at the Large Hadron Collider. The measurements are done for particles in the pseudorapidity range |η|<0.8 and the transverse momentum range 0.2<pT<5.0 GeV/c as a function of collision centrality. The results are compared with theoretical calculations and provide important constraints on the initial conditions, including initial spatial geometry and its fluctuations, as well as the ratio of the shear viscosity to entropy density of the produced system.
The second and the third order anisotropic flow, V2 and V3, are mostly determined by the corresponding initial spatial anisotropy coefficients, ε2 and ε3, in the initial density distribution. In addition to their dependence on the same order initial anisotropy coefficient, higher order anisotropic flow, Vn (n>3), can also have a significant contribution from lower order initial anisotropy coefficients, which leads to mode-coupling effects. In this Letter we investigate the linear and non-linear modes in higher order anisotropic flow Vn for n=4, 5, 6 with the ALICE detector at the Large Hadron Collider. The measurements are done for particles in the pseudorapidity range |η|<0.8 and the transverse momentum range 0.2<pT<5.0 GeV/c as a function of collision centrality. The results are compared with theoretical calculations and provide important constraints on the initial conditions, including initial spatial geometry and its fluctuations, as well as the ratio of the shear viscosity to entropy density of the produced system.
Results on the transverse spherocity dependence of light-flavor particle production (π, K, p, ϕ, K∗0, K0S, Λ, Ξ) at midrapidity in high-multiplicity pp collisions at s√=13 TeV were obtained with the ALICE apparatus. The transverse spherocity estimator (SpT=1O) categorizes events by their azimuthal topology. Utilizing narrow selections on SpT=1O, it is possible to contrast particle production in collisions dominated by many soft initial interactions with that observed in collisions dominated by one or more hard scatterings. Results are reported for two multiplicity estimators covering different pseudorapidity regions. The SpT=1O estimator is found to effectively constrain the hardness of the events when the midrapidity (|η|<0.8) estimator is used. The production rates of strange particles are found to be slightly higher for soft isotropic topologies, and severely suppressed in hard jet-like topologies. These effects are more pronounced for hadrons with larger mass and strangeness content, and observed when the topological selection is done within a narrow multiplicity interval. This demonstrates that an important aspect of the universal scaling of strangeness enhancement with final-state multiplicity is that high-multiplicity collisions are dominated by soft, isotropic processes. On the contrary, strangeness production in events with jet-like processes is significantly reduced. The results presented in this article are compared with several QCD-inspired Monte Carlo event generators. Models that incorporate a two-component phenomenology, either through mechanisms accounting for string density, or thermal production, are able to describe the observed strangeness enhancement as a function of SpT=1O.
The ALICE experiment has measured low-mass dimuon production in pp collisions at √s=7 TeV in the dimuon rapidity region 2.5<y<4. The observed dimuon mass spectrum is described as a superposition of resonance decays (η,ρ,ω,η′,ϕ) into muons and semi-leptonic decays of charmed mesons. The measured production cross sections for ω and ϕ are σω(1<pt<5 GeV/c,2.5<y<4)=5.28±0.54(stat)±0.49(syst) mb and σϕ(1<pt<5 GeV/c,2.5<y<4)=0.940±0.084(stat)±0.076(syst) mb. The differential cross sections d2σ/dydpt are extracted as a function of pt for ω and ϕ. The ratio between the ρ and ω cross section is obtained. Results for the ϕ are compared with other measurements at the same energy and with predictions by models.
The measurement of the production of deuterons, tritons and 3He and their antiparticles in Pb-Pb collisions at sNN−−−√=5.02 TeV is presented in this article. The measurements are carried out at midrapidity (|y|< 0.5) as a function of collision centrality using the ALICE detector. The pT-integrated yields, the coalescence parameters and the ratios to protons and antiprotons are reported and compared with nucleosynthesis models. The comparison of these results in different collision systems at different centre-of-mass collision energies reveals a suppression of nucleus production in small systems. In the Statistical Hadronisation Model framework, this can be explained by a small correlation volume where the baryon number is conserved, as already shown in previous fluctuation analyses. However, a different size of the correlation volume is required to describe the proton yields in the same data sets. The coalescence model can describe this suppression by the fact that the wave functions of the nuclei are large and the fireball size starts to become comparable and even much smaller than the actual nucleus at low multiplicities.
