Transformation of sodium bicarbonate and CO2 into sodium formate over NiPd nanoparticle catalyst

The present research systematically investigated, for the first time, the transformation of sodium bicarbonate and CO2 into sodium formate over a series of Ni based metal nanoparticles (NPs). Ni NPs and eight NiM (M stands for a second metal) NPs were prepared by a facile wet chemical process and then their catalytic performance were evaluated in sodium bicarbonate hydrogenation. Bimetallic NiPd NPs with a composition of 7:3 were found to be superior for this reaction, which are more active than both pure Ni and Pd NPs. Hot filtration experiment suggested the NPs to be the truly catalytic active species and kinetic analysis indicated the reaction mechanism to be different than most homogeneous catalysts. The enhanced activity of the bimetallic nanoparticles may be attributed to their smaller size and improved stability

Towards a Better Microcredit Decision

Reject inference comprises techniques to infer the possible repayment behavior of rejected cases. In this paper, we model credit in a brand new view by capturing the sequential pattern of interactions among multiple stages of loan business to make better use of the underlying causal relationship. Specifically, we first define 3 stages with sequential dependence throughout the loan process including credit granting(AR), withdrawal application(WS) and repayment commitment(GB) and integrate them into a multi-task architecture. Inside stages, an intra-stage multi-task classification is built to meet different business goals. Then we design an Information Corridor to express sequential dependence, leveraging the interaction information between customer and platform from former stages via a hierarchical attention module controlling the content and size of the information channel. In addition, semi-supervised loss is introduced to deal with the unobserved instances. The proposed multi-stage interaction sequence(MSIS) method is simple yet effective and experimental results on a real data set from a top loan platform in China show the ability to remedy the population bias and improve model generalization ability

Design and Implement a New Remote Web-Based Visualization System for the Clinical Examination and Treatment of Skin Lesions. Evaluate the System Based on Interview Feedback

In the field of dermatology research, researchers commonly take pictures of the skin lesion area with traditional cameras and measure its size over time to determine the effectiveness of the treatment process. To revolutionize this current practice, along with the help of an application that takes 3D captures and AR measurements, the proposed web-based visualization system follows user-centric design principles to clean, re-structure, process, and present the collected raw data in an intuitive, interactive, simplistic, and responsive manner. The system couples state-of-the-art modern web development with a secure and robust logical server through application programming interfaces (API) designed following best practices in the industry. An evaluation study with five participants was conducted to assess certain design choices of the system. Subjective feedbacks on the system were positive overall, with suggestions toward certain detailed aspects of the system that can be implemented in future development.Master of Science in Information Scienc

Structural and optical studies of condensed gases under extreme conditions

Dense solidified gases are sources of rich physical and chemical phenomena and model objects to be widely used in theoretical calculations. The focus of this thesis has been the structural and optical properties of simple gases and gas mixtures under extreme conditions. Three simple dense gas systems, methane (CH₄), Xe-Ar mixture and nitrogen-trifluoride (NF₃) have been studied and characterized using high pressure and high temperature techniques in combination with Raman spectroscopy and x-ray diffraction in diamond anvil cells (DACs). CH₄ is one of the major constituents of the Uranus and Neptune interiors, and large amounts of it are also present in the deep Earth. As the simplest hydrocarbon, CH₄ presents a rich variety of crystal structures at low temperature and pressure regime. However, despite being widely studied, phase relations between numerous CH₄ phases are poorly understood even at relatively low pressure. In this thesis, by combining Raman spectroscopy and in-situ high-pressure, high-temperature resistive heating techniques, we demonstrate the complexity of the phase diagram of CH₄ up to 45 GPa and 1400 K. Changes in the frequencies and Raman profiles of the ν₁ and ν₃ vibrational modes of CH₄ molecule were used to detect phase transitions and construct boundaries between individual phases in the phase diagram. A triple point between fluid, phases I, and phase alone the melting curve was found and precisely located in the studied P-T range. The melting curve changes its slope above the triple point. Moreover, previously reported sluggish transitions from phase A to phase B was found to be controlled by kinetics. These results represent a significant revision of the existing phase diagram of CH₄. The second system under investigation is the binary mixture of xenon and argon. The simple closed-shell electronic configurations make rare gases and their mixtures an ideal system for comparing experiment with theory. Rare gases are archetypal van der Waals systems. Previously, no binary compound of Xe and Ar were known. We have explored rare gas solids Xe-Ar₂ system up to pressure of 60 GPa with combined Raman spectroscopy, x-ray diffraction and first-principles density functional theory (DFT) calculations. A novel van der Waals compound XeAr₂ has been observed at 3.5 GPa. We find that pressure stabilizes the formation of this stoichiometric, solid van der Waals compound of composition XeAr₂. Synchrotron x-ray diffraction shows that this compound adopts a MgZn₂-type crystal structure, which is in a Laves phase. Our DFT calculation of the formation enthalpy indicates that XeAr₂ stays stable to at least 80 GPa. The last condensed gas solid presented here is nitrogen-trifluoride (NF₃). Since first synthesized by molten salt electrolysis, NF₃ has attracted wide interests, ranging from fundamental study to industrial applications. However, structures and phase relations on NF₃ under high pressure remains unknown. In the contributing work, NF₃ has been studied by synchrotron x-ray diffraction and Raman spectroscopy combined with DFT calculation. At 300 K, NF₃ solidifies at 3.5 GPa into the orthorhombic structure (Pnma). Phase diagram of NF₃ has been studied by Raman spectroscopy, two solid phases have been observed between 77 and 300 K up to 120 GPa. Our DFT calculations suggests NF₃ remains stable to at least 150 GPa

