175 research outputs found
Marketing Plan for Columbus State University
Marketing is an important tool for capturing customers and sales in business, and itâs equally as important to an institution like Columbus State University (CSU) aiming to capture student customers. The development of a marketing plan through extensive research and planning will contribute to the success of all involved marketing efforts. This marketing plan will include all the standard essential elements: business mission statement, situation (SWOT) analysis, objectives, marketing strategy, implementation, and evaluation control. For the mission statement, the current statement from the 2018-2023 Strategic Plan will be used. The situation analysis will include looking at the current strengths, weakness, opportunities, and threats of CSU. Based on this analysis, the objectives of the marketing plan will be created. The marketing strategy will include development of the target market strategy and the marketing mix. Target market strategy will consist of segmentation, targeting, and positioning while the marketing mix is made up of product, price, place, and promotion. An action plan will be developed to implement the marketing plan, and evaluation and control methods will be planned. The proposed plan would increase student enrollment and brand awareness of CSU
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Developments in Auxiliary Field Quantum Monte Carlo for Molecules
This thesis presents a compilation of recent work on benchmarking, applying, and developing Auxiliary Field Quantum Monte Carlo (AFQMC) for use in ab initio simulations of the electronic structure of molecules.
With Chapter 1 I begin with a benchmark of phaseless AFQMC versus experiment in obtaining gas phase ligand dissociation energies of a set of tetrahedral and octahedral transition metal complexes. ph-AFQMC is shown to acquire chemical accuracy through the use of correlated sampling (CS) and CASSCF trial wave functions selected via a black box procedure.
This is followed in Chapter 2 with another gas phase benchmark of ph-AFQMC versus experiment, this time calculating the redox potentials for a set of metallocenes, where we find a mix of correlated sampling and large CASSCF trials necessary to reproduce gas phase experimental values to within 1.7 ± 1.0 kcal/mol. Additionally, the inclusion of QZ ph-AFQMC values, either using UHF or CASSCF trials, was found to be necessary for a few systems, as opposed to using a hybrid approach with alternate methods such as coupled cluster to extrapolate to the basis set limit.
In Chapter 3, having established protocols to obtain decent results on transition metal complexes with known experimental values, I apply ph-AFQMC to successfully predict the activity of a set of new annihilators for optical upconversion. For a set of functionalized anthracene molecules, I report agreement within statistics between ph-AFQMC and a localized approximation to coupled cluster singles doubles and perturbative triples (DLPNO-CCSD(T0)), and develop intuitive guidelines for tuning the excited state energies of anthracene. For a single molecule in an additional set of functionalized benzothiadiazole (BTD) molecules, Ph-BTD, ph-AFQMC and DLPNO-CCSD(T) disagree significantly; subsequent experimental testing validates the ph-AFQMC result.
In Chapter 4 I present an approach based on localized orbitals to reduce the scaling with system size from quartic to cubic for the energy evaluation, the functional bottleneck for the majority of AFQMC calculations. Additionally, I describe the practical implementation of such an algorithm to be run on large GPU clusters. This allows AFQMC to be run for both larger systems and trials at a significantly decreased cost, while still reproducing full AFQMC results within the statistics of the method.
With Chapter 5, I conclude with the development and characterization of a novel constraint, linecut (lc-) AFQMC, which exhbits distinct behavior versus the phaseless constraint. We demonstrate benchmarks for a variety of weakly to strongly correlated molecules for which we have the exact total energies, and observe that lc-AFQMC outperforms ph-AFQMC for the majority of systems studied.
I conclude with the description of a systematic method to remove the linecut constraint, partially removing the bias and re-introducing the fermionic sign problem while still maintaining a practicable signal to noise ratio. This allows for us to recover the exact energy of FeO with a fraction of the cost of converging the trial wave function within ph-AFQMC
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels
Adversarial training (AT) formulated as the minimax optimization problem can
effectively enhance the model's robustness against adversarial attacks. The
existing AT methods mainly focused on manipulating the inner maximization for
generating quality adversarial variants or manipulating the outer minimization
for designing effective learning objectives. However, empirical results of AT
always exhibit the robustness at odds with accuracy and the existence of the
cross-over mixture problem, which motivates us to study some label randomness
for benefiting the AT. First, we thoroughly investigate noisy labels (NLs)
injection into AT's inner maximization and outer minimization, respectively and
obtain the observations on when NL injection benefits AT. Second, based on the
observations, we propose a simple but effective method -- NoiLIn that randomly
injects NLs into training data at each training epoch and dynamically increases
the NL injection rate once robust overfitting occurs. Empirically, NoiLIn can
significantly mitigate the AT's undesirable issue of robust overfitting and
even further improve the generalization of the state-of-the-art AT methods.
