Miracle-dz/UniFAR-FTUs huggingface dataset:
A ug 2 01 2 TIT / HEP-619 August 2012 S 3 / Z n partition function and dualities

We investigate S/Zn partition function of N = 2 supersymmetric gauge theories. A gauge theory on the orbifold has degenerate vacua specified by the holonomy. The partition function is obtained by summing up the contributions of saddle points with different holonomies. An appropriate choice of the phase of each contribution is essential to obtain the partition function. We determine the relative phases in the holonomy sum in a few examples by using duality to non-gauge theories. In the case of odd n the phase factors can be absorbed by modifying a single function appearing in the partition function. E-mail: imamura@phys.titech.ac.jp E-mail: d.yokoyama@th.phys.titech.ac.jp
Applicable Analysis and Discrete Mathematics

The class of even-hole-free graphs is structurally quite similar to the class of perfect graphs, which was the key initial motivation for their study. The techniques developed in the study of even-hole-free graphs were generalized to other complex hereditary graph classes, and in the case of perfect graphs led to the famous resolution of the Strong Perfect Graph Conjecture and their polynomial time recognition. The class of even-hole-free graphs is also of independent interest due to its relationship to β-perfect graphs. In this survey we describe all the different structural characterizations of even-holefree graphs, focusing on their algorithmic consequences.
0 A pr 2 01 2 Submitted to the Annals of Statistics UNIFORM CONVERGENCE AND RATE ADAPTIVE ESTIMATION OF A CONVEX FUNCTION By

This paper addresses the problem of estimating a convex regression function under both the sup-norm risk and the pointwise risk using B-splines. The presence of the convex constraint complicates various issues in asymptotic analysis, particularly uniform convergence analysis. To overcome this difficulty, we establish the uniform Lipschitz property of optimal spline coefficients in the l∞-norm by exploiting piecewise linear and polyhedral theory. Based upon this property, it is shown that this estimator attains optimal rates of convergence on the entire interval of interest over the Hölder class under both the risks. In addition, adaptive estimates are constructed under both the sup-norm risk and the pointwise risk when the exponent of the Hölder class is between one and two. These estimates achieve a maximal risk within a constant factor of the minimax risk over the Hölder class.
Surface Gravity as a Diagnostic of Stellar Youth

Young stars, those less than 100 Myr old, provide much information about the formation of planetary systems. The study of such objects is impossible without reliable indicators of stellar youth. The most reliable age diagnostic, placing candidate stars on an HR diagram, hinges on knowing the luminosity. We avoid the need to measure this quantity by finding the star's surface gravity instead. Using computer-generated spectra, we compare individual spectral lines in our data to models of known gravity. By minimizing the chi-square of the difference between the fluxes of the data and the model, we arrive at a best-fit value of log g, Te ff , and v sin i . The gravity value also fixes the star 's position on an HR diagram as either above the main-sequence, or on it . We find that this technique is, by itself, insufficient to constrain log g well enough to determine the appoximate age of stars.
Construção de portfólios coletivos em currículos tradicionais: uma proposta inovadora de ensino-aprendizagem

Education to promote health has traditionally been based on knowledge transmission methodologies. However, the current scenario calls for the training of professionals with a critical-reflective profile, who are able to work in teams. We present the report of an innovative experience using the construction of collective portfolios as instruments of learning, changing attitudes and training of undergraduates, in a traditional subject-based curriculum structure context. It is a descriptive exploratory study, with a qualitative-quantitative approach, based on analysis of collective portfolios (n=9), built by Health Policy students, together with an open questionnaire to students who attended the course (n=58) and also the staging of focus groups (n=3). The use of collective portfolios mobilized students in critical and reflective thinking on Brazilian health policy - the Unified Health System - broadening the concept on the health-disease process and practices related to health services, prioritizing teamwork and the active search for knowledge building, stressing the exercise of otherness, resilience and empowerment.
Can parametric statistical methods be trusted for fMRI based group studies?

The most widely used task fMRI analyses use parametric methods that depend on a variety of assumptions. While individual aspects of these fMRI models have been evaluated, they have not been evaluated in a comprehensive manner with empirical data. In this work, a total of 2 million random task fMRI group analyses have been performed using resting state fMRI data, to compute empirical familywise error rates for the software packages SPM, FSL and AFNI, as well as a standard non-parametric permutation method. While there is some variation, for a nominal familywise error rate of 5% the parametric statistical methods are shown to be conservative for voxel-wise inference and invalid for cluster-wise inference; in particular, cluster size inference with a cluster defining threshold of p = 0.01 generates familywise error rates up to 60%. We conduct a number of follow up analyses and investigations that suggest the cause of the invalid cluster inferences is spatial auto correlation functions that do not follow the assumed Gaussian shape. By comparison, the non-parametric permutation test, which is based on a small number of assumptions, is found to produce valid results for voxel as well as cluster wise inference. Using real task data, we compare the results between one parametric method and the permutation test, and find stark differences in the conclusions drawn between the two using cluster inference. These findings speak to the need of validating the statistical methods being used in the neuroimaging field.
N ov 2 01 2 Discrete multi-colour random mosaics with an application to network extraction

