Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multiagent-Based Decision-Making System for Semiconductor Wafer Fabrication With Hard Temporal Constraints

This paper presents a decision-making system for semiconductor wafer fabrication facilities, or wafer fabs, with hard interoperation temporal constraints. The decision-making system is developed based on a multiagent architecture that is composed of scheduling agents, workcell agents, machine agents, and product agents. The decision-making problem is to allocate lots into each workcell to satisfy both logical and temporal constraints. A dynamic planning-based approach is adopted for the decision-making mechanism so that the dynamic behaviors of the wafer fab such as aperiodic lot arrivals and reconfiguration can be taken into consideration. The scheduling agents compute quasi-optimal schedules through a bidding mechanism with the workcell agents. The proposed decision-making mechanism uses a concept of temporal constraint sets to obtain a feasible schedule in polynomial steps. The computational complexity of the decision-making mechanism is proven to be, where is the number of operations of a lot and is the cardinality of the temporal constraint set.
N-lactoyl-amino acids are ubiquitous metabolites that originate from CNDP2-mediated reverse proteolysis of lactate and amino acids

Significance Untargeted metabolomics is rapidly becoming a mainstream technique to discover biomarkers, to study the effects of interventions, and to discover the function of enzymes. Several thousand metabolites can be detected in a single untargeted metabolomics analysis, but state-of-the-art untargeted metabolomics studies still report up to 40% unidentified metabolites, indicating that large parts of the human metabolome are still unexplored. We identified an uncharacterized class of ubiquitous mammalian metabolites: N-lactoyl-amino acids. Using a powerful combination of proteomics and protein fractionation, we unexpectedly discovered that these metabolites are formed from lactate and amino acids by reversed action of the protease cytosolic nonspecific dipeptidase 2. Our approach to identify unknown metabolites and their biosynthesis has general applicability in the further exploration of the human metabolome. Despite technological advances in metabolomics, large parts of the human metabolome are still unexplored. In an untargeted metabolomics screen aiming to identify substrates of the orphan transporter ATP-binding cassette subfamily C member 5 (ABCC5), we identified a class of mammalian metabolites, N-lactoyl-amino acids. Using parallel protein fractionation in conjunction with shotgun proteomics on fractions containing N-lactoyl-Phe–forming activity, we unexpectedly found that a protease, cytosolic nonspecific dipeptidase 2 (CNDP2), catalyzes their formation. N-lactoyl-amino acids are ubiquitous pseudodipeptides of lactic acid and amino acids that are rapidly formed by reverse proteolysis, a process previously considered to be negligible in vivo. The plasma levels of these metabolites strongly correlate with plasma levels of lactate and amino acid, as shown by increased levels after physical exercise and in patients with phenylketonuria who suffer from elevated Phe levels. Our approach to identify unknown metabolites and their biosynthesis has general applicability in the further exploration of the human metabolome.
On the Origin of Radial Magnetic Fields in Young Supernova Remnants

We study the radio emission from young supernova remnants by means of three-dimensional numerical MHD simulations of the Rayleigh-Taylor instability in the shell of the remnant. The computation is carried out in spherical polar coordinates (r, θ, ϕ) by using a moving grid technique that allows us to resolve the shell finely. The three-dimensional result shows more turbulent (complex) structures in the mixing region than the two-dimensional result, and the instability is found to deform the reverse shock front. Stokes parameters (I, Q, and U) are computed to study the radio properties of the remnant. The total intensity map shows two distinctive regions (inner and outer shells). The inner shell appears to be complex and turbulent, exhibiting loop structures and plumes as a result of the Rayleigh-Taylor instability, while the outer shell is faint and laminar because of the shocked uniform ambient magnetic fields. The inner shell resembles the observed radio structure in the main shell of young SNRs, which is evidence that the Rayleigh-Taylor instability is an ongoing process in young SNRs. When only the peculiar components of the magnetic fields generated by the instability are considered, the polarization B-vector in the inner radio shell is preferentially radial with about 20%-50% of fractional polarization, which is higher than the observed value. The fractional polarization is lowest in the turbulent inner shell and increases outward, which is attributed to the geometric effect. The polarized intensity is found to be correlated with the total intensity. We demonstrate that the polarized intensity from the turbulent region can dominate over the polarized intensity from the shocked uniform fields if the amplified field is sufficiently strong. Therefore, we conclude that the Rayleigh-Taylor instability can explain the dominant radial magnetic field in the main shell of young supernova remnants. However, the outer faint shell shows a dominant tangential field orientation due to the shock-compression because this region is not mixed by the Rayleigh-Taylor instability, which is contrary to observations. Therefore, another mechanism is necessary to produce the radial components of the magnetic field at the outer shock, which we suggest to be a clumpy medium model.
Strength retention behavior of oriented PLLA, 96L/4D PLA, and 80L/20D,L PLA

