Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of complex probiotic supplementation in growing pig diets with and without palm kernel expellers on growth performance, nutrient digestibility, blood parameters, fecal microbial shedding and noxious gas emission.

In Experiment 1, a total of 100 growing pigs (Duroc × [Landrace × Yorkshire]) with an average initial body weight (BW) of 24.88 ± 1.57 kg were randomly allotted to 2 × 2 factorial arrangement with two concentrations of palm kernel expellers (PKE) in diets at 0% or 10%, and two concentrations of supplemental probiotics at 0 or 6.0 × 107 colony-forming units/kg. There were five replicate pens per treatment with five pigs per pen. In Experiment 2, eight barrows with average initial BW of 25.78 ± 0.19 kg were allotted to a replicated 4 × 4 Latin square design with four diets and four periods per square. Four experimental diets were the same as Experiment 1. In Experiment 1, dietary probiotic supplementation improved (P < 0.05) the average daily gain (ADG), nutrient digestibility and the fecal Lactobacillus counts. Furthermore, interactive effects (P < 0.05) between PKE and probiotics were observed on ADG and growth-to-feed ratio. In Experiment 2, an interactive effect (P < 0.05) of PKE and probiotics was observed in apparent ileal digestibility of nitrogen and some amino acids. In conclusion, dietary probiotics did not improve PKE utilization and the use of probiotics in non-PKE-containing diet was more favorable than in PKE-containing diet.
Learning the Nonlinear Geometry of High-Dimensional Data: Models and Algorithms

Modern information processing relies on the axiom that high-dimensional data lie near low-dimensional geometric structures. This paper revisits the problem of data-driven learning of these geometric structures and puts forth two new nonlinear geometric models for data describing “related” objects/phenomena. The first one of these models straddles the two extremes of the subspace model and the union-of-subspaces model, and is termed the metric-constrained union-of-subspaces (MC-UoS) model. The second one of these models-suited for data drawn from a mixture of nonlinear manifolds-generalizes the kernel subspace model, and is termed the metric-constrained kernel union-of-subspaces (MC-KUoS) model. The main contributions of this paper in this regard include the following. First, it motivates and formalizes the problems of MC-UoS and MC-KUoS learning. Second, it presents algorithms that efficiently learn an MC-UoS or an MC-KUoS underlying data of interest. Third, it extends these algorithms to the case when parts of the data are missing. Last, but not least, it reports the outcomes of a series of numerical experiments involving both synthetic and real data that demonstrate the superiority of the proposed geometric models and learning algorithms over existing approaches in the literature. These experiments also help clarify the connections between this work and the literature on (subspace and kernel k-means) clustering.
Natural Deduction and the Isabelle Proof Assistant

We describe our Natural Deduction Assistant (NaDeA) and the interfaces between the Isabelle proof assistant and NaDeA. In particular, we explain how NaDeA, using a generated prover that has been verified in Isabelle, provides feedback to the student, and also how NaDeA, for each formula proved by the student, provides a generated theorem that can be verified in Isabelle.
Cloud computing security challenges & solutions-A survey

Cloud Computing and its’ related security issues as well as countermeasures are one of the highly debated topics in today's research field. Though, various surveys regarding Cloud security are already prevalent, there remains a certain gap between the proper mapping of these issues to their corresponding solutions. Some surveys present the Virtualization issues and solutions while other deal with the access control mechanisms, but what lacks is a common framework that would at the same time generalize the concept of cloud security as well as intricately analyze its’ specific requirements. Moreover, countermeasures that are provided in a survey must clearly depict the issue that it is handling. Keeping all these factors in mind this survey paper has been designed so as to cover the necessary areas with a proper interconnection between them and lastly discuss a set of open problems in this domain.
Designing oppressive and libertarian interactions with the conscious body

Human-Computer Interaction literature often includes the allegory of computers oppressing users through badly designed interfaces. Analyzing this oppression relation through the critical pedagogy of Paulo Freire, it is possible to say that computers are not oppressors, yet they can be considered oppressive if one social group is using it to oppress another. This article introduces an alternative proposal that appropriates the computer as a space of possibilities to overcome oppression relations, similarly to the Theater of the Oppressed of Augusto Boal. An experiment of critical pedagogy in Interaction Design confirmed the effectiveness human body as a fulcrum to the interaction design project and as a springboard to understanding and overcoming oppressions mediated by the computer.
Specific Lobar Affection Reveals a Rostrocaudal Gradient in Functional Outcome in Spontaneous Intracerebral Hemorrhage

