Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Gradient Descent Method for Optimization of Supervised Machine Learning Problems

Gradient descent method is commonly used as an optimization algorithm for some machine learning problems such as regression analysis and classification problems. This method is highly applicable for real life of yearly demandprice commodity, agricultural products and Iris flowers. This study proposed the combination of Dai-Yuan (DY) and Saleh and Mustafa (SM) conjugate gradient methods for the optimization of supervised machine learning problems. Experiments were conducted on combined DY and SM with well-known conjugate gradient methods using a fixed learning rate. The efficiency of the combined methods and existing models was evaluated in term of number of iterations and processing time. The experimental results indicated that the combined conjugate gradient method had the better performance in term of number of iterations and processing time.
Stable Vibration-Based Communication Scheme Using Multi-Step ASK and PPM Techniques

In the information-oriented society, there are increasing needs to conduct data communication with nearby devices/people. In this light, vibration-based communication method was proposed as one of possible communication means between adjacent devices. This method has been expected to provide an intuitive and safe communication by propagating vibration to a receiver device. This study proposes two types of techniques, which are multi-step ASK (Amplitude Shift Keying) with pseudo clock and PPM (Pulse Position Modulation), to achieve a stable vibration-based communication simply using smart device functions. These proposed techniques are then evaluated through some experiments using several types of smart devices. In addition, the effectiveness of the proposed methods is discussed based on the experimental results.
A fine-scale digital elevation model of Antarctica derived from ICESat-2

Abstract. Antarctic digital elevation models (DEMs) are essential for human fieldwork, ice topography monitoring and ice mass change estimation. In the past thirty decades, several Antarctic DEMs derived from satellite data have been published. However, these DEMs either have coarse spatial resolutions or vague time stamps, which limit their further scientific applications. In this study, the new-generation satellite laser altimeter Ice, Cloud, And Land Elevation Satellite-2 (ICESat-2) is used to generate a fine-scale and specific time-stamped Antarctic DEM for both the ice sheet and ice shelves. Approximately 4.69 × 109 ICESat-2 measurement points from November 2018 to November 2019 are used to estimate surface elevations at resolutions of 250 m, 500 m and 1 km based on a spatiotemporal fitting method, which results in a modal resolution of 250 m for this DEM. Approximately 74 % of Antarctica is observed, and the remaining observation gaps are interpolated using the ordinary kriging method. National Aeronautics and Space Administration Operation IceBridge (OIB) airborne data are used to evaluate the generated Antarctic DEM (hereafter called the ICESat-2 DEM) in individual Antarctic regions and surface types. Overall, a median bias of 0.11 m and a root-mean-square deviation of 8.27 m result from approximately 1.4 × 105 spatiotemporally matched grid cells. The accuracy and uncertainty of the ICESat-2 DEM vary in relation to the surface slope and roughness, and more reliable estimates are found in the flat ice sheet interior. The ICESat-2 DEM is superior to previous DEMs derived from satellite altimeters for both spatial resolution and elevation accuracy and comparable to those derived from stereo-photogrammetry and interferometry. The decimeter-scale accuracy and specific time stamp make the ICESat-2 DEM an essential addition to the existing Antarctic DEM groups, and it can be further used for other scientific applications.
The Predictive Brain Must Have a Limitation in Short-Term Memory Capacity

Traditionally, short-term memory (STM) has been assessed by asking participants to remember words, visual objects, or numbers for a short amount of time before their recall or recognition of those items is tested. However, this focus on memory for past sensory input might have obscured potential theoretical insights into the function of this cognitive faculty. Here, we suggest that STM may have an important role in predicting future sensory input. This reconceptualization of STM may provide a functional explanation for its capacity limitation.
ENGINEERING STUDENTS

In current scenario there is strong requirement to develop Entrepreneurial mindset in engineering student. A simple method to analyses the level of existing competence among the students is to conduct Dermatoglyphics Multiple Intelligence Test of the students. DMIT test can help to get insight of the inmate characteristics of students to start Enterprise as career option. Along with DMIT Psychometric test based on questionnaire is also conducted to analyses their mindset depending upon their preconditioning and belief system. In the present work a sample of 90 students were taken and DMIT and Psychometric test was conducted on them. After the test an Entrepreneurship training program was conducted for these students. To validate the results again DMIT test and Psychometric test was conducted for these students. Test results of DMIT and psychometric analysis were carefully recorded and analyzed. KEYWORDS— Entrepreneur, Learning Style, Education, Entrepreneurial Mindset
phase filtering, fast least square algorithm for linear phase system, constrained

