Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel SAT-Based Approach to Model Based Diagnosis

This paper introduces a novel encoding of Model Based Diagnosis (MBD) to Boolean Satisfaction (SAT) focusing on minimal cardinality diagnosis. The encoding is based on a combination of sophisticated MBD preprocessing algorithms and the application of a SAT compiler which optimizes the encoding to provide more succinct CNF representations than obtained with previous works. Experimental evidence indicates that our approach is superior to all published algorithms for minimal cardinality MBD. In particular, we can determine, for the first time, minimal cardinality diagnoses for the entire standard ISCAS-85 and 74XXX benchmarks. Our results open the way to improve the state-of-the-art on a range of similar MBD problems.
Efficient Inference for Unsupervised Semantic Parsing

Unsupervised semantic parsing algorithms based on Bayesian nonparametric grammars offer a promising way to bootstrap semantic analyses for unsupervised relation/information extraction (RE/IE). Yet the form in which they currently exist makes it difficult to apply them to larger data sets. In this paper, we make progress toward scaling unsupervised semantic parsing by introducing a local, collapsed sampling scheme for a class of nonparametric dependency grammars. We show that our algorithm can rapidly discover the semantic units in a corpus, and we report edifying preliminary results on the semantic clustering task. 1 Model specification Our work centers on the Bayesian nonparametric dependency grammar introduced for unsupervised semantic parsing in [9]. In this grammar, trees are partitioned into fragments, each corresponding to a particular lexical realization of an abstract concept, which we call a “semantic class. ” Semantic classes are viewed as values c ∈ N associated with parameters dictating their behavior: a distribution φc over its lexical realizations (i.e. tree fragments); a probability of generating arguments, encoded as a probability ψct of generating any arguments of latent type t and a parameter ψ ct to a geometric distribution on the number of arguments of type t; a distribution β ct over edge labels for arcs connecting it to its arguments; and a distribution θct over semantic class labels for type t arguments of the class. A special distribution θ controls the semantic class labels at the root node. As the number of semantic classes is unknown (and likely to be large), we sample the base distribution over semantic classes according to γ ∼ PYP(α, d). The root distribution and class-specific distributions over arguments then come from a second-level PYP with base measure γ, viz. θ ∼ DP(α, γ) and θct ∼ DP(α, γ). We construct the prior on tree fragment distributions using a weak base dependency grammar H: φc ∼ PYP(α, d, H). The recursive structure of the model makes it difficult to represent in graphical form. We therefore present it as a generative process, explicitly spelling out how nodes with given context are created and expanded according to the model. A tree below a node is generated via: [9] ∗Work completed while the author was a student at the University of Cambridge.
Development and Initial Validation of the Self-Harm Behavior Questionnaire

Evaluation of suicide-related behaviors and thoughts about suicide with a newly developed self-report instrument is described. The Self-Harm Behavior Questionnaire (SHBQ; Gutierrez, 1998) generates detailed clinical information from an easy to administer and score self-report form. A sample of 342 participants were drawn from university undergraduate students enrolled in various psychology courses. Participants were then divided into a severe suicidal ideation group (n = 20) and a nonsuicidal control group (n = 20) to conduct subgroup and criterion-related validity analyses. Preliminary analyses indicate this new questionnaire is both valid and reliable when used with young adults drawn from a nonclinical population. The questionnaire should be of use to both clinicians and researchers due to the flexibility of the data generated.
Culturally Responsive Dispositions in Prospective Mathematics Teachers

Abstract Sustaining teachers in culturally and linguistically diverse schools has been a prominent issue for years. This qualitative study focused on the impact of an enhanced preparation program on the cultural dispositions of five pre-service mathematics teachers. It is postulated that if positive cultural dispositions are developed in teacher candidates, the possibility of them remaining in cultural school settings is likely. Themes emerged demonstrating that the enhanced program heightened the participants’ awareness of cultural and linguistic differences, as well as, their commitment to impacting academic achievement. Of the five participants, four teach in a culturally and linguistically school after five years in the profession, demonstrating sustainability after participating in the enhanced program.
Self Body Mapping in Mobile Robots Using Vision and Forward Models

The work presented in this paper aims at providing an agent with basic capabilities leading towards navigation through self body-mapping using a vision system. In particular, we are interested in the study of forward models which code for the sensory consequences of an agent's self-produced actions. The research is developed in the framework of cognitive robotics and embodied cognition. The agent is a robot we let interact with its environment to know the free space around it from re-enaction of sensory-motor cycles predicting collisions from visual data. From the disparity map the robot associates intensity regions with motor commands in order to predict distances to objects reported by a tactile sensor in self-motion coordinates. In order to form this multimodal association we use a forward model, coded as a system of neural networks and trained with data coming from random trajectories executed by a Pioneer 3-XD. The resulting forward model allows the agent to navigate avoiding undesired situations by performing long term predictions of the sensory consequences of its actions. The experiments validate the hypothesis that this model allows for a basic self body-mapped navigation capability.
Cognitive Behavioral Therapy Group for Reducing Self-Stigma for People With Mental Illness

