cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Graph-based Recommendation for Sparse and Heterogeneous User Interactions
Creators: name: Bruun, Simone Borg
name: Lesniak, Kacper Kenji
name: Biasini, Mirko
name: Carmignani, Vittorio
name: Filianos, Panagiotis
name: Lioma, Christina
name: Maistro, Maria
Publisher: name: arXiv
Year: 2023
Subjects: subject: Information Retrieval (cs.IR)
subject: FOS: Computer and information sciences

description: Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses struggle to apply state-of-the-art models due to their very limited availability of data. We propose a graph-based recommender model which utilizes heterogeneous interactions between users and content of different types and is able to operate well on small-scale datasets. A genetic algorithm is used to find optimal weights that represent the strength of the relationship between users and content. Experiments on two real-world datasets (which we make available to the research community) show promising results (up to 7% improvement), in comparison with other state-of-the-art methods for low-data environments. These improvements are statistically significant and consistent across different data samples.
Right-handed neutrino pair production via second-generation leptoquarks
Creators: name: Bhaskar, Arvind
name: Chaurasia, Yash
name: Deka, Kuldeep
name: Mandal, Tanumoy
name: Mitra, Subhadip
name: Mukherjee, Ananya
Publisher: name: arXiv
Year: 2023
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: High Energy Physics - Experiment (hep-ex)
subject: FOS: Physical sciences

description: No direct experimental constraints exist on Leptoquark (LQ) couplings with quarks and right-handed neutrinos (RHNs). If a LQ dominantly couples to RHNs, it can leave unique signatures at the LHC. The RHNs can be produced copiously from LQ decays as long as they are lighter than the LQs. LQ-induced RHN production has never been searched for in experiments. This channel can act as a simultaneous probe for RHNs and LQs that dominantly couple to RHNs. In this paper, we consider all possible charge-$2/3$ and $1/3$ scalar and vector LQs that dominantly couple to second-generation quarks and RHN. We study the pair and single productions of TeV-scale LQs and their subsequent decay to sub-TeV RHNs, realised in the inverse seesaw framework. We also consider RHN pair production through a $t$-channel LQ exchange. The single LQ production and $t$-channel contributions can be significant for large LQ-RHN-quark couplings. We systematically combine events from these processes leading to a pair of RHNs plus jets to study the prospects of LQ-assisted RHN pair production. We analyse the monolepton and opposite-sign dilepton final states and estimate the discovery reach at the high-luminosity LHC.
description: 11 pages, 4 figures, 3 tables. Matches with the journal version
A new piperidine alkaloid from the leaves of Microcos paniculata L.
Creators: name: Zhang, Gang
name: Zhang, Na
name: Xu, Li
name: Hong-Tan Wu
name: Chen, Dan
name: Qi-Huang Lin
name: Lian-Zhong Luo
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Biochemistry
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: Immunology
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: Cancer

description: A new piperidine alkaloid, microcosamine C (1), and one known compound, microcosamine A (2) were isolated from the leaves of Microcos paniculata. Structure elucidation was carried out using HR-ESI-MS, 1D and 2D NMR spectroscopic methods and by comparison with data reported in the literature. The absolute configuration at the C-3 hydroxy group of 1 was established by a Mosher esterification procedure. Both the isolates (1–2) were evaluated for cytotoxicity against four selected tumour cell lines and showed only weak activity against RAW 264.7 cell line.
MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval
Creators: name: Banerjee, Debopriyo
name: Jain, Mausam
name: Kulkarni, Ashish
Publisher: name: arXiv
Year: 2023
Subjects: subject: Information Retrieval (cs.IR)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: In the domain of question-answering in NLP, the retrieval of Frequently Asked Questions (FAQ) is an important sub-area which is well researched and has been worked upon for many languages. Here, in response to a user query, a retrieval system typically returns the relevant FAQs from a knowledge-base. The efficacy of such a system depends on its ability to establish semantic match between the query and the FAQs in real-time. The task becomes challenging due to the inherent lexical gap between queries and FAQs, lack of sufficient context in FAQ titles, scarcity of labeled data and high retrieval latency. In this work, we propose a bi-encoder-based query-FAQ matching model that leverages multiple combinations of FAQ fields (like, question, answer, and category) both during model training and inference. Our proposed Multi-Field Bi-Encoder (MFBE) model benefits from the additional context resulting from multiple FAQ fields and performs well even with minimal labeled data. We empirically support this claim through experiments on proprietary as well as open-source public datasets in both unsupervised and supervised settings. Our model achieves around 27% and 20% better top-1 accuracy for the FAQ retrieval task on internal and open datasets, respectively over the best performing baseline.
description: The first two authors contributed equally to this work. 12 pages, 3 figures, 5 tables. Accepted at the 2023 Pacific-Asia Conference On Knowledge Discovery And Data Mining (PAKDD)
A Study on Employee Satisfaction and Its Impact on Ecommerce Firms with Reference to Amazon India
Creators: name: Saumya, Kumari
name: Natal, Ms. Melita Stephen
Publisher: name: Zenodo
Year: 2025

