Miracle-dz/UniFAR-FTUs huggingface dataset:
Valence Instability and Superconductivity in Heavy Fermion Systems( Frontiers of Novel Superconductivity in Heavy Fermion Compounds)

Many cerium-based heavy fermion (HF) compounds have pressure–temperature phase diagrams in which a superconducting region extends far from a magnetic quantum critical point. In at least two compounds, CeCu 2 Si 2 and CeCu 2 Ge 2 , an enhancement of the superconducting transition temperature was found to coincide with an abrupt valence change, with strong circumstantial evidence for pairing mediated by critical valence, or charge transfer, fluctuations. This pairing mechanism, and the valence instability, is a consequence of a f – c Coulomb repulsion term U fc in the Hamiltonian. While some non-superconducting Ce compounds show a clear first order valence instability, analogous to the Ce α–γ transition, we argue that a weakly first order valence transition may be a general feature of Ce-based HF systems, and both magnetic and critical valence fluctuations may be responsible for the superconductivity in these systems.
Learning strengths and weaknesses of classifiers for RGB-D semantic segmentation

3D scene understanding is an open challenge in the field of computer vision. Most of the focus is on 2D methods in which the semantic labeling of each RGB pixel is considered. But, in this paper, the 3D semantic labeling of RGB-D images is considered. In the proposed method, to extract some meaningful features, the superpixel generation algorithm is applied to the RGB image to segment it into a set of disjoint pixels. After that, the set of three powerful classifiers are utilized to semantically label each superpixel. In the proposed method, the probability outputs of these classifiers are concatenated as the novel feature vector for each superpixel. Consequently, to analyze the strengths and weaknesses of each classifier, the conditional random field framework is used to improve the contextual relationships among neighboring superpixels. The unary potential function of the conditional random field is learned based on these new feature vectors. The proposed method is evaluated on the challenging NYU-V2 RGB-D dataset and improves the pixel average accuracy compared to previous methods.
Adoption of Technology-Mediated Distance Education among Higher-Education Institutions ∗

Recent developments in Information Technology (IT) and the commercialization of the Internet have generated new opportunities for the delivery of education and allowed many higher-education institutions to bring their resources closer to a broad base of potential users. Graduate business degrees are especially in high demand; about 200 accredited schools offered Master of Business Administration (MBA) degrees over the Internet in 2002 according to the educational research firm Eduventures Inc. The population of online MBA students rose from about 5,000 students in 2000 to more than 100,000 in 2002 (Braun 2003). Enrollment at the largest U.S. online college, University of Phoenix Online, rose to around 100,000 students in 2003 and is expected to rise 50% in 2004. Business Week ranked the online college 22nd in its 2004 Info Tech 100 list and placed it among the top 10 most profitable IT firms (Baker and Green 2004). These developments suggest that the U.S. higher-education sector is finally embracing the information age. Despite the importance of the higher-education sector in the U.S. economy, the strategic use of technologymediated distance education (TMDE) has received limited research attention at best. We refer to TMDE as synchronous and asynchronous instruction delivered to remote locations via a combination of computer, communication, and data management technologies. The overwhelming majority of Information Systems (IS) research on TMDE has focused largely on the student level of analysis and has provided an understanding of the critical enabling role of IT in learning environments. However, there is also a need for research that takes a broader perspective on the impact of TMDE. In their research commentary, Alavi and Leidner (1995) called for a greater depth and breadth in this area and stressed the lack of studies that focus on the organizational and program levels. As a step toward understanding the strategic impact of TMDE at a broader level, we explore the types of higher-education institutions and degree programs that are more suited to utilize the new opportunities enabled by IT. In particular, we focus on the appropriate hybrid model—part online, part traditional—that best serves the interests of an institution. We first analytically model the value of education for students who have differentiated preferences for a certain institution’s degree, and then analyze which institutional characteristics support the adoption of TMDE. The hypotheses derived from the model are tested using two separate data sets obtained from surveys of Department of Education and U.S. News & World Report.
Computing with Time : From Neural Networks to Wireless Networks

This article advocates a new computing paradigm, called computing with time, that is capable of efficiently performing a certain class of computation, namely, searching in parallel for the closest value to the given parameter. It shares some features with the idea of computing with action potentials proposed by Hopfield, which originated in the field of artificial neuron network. The basic idea of computing with time is captured in a novel distributed algorithm based on broadcast communication called the Lecture Hall Algorithm, which can compute the minimum among n positive numbers using only O(1) messages. When applied to wireless communication network, the Lecture Hall Algorithm leads to an interesting routing protocol having several desirable features that are not acquired by intentional design.
Cosmic acceleration and extra dimensions: constraints on modifications of the Friedmann equation