This research project aims to evaluate the effectiveness of a cognitive behavioral therapeutic (CBT) group in reducing self-stigma for people with mental illness in Chinese society. In this study, a quasi-experimental research method was adopted involving 71 people with mental illness receiving community-based mental health services. In total, 33 treatment group participants were assigned to a 10-session CBT group, while 38 control group participants received treatment as usual. Standardized assessment tools were used to collect data in the pre- and posttreatment periods by a research assistant. Analysis of covariance demonstrated that the CBT group was significantly more effective than the control group in terms of reducing self-stigma and depressive mood, with a moderate effect size, even after controlling for differences in various demographic variables between the treatment and control groups. This study provides evidence to support the efficacy and effectiveness of a CBT group for reducing self-stigma for people with mental illness.
AUTOMATIC RECTIFICATION OF SIDE-SCAN SONAR IMAGES

The authors present a novel procedure for the automated rectification of si de-scan sonar images. The traditional assumption of a flat seabed for the computation of t he ground projection from the slant-range sonar data can later result in difficult ge o-referenciation and registration problems. Proper rectification using actual seabed topography is required for accurate integration of side-scan images into a high-resolution mosaic, which de pends on the availability of seabed altitude maps for the area of interest. As an alte rn tive the authors propose a procedure for the estimation of the most probable seabed topography given t he acquired side-scan image and a simple scattering model. An Expectation-Max imization framework is constructed to iteratively determine the model para meters that best approximate the observed image.
Aspects in Architectural Description of Evolving Systems

Software architecture is the most important artifact in the software life cycle. The architectural description and the architectural design decide the quality and the longevity of the software. The design decisions made to arrive at a particular software architecture decide the value of quality attributes such as performance, reliability, security and modifiability. Also the percentage of design reuse is decided by the design heuristics like modularity and abstraction which are supported by metrics like cohesion and coupling. Aspects are cross-cutting concerns across components. Though concept of aspect-orientation had come up initially for the concerns cross-cutting the code, the concept is very much required to be addressed from the design stage itself. As the metrics for cohesion and coupling get affected due to these cross-cutting concerns, the scenario results in lower values for modifiability and reuse quality attributes. IEEE 1471-2000 is the standard for “Recommended practice for architectural description of software-intensive systems”. The standard uses the conceptual framework and its components like views, viewpoints and models. It does talk about concerns such as performance, reliability, security, distribution, and evolvability, but fails to accommodate the description for cross-cutting concerns. In this paper we focus on the importance of aspects in architectural description of evolving systems.
Human society inspired routing for wireless sensor network

Biological systems have been an influential source of inspiration for designing localized and adaptive algorithm in networking domain. In this paper, we propose a human-inspired routing protocol for WSN utilizing ideas from Social Cognitive Theory (SCT). In this context, geographical aware sensors exhibit some localized human traits to higher their throughput while keeping in mind energy metric. The simulation results encourage the application of this model.
An incentive aware routing for selfish opportunistic networks: A game theoretic approach

Many routing schemes in opportunistic networks (OPPNETs) assume that nodes are willing to forward messages to others. However, in practice, nodes in the networks may be selfish due to limited resources or poor social tie. Nodes' selfishness can be classified into individual selfishness and social selfishness. The existing works only consider how to solve the individual selfishness or social selfishness for data transmission respectively. But, the individual selfishness and social selfishness may co-exist in OPPNETs. In this paper, we propose an incentive aware routing for selfish OPPNETs from a game theoretic perspective, which jointly considers individual selfishness and social selfishness to improve the performance of OPPNETs. The scheme maps the message transmission between two nodes as a Rubinstein-Stahl bargaining game, which employs virtual currency and constructs proper price function, considering the nodes resources and the nodes' social ties for data transmission. Trace-driven simulations demonstrate that the effectiveness of the proposed incentive aware routing.
Mapping the distance between the brain and the inner surface of the skull and their global asymmetries