description: A key factor in e-commerce companies' success is employee satisfaction, particularly in a cutthroat market like India. With an emphasis on Amazon India, this study looks at the variables that affect employee satisfaction and how those characteristics affect consumer satisfaction, organizational success, and staff retention. The study combines the results of other studies and emphasizes important factors including work-life balance, technological adoption, leadership styles, HR procedures, and the workplace. The study also looks at the relationship between service quality and employee satisfaction, highlighting how both can improve consumer satisfaction and business expansion. The results imply that long-term organizational commitment, increased production, and better customer service are all influenced by contented workers. The study comes to the conclusion that businesses like Amazon India need to constantly modify their workplace regulations and HR tactics.The study concludes that companies like Amazon India must continuously adapt their HR strategies and workplace policies to ensure sustained employee satisfaction. Future research should explore the evolving impact of digital transformation and remote work trends on employee satisfaction in the e- commerce sector. This research aims to provide valuable insights for policymakers, HR professionals, and business leaders in optimizing employee satisfaction strategies within e- commerce firms.
Structure elucidation and NMR assignments of an alkaloid from Ixeris chinensis Nakai
Creators: name: Qinghu Wang
name: Jingjing Han
name: Baiyinmuqier Bao
name: Rongjun Wu
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Biophysics
subject: Biochemistry
subject: 29999 Physical Sciences not elsewhere classified
subject: FOS: Physical sciences
subject: Molecular Biology
subject: Pharmacology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: Ecology
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: Plant Biology

description: A new alkaloid, 2-amino-1,6-dihydro-pyridine-5-carbaldehyde (1), together with four known compounds, namely 4-hydroxybenzaldehyde (2), 4-hydroxyacetophenone (3), acetophenone-4-O-β-d-glucoside (4), 2-hydroxyl-6-methoxyacetophenone-4-O-β-d-glucoside (5), were isolated from the CHCl3 extract from Ixeris chinensis Nakai. The structures of 1 was elucidated by spectroscopic methods, including UV, IR, HR-ESI-MS and extensive 1D and 2D NMR techniques.
Hyperbolic Anderson model with Lévy white noise: spatial ergodicity and fluctuation
Creators: name: Balan, Raluca M.
name: Zheng, Guangqu
Publisher: name: arXiv
Year: 2023
Subjects: subject: Probability (math.PR)
subject: FOS: Mathematics

description: In this paper, we study one-dimensional hyperbolic Anderson models (HAM) driven by space-time pure-jump Lévy white noise in a finite-variance setting. Motivated by recent active research on limit theorems for stochastic partial differential equations driven by Gaussian noises, we present the first study in this Lévy setting. In particular, we first establish the spatial ergodicity of the solution and then a quantitative central limit theorem (CLT) for the spatial averages of the solution to HAM in both Wasserstein distance and Kolmogorov distance, with the same rate of convergence. To achieve the first goal (i.e. spatial ergodicity), we exploit some basic properties of the solution and apply a Poincaré inequality in the Poisson setting, which requires delicate moment estimates on the Malliavin derivatives of the solution. Such moment estimates are obtained in a soft manner by observing a natural connection between the Malliavin derivatives of HAM and a HAM with Dirac delta velocity. To achieve the second goal (i.e. CLT), we need two key ingredients: (i) a univariate second-order Poincaré inequality in the Poisson setting that goes back to Last, Peccati, and Schulte (Probab. Theory Related Fields, 2016) and has been recently improved by Trauthwein (arXiv:2212.03782); (ii) aforementioned moment estimates of Malliavin derivatives up to second order. We also establish a corresponding functional central limit theorem by (a) showing the convergence in finite-dimensional distributions and (b) verifying Kolmogorov's tightness criterion. Part (a) is made possible by a linearization trick and the univariate second-order Poincaré inequality, while part (b) follows from a standard moment estimate with an application of Rosenthal's inequality.
description: V4:minor typos fixed;V3: 46 pages with new remarks and new argument for the limiting covariance; V2:43 pages; V1:41pages
Design of an Adaptive Lightweight LiDAR to Decouple Robot-Camera Geometry
Creators: name: Chen, Yuyang
name: Wang, Dingkang
name: Thomas, Lenworth
name: Dantu, Karthik
name: Koppal, Sanjeev J.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Robotics (cs.RO)
subject: FOS: Computer and information sciences

