Miracle-dz/UniFAR-FTUs huggingface dataset:
Metagenomic profiling of gut microbial communities in both wild and artificially reared Bar‐headed goose (Anser indicus)

Bar‐headed goose (Anser indicus), a species endemic to Asia, has become one of the most popular species in recent years for rare bird breeding industries in several provinces of China. There has been no information on the gut metagenome configuration in both wild and artificially reared Bar‐headed geese, even though the importance of gut microbiome in vertebrate nutrient and energy metabolism, immune homeostasis and reproduction is widely acknowledged. In this study, metagenomic methods have been used to describe the microbial community structure and composition of functional genes associated with both wild and artificially reared Bar‐headed goose. Taxonomic analyses revealed that Firmicutes, Proteobacteria, Actinobacteria and Bacteroidetes were the four most abundant phyla in the gut of Bar‐headed geese. Bacteroidetes were significantly abundant in the artificially reared group compared to wild group. Through functional profiling, we found that artificially reared Bar‐headed geese had higher bacterial gene content related to carbohydrate transport and metabolism, energy metabolism and coenzyme transport, and metabolism. A comprehensive gene catalog of Bar‐headed geese metagenome was built, and the metabolism of carbohydrate, amino acid, nucleotide, and energy were found to be the four most abundant categories. These results create a baseline for future Bar‐headed goose microbiology research, and make an original contribution to the artificial rearing of this bird.
Frequency domain multiuser detectors for ultra-wideband short-range communications

In this paper, we propose an original multiuser detector for high rate short-range impulse radio ultra-wideband systems. The proposed receiver relies on both the introduction of the cyclic prefix at the transmitter and the use of a frequency domain multiuser detector at the receiver. Zero forcing (ZF) and minimum mean square error (MMSE) detection strategies have been investigated and compared with the classical RAKE, considering a scenario where several mobile terminals transmit to a base station in an indoor environment characterized by severe multipath propagation. The results show that the MMSE receiver achieves the best performance, irrespective of the number of active terminals.
The Acquisition of NPI any in English: a case study

The purpose of this short paper is to present some preliminary results from a study of the monolingual acquisition of the negative polarity item (NPI) any in English. These results are part of an ongoing research project that seeks to chart out the development of NPIs in English child language development. The working hypothesis is that children take a conservative approach to the acquisition of NPIs, following Snyder’s (2000) Grammatical Conservatism. According to Grammatical Conservatism, children are grammatically conservative, refraining from producing certain constructions until they have clear evidence of the grammatical options for that construction in their language. One observable result of this conservative strategy is the scarcity of “co-mission” errors (as opposed to omission errors) in child language. This paper is organized as follows. First, I present some background by introducing three claims from the semantics literature on NPI licensing. I then outline some of the predictions that such theoretical analyses, in conjunction with Grammatical Conservatism, make for child language acquisition, and describe the methodology used to test these predictions. Finally, I present some preliminary results from three children, and conclude the discussion.
A Framework to Improve the Requirements Engineering Process for Software Development Outsourcing

The number of IT projects outsourced for software development is increasing day by day as outsourcing is anticipated to provide substantial benefits and the organizations which outsource the projects have certain constraints. But unfortunately, a considerable number of the projects which are outsourced for development are failed in realizing the estimated results. The failure reasons are often entrenched in Requirements Engineering (RE) process. Despite this, appropriate research efforts are not made to evade these unexpected consequences of software development outsourcing. The objective of this research is to propose a framework in order to improve the RE process for outsourced software development projects. This framework is based upon empirical studies with pertinent national and multinational organizations and RE practices introduced by Sommerville and Sawyer. We have used a survey research method for the data collection and preliminary results confirm that majority of the RE practices recommended by Sommerville and Sawyer are significant for the RE concerned with outsourced software development. Keywords—software development outsourcing; IT outsourcing; on shoring; domestic outsourcing; near shoring; offshoring; distributed software development; global software development.
cAMP Response Element Binding Protein1 Is Essential for Activation of Steroyl Co-Enzyme A Desaturase 1 (Scd1) in Mouse Lung Type II Epithelial Cells

