Miracle-dz/UniFAR-FTUs huggingface dataset:
M ay 2 00 7 Robust MultiCellular Developmental Design to appear

This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange ”chemicals” with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not pre-determined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the ’flags’ problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multi-cellular organisms exhibit almost perfect self-healing characteristics.
N ov 2 00 7 LTH / 775 SQCD Vacua and Geometrical Engineering

We consider the geometrical engineering constructions for the N = 1 SQCD vacua recently proposed by Giveon and Kutasov. After one T-duality, the geometries with wrapped D5 branes become N = 1 brane configurations with NS branes and D4 branes. The field theories encoded by the geometries contain extra massive adjoint fields for the flavor group. After performing a flop, the geometries contain branes, antibranes and branes wrapped on non-holomorphic cycles. The various tachyon condensations between pairs of wrapped D5 branes and anti D5 branes together with deformations of the cycles give rise to a variety of supersymmetric and metastable non-supersymmetric vacua.
ParticleFilters for Partially-ObservedBooleanDynamical Systems

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear models with application in estimation and control of Boolean processes based on noisy and incomplete measurements. The optimal minimum mean square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the case of large systems, due to computational and memory requirements. To address this, we propose approximate MMSE filtering and smoothing algorithms based on the auxiliary particle filter (APF) method from sequential Monte-Carlo theory. These algorithms are used jointly with maximum-likelihood (ML) methods for simultaneous state and parameter estimation in POBDS models. In the presence of continuous parameters, ML estimation is performed using the expectation-maximization (EM) algorithm; we develop for this purpose a special smoother which reduces the computational complexity of the EM algorithm. The resulting particle-based adaptive filter is applied to a POBDS model of Boolean gene regulatory networks observed through noisy RNA-Seq time series data, and performance is assessed through a series of numerical experiments using the well-known cell cycle gene regulatory model.
Asyptotic Normality for Maximum Likelihood Estimation and Operational Risk

Operational risk models commonly employ maximum likelihood estimation (MLE) to fit loss data to heavy-tailed distributions. Yet several desirable properties of MLE (e.g. asymptotic normality) are generally valid only for large sample-sizes, a situation rarely encountered in operational risk. In this paper, we study how asymptotic normality does--or does not--hold for common severity distributions in operational risk models. We then apply these results to evaluate errors caused by failure of asymptotic normality in constructing confidence intervals around the MLE fitted parameters.
MCTS-based Automated Negotiation Agent (Extended Abstract)

This paper introduces a new Negotiating Agent for automated negotiation on continuous domains and without considering a specified deadline. The agent bidding strategy relies on Monte Carlo Tree Search, which is a trendy method since it has been used with success on games with high branching factor such as Go. It uses two opponent modeling techniques for its bidding strategy and its utility: Gaussian process regression and Bayesian learning. Evaluation is done by confronting the existing agents that are able to negotiate in such context: Random Walker, Tit-for-tat and Nice Tit-for-Tat. None of those agents succeeds in beating our agent; moreover the modular and adaptive nature of our approach is a huge advantage when it comes to optimize it in specific applicative contexts.
Detecting a trend change in cross-border epidemic transmission

A method for detecting a trend change in cross-border epidemic transmission is developed for a standard epidemiological SIR compartment model and a meta-population network model. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders from a time series of the number of new cases reported in multiple geographical regions. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert.
Domain Adaptation Methods : For the transfer learning problem , the existing domain adaptation methods

We envision Artificial Intelligence marketplaces to be platforms where consumers, with very less data for a target task, can obtain a relevant model by accessing many private data sources with vast number of data samples. One of the key challenges is to construct a training dataset that matches a target task without compromising on privacy of the data sources. To this end, we consider the following distributed data summarizataion problem. Given K private source datasets denoted by [Di]i∈[K] and a small target validation set Dv, which may involve a considerable covariate shift with respect to the sources, compute a summary datasetDs ⊆ ⋃ i∈[K]Di such that its statistical distance from the validation dataset Dv is minimized. We use the popular Maximum Mean Discrepancy as the measure of statistical distance. The non-private problem has received considerable attention in prior art, for example in prototype selection (Kim et al., NIPS 2016). Our work is the first to obtain strong differential privacy guarantees while ensuring the quality guarantees of the nonprivate version. We study this problem in a Parsimonious Curator Privacy Model, where a trusted curator coordinates the summarization process while minimizing the amount of private information accessed. Our central result is a novel protocol that (a) ensures the curator accesses at most O(K 1 3 |Ds| + |Dv|) points (b) has formal privacy guarantees on the leakage of information between the data owners and (c) closely matches the best known non-private greedy algorithm. Our protocol uses two hash functions, one inspired by the Rahimi-Recht random features method and the second leverages state of the art differential privacy mechanisms. Further, we introduce a novel “noiseless” differentially private auctioning protocol for winner notification, which may be of independent interest. Apart from theoretical guarantees, we demonstrate the efficacy of our protocol using real-world datasets. ∗1Equal contribution by these authors. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. ar X iv :1 91 0. 12 83 2v 1 [ cs . L G ] 2 8 O ct 2 01 9
Ju n 20 08 Burgers ’ equation in 2 D SU ( N )