The primary goal of this paper is to describe i) the pattern of pointwise distances between the human brain (pial surface) and the inner surface of the skull (endocast) and ii) the pattern of pointwise bilateral asymmetries of these two structures. We use a database of MR images to segment meshes representing the outer surface of the brain and the endocast. We propose automated computational techniques to assess the endocast-to-brain distances and endocast-and-brain asymmetries, based on a simplified yet accurate representation of the brain surface, that we call the brain hull. We compute two meshes representing the mean endocast and the mean brain hull to assess the two patterns in a population of normal controls. The results show i) a pattern of endocast-to-brain distances which are symmetrically distributed with respect to the mid-sagittal plane and ii) a pattern of global endocast and brain hull asymmetries which are consistent with the well-known Yakovlevian torque. Our study is a first step to validate the endocranial surface as a surrogate for the brain in fossil studies, where a key question is to elucidate the evolutionary origins of the brain torque. It also offers some insights into the normal configuration of the brain/skull interface, which could be useful in medical imaging studies (e.g. understanding atrophy in neurodegenerative diseases or modeling the brain shift in neurosurgery).
Comparison of Anti-Oxidant and Anti-Inflammatory Effects between Fresh and Aged Black Garlic Extracts

Numerous studies have demonstrated that aged black garlic (ABG) has strong anti-oxidant activity. Little is known however regarding the anti-inflammatory activity of ABG. This study was performed to identify and compare the anti-oxidant and anti-inflammatory effects of ABG extract (ABGE) with those of fresh raw garlic (FRG) extract (FRGE). In addition, we investigated which components are responsible for the observed effects. Hydrogen peroxide (H2O2) and lipopolysaccharide (LPS) were used as a pro-oxidant and pro-inflammatory stressor, respectively. ABGE showed high ABTS and DPPH radical scavenging activities and low ROS generation in RAW264.7 cells compared with FRGE. However, inhibition of cyclooxygenase-2 and 5-lipooxygenase activities by FRGE was stronger than that by ABGE. FRGE reduced PGE2, NO, IL-6, IL-1β, LTD4, and LTE4 production in LPS-activated RAW264.7 cells more than did ABGE. The combination of FRGE and sugar (galactose, glucose, fructose, or sucrose), which is more abundant in ABGE than in FRGE, decreased the anti-inflammatory activity compared with FRGE. FRGE-induced inhibition of NF-κB activation and pro-inflammatory gene expression was blocked by combination with sugars. The lower anti-inflammatory activity in ABGE than FRGE could result from the presence of sugars. Our results suggest that ABGE might be helpful for the treatment of diseases mediated predominantly by ROS.
Abrus precatorius Leaves: Antioxidant Activity in Food and Biological Systems, pH, and Temperature Stability

Natural antioxidants present in foods and other biological materials have attracted considerable interest because of their presumed safety and potential nutritional and therapeutic effects. Antioxidant constituents of plant materials act as radical scavengers and convert the radicals to less reactive species. Abrus precatorius (AP) was analyzed for its proximate and phytochemical composition. The leaves were extracted with methanol (ME) and analyzed for antioxidant activity by radical scavenging method, reducing power, ferric reducing capacity, and in vitro inhibition of Fenton's reagent-induced oxidation in oil emulsion and microsomes. In addition, the effect of temperature (100°C, 15, and 30 min) and pH (4.5, 7, and 9) C on the antioxidant activity of ME was investigated. The leaves were rich in total polyphenols, flavonoids, β-carotene, glutathione, α-tocopherol, and ascorbic acid. The ME exhibited varying degree of antioxidant activity in a dose-dependent manner. The AP exhibited more inhibition of oxidation in microsomes (73%) than compared to oil emulsion (21%). Heat treatment resulted in an increase of radical scavenging activity of extract (28% to 43%). At pH 4.5 the extract exhibited more antioxidant activity and stability compared to pH 7 and 9. Data indicates that potential exists for the utilization of Abrus precatorius as a natural antioxidant.
Group Dynamics and Individual Roles: A Differentiated Approach to Social-Emotional Learning

ABSTRACT Differentiated instruction is a set of strategies to help teachers meet each child where he or she is in order to improve students' engagement, lead them to do their best work, and maximize their success. This article describes a differentiated classroom management approach based in group dynamics which focuses on the development of group norms and roles. By teaching students about task and social-emotional roles, and about positive and negative roles, this approach provides venues for talking about student misbehaviors in a constructive way, helps students understand roles as something they choose rather than something they are, and gives every student new ways to shine in class by helping them discover their own unique roles that can contribute positively to the whole classroom group.
Winter Monsoon Dielectric Properties of Tropical Indian Ocean Seawater at 5 GHz

Dielectric constant e' and dielectric loss e'' of seawater samples collected during the winter monsoon from Tropical Indian Ocean are measured. The Von Hippel method is used to measure dielectric properties, both real, e', and imaginary part, e'', at 5 GHz and 30 °C using automated C-Band microwave bench set up. The dielectric constant e' and dielectric loss e'' are calculated using least square fitting technique. The salinity measurement of seawater samples are done on autosalinometer. Making use of salinity values of all samples and for 5 GHz and 30 - static dielectric constant and dielectric loss are estimated by Klein-Swift model and Ellison et al. model. Experimental and theoretical results are compared. The laboratory data obtained are significant for remote sensing applications in physical oceanography.
Towards Multi Label Text Classification through Label Propagation

