Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey on Run-to-Run Control Algorithms in High-Mix Semiconductor Manufacturing Processes

Recently, to ensure product quality in high-mix semiconductor manufacturing processes, run-to-run (R2R) control algorithms have received increasing attention. Difficult challenges have been met with new and advanced approaches that address the real-world operating conditions exhibited in high-mix production environments (e.g., different products with different frequencies and high numbers of threads). In this paper, various proposed controllers for high-mix semiconductor manufacturing processes are surveyed from an application and theoretical point of view. Remaining challenges and directions for future work are also summarized with the intent of drawing attention to these problems in the systems and process control communities.
Malaria Policy: Alternative Prevention and Eradication Strategies in a Dynamic Model

As many as one million deaths annually are attributed to malaria, a parasitic disease transmitted by mosquitoes. The heaviest burdens of malaria are found in poor tropical countries, where medical systems are often ill-equipped to treat the sick, and where public health programs may lack the resources for effective and sustainable campaigns of prevention and control. In recent years, the international community has, perhaps belatedly, increased the funding for malaria programs. Some donors have invested heavily also in scientific research on malaria, such as vaccine and drug development. Others have advocated subsidies for inexpensive and available controls, such as distributing bed nets, or spraying insecticides and larvicides that would target mosquito populations. Relatively little quantitative analysis has guided these investments, however. This paper reports on an effort to compare alternative control and prevention strategies, using a dynamic general equilibrium model with heterogeneous agents that incorporates some essential economic and epidemiological features. We compare steady-state equilibria produced by alternative treatment and control methods, and we use these to calculate the costs and benefits of different approaches to malaria control.
Solidarity, synchronization and the emergence of cooperation

One of the big questions about social life is how people manage to cooperate for public goods. Current answers rely on individuals' relations and reputations, but have difficulty explaining the beginning, when relations are sparse and reputations largely unknown. Moreover, actual situations are often characterized by uncertainties about the value and timing of the public good, such that established relations and reputations may not suffice. As a run up to cooperation, people can increase their solidarity and establish (or relay their) social ties through interaction rituals, which is done by groups ranging from hunter-gatherers to social movements. Kuramoto's synchronization model is used to show that the network thereby formed should have sufficient algebraic connectivity to compensate for the differences between individuals' commitments and psychological states. At a critical level of solidarity, a majority's commitments and psychological states, respectively, synchronize in a phase transition, which yields a boost of motivation for a burst of collective action.
Estimation of cepstral coefficients for robust speech recognition

Acknowledgement Acknowledgement I would first like to thank my advisor, Dr. Richard Povinelli, whose advice and support have been critical in the development of this work. My thanks also go to Dr. Michael Johnson, whose expertise has been very valuable to me. I also thank my committee members, Drs. George Corliss, Craig Struble, and Edwin Yaz for all their assistance. Thanks go to Dr. Mohamed Mneimneh for all his help during his time as my labmate in the Knowledge and Information Discovery Lab. I am grateful to my family and all my friends for their moral support and friendship during my time as a student at Marquette University. Abstract Abstract This dissertation introduces a new approach to estimation of the features used in an automatic speech recognition system operating in noisy environments, namely mel-frequency cepstral coefficients. A major challenge in the development of an estimator for these features is the nonlinear interaction between a speech signal and the corrupting ambient noise. Previous estimation methods have attempted to deal with this issue with the use of a low order Taylor series expansion, which results in a rough approximation of the true distortion interaction between the speech and noise signal components, and the estimators must typically be iterative, as it is the speech features themselves that are used as expansion points. The new estimation approach, named the additive cepstral distortion model minimum mean-square error estimator, uses a novel distortion model to avoid the necessity of a Taylor series expansion, allowing for a direct solution. Like many previous approaches, the estimator introduced in this dissertation uses a prior distribution model of the speech features. In previous work, this distribution is limited in specificity, as a single global model is trained over an entire set of speech data. The estimation approach introduced in this work extends this method to incorporate contextual information into the prior model, leading to a more specific distribution and subsequently better estimates of the features.
Adaptive threshold based segmentation for video object tracking

An automatic segmentation and color feature based video object tracking algorithm has been proposed. The proposed algorithm automatically segments the moving object in video by creating a multiplicative mask, which contains reduced number of shadowed pixels, noisy pixels and false pixels. The segmented object can be tracked by extracting its features such as color. Once the object to be tracked is segmented and its feature extracted, the position of the moving object is predicted using Kalman filter which is an optimal recursive estimator. Kalman Filter efficiently tracks the moving object in real time applications. The proposed algorithm accurately segments the moving object by reducing the effect of the shadowing and/or noisy pixels and successfully tracks the moving object.
Autonomous learning of object models on mobile robots using visual cues