Background and Purpose— Several studies have reported a better functional outcome in lobar intracerebral hemorrhage (ICH) compared with deep location. However, among lobar ICH, a correlation of hemorrhage site—involving the specific lobes—with functional outcome has not been established. Methods— Conservatively treated patients with supratentorial ICH, admitted to our hospital over a 5-year period (2008–2012), were retrospectively analyzed. Lobar patients were classified as isolated or overlapping ICH according to affected lobes. Demographic, clinical, and radiological characteristics were recorded and compared among lobar ICH patients using above subclassification. Functional outcome—dichotomized into favorable (modified Rankin Scale, 0–3) and unfavorable (modified Rankin Scale, 4–6)—was assessed after 3 and 12 months. Multivariate regression analysis was performed to identify predictors for favorable outcome. Results— Of overall 553 patients, 260 had lobar ICH. In isolated lobar ICH, median hematoma-volume decreased from rostral (frontal, 22.4 mL [7.3–55.5 mL]) to caudal (occipital, 7.1 mL [5.2–16.4 mL]; P=0.045), whereas the proportion of patients with favorable outcome increased (frontal: 23/63 [36.5%] versus occipital: 10/12 [83.3%]; P=0.003). Patients with overlapping lobar ICH had larger ICH volumes than isolated lobar ICH (overlapping, 48.9 mL [22.6–78.5 mL] versus 15.3 mL [5.0–44.6 mL]; P<0.001) and poorer clinical status on admission (Glasgow Coma Scale and National Institutes of Health Stroke Scale). Correlations with anatomic aspects provided evidence of a rostrocaudal gradient with increasing gray/white-matter ratio and decreasing hematoma-volume and rate of hematoma enlargement from frontal to occipital ICH location. Multivariate analysis revealed affection of occipital lobe (odds ratio, 3.75 [1.38–10.22]) and affection of frontal lobe (odds ratio, 0.52 [0.28–0.94]) to be independent predictors for favorable outcome and unfavorable outcome, respectively. Conclusions— Among patients with lobar ICH radiological and outcome characteristics differed according to location. Especially affection of the frontal lobe was frequent and associated with unfavorable outcome after 3 months.
Iron Deficiency in Seagrasses and Macroalgae in the Red Sea Is Unrelated to Latitude and Physiological Performance

Iron can limit primary production in shallow marine systems, especially in tropical waters characterized by carbonated sediments, where iron is largely trapped in a non-available form. The Red Sea, an oligotrophic ecosystem characterized by a strong N-S latitudinal nutrient gradient, is a suitable setting to explore patterns in situ of iron limitation in macrophytes and their physiological performance under different iron regimes. We assessed the interactions between environmental gradients and physiological parameters of poorly-studied Red Sea macrophytes. Iron concentration, chlorophyll a concentration, blade thickness, and productivity of 17 species of macrophytes, including 7 species of seagrasses and 10 species of macroalgae, were measured at 21 locations, spanning 10 latitude degrees, along the Saudi Arabian coast. Almost 90% of macrophyte species had iron concentrations below the levels indicative of iron sufficiency and more than 40% had critically low iron concentrations, suggesting that iron is a limiting factor of primary production throughout the Red Sea. We did not identify relationships between tissue iron concentration, chlorophyll a concentration and physiological performance of the 17 species of seagrass and macroalgae. There was also no latitudinal pattern in any of the parameters studied, indicating that the South to North oligotrophication of the Red Sea is not reflected in iron concentration, chlorophyll a concentration or productivity of Red Sea macrophytes.
The Ripple Effects of Vulnerability: The Effects of a Robot’s Vulnerable Behavior on Trust in Human-Robot Teams

Successful teams are characterized by high levels of trust between team members, allowing the team to learn from mistakes, take risks, and entertain diverse ideas. We investigated a robot’s potential to shape trust within a team through the robot’s expressions of vulnerability. We conducted a between-subjects experiment (N = 35 teams, 105 participants) comparing the behavior of three human teammates collaborating with either a social robot making vulnerable statements or with a social robot making neutral statements. We found that, in a group with a robot making vulnerable statements, participants responded more to the robot’s comments and directed more of their gaze to the robot, displaying a higher level of engagement with the robot. Additionally, we discovered that during times of tension, human teammates in a group with a robot making vulnerable statements were more likely to explain their failure to the group, console team members who had made mistakes, and laugh together, all actions that reduce the amount of tension experienced by the team. These results suggest that a robot’s vulnerable behavior can have "ripple effects" on their human team members’ expressions of trust-related behavior.
CAPS: Context Aware Personalized POI Sequence Recommender System