We introduce a class of random fields that can be understood as discrete versions of multicolour polygonal fields built on regular linear tessellations. We focus first on consistent polygonal fields, for which we show Markovianity and solvability by means of a dynamic representation. This representation forms the basis for new sampling techniques for Gibbsian modifications of such fields, a class which covers lattice based random fields. A flux based modification is applied to the extraction of the field tracks network from a SAR image of a rural area.
Guaranteeing Recoverability via Partially Constrained Transaction Logs

Transaction logging is an essential constituent to guarantee the atomicity and durability in online transaction processing (OLTP) systems. It always has a considerable impact on performance, especially in an in-memory database system. Conventional implementations of logging rely heavily on a centralized design, which guarantees the correctness of recovery by enforcing a total order of all operations such as log sequence number (LSN) allocation, log persistence, transaction committing and recovering. This strict sequential constraint seriously limits the scalability and parallelism of transaction logging and recovery, especially in the multi-core hardware environment. In this paper, we define recoverability for transaction logging and demonstrate its correctness for crash recovery. Based on recoverability, we propose a recoverable logging scheme named Poplar, which enables scalable and parallel log processing by easing the restrictions. Its main advantages are that (1) Poplar enables the parallel log persistence on multiple storage devices; (2) it replaces the centralized LSN allocation by calculating a partially ordered sequence number in a distributed manner, which allows log records to only track RAW and WAW dependencies among transactions; (3) it only demands transactions with RAW dependencies to be committed in serial order; (4) Poplar can concurrently restore a consistent database state based on the partially constrained logs after a crash. Experimental results show that Poplar scales well with the increase of IO devices and outperforms other logging approaches on both SSDs and emulated non-volatile memory.
1 9 O ct 2 01 4 A Privacy-Preserving Verifiable Incentive Mechanisms for Cr owd Sensing Applications

Crowd sensing, as a new paradigm that leverages pervasive smartphones to efficiently collect and upload sensing data, recently has been intensively explored. Incentive mechanisms with the truthfulness are proposed to attract extensive users to participate so as to achieve good service quality, enabling numerous novel applications. Although these mechanisms are so promising, there still exist many security and privacy challenges in real-life environments, such as cost privacies, sensing preferences, and the payment behavior of the platform (the crowd sensing application organizer). In this paper, we present two privacy-preserving verifiable incentive mechanisms for crowd sensing applications with homogeneous services, heterogeneous services, and submodular services under the budget constraint, not only to explore how to protect the privacy of the users and platform, but also to ensure the verifiable correctness of payments between the platform and users for crowd sensing applications. Results show that our general privacy-preserving verifiable incentive mechanisms achieve the same results as the generic one without privacy preservation.
Construction of a neuro-immune-cognitive pathway-phenotype underpinning the phenome of deficit schizophrenia.

In schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia. Nevertheless, there is a lack of knowledge how to construct pathway-phenotypes using Partial Least Squares (PLS) path modeling and Soft Independent Modeling of Class Analogy (SIMCA). This paper aims to provide a step-by-step utilization guide for the construction of pathway-phenotypes that reflect aberrations in the neuroimmune - brain circuit axis (NIBCA) in deficit schizophrenia. This NIBCA index is constructed using immune biomarkers (CCL-2, CCL-11, IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2) and neurocognitive tests (Brief Assessment of Cognition in Schizophrenia) predicting overall severity of schizophrenia (OSOS) in 120 deficit SCZ and 54 healthy participants. Using SmartPLS path analysis, a latent vector is extracted from those biomarkers and cognitive tests, which shows a good construct reliability (Cronbach alpha and composite reliability) and replicability and which is reflectively measured through its NIBCA manifestations. This NIBCA pathway-phenotype explains 75.0% of the variance in PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. Using SIMCA, we constructed a NIBCA pathway-class that defines deficit schizophrenia as a qualitatively distinct nosological entity and which allows patients with deficit schizophrenia to be authenticated as belonging to the deficit schizophrenia class. In conclusion, our nomothetic approach to develop a nomological network combining neuro-immune and neurocognitive phenome markers to predict OSOS and cross-validate a diagnostic class generated replicable models reflecting the key phenome of the illness, which may mediate the effects of genome X environmentome interactions on the final outcome phenome features, namely symptomatology and phenomenology.
A O(c^k n) 5-Approximation Algorithm for Treewidth

