Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptual Consequences of Direction and Level of Early Reﬂections in a Chamber Music Hall

Introduction Early reflections are important contributors to the sound of a concert hall. They are directly associated with clarity and spatial impression (SI). Early lateral reflections, especially, have a significant role in the perception of apparent source width (ASW) and in how the hall ”wakes up” at louder orchestral dynamics [1]. The direction distribution of such reflections is found to be important for the acoustical quality as early as 1964 [2]. Naturally, the relation between the level and direction of such reflections and the perceptual consequences are of utmost interest for concert hall design. As central element at establishing such relations, the audibility threshold of changes to individual reflections should be determined.
A Comparison of Abstraction Heuristics for Rubik’s Cube

Since its invention in 1974, the Rubik’s Cube puzzle fasci-nates people of all ages. Its rules are simple: the player gets a scrambled cube and rotates the six faces until each face contains only stickers of one color. Nevertheless, ﬁnding a short sequence of rotations to solve the cube is hard. We present the ﬁrst model of Rubik’s Cube for general problem solvers. To obtain a concise model, we require conditional effects. Furthermore, we extend counterexample-guided Cartesian abstraction reﬁnement (CEGAR) to support factored effect tasks , a class of planning tasks with a speciﬁc kind of conditional effects which includes Rubik’s Cube. Finally, we evaluate how newer types of abstraction heuristics compare against pattern database (PDB) heuristics, the state-of-the-art for solving Rubik’s Cube. We ﬁnd that PDBs still outperform the more general Cartesian and merge-and-shrink abstractions. However, in contrast to PDBs, Cartesian abstractions yield perfect heuristics up to a certain problem difﬁculty. These ﬁndings raise interesting questions for future research.
Decomposition of electricity data from a Nigerian power generating station

The need for energy planners to carry out accurate forecast of electricity generation is imperative in the realization of overall enterprise objectives. This paper presents an exquisite decomposition model which adequately describes the original data by employing a statistical decomposition of a set of 132 month (1996-2006) electricity generation data from Ughelli Power Station, Delta State, Nigeria. The multiplicative version of the decomposition model was employed to characterize the data into its various components such as Noise, Trends, Cyclical Component, and Seasonal Index. Our results show a linear increasing trend, heavy noise, cyclical activities and seasonality. The application of statistical forecasting technique which takes into consideration the various components that are buried in the set of generation data will however, serve as a good instrument that can be adjudged intriguing and intuitively appealing for energy planners in order to come up with better maintenance and operation policies that can cub the up and down nature of the present operation and maintenance (O & M).
Posterior Sampling with Stochastic Gradient Langevin Dynamics

So far in this course, we have discussed several different tasks that arise in statistical learning, and we expressed most of these tasks by certain optimization problems (such as the empirical risk minimization problem). The Maximum A-Posteriori (MAP) estimation problem being one of the examples of such optimization problems, arose naturally when we limited ourselves to the framework of probabilistic modeling. In this lecture, we will again consider the probabilistic modeling framework, but this time we will ask a different (and potentially more difficult) question than the MAP estimation problem.
Assessing the roles emission sources and atmospheric processes play in simulating δ 15 N of atmospheric NO x and NO − 3 using CMAQ (version 5.2.1) and SMOKE (version 4.6)

Nitrogen oxides (NO x = nitric oxide (NO) + nitrogen dioxide (NO 2 )) are important trace gases that affect atmospheric chemistry, air quality, and climate. Contempo-rary development of NO x emissions inventories is limited by the understanding of the roles of vegetation (net NO x source or net sink), vehicle emissions from gasoline- and diesel-powered vehicles, the application of NO x emission control technologies, and accurate veriﬁcation techniques. The nitrogen stable isotope composition ( δ 15 N) of NO x is an effective tool to evaluate the accuracy of the NO x emission inventories, which are based on different assumptions. In this study, we traced the changes in δ 15 N values of NO x along the “journey” of atmospheric NO x , driven by atmospheric processes after different sources emit NO x into the atmosphere. The 15 N was incorporated into the emission input dataset, generated from the US EPA trace gas emission model SMOKE (Sparse Matrix Operator Kernel Emis-sions). Then the 15 N-incorporated emission input dataset was used to run the CMAQ (Community Multiscale Air Quality) modeling system. By enhancing NO x deposition, we simulated the expected δ 15 N of NO − 3 , assuming no isotope fractionation during chemical conversion or deposition. The simulated spatiotemporal patterns in NO x isotopic composition for both SMOKE outputs (simulations under the “emission only” scenario) and CMAQ outputs (simulations under the “emission + transport + enhanced NO x loss” scenario) were compared with corresponding measurements in West Lafayette, Indiana, USA. The simulations under the emission + transport + enhanced NO x loss scenario were also compared to δ 15 N of NO − 3 at NADP (National Atmospheric Deposition Program) sites. The results indicate the potential underestimation of emissions from soil, livestock waste, off-road vehicles, and natural-gas power plants and the potential overestimation of emissions from on-road vehicles and coal-ﬁred power plants, if only considering the difference in NO x isotopic composition for different emission sources. After considering the mixing, dispersion, transport, and deposition of NO x emission from different sources, the estimation of atmospheric δ 15 N(NO x ) shows better agreement (by ∼ 3 ‰) with observations.
Reconciling the total carbon budget for boreal forest wildfire emissions using airborne observations