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text classification paradigms cannot efficiently classify such multifaceted text corpus. Through our paper we are proposing a novel label propagation approach based on semi supervised learning for Multi Label Text Classification. Our proposed approach models the relationship between class labels and also effectively represents input text documents. We are using semi supervised learning technique for effective utilization of labeled and unlabeled data for classification . Our proposed approach promises better classification accuracy and handling of complexity and elaborated on the basis of standard datasets such as Enron, Slashdot and Bibtex.
Learning a user-thread alignment manifold for thread recommendation in online forum

People are more and more willing to participate in online forums to share their knowledge and experience. However, it may not be easy for them to find their desired threads in online forums due to the information overload problem. Traditional recommendation approaches can not be directly applied to online forums due to two reasons. First, unlike traditional movie or music recommendation problem, there is no rating information in online forums. Second, the sparsity problem is more severe since the users may only read threads but take no actions. To address these limitations, in this paper we propose to make use of the reply relationships among users, as well as thread contents. A learning algorithm is introduced to infer a user-thread alignment manifold in which both users and thread contents can be well represented. Thus, the relatedness between users and threads can be measured on this alignment manifold, and the closest threads which can best meet the corresponding user's information needs are recommended. Experiments on a dataset crawled from digg.com have demonstrated the superiority of our algorithm over traditional recommendation algorithms.
A statistical phylogeography of influenza A H5N1

The geographic diffusion of highly pathogenic influenza A H5N1 has largely been traced from the perspective of the virus's victims. Birds of a variety of avian orders have been sampled across localities, and their infection has been identified by a general genetic test. Another approach tracks the migration from the perspective of the virus alone, by way of a phylogeography of H5N1 genetic sequences. Although several phylogenies in the literature have labeled H5N1 clades by geographic region, none has analytically inferred the history of the virus's migration. With a statistical phylogeography of 192 hemagglutinin and neuraminidase isolates, we show that the Chinese province of Guangdong is the source of multiple H5N1 strains spreading at both regional and international scales. In contrast, Indochina appears to be a regional sink, at the same time demonstrating bidirectional dispersal among localities within the region. An evolutionary trace of HA1 across the phylogeography suggests a mechanism by which H5N1 is able to infect repeated cycles of host species across localities, regardless of the host species first infected in each locale. The trace also hypothesizes amino acid replacements that preceded the first recorded outbreak of pathogenic H5N1 in Hong Kong, 1997.
Luteinizing hormone causes MAP kinase-dependent phosphorylation and closure of connexin 43 gap junctions in mouse ovarian follicles: one of two paths to meiotic resumption

Luteinizing hormone (LH) acts on ovarian follicles to reinitiate meiosis in prophase-arrested mammalian oocytes, and this has been proposed to occur by interruption of a meioisis-inhibitory signal that is transmitted through gap junctions into the oocyte from the somatic cells that surround it. To investigate this idea, we microinjected fluorescent tracers into live antral follicle-enclosed mouse oocytes, and we demonstrate for the first time that LH causes a decrease in the gap junction permeability between the somatic cells, prior to nuclear envelope breakdown (NEBD). The decreased permeability results from the MAP kinase-dependent phosphorylation of connexin 43 on serines 255, 262 and 279/282. We then tested whether the inhibition of gap junction communication was sufficient and necessary for the reinitiation of meiosis. Inhibitors that reduced gap junction permeability caused NEBD, but an inhibitor of MAP kinase activation that blocked gap junction closure in response to LH did not prevent NEBD. Thus, both MAP kinase-dependent gap junction closure and another redundant pathway function in parallel to ensure that meiosis resumes in response to LH.
A FPGA implementation of low-complexity noise removal

Impulse noise removal is a very important preprocessing operation in many computer vision applications. This paper presents a low-complexity noise removal based on a conditional technique and outlines its FPGA implementation for window sizes of (3×3) and (5×5). As the experiments show, the proposed technique performs significantly better than standard median filter and achieves superior image quality. The FPGA implementations are very compact, fast and consume low-power.
Measurement Validation of the Jamaican Macho Scale Among African American Males

Measures of masculinities have expanded in recent decades to reflect greater diversity. A comparative reading of the literature suggests that African American men may endorse the same macho ideology shared by Afro-Jamaican men, which is captured by the Jamaican Macho Scale. The current article examines whether the Macho Scale is relevant to explaining masculinity among African American males using exploratory and confirmatory factor analysis techniques. A sample of 203 African American male college students from a large university in the Eastern United States participated in a self-administered survey, which included the Macho Scale items. Results supported a two-factor model of macho ideology, specifically sexual dominance and virility, and procreative need, in the American context. Future research should examine understudied masculinity ideology constructs in the American setting and attempt to map the full content domain of African American masculinity ideologies.