Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient algorithms for online convex optimization and their applications

In this thesis we study algorithms for online convex optimization and their relation to approximate optimization. In the first part, we propose a new algorithm for a general online optimization framework called online convex optimization. Whereas previous efficient algorithms are mostly gradient-descent based, the new algorithm is inspired by the Newton-Raphson method for convex optimization, and hence called ONLINE NEWTON STEP. We prove that in certain scenarios ONLINE NEWTON STEP guarantees logarithmic regret, as opposed to polynomial bounds achieved by previous algorithms. The analysis is based on new insights concerning the natural "follow-the-leader" method for online optimization, answers some open problems regarding the latter. One application is for the portfolio management problem, for which we describe experimental results over real market data. In the second part of the thesis, we describe a general scheme of utilizing online game playing algorithms to obtain efficient algorithms for offline optimization. Using new and old online convex optimization algorithms we show how to derive the following: (1) Approximation algorithms for convex programming with linear dependence on the approximation guaranty. (2) Fast algorithms for approximate Semidefinite Programming. (3) Efficient algorithms for haplotype frequency estimation.
Performativity, managerial professionalism and the purpose of professional development: a South African case study

ABSTRACT Early post-apartheid policies envisaged a South African teacher who had autonomy to make professional judgements based on their school context and learners’ needs. However, over the last decade, the state has increasingly monitored learner achievement and teachers’ work. In this paper, we show how the professional development activities provided by the state for high school Life Sciences teachers focus primarily on measuring learner achievement and thus reflect organisational professionalism and managerial discourses which challenge the early post-apartheid vision of democratic professionalism. We present a case study of a cluster of Life Sciences teachers, generating data from interviews with eleven high school teachers and from observation of six professional development meetings. The findings show that the two main purposes of the activities in the cluster meetings are the improvement of learner results and the monitoring teachers’ curriculum coverage and assessment practices. We argue that the discourse of performativity and managerial professionalism narrows the purpose of schooling, influences the nature of state-initiated professional development opportunities and also informs the way in which the state and teachers view their work. This contradicts the initial vision of the democratic state and provides insight into how neoliberal discourses have influenced education in a post-colonial country.
Improving Bus Operations through Integrated Dynamic Holding Control and Schedule Optimization

Bus bunching can lead to unreliable bus services if not controlled properly. Passengers will suffer from the uncertainty of travel time and the excessive waiting time. Existing dynamic holding strategies to address bus bunching have two major limitations. First, existing models often rely on large slack time to ensure the validity of the underlying model. Such large slack time can significantly reduce the bus operation efficiency by increasing the overall route travel times. Second, the existing holding strategies rarely consider the impact on the schedule planning. Undesirable results such as bus overloading issues arise when the bus fleet size is limited. This paper explores analytically the relationship between the slack time and the effect of holding control. The optimal slack time determined based on the derived relationship is found to be ten times smaller than in previous models based on numerical simulation results. An optimization model is developed with passenger-orient objective function in terms of travel cost and constraints such as fleet size limit, layover time at terminals, and other schedule planning factors. The optimal choice of control stops, control parameters, and slack time can be achieved by solving the optimization. The proposed model is validated with a case study established based on field data collected from Chengdu, China. The numerical simulation uses the field passenger demand, bus average travel time, travel time variance of road segments, and signal timings. Results show that the proposed model significantly reduce passengers average travel time compared with existing methods.
GHOC: A generative model for hybrid‐order community detection

Recently, hybrid‐order community detection has been proposed for addressing the hypergraph fragmentation issue suffered by the motif‐based higher‐order community detection. However, the existing attempts of hybrid‐order community detection inadvertently damage the lower‐order connectivity pattern and the higher‐order connectivity pattern when constructing the fusion model. Additionally, like the higher‐order community detection approaches, they also adopt a two‐phase strategy that separately applies the existing graph node clustering methods to the proximity matrix derived from the lower‐order connectivity pattern and the higher‐order connectivity pattern. Therefore, the higher‐order connectivity pattern is only utilized for constructing proximity matrix and hence has no direct effect on the final community results. In this paper, to address the above issues, we propose a Generative model for Hybrid‐Order Community detection (GHOC). The main idea lies in defining a likelihood function of a generative model that finds the optimal community membership strength vectors of nodes, based on which the original lower‐order connectivity pattern and the higher‐order connectivity pattern can be directly reconstructed simultaneously. From the community membership strength vectors, the final community structure can be derived. Extensive experiments have been conducted on several data sets, and the results have confirmed the superiority of the proposed GHOC method.
An Approach to Generate Music with LSTMN