Philosophically, NoiLIn sheds light on a new perspective of learning with NLs:
NLs should not always be deemed detrimental, and even in the absence of NLs in
the training set, we may consider injecting them deliberately. Codes are
available in https://github.com/zjfheart/NoiLIn.Comment: Accepted at Transactions on Machine Learning Research (TMLR) at June
202
A Channel Ranking And Selection Scheme Based On Channel Occupancy And SNR For Cognitive Radio Systems
Wireless networks and information traffic have grown exponentially over the last decade. Consequently, an increase in demand for radio spectrum frequency bandwidth has resulted. Recent studies have shown that with the current fixed spectrum allocation (FSA), radio frequency band utilization ranges from 15% to 85%. Therefore, there are spectrum holes that are not utilized all the time by the licensed users, and, thus the radio spectrum is inefficiently exploited. To solve the problem of scarcity and inefficient utilization of the spectrum resources, dynamic spectrum access has been proposed as a solution to enable sharing and using available frequency channels. With dynamic spectrum allocation (DSA), unlicensed users can access and use licensed, available channels when primary users are not transmitting. Cognitive Radio technology is one of the next generation technologies that will allow efficient utilization of spectrum resources by enabling DSA. However, dynamic spectrum allocation by a cognitive radio system comes with the challenges of accurately detecting and selecting the best channel based on the channelĂąs availability and quality of service. Therefore, the spectrum sensing and analysis processes of a cognitive radio system are essential to make accurate decisions. Different spectrum sensing techniques and channel selection schemes have been proposed. However, these techniques only consider the spectrum occupancy rate for selecting the best channel, which can lead to erroneous decisions. Other communication parameters, such as the Signal-to-Noise Ratio (SNR) should also be taken into account. Therefore, the spectrum decision-making process of a cognitive radio system must use
techniques that consider spectrum occupancy and channel quality metrics to rank channels and select the best option. This thesis aims to develop a utility function based on spectrum occupancy and SNR measurements to model and rank the sensed channels.
An evolutionary algorithm-based SNR estimation technique was developed, which enables adaptively varying key parameters of the existing Eigenvalue-based blind SNR estimation technique. The performance of the improved technique is compared to the existing technique. Results show the evolutionary algorithm-based estimation performing better than the existing technique. The utility-based channel ranking technique was developed by first defining channel utility function that takes into account SNR and spectrum occupancy. Different mathematical functions were investigated to appropriately model the utility of SNR and spectrum occupancy rate. A ranking table is provided with the utility values of the sensed channels and compared with the usual occupancy rate based channel ranking. According to the results, utility-based channel ranking provides a better scope of making an informed decision by considering both channel occupancy rate and SNR. In addition, the efficiency of several noise cancellation techniques was investigated. These techniques can be employed to get rid of the impact of noise on the received or sensed signals during spectrum sensing process of a cognitive radio system. Performance evaluation of these techniques was done using simulations and the results show that the evolutionary algorithm-based noise cancellation techniques, particle swarm optimization and genetic algorithm perform better than the regular gradient descent based technique, which is the least-mean-square algorithm
An open virtual testbed for industrial control system security research