The measurement of the production of deuterons, tritons and 3He and their antiparticles in Pb-Pb collisions at sNN−−−√=5.02 TeV is presented in this article. The measurements are carried out at midrapidity (|y|< 0.5) as a function of collision centrality using the ALICE detector. The pT-integrated yields, the coalescence parameters and the ratios to protons and antiprotons are reported and compared with nucleosynthesis models. The comparison of these results in different collision systems at different centre-of-mass collision energies reveals a suppression of nucleus production in small systems. In the Statistical Hadronisation Model framework, this can be explained by a small correlation volume where the baryon number is conserved, as already shown in previous fluctuation analyses. However, a different size of the correlation volume is required to describe the proton yields in the same data sets. The coalescence model can describe this suppression by the fact that the wave functions of the nuclei are large and the fireball size starts to become comparable and even much smaller than the actual nucleus at low multiplicities.
The measurement of the production of deuterons, tritons and 3He and their antiparticles in Pb-Pb collisions at √sNN = 5.02 TeV is presented in this article. The measurements are carried out at midrapidity (y|< 0.5) as a function of collision centrality using the ALICE detector. The pT-integrated yields, the coalescence parameters and the ratios to protons and antiprotons are reported and compared with nucleosynthesis models. The comparison of these results in different collision systems at different center-of-mass collision energies reveals a suppression of nucleus production in small systems. In the Statistical Hadronisation Model framework, this can be explained by a small correlation volume where the baryon number is conserved, as already shown in previous fluctuation analyses. However, a different size of the correlation volume is required to describe the proton yields in the same data sets. The coalescence model can describe this suppression by the fact that the wave functions of the nuclei are large and the fireball size starts to become comparable and even much smaller than the actual nucleus at low multiplicities.
We propose and create a new data model for learning specific environments and learning analytics applications. This is motivated from the experience in the Fiber Bundle Data Model used for large - time and space dependent - data. Our proposed data model integrates file or stream-based data structures from capturing devices more easily. Learning analytics algorithms are added directly to the data, and formulation of queries and analytics is done in Python. It is designed to improve collaboration in the field of learning analytics. We leverage a hierarchical data structure, where varying data is located near the leaves. Abstract data types are identified in four distinct pathways, which allow storing most diverse data sources. We compare different implementations regarding its memory footprint and performance. Our tests indicate that LeAn Bundles can be smaller than a naïve xAPI export. The benchmarks show that the performance is comparable to a MongoDB, while having the benefit of being portable and extensible.
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific details and facilitate generalization. Here we consider the learning of abstract representations in a multi-modal setting with two or more input modalities. We treat the problem as a lossy compression problem and show that generic lossy compression of multimodal sensory input naturally extracts abstract representations that tend to strip away modalitiy specific details and preferentially retain information that is shared across the different modalities. Furthermore, we propose an architecture to learn abstract representations by identifying and retaining only the information that is shared across multiple modalities while discarding any modality specific information.
In der modernen Hochschullehre haben sich eLearning-Elemente als ein Teil des Lehrrepertoires etabliert. Der Einsatz interaktiver webbasierter Selbstlernmodule (Web Based Trainings (WBT)) ist dabei eine Option. Hochschulen und Unternehmen versprechen sich dadurch neue Möglichkeiten des Lehrens und Lernens, um z. B. einen Ausgleich heterogener Vorerfahrungen sowie eine stärkere aktive Beteiligung der Lernenden zu bewirken. Damit die Erstellung und Strukturierung dieser Inhalte mit möglichst geringem Aufwand erfolgen kann, bieten Autorensysteme Unterstützung.
