A unified early termination technique for primal-dual algorithms in mixed integer conic programming

We propose an early termination technique for mixed integer conic programming within branch-and-bound based solvers. Our approach generalizes previous early termination results for ADMM-based solvers to a broader class of primaldual algorithms, including both operator splitting and interior point methods. The complexity for checking early termination is O(n) for each termination check assuming a bounded problem domain. We show that this domain restriction can be relaxed for problems whose data satisfies a simple rank condition, in which case each check requires an O(n2) solve using a linear system that is factored only once at the root node. We further show how this approach can be used in hybrid model predictive control problems with bounded inputs. Numerical results show that our method leads to a moderate reduction in the computational time required for branch-and-bound conic solvers with interior-point based subsolvers

The hyperon mean free paths in the relativistic mean field

The $\Lambda$- and $\Xi^-$-hyperon mean free paths in nuclei are firstly calculated in the relativistic mean field (RMF) theory. The real parts of the optical potential are derived from the RMF approach, while the imaginary parts are obtained from those of nucleons with the relations: $U^{\mathrm{IY}}_{\mathrm{S}} = \alpha_{\sigma \mathrm{Y}}\cdot U_{\mathrm{S}}^{\mathrm{IN}}$ and $U^{\mathrm{IY}}_{\mathrm{V}} = \alpha_{\omega \mathrm{Y}}\cdot U_{\mathrm{V}}^{\mathrm{IN}}$ . With the assumption, the depth of the imaginary potential for $\Xi^-$ is $W_{\Xi}\simeq-$ 3.5 MeV, and for $\Lambda$ is $W_{\Lambda}\simeq-$ 7 MeV at low incident energy. We find that, the hyperon mean free path decreases with the increase of the hyperon incident energies, from 200 MeV to 800 MeV; and in the interior of the nuclei, the mean free path is about $2\sim 3$ fm for $\Lambda$, and about $4\sim 8$ fm for $\Xi^-$, depending on the hyperon incident energy.Comment: 5 figures, 6 page

Studying the Effect of Data Structures on the Efficiency of Collaborative Filtering Systems

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CERI '16 Proceedings of the 4th Spanish Conference on Information Retrieval, http://dx.doi.org/10.1145/2934732.2934747Recommender systems is an active research area where the major focus has been on how to improve the quality of gen- erated recommendations, but less attention has been paid on how to do it in an e cient way. This aspect is increas- ingly important because the information to be considered by recommender systems is growing exponentially. In this pa- per we study how di erent data structures a ect the perfor- mance of these systems. Our results with two public datasets provide relevant insights regarding the optimal data struc- tures in terms of memory and time usages. Speci cally, we show that classical data structures like Binary Search Trees and Red-Black Trees can beat more complex and popular alternatives like Hash Tables

Cross-linked cationic diblock copolymer worms are superflocculants for micrometer-sized silica particles

A series of linear cationic diblock copolymer nanoparticles are prepared by polymerization-induced self-assembly (PISA) via reversible addition–fragmentation chain transfer (RAFT) aqueous dispersion polymerization of 2-hydroxypropyl methacrylate (HPMA) using a binary mixture of non-ionic and cationic macromolecular RAFT agents, namely poly(ethylene oxide) (PEO113, Mn = 4400 g mol−1; Mw/Mn = 1.08) and poly([2-(methacryloyloxy)ethyl]trimethylammonium chloride) (PQDMA125, Mn = 31 800 g mol−1, Mw/Mn = 1.19). A detailed phase diagram was constructed to determine the maximum amount of PQDMA125 stabilizer block that could be incorporated while still allowing access to a pure worm copolymer morphology. Aqueous electrophoresis studies indicated that zeta potentials of +35 mV could be achieved for such cationic worms over a wide pH range. Core cross-linked worms were prepared via statistical copolymerization of glycidyl methacrylate (GlyMA) with HPMA using a slightly modified PISA formulation, followed by reacting the epoxy groups of the GlyMA residues located within the worm cores with 3-aminopropyl triethoxysilane (APTES), and concomitant hydrolysis/condensation of the pendent silanol groups with the secondary alcohol on the HPMA residues. TEM and DLS studies confirmed that such core cross-linked cationic worms remained colloidally stable when challenged with either excess methanol or a cationic surfactant. These cross-linked cationic worms are shown to be much more effective bridging flocculants for 1.0 μm silica particles at pH 9 than the corresponding linear cationic worms (and also various commercial high molecular weight water-soluble polymers.). Laser diffraction studies indicated silica aggregates of around 25–28 μm diameter when using the former worms but only 3–5 μm diameter when employing the latter worms. Moreover, SEM studies confirmed that the cross-linked worms remained intact after their adsorption onto the silica particles, whereas the much more delicate linear worms underwent fragmentation under the same conditions. Similar results were obtained with 4 μm silica particles

Finite Size Scaling of Domain Chaos

Numerical studies of the domain chaos state in a model of rotating Rayleigh-Benard convection suggest that finite size effects may account for the discrepancy between experimentally measured values of the correlation length and the predicted divergence near onset

Study on the Oxidative Leaching of Uranium from the Lignite in the C

Na2CO3/NaHCO3 mixtures with different oxidants were used to leach uranium in the lignite which was obtained from Lincang, Yunnan province. The experimental results showed that the optimal solid/liquid ratio and CO32-/HCO3- ratio for uranium leaching were 1 : 20 (g/mL) and 2 : 1, respectively. With the increase of carbon concentration from 0.1 mol/L to 1.1 mol/L, the leaching efficiency of uranium increased from 14.64% to 42.39% after 6 h leaching. The oxidants could significantly enhance the uranium leaching efficiency, which was up to 72.23% by injecting O2 at 1.5 L/min after 12 h leaching. The oxidative leaching process of uranium from the lignite was better fitted to the pseudo-second-order reaction model. The sequential extraction results illustrated that the oxidants could effectively enhance the leaching of organic matter bound uranium in the lignite, which was decreased from 76.86 mg/kg to 9.00 mg/kg by injecting O2. The infrared spectrum analysis demonstrated that the corresponding transmittance at about 3197 cm−1 was prominently reduced after the oxidative leaching, which intimated that the phenolic and alcoholic hydroxyl might be the main functional groups combined with uranium in the lignite

Distinct transport behaviors of LaFe1-yCoyAsO1-xFx (x=0.11) between the superconducting and nonsuperconducting metallic y regions divided by y ~ 0.05

Electrical resistivities, Hall coefficients and thermoelectric powers have been measured for polycrystalline samples of LaFe1-yCoyAsO1-xFx (x=0.11) with various values of y. The results show that there exists clear distinction of these transport behaviors between the superconducting and nonsuperconducting metallic regions of y divided by the boundary value yc~0.05. We have found that the behaviors in both regions are very similar to those of high-Tc Cu oxides in the corresponding phases. If they reflect, as in the case of Cu oxides, effects of strong magnetic fluctuations, the energy scale of the fluctuations is considered to be smaller than that of the high Cu oxides by a factor of ~1/2. Arguments on the electronic nature and superconducting symmetry are presented on the basis of the observed small rate of the Tc suppression rate by the Co doping.Comment: 8 pages, 4 figures, submitted to J. Phys. Soc. Jp

Generating Realistic Synthetic Population Datasets

Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and on US census datasets, and demonstrate its feasibility using an epidemic simulation application