Abstract. Wildfire impacts on air quality and climate are expected to be exacerbated by climate change with the most pronounced impacts in the boreal biome. Despite the large geographic coverage, there is limited information on boreal forest wildfire emissions, particularly for organic compounds, which are critical inputs for air quality model predictions of downwind impacts. In this study, airborne measurements of 193 compounds from 15 instruments, including 173 non-methane organics compounds (NMOG), were used to provide the most detailed characterization, to date, of boreal forest wildfire emissions. Highly speciated measurements showed a large diversity of chemical classes highlighting the complexity of emissions. Using measurements of the total NMOG carbon (NMOGT), the ΣNMOG was found to be 50 % ± 3 % to 53 % ± 3 % of NMOGT, of which, the intermediate- and semi-volatile organic compounds (I/SVOCs) were estimated to account for 7 % to 10 %. These estimates of I/SVOC emission factors expand the volatility range of NMOG typically reported. Despite extensive speciation, a substantial portion of NMOGT remained unidentified (47 % ± 15 % to 50 % ± 15 %), with expected contributions from more highly-functionalized VOCs and I/SVOCs. The emission factors derived in this study improve wildfire chemical speciation profiles and are especially relevant for air quality modelling of boreal forest wildfires. These aircraft-derived emission estimates were further linked with those derived from satellite observations demonstrating their combined value in assessing variability in modelled emissions. These results contribute to the verification and improvement of models that are essential for reliable predictions of near-source and downwind pollution resulting from boreal forest wildfires.
Transcription Factors and miRNAs Regulate the Mechanism of Drug Resistance in Esophageal Cancer

: The presence of drug resistance can lead to differences in treatment outcomes among patients using the same drug. Therefore, identification of key biological markers associated with drug resistance can help clinicians to quickly select the appropriate drug to prolong the survival time of patients, and it is important for drug development and detailed study of the drug’s mechanism of action. Firstly, we screened different drug resistance and sensitive cell lines in esophageal cancer cell lines to find different drug resistance related genes, and annotated these calculated drug resistance related genes into the transcriptional regulatory network we constructed. The regulatory relationships within this transcriptional regulatory network were all experimentally confirmed and further filtered by real esophageal cancer data to identify drug resistance related modules and key regulators. Thirteen drug resistance-associated modules were identified, each containing 1-6 drug resistance-associated key regulators. Among them, transcription factors SP1, has-miR-21-5p and hsa-miR-1-5p play key regulatory roles in the resistance modules of various drugs, and they regulate drug resistance-associated differentially expressed genes through one-step or multi-step regulatory relationships. Key transcription factors and miRNA in the transcriptional regulatory network that regulate drug resistance-associated genes can be used as potential biomarkers to identify drug resistance to the corresponding drugs in tumor patients.
Learning from Production Test Data: From Statistical Characterization to Modeling for Anomaly Detection

Author(s): Lin, Fan | Advisor(s): Cheng, Kwang-Ting (Tim) | Abstract: Modern test programs for post-silicon testing include a large number of test measurements applied in multiple settings such as different temperatures, supply voltages, and operation modes to meet the demanding quality requirements of the products. In addition to the pass/fail results of each test item, there exist multiple types of correlations in the huge amount of production test data. Identifying and modeling the hidden correlations in the test data could help screen test escapes, which are chips that pass all test items but fail in system-level application.This thesis focuses on developing revealing features and machine learning algorithms for classifying test escapes based on production test data. In terms of feature engineering, three types of feature sets that represent different aspects of how a chip deviates from the normal population are proposed. In addition, a linear transformation that compacts the critical information for feature reduction and a collection of nonlinear transformations that reveal additional abnormalities of the test escapes are proposed to effectively expose the test escapes as outliers in certain perspectives. We have also developed frameworks exploiting state-of-the-art machine learning algorithms including a support vector machine (SVM), a cascade of AdaBoost classifiers, and an artificial neural network.
Developing deep, widespread and sustained improvements in online learning in a Faculty of Business: An analysis of the change process

