Agent Behavior Prediction and Its Generalization Analysis

Machine learning algorithms have been applied to predict agent behaviors in real-world dynamic systems, such as advertiser behaviors in sponsored search and worker behaviors in crowdsourcing. The behavior data in these systems are generated by live agents: once the systems change due to the adoption of the prediction models learnt from the behavior data, agents will observe and respond to these changes by changing their own behaviors accordingly. As a result, the behavior data will evolve and will not be identically and independently distributed, posing great challenges to the theoretical analysis on the machine learning algorithms for behavior prediction. To tackle this challenge, in this paper, we propose to use Markov Chain in Random Environments (MCRE) to describe the behavior data, and perform generalization analysis of the machine learning algorithms on its basis. Since the one-step transition probability matrix of MCRE depends on both previous states and the random environment, conventional techniques for generalization analysis cannot be directly applied. To address this issue, we propose a novel technique that transforms the original MCRE into a higher-dimensional time-homogeneous Markov chain. The new Markov chain involves more variables but is more regular, and thus easier to deal with. We prove the convergence of the new Markov chain when time approaches infinity. Then we prove a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain, which depends on both the Markovian parameters and the covering number of the function class compounded by the loss function for behavior prediction and the behavior prediction model. To the best of our knowledge, this is the first work that performs the generalization analysis on data generated by complex processes in real-world dynamic systems

A Decision Tree Approach for Assessing and Mitigating Background and Identity Disclosure Risks

The Facebook/Cambridge Analytica data scandal shows a type of privacy threat where an adversary attacks on a massive number of people without prior knowledge about their background information. Existing studies typically assume that the adversary knew the background information of the target individuals. This study examines the disclosure risk issue in privacy breaches without such an assumption. We define the background disclosure risk and re-identification risk based on the notion of prior and conditional probabilities respectively, and integrate the two risk measures into a composite measure using the Minimum Description Length principle. We then develop a decision-tree pruning algorithm to find an appropriate group size considering the tradeoff between disclosure risk and data utility. Furthermore, we propose a novel tiered generalization method for anonymizing data at the group level. An experimental study has been conducted to demonstrate the effectiveness of our approach

Self-Assemblies of Single-Walled Carbon Nanotubes through Tunable Tethering of Pyrenes by Dextrin for Rapidly Chiral Sensing

Pyrene-modified dextrin (Py-Dex) was synthesized via the Schiff base reaction between reducing end of dextrins and 1-aminopyrene, and then self-assemblies of single-walled carbon nanotubes (SWNTs) were fabricated through the tunable tethering of pyrene to SWNTs by dextrin chains. The Py-Dex-SWNTs assemblies were found to be significantly water-soluble because of the synergistic effect of dextrin chains and pyrene moieties. Py-Dex and Py-Dex-SWNTs were adequately characterized by NMR, UV-vis, fluorescence spectroscopy, Raman spectroscopy, matrix-assisted laser desorption/ionization-time of flight mass spectroscopy, and transmission electron microscopy. The tethering effect of dextrin toward pyrene moieties was clearly revealed and was found to be tunable by adjusting the length of dextrin chains. The fluorescence of pyrene moieties was sufficiently quenched by SWNTs with the support of dextrin chains. Furthermore, the Py-Dex-SWNTs assemblies were used for chiral selective sensing by introducing cyclodextrins as chiral binding sites. The rapid chiral sensing was successfully tested for different enantiomers

Connectivity reveals homology between the visual systems of the human and macaque brains

The visual systems of humans and nonhuman primates share many similarities in both anatomical and functional organization. Understanding the homology and differences between the two systems can provide important insights into the neural basis of visual perception and cognition. This research aims to investigate the homology between human and macaque visual systems based on connectivity, using diffusion tensor imaging and resting-state functional magnetic resonance imaging to construct structural and functional connectivity fingerprints of the visual systems in humans and macaques, and quantitatively analyze the connectivity patterns. By integrating multimodal magnetic resonance imaging, this research explored the homology and differences between the two systems. The results showed that 9 brain regions in the macaque visual system formed highly homologous mapping relationships with 11 brain regions in the human visual system, and the related brain regions between the two species showed highly structure homologous, with their functional organization being essentially conserved across species. Finally, this research generated a homology information map of the visual system for humans and macaques, providing a new perspective for subsequent cross-species analysis

Topljivost CO2 u eterima 1-aliloksi-3-(4-nonilfenoksi)-2-propanola i polioksietilena