We give an algorithm that for an input n-vertex graph G and integer k>0, in time 2^[O(k)]n either outputs that the treewidth of G is larger than k, or gives a tree decomposition of G of width at most 5k+4. This is the first algorithm providing a constant factor approximation for treewidth which runs in time single-exponential in k and linear in n. Treewidth based computations are subroutines of numerous algorithms. Our algorithm can be used to speed up many such algorithms to work in time which is single-exponential in the treewidth and linear in the input size.
A rare case of neoplastic transformation in a longstanding case of fistula-in-ano

Mucinous adenocarcinoma development in a long standing case of with fistula-in-ano is rare. A case of fistula–in-ano diagnosed as low grade mucinous adenocarcinoma on biopsy and confirmed on Immunohistochemistry is discussed. It was established that the carcinoma had developed within the fistula and was not a secondary manifestation of any other tumor.
Ocular Dermoid in Crossbred calf- A Case Report

The present paper deals with a typical case of ocular dermoid cyst in three weeks old crossbred calf. The patient had a history of lacrimation and a hairy growth in the left eye. Based on the history and clinical examination, the case was diagnosed as ocular dermoid cyst and removed successfully by superficial keratectomy. Key words : Calf, Dermoid, Keratectomy, Teratology
An Object-Oriented MDP Representation for Robotic pl anning

This paper aims to implement Object-Oriented Markov Decision Process (OO-MDPs) for goal planning and navigation of robot in an indoor environment. We use the OO-MDP representation of the environment which is a natural way of modeling the environment based on objects and their interactions. The paper aims to extend the well known Taxi domain example which has been tested on grid world environment to robotics domain with larger statespaces. For the purpose of this project we have created simulation of the environment and robot in ROS (Robot operating system) with Gazebo and Rviz as visualization tools. The mobile robot uses a 2D LIDAR module to perform SLAM in the unknown environment. The goal of this project is to be able to make an autonomous agent capable of performing planning and navigation in an indoor environment to deliver boxes (passengers in Taxi domain) placed at random locations to a particular location (warehouse). The approach can be extended to a wide variety of mobile and manipulative robots
Detecting stock market colluding groups with spectral clustering

We provide the first application of spectral clustering to detecting colluding groups in a stock market. For ensuring market efficiency, stock market regulators take strict action against colluding groups who manipulate the price of stocks and create artificial liquidity. In this work, we show how to use existing machine learning techniques for algorithmically detecting suspected colluding groups based on the stock market data. A key contribution of this work is in appropriately defining ‘closeness’ between two traders that takes into account various factors like common traders and frequency, volume and price of a stock traded between them. In an earlier work, Apte and Palshikar (2008) have applied non-spectral clustering technique to the problem where they use the volume of the trades as a measure of closeness. Our experience with real data suggests that including other factors in measuring closeness helps detect colluding groups that will otherwise go undetected. We demonstrate the effectiveness of our algorithm by detecting clusters in a random Erdős-Rényi graph with hidden clusters.
Screening for brain impairment: A manual for mental health practice

Each chapter contains updated information and new empirical data, including extensive information on neurological and psychiatric disorders, adult attention deficit disorder, and screening for and assessing the emotional correlates of brain impairment. Also included are new chapters on computerized assessment and developing relations with other professionals, including clinical neuropsychologists, neurologists, primary care physicians, neurosurgeons, and psychiatrists.
DCS Paper

This paper describes a method for creating structure from heterogeneous sources, as part of an information database, or more specifically, a ‘concept base’. Structures called ‘concept trees’ can grow from the semi-structured sources when consistent sequences of concepts are presented. They might be considered to be dynamic databases, possibly a variation on the distributed Agent-Based or Cellular Automata models, or even related to Markov models. Semantic comparison of text is required, but the trees can be built more, from automatic knowledge and statistical feedback. This reduced model might also be attractive for security or privacy reasons, as not all of the potential data gets saved. The construction process maintains the key requirement of generality, allowing it to be used as part of a generic framework. The nature of the method also means that some level of optimisation or normalisation of the information will occur. This gives comparisons with databases or knowledge-bases, but a database system would firstly model its environment or datasets and then populate the database with instance values. The concept base deals with a more uncertain environment and therefore cannot fully model it beforehand. The model itself therefore evolves over time. Similar to databases, it also needs a good indexing system, where the construction process provides memory and indexing structures. These allow for more complex concepts to be automatically created, stored and retrieved, possibly as part of a more cognitive model. There are also some arguments, or more abstract ideas, for merging physical-world laws into these automatic processes.
IT ] 9 M ar 2 01 6 A Novel Design of Linear Phase Non-uniform Digital Filter