: This work proposes a linear phase sparse minimum error entropy adaptive filtering algorithm. The linear phase condition is obtained by considering symmetry or anti symmetry condition onto the system coefficients. The proposed work integrates linear constraint based on linear phase of the system and -norm for sparseness into minimum error entropy adaptive algorithm. The proposed -norm linear constrained minimum error entropy criterion ( -CMEE) algorithm makes use of high-order statistics, hence worthy for non-Gaussian channel noise. The experimental results obtained for linear phase sparse system identification in the presence of non-Gaussian channel noise reveal that the proposed algorithm has lower steady state error and higher convergence rate than other existing MEE variants.
OTT_A_248226 4359..4364

Lulu Chen Xue Han Yujuan Gao 1 Qi Zhao Yongsheng Wang Ya Jiang Sisi Liu Xue Wu Liyun Miao 1Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, People’s Republic of China; 2Department of Research and Development, Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People’s Republic of China; 3Translational Medicine Research Institute, Geneseeq Technology Inc., Toronto, Ontario, Canada Abstract: Hepatoid adenocarcinoma of the lung (HAL) is extremely rare and standardized treatment strategy for HAL has not been established. Patients with unresectable HAL have a shorter survival time ranges from 1 to 36 months. Here, we reported a 65-year-old female patient with unresectable alpha-fetoprotein-producing HAL harboring KRAS-G12V and no other targetable driver mutations. The patient was treated with multiple lines of chemotherapies followed by PD-1 inhibitor, sintilimab, due to positive staining of PD-L1, and achieved an overall survival of 52 months. Although the disease was under control, the patient experienced fifth-grade pneumonia and died after 6 months of anti-PD-1 treatment. This is the first case of KRAS-positive HAL patient achieved stable disease by PD-1 inhibitor, which may provide valuable information for the treatment strategy development of advanced HAL patients, and highlights the importance of molecular diagnosis in treatment decision-making.
Adaptive and Informative Planning for an Underwater Vehicle-Manipulator System

—Underwater motion planning is challenging due to environmental conditions such as low visibility, uncertain environmental disturbances, and complex hydrodynamics. Since these conditions pose various limitations on sensing, actuation, power, and communication, state-of-the-art solutions developed for robotic systems in the ground and aerial domains may not generalize well to the underwater domain. In this paper, we present our ongoing work to overcome these challenges and improve autonomy for underwater vehicle-manipulator systems on underwater tasks such as object retrieval. We develop a framework for planning vehicle trajectories that collect information of an object of interest in the presence of environmental disturbances. Preliminary results are shown using a closed-loop rapidly-exploring random tree algorithm. Alongside vehicle motion planning, we also present a framework for exploratory and exploitative grasp planning using underwater tactile sensing. Together, these frameworks provide a promising solution for robust underwater grasping under uncertainty.
Determinants of Bank Branch Presence in Local Areas: A Comparison Between North and South of Italy

A close relationship with customers, mainly achieved through the opening of local branches, has long been a fundamental key to competitive advantage in the banking sector. Financial innovation, changes in customer behavior and competitive dynamics have transformed this relationship over time, leading to the closure of numerous branches. This work explores the determinants of branch evolution in an advanced and notoriously bank-centric country (Italy) using NUTS 3 panel data over the 2000-2016 time-span. Results show how - in general - demographic, industrial and economic variables explain the dynamics of banks’ branch location strategy. However, more heterogeneous evidence emerges when North and South Italy are taken into consideration separately suggesting that territorial and regional dimension might play a role even in the banking sector.
Proximity to mother over the life course in the United States: Overall patterns and racial differences

BACKGROUND The spatial distance between children and parents substantially influences their lives, yet empirical evidence on life course patterns of child–parent proximity is sparse. OBJECTIVES The goal of this study is to identify salient features of child–parent spatial distance across the life course and explain differences by race in these features in the United States. METHODS Using the Panel Study of Income Dynamics, this study examines the spatial distance between residential locations of children and their mothers over 47 years (166,098 person-year observations). Point-in-time, dynamic, and multi-state life table analyses of spatial distance are conducted using location measured at the block level. Differences between Black and White people are assessed, examining various explanatory factors, including life course events. RESULTS Living very close is common across the life course, with 25% of life-years during ages 18–54 spent within five miles of, but not with, one’s mother. Children who are Black are much more likely than children who are White to live close to their mother if she is alive, but mothers of Black children are much less likely to be alive, especially when children 1 University of Michigan, USA. Email: hwajungc@umich.edu. 2 University of Michigan, USA. 3 City University of New York, Queens College, USA. 4 Cornell University, USA. Choi et al.: Proximity to mother over the life course in the United States: Overall patterns and racial differences 770 https://www.demographic-research.org are middle-aged. Observed sociodemographic characteristics of the child and mother account for a substantial share – and in some cases all – of the racial differences. CONCLUSIONS Very close residential proximity to one’s mother beyond coresidence is common across the life course even in the geographically large United States. Racial differences in mortality affect differences between Black and White people in family spatial availability. CONTRIBUTION This study provides the first national estimates of intergenerational proximity over the life course in the United States, and hence a basis for further research.
Labor Market Agglomeration and Employee-Retention Policies