The strength retention characteristics of oriented semicrystalline polylactides were monitored during hydrolytic degradation in vitro. The effects of the polymer type, the material’s initial inherent viscosity (iv), the sample diameter and the residual monomer content on strength retention were analyzed. The analyzed polylactides had similar, but not identical, strength retention characteristics. It was concluded that a higher degree of initial crystallinity was a major variable determining the earlier and more profound strength loss of PLLA than 96L/4D PLA and 80L/20 D,L PLA. Samples with a higher initial iv were found to have a longer strength retention time than lower iv samples. Size-dependency was observed, as the strength retention time was shorter for the smaller diameter samples. This size-dependency was caused by faster iv decay. The amount of residual monomer content had a remarkable impact on strength retention. Neither the sample diameter, initial iv or residual monomer content were found to have an effect on the iv range in which there was a rapid decline in strength properties. Therefore, it was concluded that the inherent viscosity and/or molecular weight of oriented PLLA, 96L/4D PLA and 80L/20 D,L PLA is a major variable determining the strength retention of these materials.
Asian Management Research: Status Today and Future Outlook

This paper reviews the existing management research on Asia. The findings highlight that Asian management research not only builds the understanding of Asia, but also makes a critical contribution to the broad domain of management studies. In particular, Asian-focused research allows researchers to extend and revise theories through the consideration of new contextual variables. This enables researchers to fine-tune theories by developing context-specific conditions and operationalization of key constructs, which in turn allows researchers to develop new theories and constructs which are generalizable to research in other contexts. This systematic examination will help to lay the foundation for the further expansion of the understanding of both Asia and of management research in general.
The Roles Elementary School Counselors Perform in the Education of Students with Autism Spectrum Disorders

This nation-wide study investigated elementary school counselors (ESC) self-reported: (a) professional background and training; (b) general knowledge of autism spectrum disorders (ASD); (c) attitudes towards ASD; and (d) roles performed with students identified with ASD. Also investigated was the predictive relationships between professional background, training, knowledge, and attitudes on roles (counseling, consultation, curriculum, and coordination) performed with students identified with ASD. Descriptive statistics were utilized to address professional background, training, knowledge, attitude and characteristics of ESC participants. These variables were also examined in relationship to the four role types. Multivariate analyses of variance (MANOVA) and Pearson’s correlation coefficients were used to test for significant relationships. A series of four multiple regression analyses predicting each of the total roles scores for counseling, consultation, curriculum, and coordination were also conducted. Results of the study suggest (a) ESC have limited training experiences of ASD, leading to self-education about this population of students, (b) ESC possess general knowledge about ASD, (c) overall, ESC have positive attitudes towards ASD, and (d) ESC perform all conceptualized roles in the education of students with ASD. Regression models revealed eight predictors found to influence roles: total knowledge, attitudes, geographic setting, U.S. region, years practiced, conference training, self-education, and ASD caseload. Significantly associated with performing roles across all four domains was the number of students with ASD on ESC caseload.
Domain Specific Learning for Newborn Face Recognition

Biometric recognition of newborn babies is an opportunity for the realization of several useful applications, such as improved security against swapping and abduction, accurate census, and effective drug delivery. This paper explores the possibility of using face recognition toward an affordable and friendly biometric modality for newborns. The paper proposes an autoencoder-based feature representation followed by problem specific distance metric learning via one-shot similarity with one class-online support vector machine. The largest publicly available database of newborns collected from various sources to study face recognition is introduced. Several existing face recognition approaches and commercial systems are also evaluated on a common benchmark protocol. The efficacy of the proposed algorithm is evaluated under both verification and identification settings. With multiple galleries, rank-1 identification accuracy of 78.5% and verification accuracy of 63.4% at 0.1% false accept rate are achieved.
A Confirmatory Factor-Analytic and Psychometric Examination of the Team Climate Inventory