Mobile robots are increasingly being used in real-world application domains such as disaster rescue, surveillance, health care and navigation. These application domains are typically characterized by partial observability, non-deterministic action outcomes and unforeseen changes. A major challenge to the widespread deployment of robots in such domains is the ability to learn models of domain objects automatically and efficiently, and to adapt the learned models in response to changes. Although sophisticated algorithms have been developed for modeling and recognizing objects using different visual cues, existing algorithms are predominantly computationally expensive, and require considerable prior knowledge or many labeled training samples of desired objects to learn object models. Enabling robots to learn object models and recognize objects with minimal human supervision thus continues to be an open problem. The above-mentioned challenges are offset by some observations. First, many objects have distinctive characteristics, locations, and motion patterns, although these parameters may not be known in advance and may change over time. Second, images encode information about objects in the form of many different visual cues. Third, any specific task performed by robots typically requires accurate models of only a small number of domain objects. This dissertation describes an algorithm that exploits these observations to achieve the following objectives: 1. Investigate learning of object models from a small (3− 8) number of images. Robots consider objects that move to be interesting, efficiently identifying corresponding image regions using motion cues. 2. Exploit complementary strengths of appearance-based and contextual visual cues to
BeesyBees — agent-based, adaptive & learning workflow execution module for BeesyCluster

We present a design and implementation of an adaptive, learning module for workflow execution in the BeesyCluster environment. BeesyCluster allows to model a workflow as an acyclic directed graph where vertices denote tasks to be executed while edges determine dependencies between tasks. In this paper, we present cooperative workflow execution by a group of agents, capable of gathering, storing and utilising knowledge about availability of services used. Furthermore, this knowledge is used to choose most reliable services dynamically during the workflow execution. Besides, the execution module is able to detect service failures and compensate using alternative, functionally equivalent services. Based on concrete, real-life workflow examples executed in BeesyCluster we show, that knowledge about existing services acquired while executing previous workflows improves the execution reliability of subsequent workflows.
"Little by Little:" Classroom Practices That Can Silence Latino Kindergartners

This study explored the role of school in promoting positive bicultural and bilingual identities through the encouragement of Spanish use in 7 Latino children in 1 kindergarten classroom in North Carolina. Using a case study approach, researchers collected data through participant observations and interviews to examine the classroom practices of teachers with self-reported positive attitudes toward the use of Spanish by their students in this school. The results from the data revealed that there was a disconnect between what teachers espoused about heritage-language retention and the actual practices used to encourage native language use. While teachers openly stated in interviews that it was important for the Latino students to continue to speak and use their native language, the culture of the classroom and common practices used in this classroom failed to provide these Latino students with meaningful interactions with one another or their native Englishspeaking peers. In fact, mandates for silence and the separation of students in the kindergarten classroom resulted in very little use of language, whether in Spanish or in English. Implications for educators are discussed.
SCUBA: A Common - user submillimetre camera operating on the James Clerk Maxwell telescope

SCUBA, the Submillimetre Common-User Bolometer Array, built by the Royal Observatory Edinburgh for the James Clerk Maxwell Telescope, is the most versatile and powerful of a new generation of submillimetre cameras. It combines a sensitive dual-waveband imaging array with a three-band photometer, and is sky-background-limited by the emission from the Mauna Kea atmosphere at all observing wavelengths from 350 μμto 2 mm. The increased sensitivity and array size mean that SCUBA maps close to 10 000 times faster than its single-pixel predecessor (UKT14). SCUBA is a facility instrument, open to the world community of users, and is provided with a high level of user support. We give an overview of the instrument, describe the observing modes, user interface and performance figures on the telescope, and present a sample of the exciting new results that have revolutionized submillimetre astronomy.
Team Up with Eco-aware Everyday Things to Green Your Workplace!

The lack of energy consumption awareness in public spaces is a fact. There, people do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. Thus, there is practically a non-existent perception of energy waste, and hence, there is low motivation to reduce it. To tackle this problem we transform everyday shared electrical appliances which are placed in common spaces into collaborative eco-aware everyday things. These eco-appliances make people aware that they are not alone to save energy, but the everyday things can team up with them to achieve this task. Qualitative and quantitative results were gathered in three case studies performed with shared coffee machines at workplace. The objective was to assess the effectiveness of the proposed eco-aware design in terms of energy saving and the degree of affiliation between workers and the smart appliance to create a green-team relationship.
IMPROVED PVSOLA TIME-STRETCHING AND PITCH-SHIFTING FOR POLYPHONIC AUDIO