In many applications such as wireless communications and su bband adaptive filtering, we need to design non-uniform filter banks (NUFB), which may lead to better performances and reduced hardware complexity whe n compared to uniform filter bank. NUFB satisfying linear phase property for a ll the constituent filters, are desirable in applications such as speech and ima ge processing and in communication. This paper proposes a novel design of non-un iform modified discrete fourier transform filter bank (MDFT FB). Here, each non -uniform channel is obtained by merging the nearby channels of a uniform MDFT F B. The reported works of non-uniform cosine modulated filter banks (CMFBs) d o not satisfy linear phase property for all the constituent filters. In this work, we introduce the design of a non-uniform MDFT FB which satisfies linear phase propert y for all the constituent filters. The proposed design of the non-uniform MDF T FB is checked utilizing the alias cancellation among the channels, disto rtion and flatness condition of the channels. A condition is derived to find the channe ls when merged with adjacent channels, will cause aliasing in M channels. I n the design and implementation of the proposed non-uniform MDFT FB, the struc ture of the uniform Preprint submitted to Elsevier March 10, 2016 MDFT filter bank is preserved and hence all the advantages of M DFT FB over the DFT filter bank, are guaranteed. Hence without increasing th e design complexity, the non-uniform MDFT FB is designed.
1 Ju l 2 01 3 Conway and iteration hemirings Parts 1 and 2

Conway hemirings are Conway semirings without a multiplicative unit. We also define iteration hemirings as Conway hemirings satisfying certain identities associated with the finite groups. Iteration hemirings are iteration semirings without a multiplicative unit. We provide an analysis of the relationship between Conway hemirings and (partial) Conway semirings and describe several free constructions. In the second part of the paper we define and study hemimodules of Conway and iteration hemirings, and show their applicability in the analysis of quantitative aspects of the infinitary behavior of weighted transition systems. These include discounted and average computations of weights investigated recently in [5, 6].
CTNN: Corticothalamic-inspired neural network

Sensory predictions by the brain in all modalities take place as a result of bottom-up and top-down connections both in the neocortex and between the neocortex and the thalamus. The bottom-up connections in the cortex are responsible for learning, pattern recognition, and object classification, and have been widely modelled using artificial neural networks (ANNs). Here, we present a neural network architecture modelled on the top-down corticothalamic connections and the behaviour of the thalamus: a corticothalamic neural network (CTNN), consisting of an auto-encoder connected to a difference engine with a threshold. We demonstrate that the CTNN is input agnostic, multi-modal, robust during partial occlusion of one or more sensory inputs, and has significantly higher processing efficiency than other predictive coding models, proportional to the number of sequentially similar inputs in a sequence. This increased efficiency could be highly significant in more complex implementations of this architecture, where the predictive nature of the cortex will allow most of the incoming data to be discarded.
Fostering Peer Learning through a New Game-Theoretical Approach in a Blended Learning Environment

Obtaining knowledge and skill achievement through peer learning can lead to higher academic achievement. However, peer learning implementation is not just about putting students together and hoping for the best. At its worst-designed, peer learning may result in one person doing all the effort for instance, or may fail to encourage the students to interact enough with the task and so enhance the task in hand. This study proposes a mechanism as well as an instructional design to foster well-organized peer learning based on game theory $(PD\_PL)$. The proposed mechanism uses prisoner's dilemma and maps the strategy and payoff concepts found in prisoner's dilemma onto a peer learning atmosphere. PD\_PL was implemented during several sessions of four university courses and with 142 computer engineering students. %The results of the pre-test and post-test exams of all the sessions were compared with R software through Paired Hotelling's T-Square analysis in order to investigate the impacts of $PD\_PL$ and the proposed instructional design on students' personal learning. The study results indicated that PD\_PL was beneficial and favourable to the students. Further analysis showed that the $PD\_PL$ had sometimes even enhanced learning by up to $47.2\%$. %The results of a subjective evaluation showed that the majority of respondents found $PD\_PL$ to be an attractive and efficient tool for learning enhancement. %Everybody who is interested in designing peer learning programs and tools will find this study interesting.
A Lyapunov-Based Small-Gain Theorem for Infinite Networks

This article presents a small-gain theorem for networks composed of a countably infinite number of finite-dimensional subsystems. Assuming that each subsystem is exponentially input-to-state stable, we show that if the gain operator, collecting all the information about the internal Lyapunov gains, has a spectral radius less than one, the overall infinite network is exponentially input-to-state stable. The effectiveness of our result is illustrated through several examples including nonlinear spatially invariant systems with sector nonlinearities and a road traffic network.