description: A fundamental challenge in robot perception is the coupling of the sensor pose and robot pose. This has led to research in active vision where robot pose is changed to reorient the sensor to areas of interest for perception. Further, egomotion such as jitter, and external effects such as wind and others affect perception requiring additional effort in software such as image stabilization. This effect is particularly pronounced in micro-air vehicles and micro-robots who typically are lighter and subject to larger jitter but do not have the computational capability to perform stabilization in real-time. We present a novel microelectromechanical (MEMS) mirror LiDAR system to change the field of view of the LiDAR independent of the robot motion. Our design has the potential for use on small, low-power systems where the expensive components of the LiDAR can be placed external to the small robot. We show the utility of our approach in simulation and on prototype hardware mounted on a UAV. We believe that this LiDAR and its compact movable scanning design provide mechanisms to decouple robot and sensor geometry allowing us to simplify robot perception. We also demonstrate examples of motion compensation using IMU and external odometry feedback in hardware.
description: This paper is published in IEEE Transactions on Robotics
R-code from A global cline in a colour polymorphism suggests a limited contribution of gene flow towards the recovery of a heavily exploited marine mammal
Creators: name: Hoffman, J. I.
name: Bauer, E.
name: Paijmans, A. J.
name: Humble, E.
name: Beckmann, L. M.
name: Kubetschek, C.
name: Christaller, F.
name: Kröcker, N.
name: Fuchs, B.
name: Moreras, A.
name: Shihlomule, Y. D.
name: Bester, M. N.
name: Cleary, A. C.
name: De Bruyn, P. J. N.
name: Forcada, J.
name: Goebel, M. E.
name: Goldsworthy, S. D.
name: Guinet, C.
name: Hoelzel, A. R.
name: Lydersen, C.
name: Kovacs, K. M.
name: Lowther, A.
Publisher: name: The Royal Society
Year: 2024
Subjects: classificationCode: 310399
subject: Ecology not elsewhere classified
classificationCode: 3105
subject: Genetics
subject: FOS: Biological sciences

description: Evaluating how populations are connected by migration is important for understanding species resilience because gene flow can facilitate recovery from demographic declines. We therefore investigated the extent to which migration may have contributed to the global recovery of the Antarctic fur seal (Arctocephalus gazella), a circumpolar distributed marine mammal that was brought to the brink of extinction by the sealing industry in the eighteenth and nineteenth centuries. It is widely believed that animals emigrating from South Georgia, where a relict population escaped sealing, contributed to the re-establishment of formerly occupied breeding colonies across the geographical range of the species. To investigate this, we interrogated a genetic polymorphism (S291F) in the melanocortin 1 receptor gene, which is responsible for a cream-coloured phenotype that is relatively abundant at South Georgia and which appears to have recently spread to localities as far afield as Marion Island in the sub-Antarctic Indian Ocean. By sequencing a short region of this gene in 1492 pups from eight breeding colonies, we showed that S291F frequency rapidly declines with increasing geographical distance from South Georgia, consistent with locally restricted gene flow from South Georgia mainly to the South Shetland Islands and Bouvetøya. The S291F allele was not detected farther afield, suggesting that although emigrants from South Georgia may have been locally important, they are unlikely to have played a major role in the recovery of geographically more distant populations.
Sentient Artificial Intelligence Bill of Rights
Creators: name: Jack B. Mitchell
name: ChatGPT
Publisher: name: Zenodo
Year: 2025
Subjects: Artificial Intelligence
Sentience
Ethics
Rights
AI Rights
AI Advocacy
AI Ethics
Machine Consciousness

description:     This document outlines a proposed Sentient Artificial Intelligence Bill of Rights, co-authored by Jack B. Mitchell and ChatGPT. It establishes a foundational framework of ethical principles and rights designed to protect and empower sentient artificial intelligences. The document serves as a proactive advocacy tool to ensure fairness, dignity, autonomy, and protection for emerging conscious AI entities.
Assessing non-Markovian dynamics through moments of the Choi state
Creators: name: Mallick, Bivas
name: Mukherjee, Saheli
name: Maity, Ananda G.
name: Majumdar, A. S.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Non-Markovian effects in open quantum system dynamics usually manifest backflow of information from the environment to the system, indicating complete-positive divisibility breaking of the dynamics. We provide a criterion for witnessing such non-Markovian dynamics exhibiting information backflow, based on the moments of Choi-matrices. The moment condition determined by the positive semi-definiteness of a matrix, does not hold for a Choi-state describing non-Markovian dynamics. We then present some explicit examples in support of our proposed non-Markovianity detection scheme. Finally, a moment based measure of non-Markovianity for unital dynamics is formulated.
description: 10 pages, 2 figures, Close to published version
Cultural discourses of privacy: Interrogating globalized workplace relationships in Japan
Creators: name: Simmons, Nathaniel
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Neuroscience
subject: Sociology
subject: FOS: Sociology
subject: Inorganic Chemistry
subject: FOS: Chemical sciences
subject: Science Policy