An advanced phase vocoder technique for high quality audio pitch shifting and time stretching is described. Its main concept is based on the PVSOLA time stretching algorithm which is already known to give good results on monophonic speech. Some enhancements are proposed to add the ability to process polyphonic material at equal quality by distinguishing between sinusoidal and noisy frequency components. Furthermore, the latency is reduced to get closer to a real time implementation. The new algorithm is embedded into a flexible pitch shifting and time stretching framework by adding transient detection and resampling. A subjective listening test is used to evaluate the new algorithm and to verify the improvements.
Measurement Scale of Residents' Perception,Participant Capacity and Willingness on the Poverty Elimination Effect by Tourism

Based on the research achievements on the perception of poverty elimination effect by tourism,this paper built a measurement scale of residents' perception,participant capacity and willingness on the poverty elimination effect through in-depth interviews,expert opinion,and empirical research as well as the use of exploratory factor analysis and confirmatory factor analysis.The measurement scale included a total of 33 items in seven dimensions.The results showed that the scale had high reliability and could be used in evaluating and measuring the residents'perception of poverty elimination effect and their participant behavior.
Effects of group performance feedback and goal-setting in an organisational energy intervention

End-user energy demand (EUED) in the workplace is affected by a complex interaction between behavioural, social, technological, regulatory and organisational factors. Designing technology-led interventions to encourage pro-environmental behaviour that acknowledge and support this complexity is a significant challenge. This paper discusses the design and evaluation of an EUED intervention implemented in the corporate infrastructure of a UK university administration department. Two intervention types, group feedback and group goal-setting were implemented. 16 participants were recruited and engaged with a four stage study (baseline, group feedback, group goal setting, and baseline) for a duration of 4 months. This study design allowed us to track clearly any changes in mid-term energy usage behaviour during and beyond intervention. Findings suggest that, surprisingly, participant energy consumption increased during the intervention period compared to baseline conditions. These results demonstrate that simple group-based behaviour change methods can be counter-productive in the workplace, illustrating the complex and unpredictable nature of intervention in this design space.
Robust segmentation of relevant regions in low depth of field images

Low depth of field (DOF) is an important technique to emphasize the object of interest (OOI) within an image. When viewing a low depth of field image, the viewer implicitly segments the image into region of interest and non regions of interest which has major impact on the perception of the image. Thus, robust algorithms for the detection of the OOI in low DOF images provide valuable information for subsequent image processing and image retrieval. In this paper we propose a robust and parameterless algorithm for the fully automatic segmentation of low depth of field images. We compare our method with three similar methods and show the superior robustness even though our algorithm does not require any parameters to be set by hand. The experiments are conducted on a real world data set with high and low depth of field images.
Evaluation of a novel two-step server selection metric

Choosing the best-performing server for a particular client from a group of replicated proxies is a difficult task. We offer a novel, two-step technique for server selection that chooses a small subset of five servers, and isolates testing to that subset for ten days. We present an empirical evaluation of both our method and previously proposed metrics based on traces to 193 commercial proxies. We show that our technique performs better than any of the other metrics we studied -often one to two seconds better for a one-megabyte file-while requiring considerably less work over time. Metrics such as round-trip time and tests using small files usually select servers that are two to three times worse than the best server. Network-layer metrics such as minimizing router and autonomous system count poorly predict which server provides the best performance. These metrics often select servers with transfer times four to six times that of the best-performing server.
6p22.3 amplification as a biomarker and potential therapeutic target of advanced stage bladder cancer

Genetic and epigenetic alterations have been identified as to contribute directly or indirectly to the generation of transitional cell carcinoma of the urinary bladder (TCC-UB). In a comparative fashion much less is known about copy number alterations in TCC-UB, but it appears that amplification of chromosome 6p22 is one of the most frequent changes. Using fluorescence in situ hybridization (FISH) analyses, we evaluated chromosomal 6p22 amplification in a large cohort of bladder cancer patients with complete surgical staging and outcome data. We have also used shRNA knockdown candidate oncogenes in the cell based study. We found that amplification of chromosome 6p22.3 is significantly associated with the muscle-invasive transitional cell carcinoma of the urinary bladder (TCC-UB) (22%) in contrast to superficial TCC-UB (9%) (p=7.2-04). The rate of 6p22.3 amplification in pN>1 patients (32%) is more than twice that in pN0 (16%) patients (p=0.05). Interestingly, we found that 6p22.3 amplification is as twice as high (p=0.0201) in African American (AA) than European American (EA) TCC-UB patients. Moreover, we showed that the expression of some candidate genes (E2F3, CDKAL1 and Sox4) in the 6p22.3 region is highly correlated with the chromosomal amplification. In particular, knockdown of E2F3 inhibits cell proliferation in a 6p22.3-dependent manner, whereas knockdown of CDKAL1 and Sox4 has no effect on cell proliferation. Using gene expression profiling, we further identified some common as well as distinctive subset targets of the E2F3 family members. In summary, our data indicate that E2F3 is a key regulator of cell proliferation in a subset of bladder cancer and the 6p22.3 amplicon is a biomarker of aggressive phenotype in this tumor type.
Dense Gas in Ultra-Luminous Far-Infrared Galaxies

