117 research outputs found
GLOBAL DEFORESTATION PREDICTION: SUMMER INTERNSHIP AT CLARK LABS
This paper is a description of our internship with Clark Labs in the summer of 2016. We worked as research assistants in the Deforestation Risk Prediction project for Ecosystem Services team. This goal of the project was to predict deforestation at global, continental and national level. Our responsibilities were to choose the variables that may influence the deforestation and to use Land Change Modeler in TerrSet to test the variables and create the deforestation prediction maps. We highly recommend this internship with Clark Labs to other GISDE students who are interested in land change analysis
A note on character square
We study the finite groups with an irreducible character χ satisfying the following hypothesis: χ2 has exactly two distinct irreducible constituents, and one of which is linear, and then obtain a result analogous to the Zhmud\u27s ([8])
Heuristics for periodical batch job scheduling in a MapReduce computing framework
Task scheduling has a significant impact on the performance of the MapReduce computing
framework. In this paper, a scheduling problem of periodical batch jobs with makespan minimization
is considered. The problem is modeled as a general two-stage hybrid flow shop
scheduling problem with schedule-dependent setup times. The new model incorporates the
data locality of tasks and is formulated as an integer program. Three heuristics are developed
to solve the problem and an improvement policy based on data locality is presented to enhance
the methods. A lower bound of the makespan is derived. 150 instances are randomly
generated from data distributions drawn from a real cluster. The parameters involved in the
methods are set according to different cluster setups. The proposed heuristics are compared
over different numbers of jobs and cluster setups. Computational results show that the performance
of the methods is highly dependent on both the number of jobs and the cluster setups.
The proposed improvement policy is effective and the impact of the input data distribution on
the policy is analyzed and tested.This work is supported by the National Natural Science Foundation of China (No. 61272377) and the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120092110027). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "RESULT - Realistic Extended Scheduling Using Light Techniques" (No. DPI2012-36243-C02-01) partially financed with FEDER funds.Xiaoping Li; Tianze Jiang; Ruiz GarcĂa, R. (2016). Heuristics for periodical batch job scheduling in a MapReduce computing framework. Information Sciences. 326:119-133. https://doi.org/10.1016/j.ins.2015.07.040S11913332
Online Nash Welfare Maximization Without Predictions
Nash welfare maximization is widely studied because it balances efficiency
and fairness in resource allocation problems. Banerjee, Gkatzelis, Gorokh, and
Jin (2022) recently introduced the model of online Nash welfare maximization
with predictions for divisible items and agents with additive
utilities. They gave online algorithms whose competitive ratios are
logarithmic. We initiate the study of online Nash welfare maximization
\emph{without predictions}, assuming either that the agents' utilities for
receiving all items differ by a bounded ratio, or that their utilities for the
Nash welfare maximizing allocation differ by a bounded ratio. We design online
algorithms whose competitive ratios only depend on the logarithms of the
aforementioned ratios of agents' utilities and the number of agents
A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system
KMT2A promotes melanoma cell growth by targeting hTERT signaling pathway.
Melanoma is an aggressive cutaneous malignancy, illuminating the exact mechanisms and finding novel therapeutic targets are urgently needed. In this study, we identified KMT2A as a potential target, which promoted the growth of human melanoma cells. KMT2A knockdown significantly inhibited cell viability and cell migration and induced apoptosis, whereas KMT2A overexpression effectively promoted cell proliferation in various melanoma cell lines. Further study showed that KMT2A regulated melanoma cell growth by targeting the hTERT-dependent signal pathway. Knockdown of KMT2A markedly inhibited the promoter activity and expression of hTERT, and hTERT overexpression rescued the viability inhibition caused by KMT2A knockdown. Moreover, KMT2A knockdown suppressed tumorsphere formation and the expression of cancer stem cell markers, which was also reversed by hTERT overexpression. In addition, the results from a xenograft mouse model confirmed that KMT2A promoted melanoma growth via hTERT signaling. Finally, analyses of clinical samples demonstrated that the expression of KMT2A and hTERT were positively correlated in melanoma tumor tissues, and KMT2A high expression predicted poor prognosis in melanoma patients. Collectively, our results indicate that KMT2A promotes melanoma growth by activating the hTERT signaling, suggesting that the KMT2A/hTERT signaling pathway may be a potential therapeutic target for melanoma
Tunable photochemical deposition of silver nanostructures on layered ferroelectric CuInPS6
2D layered ferroelectric materials such as CuInPS6 (CIPS) are promising
candidates for novel and high-performance photocatalysts, owning to their
ultrathin layer thickness, strong interlayer coupling, and intrinsic
spontaneous polarization, while how to control the photocatalytic activity in
layered CIPS remains unexplored. In this work, we report for the first time the
photocatalytic activity of ferroelectric CIPS for the chemical deposition of
silver nanostructures (AgNSs). The results show that the shape and spatial
distribution of AgNSs on CIPS are tunable by controlling layer thickness,
environmental temperature, and light wavelength. The ferroelectric polarization
in CIPS plays a critical role in tunable AgNS photodeposition, as evidenced by
layer thickness and temperature dependence experiments. We further reveal that
AgNS photodeposition process starts from the active site creation, selective
nanoparticle nucleation/aggregation, to the continuous film formation.
Moreover, AgNS/CIPS heterostructures prepared by photodeposition exhibit
excellent resistance switching behavior and good surface enhancement Raman
Scattering activity. Our findings provide new insight into the photocatalytic
activity of layered ferroelectrics and offer a new material platform for
advanced functional device applications in smart memristors and enhanced
chemical sensors.Comment: 18 pages, 5 figure
Multi-criteria decision making for identification of unbalanced bidding
Unbalanced bidding is a serious problem in the competitive bidding practices of construction projects. Identification and prevention of unbalanced bidding is an important and complexity task for owners. This paper aims to propose an identification model of unbalanced bidding from multi-criteria decision making (MCDM) perspective. The VIKOR method is employed to detect unbalanced bidding, in which the line items and bidders are considered as criteria and alternatives in MCDM, respectively. And the engineer’s estimated price is chosen as evaluation benchmarking. Then relative distances between engineer’s estimated price and each bidding unit price are calculated to build decision matrix. The weights of factors are determined using entropy weight method. To illustrate the effectiveness of the proposed model, an application example is tested in detecting unbalanced bidding. Finally, the sensitivity analysis about VIKOR method is given. It shows that the presented model would provide a robust decision making support for owner in identifying unbalanced bidding.
First published online 18 December 201
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Recent advances in artificial general intelligence (AGI), particularly large
language models and creative image generation systems have demonstrated
impressive capabilities on diverse tasks spanning the arts and humanities.
However, the swift evolution of AGI has also raised critical questions about
its responsible deployment in these culturally significant domains
traditionally seen as profoundly human. This paper provides a comprehensive
analysis of the applications and implications of AGI for text, graphics, audio,
and video pertaining to arts and the humanities. We survey cutting-edge systems
and their usage in areas ranging from poetry to history, marketing to film, and
communication to classical art. We outline substantial concerns pertaining to
factuality, toxicity, biases, and public safety in AGI systems, and propose
mitigation strategies. The paper argues for multi-stakeholder collaboration to
ensure AGI promotes creativity, knowledge, and cultural values without
undermining truth or human dignity. Our timely contribution summarizes a
rapidly developing field, highlighting promising directions while advocating
for responsible progress centering on human flourishing. The analysis lays the
groundwork for further research on aligning AGI's technological capacities with
enduring social goods
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