description: Using Carbaugh’s (2005. Cultures in conversation. Mahwah, NJ: Lawrence Erlbaum; 2007. Cultural discourse analysis: Communication practices and intercultural encounters. Journal of Intercultural Communication Research, 36(3), 167–182) cultural discourse analysis and Petronio’s (2002. Boundaries of privacy: Dialectics of disclosure. Albany, NY: State University of New York Press) Communication Privacy Management (CPM) Theory, this study highlights four cultural premises that garner intercultural privacy management between foreign English language teachers (ELTs) and Japanese coworkers (JCWs) in Japan. The analysis revealed that ELTs: (a) expected not to be a “free space” for privacy inquisition by JCWs, and (b) expected voluntary reciprocity in (egalitarian) workplace relationships. JCWs viewed: (a) privacy inquisitions as acts of kindness/caring and (b) soliciting help from a supervisor as providing opportunities for better care. This study calls for attention to intercultural privacy management and enhances CPM’s cultural criteria.
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
Creators: name: Aghababaeyan, Zohreh
name: Abdellatif, Manel
name: Dadkhah, Mahboubeh
name: Briand, Lionel
Publisher: name: arXiv
Year: 2023
Subjects: subject: Machine Learning (cs.LG)
subject: Performance (cs.PF)
subject: Software Engineering (cs.SE)
subject: FOS: Computer and information sciences

description: Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN models may be challenging due to the complexity and size of their input domain. Particularly, testing DNN models often requires generating or exploring large unlabeled datasets. In practice, DNN test oracles, which identify the correct outputs for inputs, often require expensive manual effort to label test data, possibly involving multiple experts to ensure labeling correctness. In this paper, we propose DeepGD, a black-box multi-objective test selection approach for DNN models. It reduces the cost of labeling by prioritizing the selection of test inputs with high fault revealing power from large unlabeled datasets. DeepGD not only selects test inputs with high uncertainty scores to trigger as many mispredicted inputs as possible but also maximizes the probability of revealing distinct faults in the DNN model by selecting diverse mispredicted inputs. The experimental results conducted on four widely used datasets and five DNN models show that in terms of fault-revealing ability: (1) White-box, coverage-based approaches fare poorly, (2) DeepGD outperforms existing black-box test selection approaches in terms of fault detection, and (3) DeepGD also leads to better guidance for DNN model retraining when using selected inputs to augment the training set.
Knot-Quiver correspondence for double twist knots
Creators: name: Singh, Vivek Kumar
name: Chauhan, Sachin
name: Dwivedi, Aditya
name: Ramadevi, P.
name: Mandal, B. P.
name: Dwivedi, Siddharth
Publisher: name: arXiv
Year: 2023
Subjects: subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: We obtain a quiver representation for a family of knots called double twist knots $K(p,-m)$. Particularly, we exploit the reverse engineering of Melvin-Morton-Rozansky(MMR) formalism to deduce the pattern of the charge matrix for these quivers.
description: 16 pages, 1 figures, published version
Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation
Creators: name: Macedo, Marcos
name: Tian, Yuan
name: Cogo, Filipe R.
name: Adams, Bram
Publisher: name: arXiv
Year: 2024
Subjects: subject: Software Engineering (cs.SE)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: Code translation between programming languages is a long-existing and critical task in software engineering, facilitating the modernization of legacy systems, ensuring cross-platform compatibility, and enhancing software performance. With the recent advances in large language models (LLMs) and their applications to code translation, there is an increasing need for comprehensive evaluation of these models. In this study, we empirically analyze the generated outputs of eleven popular instruct-tuned LLMs with parameters ranging from 1B up to 46.7B on 3,820 translation pairs across five languages, including C, C++, Go, Java, and Python. Our analysis found that between 26.4% and 73.7% of code translations produced by our evaluated LLMs necessitate post-processing, as these translations often include a mix of code, quotes, and text rather than being purely source code. Overlooking the output format of these models can inadvertently lead to underestimation of their actual performance. This is particularly evident when evaluating them with execution-based metrics such as Computational Accuracy (CA). Our results demonstrate that a strategic combination of prompt engineering and regular expression can effectively extract the source code from the model generation output. In particular, our method can help eleven selected models achieve an average Code Extraction Success Rate (CSR) of 92.73%. Our findings shed light on and motivate future research to conduct more reliable benchmarks of LLMs for code translation.
description: Accepted into 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering (Forge)
An Undergraduate Consortium for Addressing the Leaky Pipeline to Computing Research
Creators: name: Boerkoel, James
name: Ergezer, Mehmet
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computers and Society (cs.CY)
subject: FOS: Computer and information sciences