Music was generally considered as a simple sign that was physically produced. Music has become progressively apparent to innovation lately, which can now make a suite out of music with no necessity for human interest. It should beat different specialized obstacles to finish this work, which are portrayed exhaustively in this paper. The paper incorporates a short prologue to music and its parts, as well as references and investigations of significant work by different researchers in the field. The fundamental objective of this examination is to bid a technique for producing melodic notes with Reccurent Neural Networks (RNN’s), explicitly Long Short-Term Memory Network (LSTMN) organizations. This procedure is executed involving a model where information is addressed utilizing the ABC representation text record design for more straightforward access and understanding. Preprocessing information before it is likewise depicted to take care of it into the model. Over a solitary layered LSTMN organization, the model utilized in this study is utilized to grasp chorus melodic note successions. For more noteworthy learning, the algo should have the option to hold past subtleties of a melodic grouping and its construction. This paper presents a portrayal of the layered engineering utilized in the LSTMN model, in addition to its intertwining associations for fostering a brain organization. This study gives a slip look into weight and predisposition dispersions in each slab of the model, in addition to a point-by-point portrayal of misfortunes and exactness at each progression and group. After a careful assessment, the model delivered magnificent outcomes in the formation of new songs
Improving the Assessment (Assurance of Learning) Processes

Strategically located in downtown Denver, this state university is a popular destination for working adults and traditional students alike to get a quality education. The university’s College of Business was first accredited by the Association to Advance Collegiate Schools of Business International (AACSB) in spring 2016. Five undergraduate programs and one graduate program in Accounting were accredited; an undergraduate program in Economics was excluded from AACSB review. Since the initial accreditation, the college added three undergraduate degree programs and a second graduate program (MBA). AACSB standards stipulate assessing general knowledge & skills and subject-specific knowledge & skills for each program. University also requires each program to conduct program assessment every year. Given this huge challenge of assessment for 11 programs that includes data collection, analysis, and planning & implementing intervention to improve student learning, the faculty team was looking for ways to improve the processes to avoid duplication and data storage for easy retrieval. This paper, the first in a twopart series, explains how this challenge is being addressed by the college. Because of six sigma approaches, a technology solution is developed.
Short-Chain Fatty Acids Impair Neutrophil Antiviral Function in an Age-Dependent Manner

Half of the people living with HIV are women. Younger women remain disproportionally affected in endemic areas, but infection rates in older women are rising worldwide. The vaginal microbiome influences genital inflammation and HIV infection risk. Multiple factors, including age, induce vaginal microbial alterations, characterized by high microbial diversity that generate high concentrations of short-chain fatty acids (SCFAs), known to modulate neutrophil function. However, how SCFAs may modulate innate anti-HIV protection by neutrophils is unknown. To investigate SCFA-mediated alterations of neutrophil function, blood neutrophils from younger and older women were treated with SCFAs (acetate, butyrate and propionate) at concentrations within the range reported during bacterial vaginosis, and phenotype, migration and anti-HIV responses were evaluated. SCFA induced phenotypical changes preferentially in neutrophils from older women. Butyrate decreased CD66b and increased CD16 and CD62L expression, indicating low activation and prolonged survival, while propionate increased CD54 and CXCR4 expression, indicating a mature aged phenotype. Furthermore, acetate and butyrate significantly inhibited neutrophil migration in vitro and specifically reduced α-defensin release in older women, molecules with anti-HIV activity. Following HIV stimulation, SCFA treatment delayed NET release and dampened chemokine secretion compared to untreated neutrophils in younger and older women. Our results demonstrate that SCFAs can impair neutrophil-mediated anti-HIV responses.
Capacitorless one-transistor dynamic random-access memory based on asymmetric double-gate Ge/GaAs-heterojunction tunneling field-effect transistor with n-doped boosting layer and drain-underlap structure

