Self-adaptive algorithms for quasiconvex programming and applications to machine learning

For solving a broad class of nonconvex programming problems on an unbounded constraint set, we provide a self-adaptive step-size strategy that does not include line-search techniques and establishes the convergence of a generic approach under mild assumptions. Specifically, the objective function may not satisfy the convexity condition. Unlike descent line-search algorithms, it does not need a known Lipschitz constant to figure out how big the first step should be. The crucial feature of this process is the steady reduction of the step size until a certain condition is fulfilled. In particular, it can provide a new gradient projection approach to optimization problems with an unbounded constrained set. The correctness of the proposed method is verified by preliminary results from some computational examples. To demonstrate the effectiveness of the proposed technique for large-scale problems, we apply it to some experiments on machine learning, such as supervised feature selection, multi-variable logistic regressions and neural networks for classification

Lanthanum oxide-promoted cobalt catalyst supported on mesoporous alumina for syngas production via methane dry reforming

Methane dry reforming reaction (MDR) has recently emerged as a promising multipurpose approach for converting two greenhouse gasses, included carbon dioxide (CO2) and methane (CH4), into valuable feedstock for downstream petrochemical processes. At present, there is still a challenge in developing the highly stable and active catalysts for MDR reaction as well as better resistance to carbon deposition. Though the mesoporous alumina supported Co-based catalysts have recently appeared to be the potential catalysts. However, the common starting materials for preparing these wellordered mesoporous catalyst supports are organic precursors and anhydrous ethanol which are quite expensive and harmful to the environment. Therefore, in this study, mesoporous alumina (Al2O3), fabricated using a cheap and available inorganic aluminium precursor in binary water-ethanol solvent, was implemented as support for cobalt catalyst. This investigation aimed to design an effective cobalt-based catalyst system for MDR reaction, which overcomes coke-related deactivation barriers. The promotional effect of La2O3 on the physicochemical features of Al2O3 supported cobalt catalyst and its catalytic performance were also elucidated. The catalyst evaluations in MDR reaction were conducted for 10%Co/Al2O3 and La2O3-promoted 10%Co/Al2O3 catalysts (La loading was in 1% – 8%) in a fixed-bed reactor at temperature range of 923 – 1073 K and partial pressure of individual reactant from 10 to 40 kPa. The Al2O3 support has BET surface area of 173.4 m2 g-1 and cobalt nanoparticles were finely dispersed on the support with desired crystallite size ranged from 5.2 - 9.2 nm. The strong interaction of CoO and Al2O3 phases was confirmed by the presence of cobalt-aluminate spinel and the textural structure of catalysts was stable with reaction temperature. The promotion behavior of La2O3 facilitated H2-reduction by providing higher electron density and enhanced oxygen vacancy in 10%Co/Al2O3. The addition of La2O3 could reduce the apparent activation energy of CH4 consumption; hence, increasing CH4 conversion up to 93.7% at 1073 K. Lanthanum dioxycarbonate transitional phase formed in situ during MDR was accountable for mitigating deposited carbon via redox cycle for 17-30% relying on reaction temperature. Additionally, the oxygen vacancy degree increased to 73.3% with La2O3 promotion. 5%La loading was an optimal promoter content for reactant conversions as well as yield of H2 and CO. 5%La-10%Co/Al2O3 also exhibited the highest resistance to carbon deposition owing to the basic nature, redox feature of La2O3 dopant. The MDR reaction over 5%La-10%Co/Al2O3 catalyst was convinced to follow an associative adsorption mode of CH4 and CO2 on dual or different sites of active particles and the catalyst exhibited a good stability during 48 h reaction at 1023 K. The resulting H2/CO ratios of 0.84-0.98 are suitable for Fischer-Tropsch reaction in downstream to generate liquid hydrocarbon fuels. As a result, the employment of mesoporous alumina support and La2O3 promoter efficiently boosted the Co activity in MDR reaction along with suppressing the carbon deposition on the catalyst surface

Monitoring Heart Rate Variability Based on Self-powered ECG Sensor Tag

This paper proposes a batteryless sensing and computational device to collect and process electrocardiography (ECG) signals for monitoring heart rate variability (HRV). The proposed system comprises of a passive UHF radio frequency identification (RFID) tag, an extreme low power microcontroller, a low-power ECG circuit, and a radio frequency (RF) energy harvester. The microcontroller and ECG circuits consume less power of only ~30 µA and ~3 mA, respectively. Therefore, the proposed RF harvester operating at frequency band of 902 MHz ~ 928 MHz can sufficiently collect available energy from the RFID reader to supply power to the system within a maximum distance of ~2 m. To extract R-peak of the ECG signal, a robust algorithm that consumes less time processing is also developed. The information of R-peaks is stored into an Electronic Product Code (EPC) Class 1st Generation 1st compliant ID of the tag and read by the reader. This reader is functioned to collected the R-peak data with sampling rate of 100ms; therefore, the user application can monitor fully range of HRV. The performance of the proposed system shows that this study can provide a good solution in paving the way to new classes of healthcare applications