Cyclic AMP Response Element-Binding Protein 1 (Creb1) is a transcription factor that mediates cyclic adenosine 3′, 5′-monophosphate (cAMP) signalling in many tissues. Creb1−/− mice die at birth due to respiratory failure and previous genome-wide microarray analysis of E17.5 Creb1−/− fetal mouse lung identified important Creb1-regulated gene targets during lung development. The lipogenic enzymes stearoyl-CoA desaturase 1 (Scd1) and fatty acid synthase (Fasn) showed highly reduced gene expression in Creb1−/− lungs. We therefore hypothesized that Creb1 plays a crucial role in the transcriptional regulation of genes involved in pulmonary lipid biosynthetic pathways during lung development. In this study we confirmed that Scd1 and Fasn mRNA levels were down regulated in the E17.5 Creb1−/− mouse lung while the lipogenic-associated transcription factors SrebpF1, C/ebpα and Pparγ were increased. In vivo studies using germline (Creb1−/−) and lung epithelial-specific (Creb1EpiΔ/Δ) Creb1 knockout mice showed strongly reduced Scd1, but not Fasn gene expression and protein levels in lung epithelial cells. In vitro studies using mouse MLE-15 epithelial cells showed that forskolin-mediated activation of Creb1 increased both Scd1 gene expression and protein synthesis. Additionally, MLE15 cells transfected with a dominant-negative ACreb vector blocked forskolin-mediated stimulation of Scd1 gene expression. Lipid profiling in MLE15 cells showed that dominant-negative ACreb suppressed forskolin-induced desaturation of ether linked lipids to produce plasmalogens, as well as levels of phosphatidylethanolamine, ceramide and lysophosphatidylcholine. Taken together these results demonstrate that Creb1 is essential for the induction and maintenance of Scd1 in developing fetal mouse lung epithelial cells.
Probabilistic checkpointing

Many optimization schemes have been proposed to reduce the overhead of checkpointing. Incremental checkpointing based on memory page protection has been one of the successful schemes used to reduce the overhead and to improve the performance of checkpointing. In this paper, we propose two checkpointing schemes, called "block encoding" and "combined block encoding", which further reduce the checkpointing overhead. The smallest unit of checkpoint data in our scheme is a block, which is smaller than a page-this reduces the amount of checkpoint data required when compared with page-based incremental checkpointing. One drawback of the proposed schemes is the possibility of aliasing in encoded words. In this paper, however, we show that the aliasing probability is near zero when an 8-byte encoded word is used. The performance of the proposed schemes is analyzed and measured using experiments. First, we construct an analytic model that predicts the checkpointing overhead. By using this model, we can estimate the block size that produces the best performance for a given target program. Next, the proposed schemes are implemented on libckpt, a general-purpose checkpointing library for Unit based system which was developed at the University of Tennessee. According to our experimental results, the proposed schemes reduce the overhead by 11.7% in the best case and increase the overhead by 0.5% in the worst case in comparison with page-based incremental checkpointing. In most cases, the combined block encoding scheme shows an improvement over both block encoding and page-based incremental checkpointing.
Research on Psychological Health Education of College Students under Positive Psychology Model

The psychological health education of college students is important in higher education. It is an important task of colleges to train college students’ healthy psychology and make them form good psychological traits. The development and flourish of positive psychology provides theoretical basis for psychological health education of college students. The construction of course system in psychological health education of college students under the perspective of positive psychology should highlight positive concept. Course framework runs through the training of college students’ positive quality, the experience of college students’ positive psychology and the creation of positive course environment. Required course combines with optional course and classroom teaching combines with practical experience. Positive psychological consultation should run through psychological health education. Keywords—positive psychology; college students; psychological health education; course system
Support Super-Vector Machines in Automatic Speech Emotion Recognition

In this paper, we use super-vectors in support vector machines for automatic speech emotion recognition. In our implementation, an utterance is converted to a super-vector formed by the mean vectors of a Gaussian mixture model adapted from a universal background model. The proposed method is evaluated on FAU-Aibo database which is well-known to be used in INTERSPEECH 2009 Emotion Challenge. In the case of HMM-based dynamic modeling classiﬁer, we achieve an unweighted average (UA) recall rate of 40.0%, over a baseline of 35.5%, by using the delta features and increasing the number of mixture components. In the case of SVM-based static modeling classiﬁer, we achieve an unweighted average (UA) recall rate of 38.9%, over a baseline of 38.2%, by using the proposed super-vectors.
FEEDING PROBLEMS OF CHILDREN WITH AUTISM