This study examined the factor structure and psychometric properties of the 38-item Team Climate Inventory and the 14-item short version using a sample of 72 four-person teams of management undergraduates in a Canadian university. The confirmatory factor analyses supported the five-factor correlated model that questions the validity of the original four-factor model. The confirmatory factor analyses also supported the short version. Team Climate Inventory scale and subscale scores showed no significant differences when the Team Climate Inventory was administered at two times, separated by 9 weeks, during the team projects. The Team Climate Inventory shows promise as a multidimensional measure of the team climate construct in both student and employee teams. Finally, the short version provides a useful measure when administration time is limited.
Subsidising Consumer Services: Effects on Employment, Welfare and the Informal Economy

It is widely agreed that the opportunities for expansion of aggregate employment in the OECD area must be sought mainly in the private service sector. Thus,because of budgetary problems and voter resistance to higher tax rates, the scope for further expansion of public sector employment seems limited in most OECD countries. Furthermore, the primary sector will undoubtedly continue to release labour resources and, with normal growth rates, there is little prospect of a secular rise in manufacturing employment, since the increase in demand for manufactures is likely to be met through increases in labour productivity. In particular, the ongoing shifts in the international division of labour and the associated transition to the ‘knowledge-based economy’ within the OECD are likely to destroy a large number of manufacturing jobs for unskilled and lowskilled workers in the OECD area.
Rationale and development of a general population well-being measure: Psychometric status of the GP-CORE in a student sample

This paper presents the rationale, development, and psychometric status of a non-clinical self-report measure for the general population (GP) – including students – derived from the Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM) and hence termed the GP-CORE. In contrast to the CORE-OM, the GP-CORE does not comprise items denoting high-intensity of presenting problems or risk and thereby increases its acceptability in a non-clinical population. Uniquely, over half the items in the GP-CORE are positively keyed. Analyses showed the GP-CORE to have good reliability, to distinguish between clinical and non-clinical populations, and have convergent validity against the full version. Norms for student populations are presented. It is suggested that the GP-CORE has considerable utility as a means of tapping the psychological well being of students and can then interface with counselling and mental health services using the CORE-OM.
Beat the Machine: Challenging Humans to Find a Predictive Model's “Unknown Unknowns”

We present techniques for gathering data that expose errors of automatic predictive models. In certain common settings, traditional methods for evaluating predictive models tend to miss rare but important errors—most importantly, cases for which the model is confident of its prediction (but wrong). In this article, we present a system that, in a game-like setting, asks humans to identify cases that will cause the predictive model-based system to fail. Such techniques are valuable in discovering problematic cases that may not reveal themselves during the normal operation of the system and may include cases that are rare but catastrophic. We describe the design of the system, including design iterations that did not quite work. In particular, the system incentivizes humans to provide examples that are difficult for the model to handle by providing a reward proportional to the magnitude of the predictive model's error. The humans are asked to “Beat the Machine” and find cases where the automatic model (“the Machine”) is wrong. Experiments show that the humans using Beat the Machine identify more errors than do traditional techniques for discovering errors in predictive models, and, indeed, they identify many more errors where the machine is (wrongly) confident it is correct. Furthermore, those cases the humans identify seem to be not simply outliers, but coherent areas missed completely by the model. Beat the Machine identifies the “unknown unknowns. ” Beat the Machine has been deployed at an industrial scale by several companies. The main impact has been that firms are changing their perspective on and practice of evaluating predictive models. “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know. ” --Donald Rumsfeld
Effective and Efficient Optimization Methods for Kernel Based Classification Problems

Kernel methods are a popular choice in solving a number of problems in statistical machine learning. In this thesis, we propose new methods for two important kernel based classification problems: 1) learning from highly unbalanced large-scale datasets and 2) selecting a relevant subset of input features for a given kernel specification. The first problem is known as the rare class problem, which is characterized by a highly skewed or unbalanced class distribution. Unbalanced datasets can introduce significant bias in standard classification methods. In addition, due to the increase of data in recent years, large datasets with millions of observations have become commonplace. We propose an approach to address both the problem of bias and computational complexity in rare class problems by optimizing area under the receiver operating characteristic curve and by using a rare class only kernel representation, respectively. We justify the proposed approach theoretically and computationally. Theoretically, we establish an upper bound on the difference between selecting a hypothesis from a reproducing kernel Hilbert space and a hypothesis space which can be represented using a subset of kernel functions. This bound shows that for a fixed number of kernel functions, it is optimal to first include functions corresponding to rare class samples. We also discuss the connection of a subset kernel representation with the Nyström method for a general class of regularized loss minimization methods. Computationally, we illustrate that the rare class representation produces statistically equivalent test error results on highly unbalanced datasets compared to using the full kernel representation, but with significantly better time and space complexity. Finally, we extend the method to rare class ordinal ranking, and apply it to a recent public competition problem in health informatics. The second problem studied in the thesis is known as the feature selection problem in literature. Embedding feature selection in kernel classification leads to a non-convex optimization problem. We specify a primal formulation and solve the problem using a second-order trust region algorithm. To improve efficiency, we use the two-block Gauss-
Defensive Medicine Among Plastic and Aesthetic Surgeons in Israel.