In this work, we present a capacitorless one-transistor dynamic random-access memory (1T-DRAM) based on an asymmetric double-gate Ge/GaAs-heterojunction tunneling field-effect transistor (TFET) for DRAM applications. The n-doped boosting layer and gate2 drain-underlap structure is employed in the device to obtain an excellent 1T-DRAM performance. The n-doped layer inserted between the source and channel regions improves the sensing margin because of a high rate of increase in the band-to-band tunneling (BTBT) probability. Furthermore, because the gate2 drain-underlap structure reduces the recombination rate that occurs between the gate2 and drain regions, a device with a gate2 drain-underlap length (LG2_D-underlap) of 10 nm exhibited a longer retention performance. As a result, by applying the n-doped layer and gate2 drain-underlap structure, the proposed device exhibited not only a high sensing margin of 1.11 µA/µm but also a long retention time of greater than 100 ms at a temperature of 358 K (85 °C).
Fast near-infrared palmprint recognition using nonnegative matrix factorization extreme learning machine

Support vector machine and artificial neural network are widely used in classification applications. Extreme learning machine (ELM) is a novel and efficient learning algorithm based on the generalized single hidden layer feed forward networks, which performs well in classification applications. The research results have shown the superiority of ELM with the existing classical algorithms: support vector machine (SVM) and back propagation neural network. In this study, we firstly propose a novel nonnegative matrix factorization extreme learning machine (NMFELM) to improve the performance of standard ELM method. Then we propose a novel near-infrared palmprint recognition approach based on NMFELM classifier. As the test data, we use the near-infrared palmprint database provided by Hong Kong Polytechnic University. The experimental results demonstrate that the proposed NMFELM method outperforms the standard ELMand SVM-based methods.
Evaluation of Ionospheric Delay for Moon-Based Repeat-Pass InSAR Based on International Reference Ionosphere Model

Moon-based synthetic aperture radar (SAR) offers unprecedented temporal and spatial coverage. Its repeat-pass interferometry is expected to play a substantial role in earth science because of its large-scale, long-term, near 24.8 h revisit period, and stable earth observation ability. However, it faces a greater challenge when the signal passes through the ionosphere compared with the low Earth orbit (LEO) satellite. In this study, we constructed a total electron content (hereafter referred to as TEC) calculation model for the propagation path of a moon-based SAR signal based on International reference ionosphere model and moon-based SAR interferometry (InSAR) geometry. Subsequently, the ionospheric delay at various carrier frequencies was quantitatively evaluated under different time baseline types. The results show that the interferometric phase space gradient caused by ionospheric can reach ten times that of LEO SAR satellites with superposition of diurnal and seasonal ionospheric variations. In addition, this problem in the observation area with a large incident angle is more severe, which limits the effective swath width of moon-based repeat-pass InSAR. The ionospheric delay effects can be avoided to some extent by selecting the interference combination at nighttime or approaching the solar altitude angle. To highlight the large-scale ground deformation information in most cases and give full play to the long-term and stable observation advantages of the moon-based platform, accurate ionospheric correction or compensation must be considered.
Valuating requirements arguments in the online user's forum for requirements decision‐making: The CrowdRE‐VArg framework

User forums enable a large population of crowd‐users to publicly share their experience, useful thoughts, and concerns about the software applications in the form of user reviews. Recent research studies have revealed that end‐user reviews contain rich and pivotal sources of information for the software vendors and developers that can help undertake software evolution and maintenance tasks. However, such user‐generated information is often fragmented, with multiple viewpoints from various stakeholders involved in the ongoing discussions in the Reddit forum. In this article, we proposed a crowd‐based requirements engineering by valuation argumentation (CrowdRE‐VArg) approach that analyzes the end‐users discussion in the Reddit forum and identifies conflict‐free new features, design alternatives, or issues, and reach a rationale‐based requirements decision by gradually valuating the relative strength of their supporting and attacking arguments. The proposed approach helps to negotiate the conflict over the new features or issues between the different crowd‐users on the run by finding a settlement that satisfies the involved crowd‐users in the ongoing discussion in the Reddit forum using argumentation theory. For this purpose, we adopted the bipolar gradual valuation argumentation framework, extended from the abstract argumentation framework and abstract valuation framework. The automated CrowdRE‐VArg approach is illustrated through a sample crowd‐users conversation topic adopted from the Reddit forum about Google Map mobile application. Finally, we applied natural language processing and different machine learning algorithms to support the automated execution of the CrowdRE‐VArg approach. The results demonstrate that the proposed CrowdRE‐VArg approach works as a proof‐of‐concept and automatically identifies prioritized requirements‐related information for software engineers.
How to Use Respiratory Chain Inhibitors in Toxicology Studies—Whole-Cell Measurements

