Miracle-dz/UniFAR-FTUs huggingface dataset:
Out-of-Sample Extensions for Non-Parametric Kernel Methods

Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
Efficacy of gallium maltolate against Lawsonia intracellularis infection in a rabbit model.

Antimicrobial efficacy against Lawsonia intracellularis is difficult to evaluate in vitro, thus, the effects of gallium maltolate's (GaM) were investigated in a rabbit model for equine proliferative enteropathy (EPE). Juvenile (5-6-week-old) does were infected with 3.0 × 10(8) L. intracellularis/rabbit and allocated into three groups (n = 8). One week postinfection, one group was treated with GaM, 50 mg/kg; one, with doxycycline, 5 mg/kg; and one with a sham-treatment (control). Feces and blood were collected daily and weekly, respectively, to verify presence of L. intracellularis fecal shedding using qPCR, and seroconversion using immunoperoxidase monolayer assay. Rabbits were sacrificed after 1 week of treatment to collect intestinal tissues focusing on EPE-affected sections. Intestinal lesions were confirmed via immunohistochemistry. No difference was noted between treatments regarding EPE-lesions in jejunum (P = 0.51), ileum (P = 0.74), and cecum (P = 0.35), or in L. intracellularis fecal shedding (P = 0.64). GaM and doxycycline appear to have similar efficacy against EPE in infected rabbits.
Comprehensive Analysis of Prevalence, Epidemiologic Characteristics, and Clinical Characteristics of Monoinfection and Coinfection in Diarrheal Diseases in Children in Tanzania

Abstract The role of interactions between intestinal pathogens in diarrheal disease is uncertain. From August 2010 to July 2011, we collected stool samples from 723 children admitted with diarrhea (cases) to 3 major hospitals in Dar es Salaam, Tanzania, and from 564 nondiarrheic children (controls). We analyzed the samples for 17 pathogens and assessed interactions between coinfections in additive and multiplicative models. At least one pathogen was detected in 86.9% of the cases and 62.8%, of the controls. Prevalence of coinfections was 58.1% in cases and 40.4% in controls. Rotavirus, norovirus genogroup II, Cryptosporidium, and Shigella species/enteroinvasive Escherichia coli were significantly associated with diarrhea both as monoinfections and as coinfections. In the multiplicative interaction model, we found 2 significant positive interactions: rotavirus + Giardia (odds ratio (OR) = 23.91, 95% confidence interval (CI): 1.21, 470.14) and norovirus GII + enteroaggregative E. coli (OR = 3.06, 95% CI: 1.17, 7.98). One significant negative interaction was found between norovirus GII + typical enteropathogenic E. coli (OR = 0.09, 95% CI: 0.01, 0.95). In multivariate analysis, risk factors for death were presence of blood in stool and severe dehydration. In conclusion, coinfections are frequent, and the pathogenicity of each organism appears to be enhanced by some coinfections and weakened by others. Severity of diarrhea was not affected by coinfections.
Effect of inclusion level and adaptation duration on digestible energy and nutrient digestibility in palm kernel meal fed to growing-finishing pigs

Objective An experiment was conducted to evaluate effects of inclusion level of palm kernel meal (PKM) and adaptation duration on the digestible energy (DE) and apparent total tract digestibility (ATTD) of chemical constituents in diets fed to growing-finishing pigs. Methods Thirty crossbred barrows (Duroc×Landrace×Large White) with an average initial body weight of 85.0±2.1 kg were fed 5 diets in a completely randomized design. The diets included a corn-soybean meal basal diet and 4 additional diets in which corn and soybean meal were partly replaced by 10%, 20%, 30%, or 40% PKM. After 7 d of adaptation to the experimental diets, feces were collected from d 8 to 12, d 15 to 19, d 22 to 26, and d 29 to 33, respectively. Results The DE and ATTD of gross energy (GE), dry matter (DM), ash, organic matter (OM), neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude protein (CP) in diets decreased linearly as the dietary PKM increased within each adaptation duration (p< 0.01). Diet containing 19.5% PKM had less DE value and ATTD of all detected items compared with other diets when fed to pigs for 14 days (p<0.05). The ATTD of CP in PKM calculated by 19.5% and 39.0% linearly increased as adaptation duration prolonged from 7 to 28 days (p<0 .01). Conclusion Inclusion level of PKM and adaptation duration had an interactive effect on DE and the ATTD of GE, DM, OM, and CP (p<0.01 or 0.05) but ash, NDF, and ADF in diet (p> 0.05). Considering a stable determination, 21 days of adaptation to a diet containing 19.5% PKM is needed in pigs and a longer adaptation time is recommended as dietary PKM increases.
Effects of incorporation of cassava flour on characteristics of corn grit-rice grit-chickpea flour blend expanded extrudates