An alternative to dark energy as an explanation for the present phase of accelerated expansion of the Universe is that the Friedmann equation is modified, e.g. by extra dimensional gravity, on large scales. We explore a natural parametrization of a general modified Friedmann equation, and find that the present supernova Type Ia and cosmic microwave background data prefer a correction of the form 1/H to the Friedmann equation over a cosmological constant.
Using ontology for description of grid resources

Grid computing environments can share resources and services in a large-scale. These environments are being considered as an effective solution for many organizations to execute distributed applications to obtain high level of performance and availability. However, the use of a grid environment can be a complex task for an ordinary user, demanding a previous knowledge of the access requirements from a virtual organization. In order to improve the search of resources and its selection, in this paper we propose the use of ontology as alternative approach to help the use of grid services. This paradigm can provide a description of available resources in a grid configuration, leading users to desire operations and describing resources syntax and semantics that can form a common domain vocabulary. Our experimental result indicates that our proposal allows a better comprehension about available resources main text.
Women's perceptions of femininity and masculinity as structures of rules for behavior.

Women's Perceptions of Femininity and Masculinity As Structures of Rules for Behavior (September 1983) Mary L. Blankenship, B.A., West Georgia College M.Ed., Columbus College^ Ed.D. , University of Massachusetts Directed by: Professor Ernest D. Washington This study was designed to begin to define masculinity and femin¬ inity as structures of rules for behavior. A contrast was presented between the traditional psychometric approaches to defining femininity and masculinity and a sex-roles-as-rules model, based on a phenomeno¬ logical approach. The study sought to compile a list of rules for both women and men based on gender (the social norm), a comparison of personal norms to the social norms identified in the study, an identi¬ fication of the rules that are perceived to be unbreakable, and the costs to the individual of choosing not to obey a rule. The sample for this study was made up of 48 women in the Amherst, Massachusetts, area, divided into four age groups of 12 each (junior high, high school, college seniors, 35-40 year old women). Data con¬ sisted of interviews with each subject to identify from their own experiences the rules that defined masculine and feminine behavior. The interviews resulted in a list of 94 rules describing the ex¬ pected behavior of both men and women in the dimensions of professional
Molecular and Morphometrical Characterization of Fasciola Species Isolated from Domestic Ruminants in Ardabil Province, Northwestern Iran

Background: We aimed to describe morphological and morphometrical characteristics of Fasciola spp. in livestock from Ardabil Province, Northwest Iran. Methods: Forty adult flukes were collected from different definitive hosts (cattle and sheep). Previously specimens were identified as F. hepatica or F. gigantica based on PCR-RFLP of the ITS-1 region with RsaI enzyme. We identified Fasciola spp. based on morphological and metric assessment of external features of fresh adults, morphological and metric assessment of internal anatomy of stained mounted worms. Statistical analysis was conducted using the Student’s t-test implemented in SPSS 15.0 (SPSS, Chicago, Illinois). Then the morphometric criteria of Fasciola samples were compared with PCR-RFLP data. The results of PCR-RFLP were confirmed by COI gene sequence. Results: The differences between the body length, area of the body, peripheral of the body, succer area, cone length, cone width, in two species were significant (P < 0.05). Based on Morphological characterizations, 6 specimens had the intermediate morphological features and 19 and 15 specimens had morphological features of F. hepatica and F. gigantica, respectively. In contrast, RFLP results showed, F. hepatica was present in 20 of the isolates, and F. gigantica in 20 isolates. No hybrid forms were detected. Conclusion: PCR-RFLP method can be used for differentiation of Fasciola species, which is more reliable method than morphology. Using morphology methods, merely, is not efficient for determination of genetic diversity.
RESONANCE ERROR IN MULTISCALE FINITE ELEMENT METHODS

A multiscale nite element method (MFEM) 9, 7] has been introduced to capture the large scale solutions of elliptic equations with highly oscillatory coeecients. This is accomplished by constructing the multiscale base functions from the local solutions of the elliptic operator. Our previous study reveals that the leading order error in this approach is caused by the \resonant sampling", which leads to large error when the mesh size is close to the small scale of the continuous problem. Similar diiculty also arises in the numerical upscaling methods. Here, we present an in-depth analysis of the resonance error and the over-sampling method 7] devised to remove the error. We show two origins of the resonance error. One is the mismatch between the a priori local boundary conditions imposed for the base functions and global oscillatory characteristics of the diierential operator; the other is the mismatch between the mesh size and the \perfect" sample size. The rst origin can be eeectively removed by the over-sampling method, whereas the second one is immune to over-sampling and is more subtle. Nevertheless, we show that the error it induces is of lower order; in fact, it is generically small and diicult to observe in practice.
ecurity-constrained expansion planning of fast-response units for wind ntegration