1-allyloxy-3-(4-nonylphenoxy)-2-propanol polyoxyethylene ethers (ANAPEs), a new type of absorbent, are polymeric surfactants with different adduct numbers. In this work, ANAPEs, including SN-10 with adduct number of 10 and SN-15 with adduct number of 15, were prepared for CO2 absorption using the isochoric saturation method. Densities of the ANAPEs at atmospheric pressure were measured by a 5.567 ± 0.004 cm3 pycnometer, which decreased with increased temperature. Solubility data of CO2 in ANAPEs were measured within the pressure range of 0 – 600.0 kPa and temperature range of 303.15 – 323.15 K at 10 K intervals and could be calculated on the basis of experimental data of p, xCO2 and bCO2. The solubility of CO2 in absorbents increased linearly with increasing pressure and decreased with increasing temperature at all the pressures. The solubility of CO2 in SN-15 is the highest at all temperatures, but almost the same with SN-10 at 303.15 K over pressures (p < 350kPa), which indicates physical dissolution process. Henry’s constants were determined from solubility data. With increasing temperature, Henry’s constants increased. Thermodynamics of CO2 absorption were calculated including enthalpy, entropy, and Gibbs energy. The absolute value of ΔsolH based on Hx of SN-15 is largest at 303.15 K and indicates stronger SN-15/CO2 interactions, consistent with solubility of CO2 based on Hx. The negative enthalpy demonstrated exothermic process, which means the dissolution of CO2 in ANAPEs is favourable enthalpically. The ΔsolG shows positive value. This work is licensed under a Creative Commons Attribution 4.0 International License.Ispitana je topljivost ugljikova dioksida u eterima 1-aliloksi-3-(4-nonilfenoksi)-2-propanola i polioksietilena (ANAPE), (SN-10 i SN-15) u izohornim uvjetima pri rasponu tlakova 0 – 600 kPa i temperatura 303,15 – 323,15 K. Topljivost CO2 raste s tlakom, a pri svim tlakovima opada s temperaturom. U cijelom temperaturnom rasponu topljivost je veća u SN-15, ali pri 303,15 K i tlakovima nižim od 350 kPa gotovo je izjednačena s topljivošću u SN-10 što ukazuje na fizikalni mehanizam otapanja. Određene su Henryjeve konstante i termodinamika apsorpcije, uključujući entalpiju, entropiju i Gibbsovu energiju. Prema negativnim vrijednostima entalpije otapanje CO2 u eterima ANAPE je egzoterman proces. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Polymorphism in Growth Hormone Gene and its Association with Growth Traits in Siniperca chuatsi

Growth hormone (GH) is a candidate gene for growth traits in fish. In this study, we assessed associations between single nucleotide polymorphisms (SNPs) in GH gene with growth traits in 357 Siniperca chuatsi individuals using high-resolution melting. Two SNPs were identified in GH gene, with one mutation in exon 5 (g.5045T>C), and one mutation in intron 5 (g.5234T>G). The corrections analysis of SNPs with the four growth traits was carried out using General Linear Model (GLM) estimation. Results showed that both of them were significantly associated with growth performance in S. chuatsi. For g.5234T>G, it was significantly associated with body weight (P＜0.01), body length (P＜0.05), body depth (P＜0.01), and body width (P＜0.01), and the individuals of genotype GG grew faster than those of genotypes TT and TG (P＜0.05). A further diplotype-trait association analysis confirmed that in fish with H3H2 (TC-GG) diplotype body weight, body length, and body width was greater than in those with other diplotypes (P＜0.05). These results demonstrated GH gene SNPs could be used as potential genetic markers in future marker assisted selection of S. chuatsi

Large magnetoelectric coupling in nanoscale BiFeO3_3 from direct electrical measurements

We report the results of direct measurement of remanent hysteresis loops on nanochains of BiFeO$_3$ at room temperature under zero and $\sim$20 kOe magnetic field. We noticed a suppression of remanent polarization by nearly $\sim$40\% under the magnetic field. The powder neutron diffraction data reveal significant ion displacements under a magnetic field which seems to be the origin of the suppression of polarization. The isolated nanoparticles, comprising the chains, exhibit evolution of ferroelectric domains under dc electric field and complete 180$^o$ switching in switching-spectroscopy piezoresponse force microscopy. They also exhibit stronger ferromagnetism with nearly an order of magnitude higher saturation magnetization than that of the bulk sample. These results show that the nanoscale BiFeO$_3$ exhibits coexistence of ferroelectric and ferromagnetic order and a strong magnetoelectric multiferroic coupling at room temperature comparable to what some of the type-II multiferroics show at a very low temperature.Comment: 7 pages with 5 figures, published in Phys. Rev.