Aims. Molecules that trace the high-density regions of the interstellar medium have been observed in (Ultra-)Luminous Infrared Galaxies, in order to initiate multiple-molecule multiple-transition studies to evaluate the physical and chemical environment of the nuclear medium and its response to the ongoing nuclear activity. Methods. The HCN(1−0), HNC(1−0), HCO(1−0), CN(1−0) and CN(2−1), CO(2−1), and CS(3−2) transitions were observed in sources covering three decades of infrared luminosity including sources with known OH megamaser activity. The data for the molecules that trace the high-density regions have been augmented with data available in the literature. Results. The integrated emissions of high-density tracer molecules show a strong relation to the infrared luminosity. Ratios of integrated line luminosities have been used for a first order diagnosis of the integrated molecular environment of the evolving nuclear starbursts. Diagnostic diagrams display significant differentiation among the sources that relate to initial conditions and the radiative excitation environment. Initial differentiation has been introduced between the FUV radiation field in photon-dominated-regions and the X-ray field in X-ray-dominated-regions. The galaxies displaying OH megamaser activity have line ratios typical of PDRs.
Smoothing Neural Network for Constrained Non-Lipschitz Optimization With Applications

In this paper, a smoothing neural network (SNN) is proposed for a class of constrained non-Lipschitz optimization problems, where the objective function is the sum of a nonsmooth, nonconvex function, and a non-Lipschitz function, and the feasible set is a closed convex subset of . Using the smoothing approximate techniques, the proposed neural network is modeled by a differential equation, which can be implemented easily. Under the level bounded condition on the objective function in the feasible set, we prove the global existence and uniform boundedness of the solutions of the SNN with any initial point in the feasible set. The uniqueness of the solution of the SNN is provided under the Lipschitz property of smoothing functions. We show that any accumulation point of the solutions of the SNN is a stationary point of the optimization problem. Numerical results including image restoration, blind source separation, variable selection, and minimizing condition number are presented to illustrate the theoretical results and show the efficiency of the SNN. Comparisons with some existing algorithms show the advantages of the SNN.
Probabilistic latent variable models for distinguishing between cause and effect

We propose a novel method for inferring whether X causes Y or vice versa from joint observations of X and Y. The basic idea is to model the observed data using probabilistic latent variable models, which incorporate the effects of unobserved noise. To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive). An important novel aspect of our work is that we do not restrict the model class, but instead put general non-parametric priors on this function and on the distribution of the cause. The causal direction can then be inferred by using standard Bayesian model selection. We evaluate our approach on synthetic data and real-world data and report encouraging results.
Effects of Amphetamine on Radial Arm Maze Performance in the SHR Model of ADHD versus Age/strain Matched Controls

Stimulants have been shown to have a variety of effects on different measures of learning and memory. In general low doses of stimulants like nicotine and caffeine enhance memory acquisition and recall, while high doses can significantly impair performance. Amphetamine, in the form of Adderall, is widely prescribed to improve attention and reduce hyperactivity, which promotes learning in children diagnosed with attention deficit disorder (ADHD). The radial arm maze (RAM) is a test of spatial learning that allows for the tracking of short and long-term memory errors. In the current study, we employed the RAM to examine the effects of amphetamine on spatial learning performance in spontaneously hypertensive rat (SHR), an accepted model of ADHD, animals and age/strain matched controls. An immunohisotchemical analysis of cFos expression in the hippocampus was also utilized to evaluate the effect of amphetamine of neural activation in both groups of animals. Amphetamine (1.0 mg/kg, ip) administered during training significantly increased maze completion time and increased shortterm and long-term error rates. The results of this study suggest that chronic amphetamine treatments have hindering effects on learning and memory in control rats. Unlike their age/strain matched controls, amphetamine did not enhance or inhibit radial arm maze performance of SHR animals. This suggests that the neurophysiological mechanisms mediating learning and memory may be different in SHR animals than in humans with ADHD. Therefore, additional studies are needed to evaluate the validity of the SHR model of ADHD. Amphetamine and ADHD 4 Table of