Children with autism generally have feeding problems especially in their mealtime behavior. The purpose of this study is to identify the feeding problems of children with autism. To be able to determine the problems of children with autism BAMBI (Brief Autism Mealtime Behavior Inventory), (Demand, Johnson and Fokles, 2015; Meral and Fidan, 2014) is used. Twenty-one families of children with autism were participated in the study. The results of our study showed that according to the parental reports children with autism have feeding problems in terms of mealtime behavioral problems such as crying and yelling throughout meals. Other results of the study and recommendations are discussed in discussion. Keywords Feeding behaviors Feeding behaviors, autism, eating problems, family’s perception
Fundamental Concepts of Object Oriented Databases

It is claimed that object oriented databases (OODBs) overcome many of the limitations of the relational model. However, the formal foundation of OODB concepts is still an open problem. Even worse, for relational databases a commonly accepted datamodel existed very early on whereas for OODBs the uniication of concepts is missing. The work reported in this paper contains the results of our rst investigations on a formally founded object oriented datamodel (OODM) and is intended to contribute to the development of a uniform mathematical theory of OODBs. A clear distinction between objects and values turns out to be essential in the OODM. Types and Classes are used to structure values and objects repectively. Then the problem of unique object identiication occurs. We show that this problem can be be solved for classes with extents that are completely representable by values. Such classes are called value-representable. Another advantage of the relational approach is the existence of structurally determined generic update operations. We show that this property can be carried over to object-oriented datamodels if classes are value-representable. Moreover, in this case database consistency with respect to implicitly speciied referential and inclusion constraints will be automatically preserved. This result can be generalized with respect to distinguished classes of explicitly stated static constraints. Given some arbitrary method and some integrity constraint there exists a greatest consistent specialization (GCS) that behaves nice in that it is compatible with the conjunction of constraints. We present an algorithm for the GCS construction of user-deened methods and describe the GCSs of generic update operations that are required herein.
Feasibility Study of Applying Descriptive ILP to Large Geographic Databases

This paper discusses a case study in which the aim is to discover regularities and anomalies in large databases containing geographic data, to improve and maintain the overall data quality. The application of Inductive Logic Programming (ILP) and descriptive ILP in particular to this case is discussed and motivated. In an experiment on real-world data, a classical descriptive ILP algorithm (WARMR) is applied to the hamlet of Beggen, Luxemburg, to mine for rules describing regularities. The algorithm adopts the setting of learning from interpretations. In a next stage, the violating interpretations of the rules could be traced to identify candidate anomalies. A rule export module was set up to feed the results to a rule checking engine for further validation of this experiment. Finally, the results are discussed, the feasibilities of the system used in the case study are assessed and possibilities w.r. t. a larger scale application of the experiment are discussed.
Improving the performance of an HMM-based fingerprint recognition system

In this paper, general structure of a HMM-based fingerprint system is described. Since the HMMs have one-dimensional nature, in the presented system, Hilbert space-filling curves have been employed to map the fingerprint sample to a one-dimensional vector. The system have been simulated and tested on FVC2002-DB1 database with the EER rate of 4.2%. However, by investigating the samples that the system failed to recognize, it was concluded that by adding a sub-stage to the preprocessing, a portion of them can be detected. By applying this sub-stage, the EER rate were lowered to 2.85% at the cost of rejecting some samples.
An Improved Single Image Haze Removal Algorithm Based on Dark Channel Prior and Histogram Specification

We introduce an improved single image haze removal algorithm, which combines dark channel prior (DCP) and histogram specification. First, the dark channel prior knowledge proposed by Kaiming He is analyzed and a conclusion is drawn that the haze removal image based on dark channel prior will have a tendency to dim and indistinct in some specific situations. Especially, when cleaning the haze in the image with large background area and low contrast, DCP result appears obvious anamorphose. Next, in order to improve the dehazing result of this kind of image, we propose an approach to change the contrast and intensity of haze removal image after DCP method by rebuilding the histogram of the image. Then, a modified approach is applied to fit general haze image. We experiment our method with a variety of outdoor haze images. The effectiveness of our method is demonstrated in comparison with DCP result when the input image contains low contrast scene and large background area, such as thick fog or dark surroundings in dusk. Our job makes up the deficiency of the dark channel model for this kind of image and enhance the contrast of the scene. Furthermore, the experimental results show that the dehazing effect on general haze image appears more close to real scene than dark channel model.
Cardiac Troponin Elevation, Cardiovascular Morbidity, and Outcome After Subarachnoid Hemorrhage