The revolution of World Wide Web (WWW) and smart-phone technologies have been the key-factor behind remarkable success of social networks. With the ease of availability of check-in data, the location-based social networks (LBSN) (e.g., Facebook1, etc.) have been heavily explored in the past decade for Point-of-Interest (POI) recommendation. Though many POI recommenders have been defined, most of them have focused on recommending a single location or an arbitrary list that is not contextually coherent. It has been cumbersome to rely on such systems when one needs a contextually coherent list of locations, that can be used for various day-to-day activities, for e.g., itinerary planning. This paper proposes a model termed as CAPS (Context-Aware Personalized POI Sequence Recommender System) that generates contextually coherent POI sequences relevant to user preferences. To the best of our knowledge, CAPS is the first attempt to formulate the contextual POI sequence modeling by extending Recurrent Neural Network (RNN) and its variants. CAPS extends RNN by incorporating multiple contexts to the hidden layer and by incorporating global context (sequence features) to the hidden layers and the output layer. It extends the variants of RNN (e.g., Long-short term memory (LSTM)) by incorporating multiple contexts and global features in the gate update relations. The major contributions of this paper are: (i) it models the contextual POI sequence problem by incorporating personalized user preferences through multiple constraints (e.g., categorical, social, temporal, etc.), (ii) it extends RNN to incorporate the contexts of individual item and that of the whole sequence. It also extends the gated functionality of variants of RNN to incorporate the multiple contexts, and (iii) it evaluates the proposed models against two real-world data sets.
Neural architectures for open-type relation argument extraction

Abstract In this work, we focus on the task of open-type relation argument extraction (ORAE): given a corpus, a query entity Q, and a knowledge base relation (e.g., “Q authored notable work with title X”), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, for example, X: the title of a book or a work of art) from the corpus. We develop and compare a wide range of neural models for this task yielding large improvements over a strong baseline obtained with a neural question answering system. The impact of different sentence encoding architectures and answer extraction methods is systematically compared. An encoder based on gated recurrent units combined with a conditional random fields tagger yields the best results. We release a data set to train and evaluate ORAE, based on Wikidata and obtained by distant supervision.
Industrial Injury Hospitalizations Billed to Payers Other Than Workers' Compensation: Characteristics and Trends by State

OBJECTIVE To describe characteristics of industrial injury hospitalizations, and to test the hypothesis that industrial injuries were increasingly billed to non-workers' compensation (WC) payers over time. DATA SOURCES Hospitalization data for 1998-2009 from State Inpatient Databases, Healthcare Cost and Utilization Project, and Agency for Healthcare Research and Quality. STUDY DESIGN Retrospective secondary analyses described the distribution of payer, age, gender, race/ethnicity, and injury severity for injuries identified using industrial place of occurrence codes. Logistic regression models estimated trends in expected payer. PRINCIPAL FINDINGS There was a significant increase over time in the odds of an industrial injury not being billed to WC in California and Colorado, but a significant decrease in New York. These states had markedly different WC policy histories. Industrial injuries among older workers were more often billed to a non-WC payer, primarily Medicare. CONCLUSIONS Findings suggest potentially dramatic cost shifting from WC to Medicare. This study adds to limited, but mounting evidence that, in at least some states, the burden on non-WC payers to cover health care for industrial injuries is growing, even while WC-related employer costs are decreasing-an area that warrants further research.
Energy Consumption Scheduling of HVAC Considering Weather Forecast Error Through the Distributionally Robust Approach

In this paper, the distributionally robust optimization approach (DROA) is proposed to schedule the energy consumption of the heating, ventilation and air conditioning (HVAC) system with consideration of the weather forecast error. The maximum interval of the outdoor temperature is partitioned into subintervals, and the proposed DROA constructs the ambiguity set of the probability distribution of the outdoor temperature based on the probabilistic information of these subintervals of historical weather data. The actual energy consumption will be adjusted according to the forecast error and the scheduled consumption in real time. The energy consumption scheduling of HVAC through the proposed DROA is formulated as a nonlinear problem with distributionally robust chance constraints. These constraints are reformulated to be linear and then the problem is solved via linear programming. Compared with the method that takes into account the weather forecast error based on the mean and the variance of historical data, simulation results demonstrate that the proposed DROA effectively reduces the electricity cost with less computation time, and the electricity cost is reduced compared with the traditional robust method.
Evolutionary Dynamics of Abundant Stop Codon Readthrough