This paper proposes a stochastic expansion planning of fast-response thermal units for the large-scale integration of wind generation (WG). The paper assumes that the WG integration level is given and considers the short-term thermal constraints and the volatility of wind units in the planning of fastresponse thermal units. The new fast-response units are proposed by market participants. The securityvailable online 14 August 2010
Preliminary Analysis of Multiple Kernel Learning : Flat Maxima , Diversity and Fisher Information

Gaining an insight into the potential effectiveness of Multiple Kernel Learning (MKL) methods requires analysis of the underlying ensemble process and resulting loss. An attempt to suggest such directions is described in this work which presents some preliminary results based on the “Flat Maximum Effect” and decompositions of the loss which leads to MKL problem being recast as one of optimal Design of Experiments (DoE).
Highly Reliable Inference System of Neural Networks Using Gated Schottky Diodes

An inference system using gated Schottky diode (GSD) is proposed for highly reliable hardware-based neural networks (HNNs). We explain the characteristics of the GSD and present circuits that take into account the characteristics of the device. The reverse current of the GSD, which is the synaptic current, is saturated with respect to input voltage, which results in immunity of input and output noise and overcoming the IR drop problem in metal wire. In order to take advantages of this saturated $I-V$ characteristics, pulse-width modulation (PWM) of input data instead of amplitude modulation is proposed. In addition, by applying identical pulses to the bottom gate, the synaptic current of the GSD increases linearly, which makes it easy to transfer the calculated weights to the conductance of GSDs. By considering these characteristics, electronic circuits for PWM, current sum, and activation function are designed. Through SPICE simulation, we evaluate the inference accuracy of a 2-layer neural network. The classification accuracy rate of 100 images of MNIST test sets is 94% accuracy obtained with software.
The pragmatic derivation and validation of measures of adaptive and maladaptive coping styles

Abstract There remain challenges involving the theoretical and empirical variability of the operationalization of coping styles. The current analyses sought to identify a parsimonious number of coping styles, from the commonly used Ways of Coping Questionnaire presented in dispositional format, and present internal consistency, construct validity and effectiveness information for the obtained scales. Data were collected from 317 adult community participants. Measures assessed coping, personality, stress, and demographic characteristics such as age and gender. Factor analyses identified that only two unique constructs, and therefore subscales, could be supported by the available data. However, three reliable parallel versions for each coping style, comprising 20, 10 and 5 items, respectively, were able to be derived. Face validity led to these uncorrelated scales being termed adaptive and maladaptive coping. Personality, stress and demographic details provided initial construct validity for these scales. This research proposes empirically sound measures for adaptive and maladaptive coping styles in dispositional format derived from a popular existing instrument. The independence of the scales demonstrates the fundamental distinction between styles, while the availability of scales of differing length with equal reliability offers particular value. These new options will provide clarity and consistency in the quantification of two commonly cited coping styles.
THE MODERATING EFFECT OF PARTNER KNOWLEDGE DISTANCE ON THE INVENTIVE BENEFITS OF FIRM KNOWLEDGE VARIETY

The Knowledge Based View recognizes the virtues of hierarchy for solving complex problems but it does not address why some firms produce better solutions. We theorize that differences in solution usefulness derive from variation in firms’ internal knowledge variety (IKV) and access to external knowledge. A firm’s IKV delineates its ability to identify and explore promising areas on the solution landscape, and the degree to which its communication channels support cross domain knowledge flows during search. A firm’s IKV thus delimits the value of the solutions it generates. We theorize that knowledge accessible at partners augments IKV-related strengths for searching rugged landscapes. We find support for these ideas in the context of drug discovery with data on invention at 229 pharmaceutical firms over 20 years.
CARRX Model Based on LSSVR Optimized by Adaptive PSO

CARRX model measures financial volatility using range. To improve the forecasting ability of CARRX model, a new volatility forecasting method combining least squares support vector regression (LSSVR) with adaptive particle swarm optimization (APSO) is proposed to the traditional CARRX model. The non-parametric CARRX model is constructed by the LSSVR and APSO algorithm is designed to select the optimal parameters of LSSVR (LSSVR-APSO-CARRX). The results of application on China stock market show that the LSSVR-APSO-CARRX model is better than the LSSVR-CARRX and CARRX model in out-of-sample forecasting performance.
Religious feeling, morality and ethical feelings: the case study on Indonesia