Zu den Grundfunktionen von Autorensystemen gehören unter anderem, das Einbinden gebräuchlicher Medienformate, die einfache Erstellung von Fragen sowie verschiedene Auswertungs- und Feedbackmöglichkeiten. Obwohl Autorensysteme schon vor vielen Jahren ihre erste praktische Anwendung fanden, gibt es nach wie vor Schwachstellen, die sich auf den gesamten Erstellungsprozess wie auch auf einzelne Funktionen beziehen. Im Detail wird bemängelt, dass die Werkzeuge zu komplex und unflexibel sind. Darüber hinaus fehlt häufig eine zufriedenstellende Verknüpfung der vielen Werkzeuge entlang der Prozesskette zu einer Gesamtlösung.
Des Weiteren wird die Konzentration auf die Produktionsphase kritisiert, wodurch andere wichtige Prozesse in den Hintergrund treten bzw. außer Acht gelassen werden.
Im Rahmen der Zusammenarbeit mit einem Automobilhersteller, für den die erste Version des Autorensystems LernBar weiterentwickelt wurde, spielte der Begriff „Lean Production“ inhaltlich in der Umsetzung der WBTs eine wesentliche Rolle. Die Lean Production, die über viele Jahre für die Automobilindustrie entwickelt, verbessert und angepasst wurde, liefert Optimierungsansätze für den Produktionsbereich. Ein wirtschaftlicher Nutzen des Lean-Ansatzes wird auch in anderen Bereichen gesehen wie z. B. in der Softwareentwicklung („Lean Software Development“) oder im Management („Lean Management“). Dabei bietet die Wertschöpfungsorientierung Lösungen für die widersprüchlichen Ziele mehr Leistungen zu geringeren Kosten, schneller und in höherer Qualität zugleich zu liefern. Aus der Grundidee der Lean Production entwickelte sich vorliegendes Dissertationsthema in Bezug darauf, inwiefern sich diese Prinzipien auf den WBT-Produktionsprozess übertragen lassen und die LernBar (das hierfür weiterentwickelnde Autorensystem) dabei Unterstützung bieten kann.
Zunächst wurde analysiert, welche Werkzeuge und Hilfestellungen benötigt werden, um unter dem Aspekt der Lean Production WBTs im universitären Umfeld erstellen zu können. In diesem Zusammenhang wurden Merkmale einer „Lean Media Production“ definiert sowie konzeptionell und technisch umgesetzt. Zur Verbesserung der Prozesse flossen Ergebnisse aus empirischer und praktischer Forschung ein. Im Vergleich zu anderen Entwicklungen bei denen häufig das Hauptziel eine umfangreiche Funktionalität ist, werden u.a. folgende übertragbare Ziele bei der Umsetzung verfolgt: Verschwendung vermeiden, eine starke Einbeziehung der Kunden, Werkzeuge die nahtlos ineinandergreifen, eine hohe Flexibilität und eine stetige Qualitätsverbesserung.
Zur Erreichung dieser Zielsetzungen wurden alle Prozesse kontinuierlich verbessert, sich auf das Wesentliche und die Wertschöpfung konzentriert sowie überflüssige Schritte eliminiert. Demnach ist unter dem Begriff „Lean Media Production“ ein skalierbarer, effizienter und effektiver Produktionsprozess zu verstehen, in dem alle Werkzeuge ineinandergreifen.
Die Realisierung der „Lean Media Production“ erfolgte anhand des Autorensystems LernBar, wobei die typischen Softwareentwicklungsphasen Entwurf, Implementierung und Evaluierung mehrfach durchlaufen wurden. Ausschlaggebend dabei war, dass der „Lean“-Aspekt berücksichtigt wurde und dies somit eine neue Vorgehensweise bei der Umsetzung eines Autorensystems darstellt. Im Verlauf der Entwicklungen ergaben sich, durch eine formative Evaluation, den Einsatz in Projekten und eine empirische Begleitforschung, neue Anforderungen an das System. Ein Vergleich der zwei Produktionssysteme, Automobil vs. WBT-Produktion, zeigt und bestätigt die Erwartung, dass nicht alle Prinzipien der Lean Production übertragbar sind.