ICS security has been a topic of scrutiny and research for several years, and many security issues are well known. However, research efforts are impeded by a lack of an open virtual industrial control system testbed for security research. This thesis describes a virtual testbed framework using Python to create discrete testbed components (including virtual devices and process simulators). This testbed is designed such that the testbeds are interoperable with real ICS devices and that the virtual testbeds can provide comparable ICS network behavior to a laboratory testbed. Two testbeds based on laboratory testbeds have been developed and have been shown to be interoperable with real industrial control systemequipment and vulnerable to attacks in the samemanner as a real system. Additionally, these testbeds have been quantitatively shown to produce traffic close to laboratory systems (within 90% similarity on most metrics)
State of Academic Affairs Report
A comprehensive overview of the Office for Academic Affairs covering the years 2013 through 2015 as it is described by the Faculty Hanbook policy A83 Annual Reports. This includes reports on all 12 schools and colleges, and on all administrative units including Enrollment Management and GEO
Bench-Ranking: ettekirjutav analĂŒĂŒsimeetod suurte teadmiste graafide pĂ€ringutele
Relatsiooniliste suurandmete (BD) töötlemisraamistike kasutamine suurte teadmiste graafide töötlemiseks kĂ€tkeb endas vĂ”imalust pĂ€ringu jĂ”udlust optimeerimida. Kaasaegsed BD-sĂŒsteemid on samas keerulised andmesĂŒsteemid, mille konfiguratsioonid omavad olulist mĂ”ju jĂ”udlusele. Erinevate raamistike ja konfiguratsioonide vĂ”rdlusuuringud pakuvad kogukonnale parimaid tavasid parema jĂ”udluse saavutamiseks. Enamik neist vĂ”rdlusuuringutest saab liigitada siiski vaid kirjeldavaks ja diagnostiliseks analĂŒĂŒtikaks. Lisaks puudub ĂŒhtne standard nende uuringute vĂ”rdlemiseks kvantitatiivselt jĂ€rjestatud kujul. Veelgi enam, suurte graafide töötlemiseks vajalike konveierite kavandamine eeldab tĂ€iendavaid disainiotsuseid mis tulenevad mitteloomulikust (relatsioonilisest) graafi töötlemise paradigmast. Taolisi disainiotsuseid ei saa automaatselt langetada, nt relatsiooniskeemi, partitsioonitehnika ja salvestusvormingute valikut. KĂ€esolevas töös kĂ€sitleme kuidas me antud uurimuslĂŒnga tĂ€idame. Esmalt nĂ€itame disainiotsuste kompromisside mĂ”ju BD-sĂŒsteemide jĂ”udluse korratavusele suurte teadmiste graafide pĂ€ringute tegemisel. Lisaks nĂ€itame BD-raamistike jĂ”udluse kirjeldavate ja diagnostiliste analĂŒĂŒside piiranguid suurte graafide pĂ€ringute tegemisel. SeejĂ€rel uurime, kuidas lubada ettekirjutavat analĂŒĂŒtikat jĂ€rjestamisfunktsioonide ja mitmemÔÔtmeliste optimeerimistehnikate (nn "Bench-Ranking") kaudu. See lĂ€henemine peidab kirjeldava tulemusanalĂŒĂŒsi keerukuse, suunates praktiku otse teostatavate teadlike otsusteni.Leveraging relational Big Data (BD) processing frameworks to process large knowledge graphs yields a great interest in optimizing query performance. Modern BD systems are yet complicated data systems, where the configurations notably affect the performance. Benchmarking different frameworks and configurations provides the community with best practices for better performance. However, most of these benchmarking efforts are classified as descriptive and diagnostic analytics. Moreover, there is no standard for comparing these benchmarks based on quantitative ranking techniques. Moreover, designing mature pipelines for processing big graphs entails considering additional design decisions that emerge with the non-native (relational) graph processing paradigm. Those design decisions cannot be decided automatically, e.g., the choice of the relational schema, partitioning technique, and storage formats. Thus, in this thesis, we discuss how our work fills this timely research gap. Particularly, we first show the impact of those design decisionsâ trade-offs on the BD systemsâ performance replicability when querying large knowledge graphs. Moreover, we showed the limitations of the descriptive and diagnostic analyses of BD frameworksâ performance for querying large graphs. Thus, we investigate how to enable prescriptive analytics via ranking functions and Multi-Dimensional optimization techniques (called âBench-Rankingâ). This approach abstracts out from the complexity of descriptive performance analysis, guiding the practitioner directly to actionable informed decisions.https://www.ester.ee/record=b553332
UMSL Bulletin 2020-2021
The 2020-2021 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1084/thumbnail.jp
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