The Faculty of Business at Charles Sturt University initiated the Online Course Innovation (OCI) project in 2012. This project focuses on the transition of 280 print-based subjects into quality online offerings through the mindful redesign of resources, interactions and experiences. It is led by the Associate Dean (Courses), and involves a multi-disciplinary team of educational designers, academics and divisional staff. The aim of this research project is to explore this transition to the online learning and teaching environment and identify critical organisational elements that contribute to achieving quality outcomes. Specifically, this study seeks to identify factors that support or inhibit the academic’s ability to effectively design and teach quality online subjects, and the impact of leadership on the design and teaching process. Data is currently being collected from a range of institutional stakeholders involved with the project. Initial findings from the data analysis will be shared through the conference presentation.
Additive antimicrobial effects of the active compnents of the essential oil of Thymus vulgaris - Chemotype Carvacrol

Herbal remedies are multi component mixtures by their nature as well as by pharmaceutical definition. Being a multi component mixture is not only a crucial property of herbal remedies, it also represents a pre-condition for interactions such as synergism or antagonism. Until now, only few phytomedicines are accurately described concerning the interactions of their active components. The aim of this study was to search for interactions within such a naturally given multi component mixture and to discuss the pharmaceutical and clinical impact. The thyme oil chosen for the examination belongs to the essential oils with the most pronounced antimicrobial activity. Antibiotic activity of thyme oil and single active components were tested against six different strains of microorganisms. The checkerboard assay was used to search for interactions. Time-kill-assay was used to verify the observed effects and to get information about the temporal resolution of the antimicrobial activity. The degree of the detected interactions corresponded with the demarcating FICI-measure of 0.5, which separates the additive from the over additive (synergistic) effects. Therefore, the observed effect was called a “borderline case of synergism” respectively “partial synergism”. Partial synergism was observed only in the presence of Klebsiella pneumoniae. Additive antimicrobial activity was observed for the combination of the two monosubstances carvacrol plus linalool and thymol plus linalool as well as with the combination of the two essential oils of the carvacrol and linalool chemotypes. An increase of the carvacrol-oil concentration from one to two times the MIC resulted in a considerable acceleration of the kill-rate. Thyme oil is composed of several different components that show antimicrobial activity (at least: carvacrol, thymol and linalool). The antimicrobial activity of thyme oil is partly based on additive effects, which might especially enhance the rapidity of the antimicrobial action. In addition, a mixture of several active ingredients that varies in its composition from year to year and from lot to lot as is the case with herbal remedies may be more stable concerning the antimicrobial activity than mixtures containing just a single active component.
On Security Threats and Solutions for the Future Smart Home Exploring the Viability of Intrusion Detection in a Centralized Smart Hub Master’s thesis in Computer Systems and Networks

Security in smart homes is becoming increasingly important as more and more devices are becoming available on the market and the information that they handle is of a sensitive nature. Historical attacks have shown that smart homes can contribute to attacks that could have severe consequences both for their users and for society. While much work is being done on finding security solutions for IoT and smart homes, there has yet to be a consensus on what solutions are optimal. In this thesis we present the state of research in the field and introduce a potential security solution for the smart home. The security solution, called the security supervisor, is a network-based solution located in a smart hub which detects malicious activity within a smart home network. Detection of two attacks are implemented as a proof-of-concept, namely botnet device detection and Evil-Twin attack detection. The results show that the security supervisor is able to detect the former but not the latter, but the authors argue that a similar platform would be able to contribute to increased security and mitigate many threats towards smart homes.
Recovery of clinical, cognitive and cortical activity measures following mild traumatic brain injury (mTBI): A longitudinal investigation