A rice grit-corn grit-chickpea flour blend was used as a model feed system for extrusion. Response surface methodology was used to study the effects of level of incorporation of cassava flour in feed composition (5-25%), feed moisture (12-16%), screw speed (1000-1400 rpm) and barrel temperature (80-120°C) by using a single screw extruder. Among the independent variables, feed moisture was found to be the most influential (p<0.05) to bulk density (BD), water solubility index (WSI) and water absorption index (WAI). Cassava flour level affected lateral expansion (LE) most significantly, however, temperature affected all the dependent variables equally at p<0.05, while screw speed altered significantly BD, WSI and WAI only. Key words: Single screw extrusion, response surface methodology, rice and corn grit, chickpea flour, cassava flour.
Determination of the chemical and functional properties of yam bean (Pachyrhizus erosus (L.) Urban) flour for food systems

Abstract Many plant species that are suitable for food across the world are neglected and underutilized. In order to increase their diversified food uses and thus help enhance food and nutrition security, we studied the chemical and functional properties of Pachyrhizus erosus (yam bean), which is a neglected and underutilized legume species. The chemical properties of flour produced from the yam bean include 5.8% moisture content, 5.7% crude fat, 6.2% crude fiber, and 85% available carbohydrate, indicating appropriate shelf‐stable flour, low fat, and abundant energy. The results also showed a reducing sugar content of 2.0% and 21.0% starch. Pasting temperature was 70.6°C with peak viscosity of 14.5 BU, which supports ease of cooking of the flour. The swelling power obtained was 752.9 g/100 g at 85°C with a solubility index of 54%. Water holding capacity (WHC) obtained for the flour was 363.88%, whereas swelling volume was 14.0 ml and makes the flour appropriate for the production of infant foods. The P. erosus flour therefore exhibits good functional and chemical properties that would make the flour quite suitable as a substitute for other flours in food systems.
Ionic liquids for addressing unmet needs in healthcare

Abstract Advances in the field of ionic liquids have opened new applications beyond their traditional use as solvents into other fields especially healthcare. The broad chemical space, rich with structurally diverse ions, and coupled with the flexibility to form complementary ion pairs enables task‐specific optimization at the molecular level to design ionic liquids for envisioned functions. Consequently, ionic liquids now are tailored as innovative solutions to address many problems in medicine. To date, ionic liquids have been designed to promote dissolution of poorly soluble drugs and disrupt physiological barriers to transport drugs to targeted sites. Also, their antimicrobial activity has been demonstrated and could be exploited to prevent and treat infectious diseases. Metal‐containing ionic liquids have also been designed and offer unique features due to incorporation of metals. Here, we review application‐driven investigations of ionic liquids in medicine with respect to current status and future potential.
Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture

Household-level panel data from a nationally representative sample of rural Indian households describing the adoption and profitability of high-yielding seed varieties (HYVs) associated with the Green Revolution are used to test the implications of a model incorporating learning by doing and learning spillovers. The estimates indicate that (i) imperfect knowledge about the management of the new seeds was a significant barrier to adoption; (ii) this barrier diminished as farmer experience with the new technologies increased; (iii) own experience and neighbors' experience with HYVs significantly increased HYV profitability; and (iv) farmers do not fully incorporate the village returns to learning in making adoption decisions.
The influence of patient size on dose conversion coefficients: a hybrid phantom study for adult cardiac catheterization