There is no guarantee that people will follow their professional code of ethics. Large number of violation occurred in almost every organization. In this study we argued that commitment toward code of ethics, which is related to ethical feelings, is imperative to predict whether a person will obey their professional code. This study predicted that commitment to the code of ethics is determined by individual morality (i.e. moral judgment and moral maturity), and religious feeling. The survey was conducted through online questionnaire to Indonesian employees from various sectors and undergraduate students. The analysis revealed that moral judgment cannot predict commitment toward code of ethics. The result showed that religious feeling and moral maturity have positive association with commitment to code of ethics. In addition, these two concepts also produced favorable effect on moral judgment. Discussion, implication, and limitation are provided in the final part of article.
Early Annihilation and Diffuse Backgrounds in Models of Weakly Interacting Massive Particles in Which the Cross Section for Pair Annihilation Is Enhanced by 1/v

Recent studies have considered modifications to the standard weakly interacting massive particle scenario in which the pair annihilation cross section (times relative velocity v) is enhanced by a factor 1/v~10^-3 in the Galaxy, enough to explain several puzzling Galactic radiation signals. We show that in these scenarios a burst of weakly interacting massive particle annihilation occurs in the first collapsed dark-matter halos. We show that severe constraints to the annihilation cross section derive from measurements of the diffuse extragalactic radiation and from ionization and heating of the intergalactic medium.
The Sample Allocation Problem and Non-Uniform Compressive Sampling

This paper discusses sample allocation problem (SAP) in frequency-domain Compressive Sampling (CS) of time-domain signals. An analysis that is relied on two fundamental CS principles; the Uniform Random Sampling (URS) and the Uncertainty Principle (UP), is presented. We show that CS on a single- and multi-band signals performs better if the URS is done only within the band and suppress the out-band parts, compared to ordinary URS that ignore the band limits. It means that sampling should only be done at the signal support, while the non-support should be masked and suppressed in the reconstruction process. We also show that for an N-length discrete time signal with K-number of frequency components (Fourier coefficients), given the knowledge of the spectrum, URS leads to exact sampling on the location of the K-spectral peaks. These results are used to formulate a sampling scheme when the boundaries of the bands are not sharply distinguishable, such as in a triangular- or a stacked-band- spectral signals. When analyzing these cases, CS will face a paradox; in which narrowing the band leads to a more number of required samples, whereas widening it leads to lessen the number. Accordingly; instead of signal analysis by dividing the signal's spectrum vertically into bands of frequencies, slicing horizontally magnitude-wise yields less number of required sample and better reconstruction results. Moreover, it enables sample reuse that reduces the sample number even further. The horizontal slicing and sample reuse methods imply non-uniform random sampling, where larger-magnitude part of the spectrum should be allocated more sample than the lower ones.
Encoded Invariance in Convolutional Neural Networks

Deep neural networks have proven remarkably effective on tasks where euclidean distance in the raw input space lacks semantic meaning and thus where an intermediate representation must be constructed before the usual suite of classifiers can be used effectively. Traditionally, these representations were painstakingly handcrafted in order to capture a sufficiently strong signal while reducing dimension. However, deep learning methods attempt to learn this representation over many examples. In this paper, we review and propose methods of encoding invariance in the learnt representations whereby the requisite dimension reduction can be achieved while nonetheless preserving the signal.
A General Methodology for Mapping EuroWordNets to the Suggested Upper Merged Ontology

This paper presents a general methodology to mapping EuroWordNets (Vossen, 1998) to the Suggested Upper Merged Ontology (SUMO; Niles and Pease (2001)), and we show its application to the French EuroWordNet. The process makes use of existing work on mapping Princeton WordNet (Fellbaum, 1998) to SUMO (Niles and Pease, 2003). After a general discussion of the usefulness of our approach, we provide details on the procedure of mapping individual EuroWordNet synsets to SUMO conceptual classes, and discuss issues arising from a fully automatic mapping. In addition to this, we present a quantitative analysis of the thus created semantic resource and discuss how the accuracy in determining the correct SUMO class for a particular EuroWordNet synset might be improved. Finally, we briefly hint at how such resources may be used, e.g. in order to extract selectional preferences of verbal predicates with respect to the ontological categories of their syntactic arguments.
Scaling Relational Inference Using Proofs and Refutations

Many inference problems are naturally formulated using hard and soft constraints over relational domains: the desired solution must satisfy the hard constraints, while optimizing the objectives expressed by the soft constraints. Existing techniques for solving such constraints rely on efficiently grounding a sufficient subset of constraints that is tractable to solve. We present an eager-lazy grounding algorithm that eagerly exploits proofs and lazily refutes counterexamples. We show that our algorithm achieves significant speedup over existing approaches without sacrificing soundness for real-world applications from information retrieval and program analysis.