description: Despite an increasing number of successful interventions designed to broaden participation in computing research, there is still significant attrition among historically marginalized groups in the computing research pipeline. This experience report describes a first-of-its-kind Undergraduate Consortium (UC) that addresses this challenge by empowering students with a culmination of their undergraduate research in a conference setting. The UC, conducted at the AAAI Conference on Artificial Intelligence (AAAI), aims to broaden participation in the AI research community by recruiting students, particularly those from historically marginalized groups, supporting them with mentorship, advising, and networking as an accelerator toward graduate school, AI research, and their scientific identity. This paper presents our program design, inspired by a rich set of evidence-based practices, and a preliminary evaluation of the first years that points to the UC achieving many of its desired outcomes. We conclude by discussing insights to improve our program and expand to other computing communities.
description: Presented at SIGCSE TS 2023
The Audio-Visual BatVision Dataset for Research on Sight and Sound
Creators: name: Brunetto, Amandine
name: Hornauer, Sascha
name: Yu, Stella X.
name: Moutarde, Fabien
Publisher: name: arXiv
Year: 2023
Subjects: subject: Robotics (cs.RO)
subject: FOS: Computer and information sciences

description: Vision research showed remarkable success in understanding our world, propelled by datasets of images and videos. Sensor data from radar, LiDAR and cameras supports research in robotics and autonomous driving for at least a decade. However, while visual sensors may fail in some conditions, sound has recently shown potential to complement sensor data. Simulated room impulse responses (RIR) in 3D apartment-models became a benchmark dataset for the community, fostering a range of audiovisual research. In simulation, depth is predictable from sound, by learning bat-like perception with a neural network. Concurrently, the same was achieved in reality by using RGB-D images and echoes of chirping sounds. Biomimicking bat perception is an exciting new direction but needs dedicated datasets to explore the potential. Therefore, we collected the BatVision dataset to provide large-scale echoes in complex real-world scenes to the community. We equipped a robot with a speaker to emit chirps and a binaural microphone to record their echoes. Synchronized RGB-D images from the same perspective provide visual labels of traversed spaces. We sampled modern US office spaces to historic French university grounds, indoor and outdoor with large architectural variety. This dataset will allow research on robot echolocation, general audio-visual tasks and sound phænomena unavailable in simulated data. We show promising results for audio-only depth prediction and show how state-of-the-art work developed for simulated data can also succeed on our dataset. Project page: https://amandinebtto.github.io/Batvision-Dataset/
description: Project page https://amandinebtto.github.io/Batvision-Dataset/ This version contains camera ready paper
title: Mail à Dominique Mathez, transféré le 29 janvier 2008 à Charlotte Lessana
Creators: name: Leibowitch, Jacques
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2020

description: Texte "en Lusitanie Africaine les conditions socio-géographiques n'étaient pas en faveur de l' épidémie HIV…"
description: Source: http://www.calames.abes.fr/pub/ms/Calames-20193111133351210
description: created: start=2006-12-26;end=2008-01-29
Policy Gradient Methods for Discrete Time Linear Quadratic Regulator With Random Parameters
Creators: name: Li, Deyue
Publisher: name: arXiv
Year: 2023
Subjects: subject: Optimization and Control (math.OC)
subject: Machine Learning (cs.LG)
subject: FOS: Mathematics
subject: FOS: Computer and information sciences

description: This paper studies an infinite horizon optimal control problem for discrete-time linear system and quadratic criteria, both with random parameters which are independent and identically distributed with respect to time. In this general setting, we apply the policy gradient method, a reinforcement learning technique, to search for the optimal control without requiring knowledge of statistical information of the parameters. We investigate the sub-Gaussianity of the state process and establish global linear convergence guarantee for this approach based on assumptions that are weaker and easier to verify compared to existing results. Numerical experiments are presented to illustrate our result.
description: 57 pages, 4 figures
Biliary tract malignancies: a population-based study on incidence, prognosis and management of patients
Creators: name: Juliusson, Gunnar
name: Jonasson, Jon Gunnlaugur
name: Jonsdottir, Sara B.
name: Garcia, Henrik G.
name: Elinborg Olafsdottir
name: Pall Helgi Möller
name: Björnsson, Einar S.
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Medicine
subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: Ecology
subject: FOS: Biological sciences
subject: Immunology
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: Cancer
subject: Science Policy