The mechanisms that underpin recovery following mild traumatic brain injury (mTBI) remain poorly understood. Identifying neurophysiological markers and their functional significance is necessary to develop diagnostic and prognostic indicators of recovery. The current study assessed 30 participants in the subacute phase of mTBI (10-31 days post-injury) and 28 demographically matched controls. Participants also completed 3 month (mTBI: N = 21, control: N = 25) and 6 month (mTBI: N = 15, control: N = 25) follow up sessions to track recovery. At each time point, a battery of clinical, cognitive, and neurophysiological assessments was completed. Neurophysiological measures included resting-state electroencephalography (EEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG). Outcome measures were analysed using mixed linear models (MLM). Group differences in mood, post-concussion symptoms and resting-state EEG resolved by 3 months, and recovery was maintained at 6 months. On TMS-EEG derived neurophysiological measures of cortical reactivity, group differences ameliorated at 3 months but re-emerged at 6 months, while on measures of fatigue, group differences persisted across all time points. Persistent neurophysiological changes and greater fatigue in the absence of measurable cognitive impairment may suggest the impact of mTBI on neuronal communication may leads to increased neural effort to maintain efficient function. Neurophysiological measures to track recovery may help identify both temporally optimal windows and therapeutic targets for the development of new treatments in mTBI.
Cortical activation predicts posttraumatic improvement in youth treated with TF-CBT or CCT

Background: Identifying neural activation patterns that predict youths' treatment response may aid in the development of imaging-based assessment of emotion dysregulation following trauma and foster tailored intervention. Changes in cortical hemodynamic activity measured with functional near-infrared spectroscopy (fNIRS) may provide a time and cost-effective option for such work. We examined youths' PTSD symptom change following treatment and tested if previously identified activation patterns would predict treatment response. Methods: Youth (N = 73, mean age = 12.97, SD = 3.09 years) were randomly assigned to trauma-focused cognitive behavioral therapy (TF-CBT), cue-centered therapy (CCT), or treatment as usual (TAU). Parents and youth reported on youth's PTSD symptoms at pre-intervention, post-intervention, and follow-up. Neuroimaging data (N = 31) assessed at pre-intervention were obtained while youth engaged in an emotion expression task. Treatment response slopes were calculated for youth's PTSD symptoms. Results: Overall, PTSD symptoms decreased from pre-intervention through follow-up across conditions, with some evidence of relative benefit of TF-CBT and CCT over TAU but significant individual variation in treatment response. Cortical activation patterns were correlated with PTSD symptom improvement slopes (r = 0.53). In particular, cortical responses to fearful and neutral facial stimuli in six fNIRS channels in the bilateral dlPFC were important predictors of PTSD symptom improvement. Conclusions: The use of fNIRS provides a method of monitoring and assessing cortical activation patterns in a relatively inexpensive and portable manner. Associations between functional activation and youths' PTSD symptoms improvement may be a promising avenue for understanding emotion dysregulation in clinical populations.
NATIONAL ADVISORY COMMITTEE FOR AERONAUTICS

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[PhD Thesis] Contract Specification for Compliance Checking of Business Interactions

In the business world, contracts are used to regulate business interactions between trading parties. When business transactions are conducted over an electronic channel, electronic forms of contracts are needed; and because of the additional capabilities of an electronic means, their function can be extended to include compliance checking for the interactions of the parties, and enforcement of contractual clauses when needed. A contract is assumed to be a document that stipulates a list of clauses stating rights, obligations and prohibitions, and their associated constraints, that business partners are expected to honour. Compliance checking is taken to mean checking if business operations executed by business partners match with their rights, obligations and prohibitions as stipulated in the contract. We intend enforcement as making sure that business operations match the rights, obligations, and prohibitions of the parties, possibly compensating for deviations from expected behaviour. In traditional business interactions, compliance checking and enforcement are carried out manually. With electronic business interactions, such tasks can ideally be automated. This requires a model for the process of checking contract compliance, and an electronic language for the specification of the actual contract. The first main contribution of this thesis is such a model. The EROP model (from Events, Rights, Obligations and Prohibitions), composed of an ontology and an architecture, observes the interactions between the business partners, forms an interpretation of their outcome from a neutral perspective and checks their contractual compliance by matching executed operations with their sets of rights, obligations, and prohibitions, and reacting accordingly to them. Implementations of the EROP ontology and of an experimental prototype of the architecture are also presented. The second main contribution of this thesis is the EROP language, designed to specify contractual compliance, and to regulate execution of business operations through the manipulation of the sets of rights, obligations and prohibitions of the business partners. The EROP language is rule-based and event-driven, and, in a similar fashion to contracts in natural language, contractual clauses are expressed as business rules, conditional statements associating events and conditions to lists of actions altering the rights, obligations and prohibitions of the participants. The practicality of the approach taken with the EROP language is evaluated presenting a larger, complete scenario and a v number of smaller ones taken from comparable work. Notes on the translation of the EROP language to one on a lower level of abstraction that relies on the implementation of the EROP ontology are also presented. The Appendix presents a formal grammar for the language.