In this study, the influence of patient size on organ and effective dose conversion coefficients (DCCs) was investigated for a representative interventional fluoroscopic procedure—cardiac catheterization. The study was performed using hybrid phantoms representing an underweight, average and overweight American adult male. Reference body sizes were determined using the NHANES III database and parameterized based on standing height and total body mass. Organ and effective dose conversion coefficients were calculated for anterior–posterior, posterior–anterior, left anterior oblique and right anterior oblique projections using the Monte Carlo code MCNPX 2.5. 0 with the metric dose area product being used as the normalization factor. Results show body size to have a clear influence on DCCs which increased noticeably when body size decreased. It was also shown that if patient size is neglected when choosing a DCC, the organ and effective dose will be underestimated to an underweight patient and will be overestimated to an underweight patient, with errors as large as 113% for certain projections. Results were further compared with those published for a KTMAN-2 Korean patient-specific tomographic phantom. The published DCCs aligned best with the hybrid phantom which most closely matched in overall body size. These results highlighted the need for and the advantages of phantom-patient matching, and it is recommended that hybrid phantoms be used to create a more diverse library of patient-dependent anthropomorphic phantoms for medical dose reconstruction.
Large-Scale Fiber Tracking Through Sparsely Sampled Image Sequences of Composite Materials

Fast and accurate characterization of fiber micro-structures plays a central role for material scientists to analyze physical properties of continuous fiber reinforced composite materials. In materials science, this is usually achieved by continuously cross-sectioning a 3D material sample for a sequence of 2D microscopic images, followed by a fiber detection/tracking algorithm through the obtained image sequence. To speed up this process and be able to handle larger size material samples, this paper proposes sparse sampling with larger inter-slice distance in cross sectioning and develops a new algorithm that can robustly track large-scale fibers from such a sparsely sampled image sequence. In particular, the problem is formulated as multi-target tracking, and the Kalman filters are applied to track each fiber along the image sequence. One main challenge in this tracking process is to correctly associate each fiber to its observation given that: fiber observations are of large scale, crowded, and show very similar appearances in a 2D slice and there may be a large gap between the predicted location of a fiber and its observation in the sparse sampling. To address this challenge, a novel group-wise association algorithm is developed by leveraging the fact that fibers are implanted in bundles and the fibers in the same bundle are highly correlated through the image sequence. In experiments, the proposed algorithm is tested on three tiles of 100-slice S200 material samples and the tracking performance is evaluated using 1136 human annotated ground-truth fiber tracks. Both quantitative and qualitative results show that the proposed algorithm clearly outperforms the state-of-the-art multiple-target tracking algorithms on sparsely sampled image sequences.
Text Matching as Image Recognition

Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.
Effects of exogenous inosine monophosphate on growth performance, flavor compounds, enzyme activity, and gene expression of muscle tissues in chicken

ABSTRACT The goal of this experiment was to examine effects of diets supplemented with exogenous inosine monophosphate (IMP) on the growth performance, flavor compounds, enzyme activity and gene expression of chicken. A total of 1,500 healthy, 1‐day‐old male 3‐yellow chickens were used for a 52‐d experimental period. Individuals were randomly divided into 5 groups (group I, II, III, IV, V) with 6 replicates per group, and fed a basal diet supplemented with 0.0, 0.05, 0.1, 0.2, and 0.3% IMP, respectively. There was no significant response to the increasing dietary IMP level in average daily feed intake (ADFI), average daily gain (ADG), and feed:gain ratio (F/G) (P ≥ 0.05). IMP content of the breast and thigh muscle showed an exponential and linear response to the increasing dietary IMP level (P < 0.05), the highest IMP content was obtained when the diet with 0.3% and 0.2% exogenous IMP was fed. There were significant effects of IMP level in diet on free amino acids (FAA) (exponential, linear and quadratic effect, P < 0.05) and delicious amino acids (DAA) (quadratic effect, P < 0.01) content in breast muscle. FAA and DAA content in thigh muscle showed an exponential and linear response (P < 0.05), and quadratic response (P < 0.01) to the increasing dietary IMP level, the highest FAA and DAA content was obtained when the diet with 0.2% exogenous IMP was fed. Dietary IMP supplementation had a quadratic effect on 5′‐NT and the alkaline phosphatase (ALP) enzyme activity in the breast muscle (P < 0.05), and the adenosine triphosphate (ATP) enzyme activity in the thigh muscles increased exponentially and linearly with increasing IMP level in diet (exponential effect, P = 0.061; linear effect, P = 0.059). Cyclohydrolase (ATIC) gene expression in thigh muscle had a quadratic response to the increasing dietary IMP level (P < 0.05), 0.2% exogenous IMP group had the highest (AMPD1) gene expression of the breast muscle and ATIC gene expression of the thigh muscle. These results indicate that dietary IMP did not affect the growth performance of chicken, the diet with 0.2 to 0.3% exogenous IMP is optimal to improve the meat flavor quality in chicken.
The conceptualisation of forgiveness among Turkish children and adolescents

