Transfer Learning for Content-Based Recommender Systems using Tree Matching

In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users' preferences in the target domain are either scarce or unavailable, but the necessary information on the preferences exists in another domain. We show that training a system to use such information across domains can produce better performance. Specifically, we represent users' behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users' behavior is defined as the items they rated and the items' rating values. In the next step we find a correlation between behavior patterns in the source domain and behavior patterns in the target domain. This mapping is considered a bridge between the two domains. Based on the correlation and content-attributes of the items, we train a machine learning model to predict users' ratings in the target domain. When we compare our approach to the popularity approach and KNN-cross-domain on a real world dataset, the results show that on an average of 83$$ of the cases our approach outperforms both methods

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers

The origin of paramagnetic magnetization in field-cooled YBa2Cu3O7 films

Temperature dependences of the magnetic moment have been measured in YBa_2Cu_3O_{7-\delta} thin films over a wide magnetic field range (5 <= H <= 10^4 Oe). In these films a paramagnetic signal known as the paramagnetic Meissner effect has been observed. The experimental data in the films, which have strong pinning and high critical current densities (J_c ~ 2 \times 10^6 A/cm^2 at 77 K), are quantitatively shown to be highly consistent with the theoretical model proposed by Koshelev and Larkin [Phys. Rev. B 52, 13559 (1995)]. This finding indicates that the origin of the paramagnetic effect is ultimately associated with nucleation and inhomogeneous spatial redistribution of magnetic vortices in a sample which is cooled down in a magnetic field. It is also shown that the distribution of vortices is extremely sensitive to the interplay of film properties and the real experimental conditions of the measurements.Comment: RevTex, 8 figure

Statistical Models of Nuclear Fragmentation

A method is presented that allows exact calculations of fragment multiplicity distributions for a canonical ensemble of non-interacting clusters. Fragmentation properties are shown to depend on only a few parameters. Fragments are shown to be copiously produced above the transition temperature. At this transition temperature, the calculated multiplicity distributions broaden and become strongly super-Poissonian. This behavior is compared to predictions from a percolation model. A corresponding microcanonical formalism is also presented.Comment: 12 pages, 5 figure

A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic prediction, a setting that occurs when both objects in an out-of-sample dyad have not been observed during training, or if one of them has been observed, but very few times. A popular approach for addressing this problem is to train a model that makes predictions based on a pairwise feature representation of the dyads, or, in case of kernel methods, based on a tensor product pairwise kernel. As an alternative to such a kernel approach, we introduce a novel two-step learning algorithm that borrows ideas from the fields of pairwise learning and spectral filtering. We show theoretically that the two-step method is very closely related to the tensor product kernel approach, and experimentally that it yields a slightly better predictive performance. Moreover, unlike existing tensor product kernel methods, the two-step method allows closed-form solutions for training and parameter selection via cross-validation estimates both in the full and almost full cold start settings, making the approach much more efficient and straightforward to implement

Quantum magneto-oscillations in a two-dimensional Fermi liquid

Quantum magneto-oscillations provide a powerfull tool for quantifying Fermi-liquid parameters of metals. In particular, the quasiparticle effective mass and spin susceptibility are extracted from the experiment using the Lifshitz-Kosevich formula, derived under the assumption that the properties of the system in a non-zero magnetic field are determined uniquely by the zero-field Fermi-liquid state. This assumption is valid in 3D but, generally speaking, erroneous in 2D where the Lifshitz-Kosevich formula may be applied only if the oscillations are strongly damped by thermal smearing and disorder. In this work, the effects of interactions and disorder on the amplitude of magneto-oscillations in 2D are studied. It is found that the effective mass diverges logarithmically with decreasing temperature signaling a deviation from the Fermi-liquid behavior. It is also shown that the quasiparticle lifetime due to inelastic interactions does not enter the oscillation amplitude, although these interactions do renormalize the effective mass. This result provides a generalization of the Fowler-Prange theorem formulated originally for the electron-phonon interaction.Comment: 4 pages, 1 figur

Model of multifragmentation, Equation of State and phase transition

We consider a soluble model of multifragmentation which is similar in spirit to many models which have been used to fit intermediate energy heavy ion collision data. We draw a p-V diagram for the model and compare with a p-V diagram obtained from a mean-field theory. We investigate the question of chemical instability in the multifragmentation model. Phase transitions in the model are discussed.Comment: Revtex, 9 pages including 6 figures: some change in the text and Fig.

Isospin influences on particle emission and critical phenomenon in nuclear dissociation

Features of particle emission and critical point behavior are investigated as functions of the isospin of disassembling sources and temperature at a moderate freeze-out density for medium-size Xe isotopes in the framework of isospin dependent lattice gas model. Multiplicities of emitted light particles, isotopic and isobaric ratios of light particles show the strong dependence on the isospin of the dissociation source, but double ratios of light isotope pairs and the critical temperature determined by the extreme values of some critical observables are insensitive to the isospin of the systems. Values of the power law parameter of cluster mass distribution, mean multiplicity of intermediate mass fragments ($IMF$), information entropy ($H$) and Campi's second moment ($S_2$) also show a minor dependence on the isospin of Xe isotopes at the critical point. In addition, the slopes of the average multiplicites of the neutrons ($N_n$), protons ($N_p$), charged particles ($N_{CP}$), and IMFs ($N_{imf}$), slopes of the largest fragment mass number ($A_{max}$), and the excitation energy per nucleon of the disassembling source ($E^*/A$) to temperature are investigated as well as variances of the distributions of $N_n$, $N_p$, $N_{CP}$, $N_{IMF}$, $A_{max}$ and $E^*/A$. It is found that they can be taken as additional judgements to the critical phenomena.Comment: 9 Pages, 8 figure

IBM-1 description of the fission products 108,110,112^{108,110,112}Ru

IBM-1} calculations for the fission products $^{108,110,112}$Ru have been carried out. The even-even isotopes of Ru can be described as transitional nuclei situated between the U(5) (spherical vibrator) and SO(6) ($\gamma$-unstable rotor) symmetries of the Interacting Boson Model. At first, a Hamiltonian with only one- and two-body terms has been used. Excitation energies and $B$(E2) ratios of gamma transitions have been calculated. A satisfactory agreement has been obtained, with the exception of the odd-even staggering in the quasi-$\gamma$ bands of $^{110,112}$Ru. The observed pattern is rather similar to the one for a rigid triaxial rotor. A calculation based on a Hamiltonian with three-body terms was able to remove this discrepancy. The relation between the IBM and the triaxial rotor model was also examined.Comment: 22 pages, 8 figure