It is shown that the logarithmic derivative of the characteristic polynomial of a Wilson loop in two dimensional pure Yang Mills theory with gauge group SU(N) exactly satisfies Burgers’ equation, with viscosity given by 1/(2N). The Wilson loop does not intersect itself and Euclidean space-time is assumed flat and infinite. This result provides a precise framework in 2D YM for recent observations of Blaizot and Nowak and was inspired by their work.
Ju n 20 13 Linear Dependent Types for Domain Specific Program Analysis ( Abstract presented at HOPA 2013 )

In this tutorial I will present how a combination of linear an d dependent type can be useful to describe different properties abo ut higher order programs. Linear types have been proved particularly useful to express properties of functions; dependent types are useful to describe the behav ior of the program in terms of its control flow. This two ideas fits together well whe n one is interested in analyze properties of functions depending on the control flow of the program. I will present these ideas with example taken by complexity a nalysis and sensitivity analysis. I will conclude the tutorial by arguing abo ut the generality of this
Nonlinear filtering for CT radiation dose reduction

Nonlinear bilateral filters (BF) deliver a fine blend of computational simplicity and blurfree denoising. However, little is known about their nature, noise-suppressing properties, and optimal choices of filter parameters. Our study is meant to fill this gap—explaining the underlying mechanism of bilateral filtering and providing the methodology for optimal filter selection. Practical application to CT image denoising is discussed to illustrate our results.
A memory enhanced LSTM for modeling complex temporal dependencies

In this paper, we present Gamma-LSTM, an enhanced long short term memory (LSTM) unit, to enable learning of hierarchical representations through multiple stages of temporal abstractions. Gamma memory, a hierarchical memory unit, forms the central memory of Gamma-LSTM with gates to regulate the information flow into various levels of hierarchy, thus providing the unit with a control to pick the appropriate level of hierarchy to process the input at a given instant of time. We demonstrate better performance of Gamma-LSTM model regular and stacked LSTMs in two settings (pixel-by-pixel MNIST digit classification and natural language inference) placing emphasis on the ability to generalize over long sequences.
Fast Evaluation and Approximation of the Gauss-Newton Hessian Matrix for the Multilayer Perceptron

We introduce a fast algorithm for entry-wise evaluation of the Gauss-Newton Hessian (GNH) matrix for the multilayer perceptron. The algorithm has a precomputation step and a sampling step. While it generally requires $O(Nn)$ work to compute an entry (and the entire column) in the GNH matrix for a neural network with $N$ parameters and $n$ data points, our fast sampling algorithm reduces the cost to $O(n+d/\epsilon^2)$ work, where $d$ is the output dimension of the network and $\epsilon$ is a prescribed accuracy (independent of $N$). One application of our algorithm is constructing the hierarchical-matrix (\hmatrix{}) approximation of the GNH matrix for solving linear systems and eigenvalue problems. While it generally requires $O(N^2)$ memory and $O(N^3)$ work to store and factorize the GNH matrix, respectively. The \hmatrix{} approximation requires only $\bigO(N r_o)$ memory footprint and $\bigO(N r_o^2)$ work to be factorized, where $r_o \ll N$ is the maximum rank of off-diagonal blocks in the GNH matrix. We demonstrate the performance of our fast algorithm and the \hmatrix{} approximation on classification and autoencoder neural networks.
Imperial / TP / 2009 / DW / 01 E 7 ( 7 ) formulation of N = 2 backgrounds

In this paper we reformulate N = 2 supergravity backgrounds arising in type II string theory in terms of quantities transforming under the U-duality group E7(7). In particular we combine the Ramond–Ramond scalar degrees of freedom together with the O(6, 6) pure spinors which govern the Neveu-Schwarz sector by considering an extended version of generalised geometry. We give E7(7)-invariant expressions for the Kähler and hyperkähler potentials describing the moduli space of vector and hypermultiplets, demonstrating that both correspond to standard E7(7) coset spaces. We also find E7(7) expressions for the Killing prepotentials defining the scalar potential, and discuss the equations governing N = 1 vacua in this formalism.
3 Theory of Shape Space for Triangulated 3 D Polygons