Dennoch war diese Untersuchung notwendig, da sie Denkanstöße zur Entwicklung und Optimierung des Erstellungsprozesses eines WBTs gab. Auch die Ergebnisse der abschließenden Online-Befragung ergaben, dass die Ziele der Arbeit erreicht wurden, dass aber weiterer Optimierungsbedarf besteht. Die LernBar Release 3 bietet für alle Produktionsphasen Werkzeuge an, durch die eine effektive und effiziente Erstellung von WBTs von der Idee bis zur Distribution möglich ist.
Stand noch vor fünf Jahren zu Beginn dieser Arbeit das Endprodukt bei der LernBar Entwicklung im Vordergrund, verlagerte sich durch den Einfluss dieser Dissertation der Schwerpunkt auf den gesamten Produktionsprozess. Unter Berücksichtigung der in diesem Zusammenhang entwickelten Prinzipien einer „Lean Media Production“, nehmen bspw. die Wirtschaftlichkeit und die starke Kundenorientierung während des Produktionsprozesses einen wichtigen Stellenwert ein. Dieser Ansatz ist eine neue Vorgehensweise im Bereich der Entwicklung von Autorensystemen, der seine Anerkennung und Professionalität durch die Ergebnisse des selbstentwickelten Evaluationsbogens sowie dem stetig wachsenden Einsatz in Schulen, Hochschulen und Unternehmen belegen kann.
In weiteren Forschungsarbeiten ist zu untersuchen, welche Lean Production Prinzipien zu verwenden oder anzupassen sind, wenn z. B. in größeren Teams oder mobil produziert wird. Des Weiteren sollte überprüft werden, inwieweit die Lernenden mit dem Endprodukt zufrieden sind und in ihrem Lernprozess unterstützt werden. Durch diese Forschungsarbeit wurde ein Beitrag dazu geleistet, die Lehre und Ausbildung zu optimieren, indem die Autoren/Lehrende in der Erstellung ihrer digitalen Lerninhalte im gesamten Prozess von aufeinander abgestimmten Werkzeugen unterstützt werden.
We present a novel practical algorithm that given a lattice basis b1, ..., bn finds in O(n exp 2 *(k/6) exp (k/4)) average time a shorter vector than b1 provided that b1 is (k/6) exp (n/(2k)) times longer than the length of the shortest, nonzero lattice vector. We assume that the given basis b1, ..., bn has an orthogonal basis that is typical for worst case lattice bases. The new reduction method samples short lattice vectors in high dimensional sublattices, it advances in sporadic big jumps. It decreases the approximation factor achievable in a given time by known methods to less than its fourth-th root. We further speed up the new method by the simple and the general birthday method. n2
We report on improved practical algorithms for lattice basis reduction. We propose a practical floating point version of theL3-algorithm of Lenstra, Lenstra, Lovász (1982). We present a variant of theL3-algorithm with "deep insertions" and a practical algorithm for block Korkin—Zolotarev reduction, a concept introduced by Schnorr (1987). Empirical tests show that the strongest of these algorithms solves almost all subset sum problems with up to 66 random weights of arbitrary bit length within at most a few hours on a UNISYS 6000/70 or within a couple of minutes on a SPARC1 + computer.