Game Model for “Shortage of Logistics” in Online Shopping in Service Engineering

AbstractThis paper analyzes the imbalance between e-commerce and logistics service by using factor sub-game perfect Nash equilibrium as an analytical tool from the view of system and links up the bargaining process between sellers and express enterprise involved in service engineering during online shopping with discount factor. The change of interests between sellers and express enterprise is systematically analyzed from the perspective of discount factor on the equilibrium solution through the application of model towards service engineering during holidays online shopping. Finally it is concluded that discount factor is a key factor influencing the express fee between sells and express enterprise in logistic system, and some recommendations are put forward accordingly

A Survey on Fairness in Large Language Models

Large language models (LLMs) have shown powerful performance and development prospect and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks. Unfair LLM systems have undesirable social impacts and potential harms. In this paper, we provide a comprehensive review of related research on fairness in LLMs. First, for medium-scale LLMs, we introduce evaluation metrics and debiasing methods from the perspectives of intrinsic bias and extrinsic bias, respectively. Then, for large-scale LLMs, we introduce recent fairness research, including fairness evaluation, reasons for bias, and debiasing methods. Finally, we discuss and provide insight on the challenges and future directions for the development of fairness in LLMs.Comment: 12 pages, 2 figures, 101 reference

Mitigating Algorithmic Bias with Limited Annotations

Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information. When sensitive attributes are not disclosed or available, it is needed to manually annotate a small part of the training data to mitigate bias. However, the skewed distribution across different sensitive groups preserves the skewness of the original dataset in the annotated subset, which leads to non-optimal bias mitigation. To tackle this challenge, we propose Active Penalization Of Discrimination (APOD), an interactive framework to guide the limited annotations towards maximally eliminating the effect of algorithmic bias. The proposed APOD integrates discrimination penalization with active instance selection to efficiently utilize the limited annotation budget, and it is theoretically proved to be capable of bounding the algorithmic bias. According to the evaluation on five benchmark datasets, APOD outperforms the state-of-the-arts baseline methods under the limited annotation budget, and shows comparable performance to fully annotated bias mitigation, which demonstrates that APOD could benefit real-world applications when sensitive information is limited

DISPEL: Domain Generalization via Domain-Specific Liberating

Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains. However, existing domain generalization approaches often bring in prediction-irrelevant noise or require the collection of domain labels. To address these challenges, we consider the domain generalization problem from a different perspective by categorizing underlying feature groups into domain-shared and domain-specific features. Nevertheless, the domain-specific features are difficult to be identified and distinguished from the input data. In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing fine-grained masking approach that can filter out undefined and indistinguishable domain-specific features in the embedding space. Specifically, DISPEL utilizes a mask generator that produces a unique mask for each input data to filter domain-specific features. The DISPEL framework is highly flexible to be applied to any fine-tuned models. We derive a generalization error bound to guarantee the generalization performance by optimizing a designed objective loss. The experimental results on five benchmarks demonstrate DISPEL outperforms existing methods and can further generalize various algorithms