Translational stop codon readthrough emerged as a major regulatory mechanism affecting hundreds of genes in animal genomes, based on recent comparative genomics and ribosomal profiling evidence, but its evolutionary properties remain unknown. Here, we leverage comparative genomic evidence across 21 Anopheles mosquitoes to systematically annotate readthrough genes in the malaria vector Anopheles gambiae, and to provide the first study of abundant readthrough evolution, by comparison with 20 Drosophila species. Using improved comparative genomics methods for detecting readthrough, we identify evolutionary signatures of conserved, functional readthrough of 353 stop codons in the malaria vector, Anopheles gambiae, and of 51 additional Drosophila melanogaster stop codons, including several cases of double and triple readthrough and of readthrough of two adjacent stop codons. We find that most differences between the readthrough repertoires of the two species arose from readthrough gain or loss in existing genes, rather than birth of new genes or gene death; that readthrough-associated RNA structures are sometimes gained or lost while readthrough persists; that readthrough is more likely to be lost at TAA and TAG stop codons; and that readthrough is under continued purifying evolutionary selection in mosquito, based on population genetic evidence. We also determine readthrough-associated gene properties that predate readthrough, and identify differences in the characteristic properties of readthrough genes between clades. We estimate more than 600 functional readthrough stop codons in mosquito and 900 in fruit fly, provide evidence of readthrough control of peroxisomal targeting, and refine the phylogenetic extent of abundant readthrough as following divergence from centipede.
Minimizing Development Cost With Reliability Goal for Automotive Functional Safety During Design Phase

ISO 26262 is a functional safety standard specifically made for automotive systems, in which the automotive safety integrity level (ASIL) is the representation of the criticality level. Recently, most studies have used ASIL decomposition to reduce the development cost of automotive functions. However, these studies have not paid special attention to the problem that the reliability goal may not be satisfied when ASIL decomposition is performed. In this study, we solve the problem of minimizing the development cost of a distributed automotive function while satisfying its reliability goal during the design phase by presenting two heuristic algorithms, reliabilitycalculation of scheme (RCS) and minimizing development cost with reliability goal (MDCRG). We first use RCS to calculate the reliability value of each ASIL decomposition scheme; then, the MDCRG is used to select the scheme with the minimum development cost while satisfying the reliability goal. Real-life benchmark and simulated functions based on real parameter values are used in experiments, and results show the effectiveness of the proposed algorithms.
Measurements of rainfall rates from videos

Measuring rainfall rates from videos is a novel research topic. Due to rain motion, reflection of light and background clutter, it is extremely challenging to obtain accurate measurements. In this paper, we propose a new technique for measuring rainfall rates from videos, which consists of the following technical steps: first, we detect raindrops in an image using gray-tone functions and direction of rain streaks; we then select the focused raindrops, based on two features: average color tensor response and average intensity difference. Afterwards, the size of the raindrops is estimated and a raindrop size distribution (RSD) curve is created according to the use of the RSD in meteorology. Finally, a rainfall rate is obtained by fitting the RSD curve with a Gamma distribution model. In the experiment section presented in this paper, the proposed algorithm is evaluated under different light, moderate and heavy rainy conditions. The measurement results of the proposed algorithm are consistent with those of a can-type rain gauge.
How to Start Training: The Effect of Initialization and Architecture

We investigate the effects of initialization and architecture on the start of training in deep ReLU nets. We identify two common failure modes for early training in which the mean and variance of activations are poorly behaved. For each failure mode, we give a rigorous proof of when it occurs at initialization and how to avoid it. The first failure mode, exploding/vanishing mean activation length, can be avoided by initializing weights from a symmetric distribution with variance 2/fan-in. The second failure mode, exponentially large variance of activation length, can be avoided by keeping constant the sum of the reciprocals of layer widths. We demonstrate empirically the effectiveness of our theoretical results in predicting when networks are able to start training. In particular, we note that many popular initializations fail our criteria, whereas correct initialization and architecture allows much deeper networks to be trained.
Estimating risk factors for HIV infection among women in Mozambique using population-based survey data

The use of population-based survey data together with sound statistical methods can enhance better estimation of HIV risk factors and explain variations across subgroups of the population. The distribution and determinants of HIV infection in populations must be taken into consideration. We analysed data from the HIV Prevalence and Behaviour Survey in Mozambique aiming to find risk factors associated with HIV infection among Mozambican women. The paper provides a complex survey logistic regression model to explain the variation in HIV seropositivity using demographic, socio-economic and behavioural factors. Results show that women aged 25–29 years, living in female-headed households, living in richer households and those widowed, divorced or not living with a partner have higher odds of being HIV-positive. Findings from our study provide a unique and integrated perspective on risk factors for being HIV-positive among Mozambican women and could support the implementation of programmes aiming to reduce HIV infection in Mozambique.