Background— Cardiac troponin I (cTI) release occurs frequently after subarachnoid hemorrhage (SAH) and has been associated with a neurogenic form of myocardial injury. The prognostic significance and clinical impact of these elevations remain poorly defined. Methods and Results— We studied 253 SAH patients who underwent serial cTI measurements for clinical or ECG signs of potential cardiac injury. These patients were drawn from an inception cohort of 441 subjects enrolled in the Columbia University SAH Outcomes Project between November 1998 and August 2002. Peak cTI levels were divided into quartiles or classified as undetectable. Adverse in-hospital events were prospectively recorded, and outcome at 3 months was assessed with the modified Rankin Scale. Admission predictors of cTI elevation included poor clinical grade, intraventricular hemorrhage, loss of consciousness at ictus, global cerebral edema, and a composite score of physiological derangement (all P≤0.01). Peak cTI level was associated with an increased risk of echocardiographic left ventricular dysfunction (odds ratio [OR], 1.3 per quintile; 95% CI, 1.0 to 1.7; P=0.03), pulmonary edema (OR, 2.1 per quintile; 95% CI, 1.6 to 2.7; P<0.001), hypotension requiring pressors (OR, 1.9 per quintile; 95% CI, 1.5 to 2.3; P<0.001), and delayed cerebral ischemia from vasospasm (OR, 1.3 per quintile; 95% CI, 1.07 to 1.7; P=0.01). Peak cTI levels were predictive of death or severe disability at discharge after controlling for age, clinical grade, and aneurysm size (adjusted OR, 1.4 per quintile; 95% CI, 1.1 to 1.9; P=0.02), but this association was no longer significant at 3 months. Conclusions— cTI elevation after SAH is associated with an increased risk of cardiopulmonary complications, delayed cerebral ischemia, and death or poor functional outcome at discharge.
Common Principles Underlying Models of Sequential Effects

Human behavior fluctuates—an appropriate action at one moment in time may not be appropriate at the next moment. The study of sequential effects seeks to explicate the dependence these fluctuations have on the exact sequence of recent events. For example, in a simple choice experiment, how will an individual’s speed of response vary as a function of the sequence of previous stimuli? Though often studied in controlled experiments, sequential effects are not just a curiosity of the laboratory and in fact arise in many real world settings. They have been identified in automobile driving, legal reasoning, jury evidence interpretation, clinical assessments, financial decisions, and basketball players’ shot choice. Certainly it seems that if you look for sequential effects in any domain, you find them. Because of this widespread presence, there are numerous theoretical and computational accounts. Though some synthesis has been offered within particular domains, the field is lacking an organization that spans the entire literature. The goal of this work is to bring clarity to this scattered field by presenting a few computational-level principles that characterize most models of sequential effects. By focusing on a computational level of analysis, we aim to uncover the primary purpose of sequential effects in cognition (i.e., why they exist instead of how they are produced). We present three main computational explanations for sequential effects: (1) they reflect the attempt to predict future events in a dynamic environment; (2) they result from the need to continually adjust the sensitivity of perceptual and decisional processing; and (3) they reflect the generalization of knowledge from the recent past to new experiences. In addition, we identify two general classes that encompass many mechanistic models which lack a clear computational perspective: neural inertia models and incremental learning models. In the process of categorizing these models, we highlight a strong connection between sequential effects and adaptation across all explanations. Furthermore, we suggest the potential value to be gained in the latter two computational explanations by rooting models in normative principles of adaptation as has been done with some models classified in the first computational explanation. We believe that our theoretical perspective will provide important guidance in interpreting and understanding sequential effects, developing new models, and exploring the role of adaptation across all domains of cognition.
Surgery Sequencing and Scheduling in Multiple ORs with PACU Constraints

This paper addresses a surgery sequencing and scheduling problem in multiple operating rooms (OR) constrained by shared downstream post-anesthesia care unit (PACU) capacity. We propose a two-stage solution method to minimize the expected value of a cost function that includes patient waiting time, surgeon idle time, OR blocking time, OR overtime and PACU overtime. In the first stage, we propose a time-indexed mixed-integer programming model with a surrogate objective that is easier to solve than the impractically difficult original problem. The Lagrangian relaxation of the surrogate model can be decomposed by patients into network-structured subproblems which can be efficiently solved by dynamic programming. The first-stage model is thus solved by the subgradient method to determine the surgery sequence in each OR. Given the surgery sequence, scheduled start times of surgeries are determined in the second stage using a sample-gradient-based algorithm. Our solution method is shown to outperform benchmark heuristics in numerical experiments. We demonstrate that possible cost savings are significant in hospitals with constrained PACU capacity. We also provide some insights into the PACU utilization after considering PACU constraints.