Mitochondrial electron transport chain (ETC) inhibition is a phenomenon interesting in itself and serves as a tool for studying various cellular processes. Despite the fact that searching the term “rotenone” in PubMed returns more than 6900 results, there are many discrepancies regarding the directions of changes reported to be caused by this RTC inhibitor in the delicate redox balance of the cell. Here, we performed a multifaceted study of the popular ETC inhibitors rotenone and antimycin A, involving assessment of mitochondrial membrane potential and the production of hydrogen peroxide and superoxide anions at cellular and mitochondrial levels over a wide range of inhibitor concentrations (1 nmol/dm3–100 µmol/dm3). All measurements were performed with whole cells, with accompanying control of ATP levels. Antimycin A was more potent in hindering HepG2 cells’ abilities to produce ATP, decreasing ATP levels even at a 1 nmol/dm3 concentration, while in the case of rotenone, a 10,000-times greater concentration was needed to produce a statistically significant decrease. The amount of hydrogen peroxide produced in the course of antimycin A biological activity increased rapidly at low concentrations and decreased below control level at a high concentration of 100 µmol/dm3. While both inhibitors influenced cellular superoxide anion production in a comparable manner, rotenone caused a greater increase in mitochondrial superoxide anions compared to a modest impact for antimycin A. IC50 values for rotenone and antimycin A with respect to HepG2 cell survival were of the same order of magnitude, but the survival curve of cells treated with rotenone was clearly biphasic, suggesting a concentration-dependent mode of biological action. We propose a clear experimental setup allowing for complete and credible analysis of the redox state of cells under stress conditions which allows for better understanding of the effects of ETC inhibition.
Do the Temperamental Characteristics of Both Mother and Child Influence the Well-Being of Adopted and Non-Adopted Children?

(1) Background: For decades, the temperaments of infants and small children have been a focus of studies in human development and been seen as a potential contributor to children’s developmental patterns. However, less is known about the interplay between the temperamental characteristics of mothers and their children in the context of explaining variations in developmental outcomes. The aim of our study was to explore the associations—with or without genetic links—of the temperaments and psychological distress of mothers and the temperaments of children with behavioral problems in a group of internationally adopted children and their adoptive mothers and in a group of non-adopted children and their mothers. (2) Methods: Data (n = 170) were derived from the ongoing Finnish Adoption (FinAdo) follow-up study. The children included were under the age of 7 years; 74 were adopted internationally through legal agencies between October 2010 and December 2016, and the remaining 96 were non-adopted children living with their birth parents (biological group) recruited from day-care centers. We used Mary Rothbart’s temperament questionnaires to assess temperament, the Child Behavior Checklist (CBCL) to obtain data on the children’s behavioral/emotional problems and competencies, and the General Health Questionnaire (GHQ) to assess parental psychological distress. The study was approved by the Ethics Committee of the Hospital District of Southwest Finland, and written informed consent was obtained from the parents and the children themselves. (3) Results: The negative affectivities of both mothers and children were associated with the total CBCL and with both internalizing and externalizing problem behaviors after adjusting for age, gender, and adoption status. Both relationships remained significant when tested simultaneously, suggesting additive effects. Maternal negative affect was associated with problem behavior irrespective of child extraversion/surgency. Child extraversion/surgency was associated with lower levels of all internalizing behavioral problems when adjusted for maternal sociability. Child negative affect was associated with all behavioral problem measures irrespective of maternal sociability or maternal psychological distress. Maternal distress was associated with child problem behaviors only in children with low extraversion/surgency. (4) Limitations: The sample size was relatively small, and the information was gathered solely with questionnaires. (5) Conclusions: The results of the study may be clinically significant. Child negative affect, maternal negative affect, and maternal experienced distress, combined with low child extraversion/surgency, may increase the risk of child problem behaviors in both adoptees and non-adoptees.
Development of Fast Derivation Method of Revenue Distribution for Power Trading Support

We are developing the power trading support system for judgment on making contract of negotiation transaction. About this system, fast derivation method of revenue distribution is developed. Calculation amount of deriving revenue is very large, because risk factors take a various value. Revenue is derived using “cost function” which represents the price against the amount of electricity generation. The total number is same as combination of risk factors. So, in developed method, risk factors are considered sequentially in calculation, and temporary cost function is assembled on each step. We evaluated the trade‐off of between effect of acceleration and error amount by example. We got the prospects that cumulative probability error is within 2% and calculation time is within 1 min.