BACKGROUND Defensive medicine (DM) includes medical practices that are aimed at avoiding liability rather than benefitting the patient. DM has not been well characterized among plastic surgeons. OBJECTIVES The authors examined the extents of intended and unintended DM among members of the Israeli Society of Plastic and Aesthetic Surgery (ISPAS) and identified risk factors for DM. METHODS A total of 108 ISPAS members were asked to complete a questionnaire that addressed physician attitudes toward DM and intended or unintended DM practices. RESULTS Seventy-eight surgeons (72.2% response rate) returned the questionnaire, although some questionnaires were returned incomplete. Forty respondents acknowledged practicing DM (ie, DM group), and 33 respondents did not (ie, non-DM group). There were no between-group differences in gender, years of practice, or number of previous litigations. Thirty-one percent of respondents in the DM group indicated that they avoid certain surgical procedures, compared with 6% of respondents in the non-DM group (P = .008). In private practice, 66.2% of respondents stated that they obtain written informed consent twice before surgery, and 100% request preoperative blood-coagulation testing. In contrast, 40% and 74% of respondents in public practice, respectively, acknowledged these behaviors (for consent, P = .027; for testing, P = .0059). Sixty-three percent of respondents prescribe antibiotics for more than 24 hours postoperatively, and this practice was slightly more common in the DM group (34 prescribe antibiotics vs 21 in the non-DM group; P = .079). CONCLUSIONS DM is highly integrated into the daily medical practices of plastic surgeons in Israel.
Effect on milk production of controlling muscid flies, and reducing fly-avoidance behaviour, by the use of Fenvalerate ear tags during the dry period

Summary Fenvalerate ear tags reduced fly loads on dry dairy cattle by 95% between July and September. Fly dislodging behaviour, such as ear flicks which correlated with numbers of Musca autumnalis on the face and stamps/kicks which correlated with numbers of Stomoxys calcitrans on the legs, was also significantly reduced. There was no significant difference between the tagged and untagged groups in the total time spent grazing each day. Milk yields were not statistically significantly different, but the tagged group showed a greater increase in milk yield between lactations, of 1·45 kg/cow daily in the first 12 weeks of the lactation.
New Zealand Attitudes towards Foreign-accented English

This study investigates how a variety of non-indigenous English accents are evaluated by New Zealanders against their own speech. In a series of subjective reaction tests conducted in Christchurch, eighty subjects rated fifteen recorded readings by both L1 English and ESL speakers in terms of three solidarity, three competence and three status traits, while attempting to identify each reader's age, occupation, and first language and/or regional accent of English. The analysis indicates that a New Zealand accent is most consistently identified and rated best on nearly all the traits, in sharp contrast to previous studies concluding that New Zealanders found North American English the more prestigious and attractive accent. It also shows that some ESL accents are perceived as favourably as native accents of English particularly with regard to status traits, contradicting past research suggesting that ESL accents generally were downgraded.
Safety overview of FDA-approved medications for the treatment of the motor symptoms of Parkinson’s disease

Introduction: Parkinson’s disease (PD) is among the most common of the neurodegenerative disorders. Treatment is primarily focused on correcting neurotransmitter imbalances. Several classes of medication are available for this purpose. Areas covered: A Medline search was performed to gather information about the safety of the medications approved for the treatment of the motor symptoms of PD. This was supplemented with additional articles obtained from online sources and information provided by the FDA and the manufacturers. The focus of this review is the side-effect and safety profiles of carbidopa/levodopa, dopamine agonists, selective monoamine oxidase inhibitors, catechol-o-methyltransferase inhibitors, anticholinergics and amantadine. Expert opinion: Though serious side-effects may occur, as a group, the medications used for the treatment of PD motor symptoms tend to produce side-effects that are mild to moderate in nature, and that primarily reflect the focus on dopaminergic therapies. Care plans for Parkinson’s patients should be approached based on the needs of the individual as disease presentation, lifestyle, level of disability, concurrent disease states and the presence of non-motor symptoms make each case unique. Patients and caregivers must have realistic expectations about the use of PD medications.