The human brain is an unparalleled system: Through millions of years of evolution and during a lifespan of learning, our brains have developed remarkable abilities for dealing with incoming sensory data, extracting structure and useful information, and finally drawing the conclusions that result in the actions we take. Understanding the principles behind this machinery and building artificial systems that mimic at least some of these capabilities is a long standing goal in both the scientific and the engineering communities. While this goal still seems unreachable, we have seen tremendous progress when it comes to training data-driven algorithms on vast amounts of training data, e.g. to learn an optimal data model and its parameters in order to accomplish some task. Such algorithms are now omnipresent: they are part of recommender systems, they perform speech recognition and generally build the foundation for many semi-autonomous systems. They start to be integral part of many technical systems modern technical societies rely on for their everyday functioning. Many of these algorithms were originally inspired by biological systems or act as models for sensory data processing in mammalian brains. The response properties of a certain population of neurons in the first stages of the mammalian visual pathway, for example, can be modeled by algorithms such as Sparse Coding (SC), Independent Component Analysis (ICA) or Factor Analysis (FA). These well established learning algorithms typically assume linear interactions between the variables of the model. Most often these relationships are expressed in the form of a matrix-vector products between a matrix with learned dictionary-elements (basis vectors as column vectors) and the latent variables of these models. While on the one hand this linear interaction can sometimes be justified by the physical process for which the machine learning model is proposed, it is on the other hand often chosen just because of its mathematical and practical convenience. From an optimal coding point of view though, one would generally expect that the ideal model closely reflect the core interactions of the system it is modeling. In vision for example, one of the dominant processes giving rise to our sensory percepts are occlusions. Occluding objects are omnipresent in visual scenes and it would not be surprising if the mammalian visual system would be optimized to process occluding structures in the visual data stream. Yet, the established mathematical models of the first stages of the visual processing path (like, e.g., SC, ICA or FA) all assume linear interactions between the active image components. In this thesis we will discuss new models that aim to approximate the effects of occluding components by assuming nonlinear interactions between their activated dictionary elements. We will present learning algorithms that infer optimal parameters for these models given data. In the experiments, we will validate the algorithms on artificial ground truth data and demonstrate their ability to recover the correct model parameters. We will show that the predictions made by these nonlinear models correspond better to the experimental data measured in-vivo than the predictions made by the established linear models. Furthermore, we systematically explore and compare a large space of plausible combinations of hyperparameters and preprocessing schemes in order to eliminate any effects of artefacts on the observed results. Training nonlinear sparse coding models is computationally more demanding than training linear models. In order to perform the numerical experiments described in this thesis we developed a software framework that facilitates the implementation of massive parallel expectation maximization (EM) based learning algorithms. This infrastructure was used for all experiments described in here, as well as by collaborators in projects we will not discuss. Some of the experiments required more than 1017 floating point operations and were run on a computer cluster running on up to 5000 CPU Cores in parallel. Our parallel framework enabled these experiments to be performed.
The production of K∗(892)± meson resonance is measured at midrapidity (|y|<0.5) in Pb-Pb collisions at sNN−−−√=5.02 TeV using the ALICE detector at the LHC. The resonance is reconstructed via its hadronic decay channel K∗(892)±→K0Sπ±. The transverse momentum distributions are obtained for various centrality intervals in the pT range of 0.4-16 GeV/c. The reported measurements of integrated yields, mean transverse momenta, and particle yield ratios are consistent with previous ALICE measurements for K∗(892)0. The pT-integrated yield ratio 2K∗(892)±/(K++K−) in central Pb-Pb collisions shows a significant suppression (9.3σ) relative to pp collisions. Thermal model calculations overpredict the particle yield ratio. Although both simulations consider the hadronic phase, only HRG-PCE accurately represents the measurements, whereas MUSIC+SMASH tends to overpredict them. These observations, along with the kinetic freeze-out temperatures extracted from the yields of light-flavored hadrons using the HRG-PCE model, indicate a finite hadronic phase lifetime, which increases towards central collisions. The pT-differential yield ratios 2K∗(892)±/(K++K−) and 2K∗(892)±/(π++π−) are suppressed by up to a factor of five at pT<2 GeV/c in central Pb-Pb collisions compared to pp collisions at s√= 5.02 TeV. Both particle ratios and are qualitatively consistent with expectations for rescattering effects in the hadronic phase. The nuclear modification factor shows a smooth evolution with centrality and is below unity at pT>8 GeV/c, consistent with measurements for other light-flavored hadrons. The smallest values are observed in most central collisions, indicating larger energy loss of partons traversing the dense medium.