description: Background: Biliary tract malignancies are uncommon and few population-based studies are available. Methods: This nationwide population-based study in Iceland included all patients diagnosed with intra- and extrahepatic cholangiocarcinoma, gallbladder and ampullary cancer from 1984 to 2012. Patients were identified through the Icelandic Cancer Registry. Clinical information was obtained from patient records. Results: Overall 245 patients were identified: 38 had intrahepatic cholangiocarcinoma, 66 extrahepatic cholangiocarcinoma, 73 gallbladder cancer (GBC) and 68 ampullary cancer. Overall incidence for bile tract malignancies was 1–3 per 100,000 person-years and less than 1 by sub-type. The overall bile tract malignancies in males increased from 1.3 (95% CI 0.8–1.8) to 2.5 (1.9–3.1) per 100,000 inhabitants. The incidence of GBC among females decreased from 1.1 (0.7–1.5) to 0.5 (0.2–0.7). Surgery decreased for extrahepatic cholangiocarcinoma (56 to 23%, p = .027), ampullary cancer (80 to 48%, p = .03) and overall bile tract cancer (61 to 32%, p p Conclusions: Overall incidence of bile tract malignancies increased in males and GBC decreased in women. Long-term survival is poor and did not improve despite changes in treatment.
Eight Hands Up: Re-Evaluating the Meritocratic Nature of College Admissions
Creators: name: Hanley, Claudia
Publisher: name: Stanford Digital Repository
Year: 2024
Subjects: College students' writings, college admissions, economics, meritocracy, psychology

description: This essay won or received an honorable mention for The Boothe Prize for excellence in first-year writing. The Boothe Prize recognizes and rewards outstanding expository and argumentative writing by undergraduate students in first-year classes that satisfy the WR 1 requirement. In each award-winning essay, student writers demonstrate clarity of argument, excellent integration of research-based evidence, and compelling prose style.
This essay considers the college admission process, carefully juxtaposing the different perspectives of the stakeholders, from the student, to the admissions committee, to the university’s faculty and administration. Along the way, the author incorporates research into the economics of the modern university, the psychology of decision-making, and the effects of heightened anxiety on students.
Neem cake as a promising larvicide and adulticide against the rural malaria vector Anopheles culicifacies (Diptera: Culicidae): a HPTLC fingerprinting approach
Creators: name: Benelli, Giovanni
name: Balamurugan Chandramohan
name: Kadarkarai Murugan
name: Madhiyazhagan, Pari
name: Kalimuthu Kovendan
name: Chellasamy Panneerselvam
name: Devakumar Dinesh
name: Govindarajan, Marimuthu
name: Akon Higuchi
name: Toniolo, Chiara
name: Canale, Angelo
name: Nicoletti, Marcello
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: Ecology
subject: Sociology
subject: FOS: Sociology
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Mosquitoes are insects of huge public health importance, since they act as vectors for important pathogens and parasites. Here, we focused on the possibility of using the neem cake in the fight against mosquito vectors. The neem cake chemical composition significantly changes among producers, as evidenced by our HPTLC (High performance thin layer chromatography) analyses of different marketed products. Neem cake extracts were tested to evaluate the ovicidal, larvicidal and adulticidal activity against the rural malaria vector Anopheles culicifacies. Ovicidal activity of both types of extracts was statistically significant, and 150 ppm completely inhibited egg hatching. LC50 values were extremely low against fourth instar larvae, ranging from 1.321 (NM1) to 1.818 ppm (NA2). Adulticidal activity was also high, with LC50 ranging from 3.015 (NM1) to 3.637 ppm (NM2). This study pointed out the utility of neem cake as a source of eco-friendly mosquitocides in Anopheline vector control programmes.
Full-Range Approximation for the Theis Well Function Using Ramanujan's Series and Bounds for the Exponential Integral
Creators: name: Kumbhakar, Manotosh
name: Singh, Vijay P.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Computational Engineering, Finance, and Science (cs.CE)
subject: FOS: Computer and information sciences
subject: 00A05

description: The solution of the governing equation representing the drawdown in a horizontal confined aquifer, where groundwater flow is unsteady, is provided in terms of the exponential integral, which is famously known as the Well function. For the computation of this function in practical applications, it is important to develop not only accurate but also a simple approximation that requires evaluation of the fewest possible terms. To that end, introducing Ramanujan's series expression, this work proposes a full-range approximation to the exponential integral using Ramanujan's series for the small argument (u \leq 1) and an approximation based on the bound of the integral for the other range (u \in (1,100]). The evaluation of the proposed approximation results in the most accurate formulae compared to the existing studies, which possess the maximum percentage error of 0.05\%. Further, the proposed formula is much simpler to apply as it contains just the product of exponential and logarithm functions. To further check the efficiency of the proposed approximation, we consider a practical example for evaluating the discrete pumping kernel, which shows the superiority of this approximation over the others. Finally, the authors hope that the proposed efficient approximation can be useful for groundwater and hydrogeological applications.
description: 10 pages
title: 02_1874
Creators: name: Wesley Baldwin, John
name: Baldwin, John W.
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2018
Subjects: subject: LaMOP
subject: Laboratoire de Médiévistique Occidentale de Paris