Determinants Influencing Consumers’ Attitude Towards Online Shopping: An Extension of the Technology Acceptance Model

This research is conducted for investigating determinants influencing consumers’ attitude towards online shopping. The survey was based on 423 Vietnamese Internet users. Data collected was analyzed in accordance with the process from Cronbach's Alpha to EFA and multiple regression technique. The results show that consumers’ attitude towards online shopping was impacted by perceived usefulness, compatibility and trust. Based on the findings, some recommendations are given for retailers to improve customers’ attitude toward online shopping in the context of Vietnam in particular and in emerging countries in general. Keywords: Attitude, Online shopping, Perceived usefulness, Trust

Improving Pareto Front Learning via Multi-Sample Hypernetworks

Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.Comment: Accepted to AAAI-2

A neurodynamic approach for a class of pseudoconvex semivectorial bilevel optimization problem

The article proposes an exact approach to find the global solution of a nonconvex semivectorial bilevel optimization problem, where the objective functions at each level are pseudoconvex, and the constraints are quasiconvex. Due to its non-convexity, this problem is challenging, but it attracts more and more interest because of its practical applications. The algorithm is developed based on monotonic optimization combined with a recent neurodynamic approach, where the solution set of the lower-level problem is inner approximated by copolyblocks in outcome space. From that, the upper-level problem is solved using the branch-and-bound method. Finding the bounds is converted to pseudoconvex programming problems, which are solved using the neurodynamic method. The algorithm's convergence is proved, and computational experiments are implemented to demonstrate the accuracy of the proposed approach

A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications

Pareto Front Learning (PFL) was recently introduced as an efficient method for approximating the entire Pareto front, the set of all optimal solutions to a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping between a preference vector and a Pareto optimal solution is still ambiguous, rendering its results. This study demonstrates the convergence and completion aspects of solving MOO with pseudoconvex scalarization functions and combines them into Hypernetwork in order to offer a comprehensive framework for PFL, called Controllable Pareto Front Learning. Extensive experiments demonstrate that our approach is highly accurate and significantly less computationally expensive than prior methods in term of inference time.Comment: Under Review at Neural Networks Journa

Reclamation of Marine Chitinous Materials for Chitosanase Production via Microbial Conversion by Paenibacillus macerans

[[abstract]]: Chitinous materials from marine byproducts elicit great interest among biotechnologists for their potential biomedical or agricultural applications. In this study, four kinds of marine chitinous materials (squid pens, shrimp heads, demineralized shrimp shells, and demineralized crab shells) were used to screen the best source for producing chitosanase by Paenibacillus macerans TKU029. Among them, the chitosanase activity was found to be highest in the culture using the medium containing squid pens as the sole carbon/nitrogen (C/N) source. A chitosanase which showed molecular weights at 63 kDa was isolated from P. macerans cultured on a squid pens medium. The purified TKU029 chitosanase exhibited optimum activity at 60 ◦C and pH 7, and was stable at temperatures under 50 ◦C and pH 3-8. An analysis by MALDI-TOF MS revealed that the chitosan oligosaccharides (COS) obtained from the hydrolysis of water-soluble chitosan by TKU029 crude enzyme showed various degrees of polymerization (DP), varying from 3–6. The obtained COS enhanced the growth of four lactic acid bacteria strains but exhibited no effect on the growth of E. coli. By specialized growth enhancing effects, the COS produced from hydrolyzing water soluble chitosan with TKU029 chitinolytic enzymes could have potential for use in medicine or nutraceuticals.[[sponsorship]]MOST[[notice]]補正完

Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning

Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability. In this paper, we propose a framework to address such issues in polygonal building extraction. First, super resolution is employed to enhance the spatial resolution of aerial image, allowing for finer details to be captured. This enhanced imagery serves as input to a multitask learning module, which consists of a segmentation head and a frame field learning head to effectively handle the irregular building structures. Our model is supervised by adaptive loss weighting, enabling extraction of sharp edges and fine-grained polygons which is difficult due to overlapping buildings and low data quality. Extensive experiments on a slum area in India that mimics a dense area demonstrate that our proposed approach significantly outperforms the current state-of-the-art methods by a large margin.Comment: Accepted at The 12th International Conference on Awareness Science and Technolog