This study examined (a) how Turkish children and adolescents define forgiveness, (b) the association between self-reported forgiveness and the concepts participants hold and (c) the association between self-reported forgiveness and age. Three hundred and sixty-seven Turkish children in primary (N = 220) and secondary schools (N = 147) were involved in the study. Participants were asked to define forgiveness, and the study used the Enright Forgiveness Inventory for Children (EFI-C). Participants' conceptions of forgiveness were categorised into four groups: conditional forgiveness, reconciliation, ignoring the hurtful event and affective reactions. Half of all the participants in the study were found to be in the conditional forgiveness category. Turkish children were found to be mostly in the category of conditional forgiveness, reconciliation and affective reactions compared with adolescents. Adolescents were mainly found to be in the ignoring the hurtful event category. The highest self-reported forgiveness mean was in the affective reactions category. Participants were commonly offended by friends, siblings, teachers and fathers. No correlation was found between self-reported forgiveness and age. The present results expand the literature of forgiveness by presenting evidence that Turkish children's and adolescents' understanding of forgiveness moderately relates to theoretical definitions.
AllenNLP: A Deep Semantic Natural Language Processing Platform

Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.
Multi-shot ASP solving with clingo

Abstract We introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP system clingo. Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. This evolutionary aspect adds another dimension to ASP since it brings about state changing operations. We address this issue by providing an operational semantics that characterizes grounding and solving processes in multi-shot ASP solving. This characterization provides a semantic account of grounder and solver states along with the operations manipulating them. The operative nature of multi-shot solving avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities. clingo accomplishes this by complementing ASP's declarative input language with control capacities. On the declarative side, a new directive allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side. To this end, clingo offers a new application programming interface that is conveniently accessible via scripting languages. By strictly separating logic and control, clingo also abolishes the need for dedicated systems for incremental and reactive reasoning, like iclingo and oclingo, respectively, and its flexibility goes well beyond the advanced yet still rigid solving processes of the latter.
Prior Knowledge-Based Simultaneous Multibeam Power Allocation Algorithm for Cognitive Multiple Targets Tracking in Clutter

In this paper, a power allocation scheme for tracking multiple targets, with radar measurements either target generated or false alarms, is developed for colocated multiple-input multiple-output (MIMO) radar system. Such a system adopts a multibeam concept, in which multiple simultaneous transmit beams are synthesized by different probing signals from various colocated transmitters. To ensure that the limited power resource can be exploited effectively, we adjust the transmit power of each beam according to the prior knowledge predicted from the tracking recursion cycle. Specifically, the whole algorithm can be viewed as a reaction of the cognitive transmitters to the environment, in order to improve the worst case tracking performance of the multiple targets. By incorporating an information reduction factor (IRF), the Bayesian Cramér-Rao lower bound (BCRLB) gives a measure of the best achievable performance for target tracking in clutter. Hence, it is derived and utilized as an optimization criterion for the simultaneous multibeam power allocation algorithm. The optimization problem is nonconvex and is solved by the modified gradient projection (MGP) method in this paper. Simulation results show that the proposed algorithm significantly outperforms equal power allocation, in terms of the worst case tracking root mean-square error (RMSE).