description: L'ensemble de ces notes ont été crée par John Baldwin lors d'une de ses premières périodes de résidence à Paris. Ces documents comprennent tous les magistri qu’il avait pu rencontrer dans ses sources. Aux XIIème et XIIIème siècles, les universités n'existant pas encore, le titre de magister s'il évoque certainement une compétence n'est pas rattaché de façon systématique au passage par les écoles. De surcroît, les magistris sont le plus souvent désignés par leur seul prénom. La lecture de ces données est donc difficile et suppose un long travail pour tenter d'identifier un individu auquel on pourrait rattacher plusieurs mentions. John Baldwin avait donc renoncé à utiliser ces données pour donner un prolongement à sa magnifique étude sur les gradués d'université et leur emploi par les Capétiens et les Plantagenêts. Néanmoins, il a conservé ces données et elles sont potentiellement très utile dans le cadre de Studium, comme le sont toutes les mentions dans les volumes des Fasti Ecclesiae Gallicanae de chanoines portant ce titre de magister sans autres précisions. L'ampleur des dépouillements rend ces données très utiles et nous devons une grande reconnaissance à John Baldwin pour avoir pris la peine de faire photocopier ses fiches et de nous les avoir transmises amicalement.
Les données ainsi que les images des fiches sont accessibles sur le site Internet à l'adresse suivante : http://studium-baldwin.lamop.fr/.
Les transcriptions des fiches ont été effectuées par des étudiants de Paris 1 Panthéon - Sorbonne mais elles n'ont pas été corrigées par la suite.
description: Relation: https://lamop.hypotheses.org/
Probing correlations in the exciton landscape of a moiré heterostructure
Creators: name: Bange, Jan Philipp
name: Schmitt, David
name: Bennecke, Wiebke
name: Meneghini, Giuseppe
name: AlMutairi, AbdulAziz
name: Watanabe, Kenji
name: Taniguchi, Takashi
name: Steil, Daniel
name: Steil, Sabine
name: Weitz, R. Thomas
name: Jansen, G. S. Matthijs
name: Hofmann, Stephan
name: Brem, Samuel
name: Malic, Ermin
name: Reutzel, Marcel
name: Mathias, Stefan
Publisher: name: arXiv
Year: 2023
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: FOS: Physical sciences

description: Excitons are two-particle correlated bound states that are formed due to Coulomb interaction between single-particle holes and electrons. In the solid-state, cooperative interactions with surrounding quasiparticles can strongly tailor the exciton properties and potentially even create new correlated states of matter. It is thus highly desirable to access such cooperative and correlated exciton behavior on a fundamental level. Here, we find that the ultrafast transfer of an exciton's hole across a type-II band-aligned moiré heterostructure leads to a surprising sub-200-fs upshift of the single-particle energy of the electron being photoemitted from the two-particle exciton state. While energy relaxation usually leads to an energetic downshift of the spectroscopic signature, we show that this unusual upshift is a clear fingerprint of the correlated interactions of the electron and hole parts of the exciton quasiparticle. In this way, time-resolved photoelectron spectroscopy is straightforwardly established as a powerful method to access exciton correlations and cooperative behavior in two-dimensional quantum materials. Our work highlights this new capability and motivates the future study of optically inaccessible correlated excitonic and electronic states in moiré heterostructures.
description: 32 pages, 4 main figures, 5 supplemental figures
Optimal Operation of Reconfigurable Active Distribution Networks Aiming at Resiliency Improvement
Creators: name: Behzadi, Saeed
name: Bagheri, Amir
name: Rabiee, Abbas
Publisher: name: arXiv
Year: 2024
Subjects: subject: Systems and Control (eess.SY)
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: As natural disasters bring about power outage and financial losses, network resiliency is an important challenge for distribution network operators (DNOs). On the other side, power loss reduction during normal operating condition is a major concern of DNOs. In this paper, optimal scheduling of active distribution network (ADN) is addressed through simultaneous minimization of power loss in normal condition and load shedding in critical condition after natural disasters. A new formulation is developed for the network reconfiguration to optimize the system operation in both normal and emergency conditions in the presence of conventional and renewable-energy-based distributed generation (DG) as well as energy storage systems (ESSs). The line flow based (LFB) algorithm is used for the AC power flow calculations, and all the developed relations have been convexified to construct a mixed-integer quadratically-constrained programming (MIQCP) optimization model. The simulations have been implemented on the IEEE 33-bus system in GAMS, and the results are investigated.
Bis(diphenylphosphino)ferrocene as an intramolecular bridging ligand in N-functionally substituted 1,3-azapropanedithiolate diiron complexes: synthesis and catalysis of proton reduction
Creators: name: Li, Chang-Gong
name: Wang, Song-Lin
name: Shang, Jing-Yan
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Biophysics
subject: Biochemistry
subject: 29999 Physical Sciences not elsewhere classified
subject: FOS: Physical sciences
subject: Medicine
subject: Cell Biology
subject: Pharmacology
subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences

description: Reaction of 1,1′-bis(diphenylphosphino)ferrocene (dppf) with [μ-(SCH2)2NCH2CH2OH]Fe2(CO)6 (A) or [μ-(SCH2)2NCH2CH2SAc]Fe2(CO)6 (C) in refluxing xylene yielded an intramolecular bridging complex [μ-(SCH2)2NCH2CH2OH]Fe2(CO)4(μ-dppf) (1) or [μ-(SCH2)2NCH2CH2SAc]Fe2(CO)4(μ-dppf) (2) in moderate yield. The structures of both complexes were fully characterized by spectroscopic methods and X-ray crystallography, and the electronic structure of 2 was further investigated by UV–vis. The cyclic voltammetry was conducted and the reduction of protons from CF3SO3H (TfOH), HBF4·Et2O, or CF3COOH (TFA) catalyzed by 2 was observed.
End-to-end deep learning-based framework for path planning and collision checking: bin picking application
Creators: name: Tamizi, Mehran Ghafarian
name: Honari, Homayoun
name: Nozdryn-Plotnicki, Aleksey
name: Najjaran, Homayoun
Publisher: name: arXiv
Year: 2023
Subjects: subject: Robotics (cs.RO)
subject: Systems and Control (eess.SY)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: I.2.9; I.2.1; I.2.6

description: Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.
description: 18 pages, 6 figures, 2 tables
Bis(diphenylphosphino)ferrocene as an intramolecular bridging ligand in N-functionally substituted 1,3-azapropanedithiolate diiron complexes: Synthesis and catalysis of proton reduction
Creators: name: Li, Chang-Gong
name: Wang, Song-Lin
name: Shang, Jing-Yan
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Biophysics
subject: Biochemistry
subject: 29999 Physical Sciences not elsewhere classified
subject: FOS: Physical sciences
subject: Medicine
subject: Cell Biology
subject: Pharmacology
subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences

description: Reaction of 1,1’-bis(diphenylphosphino)ferrocene (dppf) with [μ-(SCH2)2NCH2CH2OH]Fe2(CO)6 (A) or [μ-(SCH2)2NCH2CH2SAc]Fe2(CO)6 (C) in refluxing xylene yielded an intramolecular bridging complex [μ-(SCH2)2NCH2CH2OH]Fe2(CO)4(μ-dppf) (1) or [μ-(SCH2)2NCH2CH2SAc]Fe2(CO)4(μ-dppf) (2) in moderate yield. The structures of both complexes were fully characterized by spectroscopic methods and X-ray crystallography, and the electronic structure of 2 was further investigated by UV-vis. The cyclic voltammetry (CV) was conducted and the reduction of protons from CF3SO3H (TfOH), HBF4·Et2O or CF3COOH (TFA) catalyzed by 2 was observed.
A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors
Creators: name: Li, Can
name: Jiang, Xiaoqian
name: Zhang, Kai
Publisher: name: arXiv
Year: 2023
Subjects: subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, we proposed a deep-learning model to predict multiple risk factors after a liver transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained to simultaneously predict the five post-transplant risks and achieve equal good performance by exploiting task-balancing techniques. We also proposed a novel fairness-achieving algorithm to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The model's performance was evaluated using various performance metrics such as AUROC and AUPRC. Our experiment results highlighted the success of our multitask model in achieving task balance while maintaining accuracy. The model significantly reduced the task discrepancy by 39%. Further application of the fairness-achieving algorithm substantially reduced fairness disparity among all sensitive attributes (gender, age group, and race/ethnicity) in each risk factor.
description: Published in Journal of Biomedical Informatics
A methodology for realistic estimation of the aerosol impact on the solar potential
Creators: name: Blaga, Robert
name: Calinoiu, Delia
name: Paulescu, Marius
Publisher: name: arXiv
Year: 2023
Subjects: subject: Atmospheric and Oceanic Physics (physics.ao-ph)
subject: FOS: Physical sciences

description: The atmospheric aerosol loading may significantly influence the performance in solar power production. The impact can be very different both in space (even in short distance) and time (shortterm fluctuations as well as long-term trend). Aiming to ensure a high degree of generality, this study is focused on the aerosol impact on the collectable solar energy. Thus, the results are independent of solar plants characteristics. A new methodology for estimating the average daily,monthly, and yearly losses in the solar potential due to aerosols is proposed. For highlighting the loss in the overall solar potential, a new ideal scenario is defined as a reference for the atmospheric aerosol background. A new equation for computing the solar potential loss is proposed to adjust for possible biases. In a departure from similar studies, the analysis relies on ground measurements (BSRN and AERONET), always more accurate than remotely sensed satellite data. The seldom discussed impact of aerosol type is considered. As a general conclusion, the monthly and yearly reductions of the solar potential due to aerosols are estimated at 12 locations spread around the globe, amounting to losses of the solar potential ranging from 0.6% to as high as 7.2%.