We construct a measure of a firm’s exposure to labor market agglomeration in the locality—a measure we label LEO—and examine whether such exposure is a key factor in firms’ employee-related policies. We find that when LEO is high, firms grant more broad-based stock options, provide more employee-friendly work environment, and maintain financial flexibility. This relation is present only for high-skilled workers and stronger for firms investing in R&D. Our results show that firms strategically adopt the policies to retain employees who face better local opportunities in a large pooled market for their skills. Two quasi-natural experiments, the staggered adoption of the Inevitable Disclosure Doctrine and Hurricane Katrina, support a causal relation.
Allocating to Thematic Investments

Abstract We introduce the notion of themes as an additional investment dimension beyond asset classes, regions, sectors and styles, and propose a framework to allocate to thematic investments at a strategic asset allocation level. Allocating to themes requires discipline because thematic investments are not only exposed to the theme but also to the traditional risk factors. Our approach uses a framework based on robust portfolio optimisation, which accounts for the expected excess return from the exposure to the theme and from exposures to traditional risk factors. We provide an example to illustrate how thematic investments fit in traditional multi-asset portfolios.
Hype Cycles: Dynamic Information Design with Two Audiences

We study dynamic Bayesian persuasion in an entry game. A sender publicly reveals information to an adopter and a competitor. When the sender's loss from competition is small, the optimal policy features hype cycles: the sender first exaggerates the value of a technology to attract the adopter, and then reveals negative information to deter the competitor. Otherwise, the optimal policy features caution: the sender first underplays the value of the technology and reveals positive information later. Hype cycles are more severe in stagnant industries and with higher threat of competition, and arise in industries where the adopter's and the competitor's entry decisions are complementary.
Does Airbnb Reduce Matching Frictions in the Housing Market?

There is growing concern about the impact of the home-sharing markets on housing affordability, yet the underlying mechanisms are not well-studied. We use a theoretical model to provide key results on the mechanisms through which home-sharing can improve the quality of matches between buyers and sellers in the housing market. We then test these predictions empirically using daily Airbnb data for the entire U.S. and a novel shift-share approach. We find that an increase in Airbnb increases house prices, reduces total sales, increases for-sales inventory, increases sellers' time on the market, and reduces the probability of selling a house. The empirical evidence supports the hypothesis that Airbnb has reduced matching frictions in the housing market. We then examine heterogeneous responses to Airbnb using Generalized Random Forest (GRF). Consistent with our theoretical model, results from the GRF model indicate that locations with a less elastic housing supply respond more to the Airbnb growth.
Private Means Better? A Water and Sanitation Quasi-Experimental Design

This paper compares water and sanitation services in municipalities that entered into a concession arrangement with a private operator versus those in a comparable control group of municipalities that continued with a public operator, and we explore five variables of interest: (i) water coverage; (ii) sewage collection; (iii) sewage treatment; (iv) average tariff; and (v) water losses. Using an empirical strategy and making improvements over previous literature, after controlling for municipality peculiarities, we adopt a difference-in-differences model with nearest neighbor matching (NNM) to evaluate private sector management impacts on these variables. We find a greater tariff increase in the first four years after a private operator's start after concession. We only identify weak evidence of greater sewage treatment increase in municipalities that change to a private operator and find no relevant or significant results for the other variables. In terms of policy, this paper contributes with the government clarifying the main benefits of privatizing water and sanitation services.
CRMP5 regulates cell proliferation and development of colorectal cancer via MAPK-dependent signaling

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Collapsin response mediator protein 5 (CRMP5) belongs to a family of five cytosolic proteins that serve a major role in neural development. CRMP5 has been identified as a biomarker of numerous cancer types, including lung cancer and glioblastoma. However, the role of CRMP5 in CRC remains unclear. In the present study, CRMP5 was characterized as a novel biomarker of poor survival in CRC. CRMP5-overexpression in CRC cells promoted cell proliferation and migration while CPMP5-knockdown decreased cell growth and migration. A novel mechanism was uncovered, by which CRMP5 regulates MAPK signaling to drive CRC cell proliferation and development. Furthermore, CRMP5-overexpression induced chemotherapy resistance and tumor recurrence in CRC. Taken together, these results demonstrated the important role of CRMP5 in the development and proliferation of CRC cells and suggested that CRMP5 may be a novel therapeutic target for CRC.
Cognitive Dissonance of Self-Standards: A Negative Interaction of Green Compensation and Green Training on Employee Pro-Environmental Behavior in China

