Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca

Inferring pleiotropy by network analysis: linked diseases in the human PPI network

<p>Abstract</p> <p>Background</p> <p>Earlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these mediators shows that proteins connecting heart disease and diabetes largely overlap with the ones connecting heart disease and obesity.</p> <p>Results</p> <p>We quantified their overlap, and based on the identified topological patterns, we inferred the structural disease-relatedness of several proteins. Literature data provide a functional look of them, well supporting our findings. For example, the inferred structurally important role of the PDZ domain-containing protein GIPC1 in diabetes is supported despite the lack of this information in the Online Mendelian Inheritance in Man database. Several key mediator proteins identified here clearly has pleiotropic effects, supported by ample evidence for their general but always of only secondary importance.</p> <p>Conclusions</p> <p>We suggest that studying central nodes in mediator networks may contribute to better understanding and quantifying pleiotropy. Network analysis provides potentially useful tools here, as well as helps in improving databases.</p

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks

<p>Abstract</p> <p>Background</p> <p>Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure.</p> <p>Results</p> <p>Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined.</p> <p>Conclusion</p> <p>Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene-encoded proteins are attached to the core at more peripheral positions of the networks.</p

Improving Performance of CIGS Solar Cells by Annealing ITO Thin Films Electrodes

Indium tin oxide (ITO) thin films were grown on glass substrates by direct current (DC) reactive magnetron sputtering at room temperature. Annealing at the optimal temperature can considerably improve the composition, structure, optical properties, and electrical properties of the ITO film. An ITO sample with a favorable crystalline structure was obtained by annealing in fixed oxygen/argon ratio of 0.03 at 400°C for 30 min. The carrier concentration, mobility, resistivity, band gap, transmission in the visible-light region, and transmission in the near-IR regions of the ITO sample were -1.6E+20 cm−3, 2.7E+01 cm2/Vs, 1.4E-03 Ohm-cm, 3.2 eV, 89.1%, and 94.7%, respectively. Thus, annealing improved the average transmissions (400–1200 nm) of the ITO film by 16.36%. Moreover, annealing a copper-indium-gallium-diselenide (CIGS) solar cell at 400°C for 30 min in air improved its efficiency by 18.75%. The characteristics of annealing ITO films importantly affect the structural, morphological, electrical, and optical properties of ITO films that are used in solar cells

Adversarially Robust Submodular Maximization under Knapsack Constraints

We propose the first adversarially robust algorithm for monotone submodular maximization under single and multiple knapsack constraints with scalable implementations in distributed and streaming settings. For a single knapsack constraint, our algorithm outputs a robust summary of almost optimal (up to polylogarithmic factors) size, from which a constant-factor approximation to the optimal solution can be constructed. For multiple knapsack constraints, our approximation is within a constant-factor of the best known non-robust solution. We evaluate the performance of our algorithms by comparison to natural robustifications of existing non-robust algorithms under two objectives: 1) dominating set for large social network graphs from Facebook and Twitter collected by the Stanford Network Analysis Project (SNAP), 2) movie recommendations on a dataset from MovieLens. Experimental results show that our algorithms give the best objective for a majority of the inputs and show strong performance even compared to offline algorithms that are given the set of removals in advance.Comment: To appear in KDD 201

Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications

We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geometric patterns that emerge in the TF domain due to such non-sinusoidal oscillations. We term this method \textit{shape-adaptive mode decomposition-based multiple harmonic ridge detection} (\textsf{SAMD-MHRD}). A swift implementation is available when supplementary information is at hand. We demonstrate the practical utility of \textsf{SAMD-MHRD} through its application to a real-world challenge. We employ it to devise a cutting-edge walking activity detection algorithm, leveraging accelerometer signals from an inertial measurement unit across diverse body locations of a moving subject

Transcutaneous Electrical Stimulation of Acupoints Changes Body Composition and Heart Rate Variability in Postmenopausal Women with Obesity

This study aimed to evaluate the effect of transcutaneous electric acupoint stimulations (TEAS) on body composition and heart rate variability (HRV) in postmenopausal women with obesity. In this prospective study, 49 postmenopausal women were recruited in Taiwan. Body composition was used as a screening test for obesity (percentage body fat > 30%, waist circumference > 80 cm). The experimental group (n = 24) received TEAS treatment 30 min twice per week for 12 weeks at the Zusanli (ST 36) and Sanyinjiao (SP 6) acupoints. The control group (n = 25) did not receive any intervention. The study of HRV was analyzed by time (standard deviation of the normal-to-normal (NN) intervals (SDNN) and square root of the mean squared differences of successive NN intervals (RMSSD) indices) and frequency domain methods. Power spectral components were obtained at low (LF) and high (HF) frequencies. Body composition and HRV values were measured at the 4th, 8th, and 12th weeks. A total of 40 subjects completed this study. Waist circumference and percentage body fat in the experimental group (n = 20) were significantly less than those of the control group (n = 20) at the 8th and 12th weeks (all P < .05). Additionally, at the same time points, percentage lean body mass in the experimental group was significantly greater than that in the control group (P < .05). SDNN values increased significantly at the 4th and 8th weeks when compared with the control group (all P < .05). At 12 weeks, SDNN value was not significantly different from that of the control group (P = .105). TEAS treatment improves body composition, and has a transient effect on the HRV in postmenopausal women with obesity

Interferon-gamma inducible protein 10 (IP10) induced cisplatin resistance of HCC after liver transplantation through ER stress signaling pathway.

Tumor recurrence remains an obstacle after liver surgery, especially in living donor liver transplantation (LDLT) for patients with hepatocellular carcinoma (HCC). The acute-phase liver graft injury might potentially induce poor response to chemotherapy in recurrent HCC after liver transplantation. We here intended to explore the mechanism and to identify a therapeutic target to overcome such chemoresistance. The associations among graft injury, overexpression of IP10 and multidrug resistant genes were investigated in a rat liver transplantation model, and further validated in clinical cohort. The role of IP10 on HCC cell proliferation and tumor growth under chemotherapy was studied both in vitro and in vivo. The underlying mechanism was revealed by detecting the activation of endoplasmic reticulum (ER) stress signaling pathways. Moreover, the effect of IP10 neutralizing antibody sensitizing cisplatin treatment was further explored. In rat liver transplantation model, significant up-regulation of IP10 associated with multidrug resistant genes was found in small-for-size liver graft. Clinically, high expression of circulating IP10 was significant correlated with tumor recurrence in HCC patients underwent LDLT. Overexpression of IP10 promoted HCC cell proliferation and tumor growth under cisplatin treatment by activation of ATF6/Grp78 signaling. IP10 neutralizing antibody sensitized cisplatin treatment in nude mice. The overexpression of IP10, which induced by liver graft injury, may lead to cisplatin resistance via ATF6/Grp78 ER stress signaling pathway. IP10 neutralizing antibody could be a potential adjuvant therapy to sensitize cisplatin treatment