The production of K∗(892)± meson resonance is measured at midrapidity (|y|<0.5) in Pb-Pb collisions at sNN−−−√=5.02 TeV using the ALICE detector at the LHC. The resonance is reconstructed via its hadronic decay channel K∗(892)±→K0Sπ±. The transverse momentum distributions are obtained for various centrality intervals in the pT range of 0.4-16 GeV/c. The reported measurements of integrated yields, mean transverse momenta, and particle yield ratios are consistent with previous ALICE measurements for K∗(892)0. The pT-integrated yield ratio 2K∗(892)±/(K++K−) in central Pb-Pb collisions shows a significant suppression (9.3σ) relative to pp collisions. Thermal model calculations overpredict the particle yield ratio. Although both simulations consider the hadronic phase, only HRG-PCE accurately represents the measurements, whereas MUSIC+SMASH tends to overpredict them. These observations, along with the kinetic freeze-out temperatures extracted from the yields of light-flavored hadrons using the HRG-PCE model, indicate a finite hadronic phase lifetime, which increases towards central collisions. The pT-differential yield ratios 2K∗(892)±/(K++K−) and 2K∗(892)±/(π++π−) are suppressed by up to a factor of five at pT<2 GeV/c in central Pb-Pb collisions compared to pp collisions at s√= 5.02 TeV. Both particle ratios and are qualitatively consistent with expectations for rescattering effects in the hadronic phase. The nuclear modification factor shows a smooth evolution with centrality and is below unity at pT>8 GeV/c, consistent with measurements for other light-flavored hadrons. The smallest values are observed in most central collisions, indicating larger energy loss of partons traversing the dense medium.
The production of K∗(892)± meson resonance is measured at midrapidity (|y|<0.5) in Pb−Pb collisions at √sNN=5.02 TeV using the ALICE detector at the CERN Large Hadron Collider. The resonance is reconstructed via its hadronic decay channel K∗(892)±→K0Sπ±. The transverse momentum distributions are obtained for various centrality intervals in the pT range of 0.4−16 GeV/c . Measurements of integrated yields, mean transverse momenta, and particle yield ratios are reported and found to be consistent with previous ALICE measurements for K∗(892)0 within uncertainties. The pT-integrated yield ratio 2K∗(892)±/(K++K−) in central Pb−Pb collisions shows a significant suppression at a level of 9.3σ relative to pp collisions. Thermal model calculations result in an overprediction of the particle yield ratio. Although both hadron resonance gas in partial chemical equilibrium (HRG-PCE) and music + smash simulations consider the hadronic phase, only HRG-PCE accurately represents the measurements, whereas music + smash simulations tend to overpredict the particle yield ratio. These observations, along with the kinetic freeze-out temperatures extracted from the yields measured for light-flavored hadrons using the HRG-PCE model, indicate a finite hadronic phase lifetime, which decreases with increasing collision centrality percentile. The pT-differential yield ratios 2K∗(892)±/(K++K−) and 2K∗(892)±/(π++π−) are presented and compared with measurements in pp collisions at √s=5.02 TeV. Both pa rticle ratios are found to be suppressed by up to a factor of five at pT<2.0 GeV/c in central Pb−Pb collisions and are qualitatively consistent with expectations for rescattering effects in the hadronic phase. The nuclear modification factor (RAA) shows a smooth evolution with centrality and is found to be below unity at pT>8 GeV/c, consistent with measurements for other light-flavored hadrons. The smallest values are observed in most central collisions, indicating larger energy loss of partons traversing the dense medium.
The production of K∗(892)0 and ϕ(1020) in pp collisions at s√ = 8 TeV was measured using Run 1 data collected by the ALICE collaboration at the LHC. The pT-differential yields d2N/dydpT in the range 0<pT<20 GeV/c for K∗0 and 0.4<pT<16 GeV/c for ϕ have been measured at midrapidity, |y|<0.5. Moreover, improved measurements of the K∗(892)0 and ϕ(1020) at s√=7TeV are presented. The collision energy dependence of pT distributions, pT-integrated yields and particle ratios in inelastic pp collisions are examined. The results are also compared with different collision systems. The values of the particle ratios are found to be similar to those measured at other LHC energies. In pp collisions a hardening of the particle spectra is observed with increasing energy, but at the same time it is also observed that the relative particle abundances are independent of the collision energy. The pT-differential yields of K∗0 and ϕ in pp collisions at s√=8 TeV are compared with the expectations of different Monte Carlo event generators.