Purpose Studies of GHRM practices number in thousands; however, they have failed to provide Chinese contextual evidence for their interactive effects on employee pro-environmental behavior (EPEB). To bridge this research gap as well as to address organizational practitioners’ concern in GHRM practices, our study explores the possible interactive effect of green compensation (GC) and green training (GT), which are two core practices of GHRM and are widely employed by Chinese organizations simultaneously, on EPEB drawing on self-determination theory, and unravels the underlying mechanism by introducing employee green self-accountability (EGSA) as a mediator based on the cognitive dissonance theory of self-standards. Methods Using on-line survey and five-point Likert rating method, employees (N=847) working in Chinese organizations were requested to self-rate GC, GT, and EGSA; their direct supervisors were invited to evaluate EPEB. The mediated moderation testing procedures with SPSS and the bootstrapping approach with MPLUS were adopted to test the mediated moderation. Results When being used separately, GC and GT are positively related to EPEB (β=0.426, p < 0.001; β=0.368, p < 0.001). When being adopted simultaneously, a negative relationship (the simple slope=−0.454, t=3.671, p=0.000) exists between GC and EPEB at higher-level GT. EGSA partially mediates the negative interaction with 95% bias-corrected confidence intervals [−0.054, −0.018]. Conclusion In the Chinese context, when being used simultaneously with high–high combination, GC and GT negatively interact with each other to engender the squeezed effect of intrinsic motivation by extrinsic motivation, which directly impairs EPEB, and cause employee cognitive dissonance of self-standards, which indirectly weakens EPEB through reducing EGSA. This paper is an attempt to show novelty in identifying negative interactions between GC and GT in EPEB in China and a mediating role of EGSA. Additionally, it addresses organizational practitioners’ concern well and provides important implications for decision-making in GHRM practices and EPEB enhancement.
Regulatory roles of RNA modifications in breast cancer

Abstract Collectively referred to as the epitranscriptome, RNA modifications play important roles in gene expression control regulating relevant cellular processes. In the last few decades, growing numbers of RNA modifications have been identified not only in abundant ribosomal (rRNA) and transfer RNA (tRNA) but also in messenger RNA (mRNA). In addition, many writers, erasers and readers that dynamically regulate the chemical marks have also been characterized. Correct deposition of RNA modifications is prerequisite for cellular homeostasis, and its alteration results in aberrant transcriptional programs that dictate human disease, including breast cancer, the most frequent female malignancy, and the leading cause of cancer-related death in women. In this review, we emphasize the major RNA modifications that are present in tRNA, rRNA and mRNA. We have categorized breast cancer-associated chemical marks and summarize their contribution to breast tumorigenesis. In addition, we describe less abundant tRNA modifications with related pathways implicated in breast cancer. Finally, we discuss current limitations and perspectives on epitranscriptomics for use in therapeutic strategies against breast and other cancers.
Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review

Background: Machine learning (ML) has emerged as a branch of artificial intelligence dealing with the analysis of large amounts of data. The applications of ML algorithms have also expanded to health care, including dentistry. Recent advances in this field point to future improvements in diagnostic techniques and the prognosis of various diseases of the teeth and other maxillofacial structures. Aim: The aim of this literature review is to describe the basis for ML being applied to different dental sub-fields in recent years, to identify typical algorithms used in the studies, and to summarize the scope and challenges of using these techniques in dental clinical practice. Relevance for Patients: The proficiency of emerging technologies that have begun to show encouraging results in the diagnosis and prognosis of oral diseases can improve the precision in the selection of treatment for patients. It is necessary to understand the challenges associated with using these tools to effectively use them in dental services and ensure a higher quality of care for patients.
Politically connected companies are less likely to shutdown due to COVID‐19 restrictions

Abstract Objective While the aim of COVID‐19 policies is to suppress the pandemic, many fear that the burden of the restrictions will fall more heavily on less privileged groups. We show one potential mechanism for COVID‐19 responses to increase inequality by examining the intersection of business restrictions and business political connections. Methods We fielded an online survey of 2735 business employees and managers in Ukraine, Egypt, and Venezuela over the summer of 2020 to collect data on companies' closures due to COVID‐19 and nuanced information about their political connections. Findings We show that businesses with political connections to government officials were significantly less likely to shut down as a result of COVID‐19 policies. This finding suggests that measures designed to mitigate COVID‐19 are ineffective in countries with a weak rule of law if politically connected firms are able to circumvent restrictions by leveraging political connections to receive preferential treatment. In addition, politically connected firms are no more likely—and sometimes even less likely—to engage in social‐distancing policies to mitigate the pandemic despite the fact that they are more likely to remain open.