We present a novel approach to morph between two isometric poses of the same non-rigid object given as triangular meshes. We model the morphs as linear interpolations in a suitable shape space S. For triangulated 3D polygons, we prove that interpolating linearly in this shape space corresponds to the most isometric morph in R. We then extend this shape space to arbitrary triangulations in 3D using a heuristic approach and show the practical use of the approach using experiments. Furthermore, we discuss a modified shape space that is useful for isometric skeleton morphing. All of the newly presented approaches solve the morphing problem without the need to solve a minimization problem.
Primal Meaning Recommendation via On-line Encyclopedia

Polysemy is a very common phenomenon in modern languages. Under many circumstances, there exists a primal meaning for the expression. We define the primal meaning of an expression to be a frequently used sense of that expression from which its other frequent senses can be deduced. Many of the new appearing meanings of the expressions are either originated from a primal meaning, or are merely literal references to the original expression, e.g., apple (fruit), Apple (Inc), and Apple (movie). When constructing a knowledge base from on-line encyclopedia data, it would be more efficient to be aware of the information about the importance of the senses. In this paper, we would like to explore a way to automatically recommend the primal meaning of an expression based on the textual descriptions of the multiple senses of an expression from on-line encyclopedia websites. We propose a hybrid model that captures both the pattern of the description and the relationship between different descriptions with both weakly supervised and unsupervised models. The experiment results show that our method yields a good result with a P@1 (precision) score of 83.3 per cent, and a MAP (mean average precision) of 90.5 per cent, surpassing the UMFS-WE baseline by a big margin (P@1 is 61.1 per cent and MAP is 76.3 per cent).
Personalized Context-Aware Multi-Modal Transportation Recommendation

This study proposes to find the most appropriate transport modes with awareness of user preferences (e.g., costs, times) and trip characteristics (e.g., purpose, distance). The work was based on real-life trips obtained from a map application. Several methods including gradient boosting tree, learning to rank, multinomial logit model, automated machine learning, random forest, and shallow neural network have been tried. For some methods, feature selection and over-sampling techniques were also tried. The results show that the best performing method is a gradient boosting tree model with synthetic minority over-sampling technique (SMOTE). Also, results of the multinomial logit model show that (1) an increase in travel cost would decrease the utility of all the transportation modes; (2) people are less sensitive to the travel distance for the metro mode or a multi-modal option that containing metro, i.e., compared to other modes, people would be more willing to tolerate long-distance metro trips. This indicates that metro lines might be a good candidate for large cities.
A global workspace theory model for trust estimation in human-robot interaction

Successful and genuine social connections between humans are based on trust, even more when the people involved have to collaborate to reach a shared goal. With the advent of new findings and technologies in the field of robotics, it appears that this same key factor that regulates relationships between humans also applies with the same importance to human-robot interactions (HRI). Previous studies have proven the usefulness of a robot able to estimate the trustworthiness of its human collaborators and in this position paper we discuss a method to extend an existing state-of-the-art trust model with considerations based on social cues such as emotions. The proposed model follows the Global Workspace Theory (GWT) principles to build a novel system able to combine multiple specialised expert systems to determine whether the partner can be considered trustworthy or not. Positive results would demonstrate the usefulness of using constructive biases to enhance the teaming skills of social robots.
Implementation of decision trees for data streams in the Spark Streaming platform

In the era of big data, enormous amounts of data are created, replicated and transferred every day. The current technology for handling and analyzing vast amounts of data allows us to develop applications for various problems (e.g., DNA sequence analysis, medical imaging, traffic control) that could not previously be solved efficiently. More precisely, the time required to process large volumes of data can be minimized by using distributed computing platforms such as Apache Spark. The Apache Spark framework includes various implementations for large-scale machine learning, distributed data streaming processing and parallel graph analytics. The Spark Streaming platform provides scalable and fault-tolerant data streaming processing. However, there is only a limited number of implemented distributed incremental machine learning algorithms available in the Spark Streaming platform. In this thesis, we propose a parallel implementation of an incremental and scalable tree learning method for classification in Spark Streaming, the Hoeffding decision tree. Our proposed implementation performs horizontal data parallelism in the shared-nothing architecture of Spark. The Hoeffding bound guarantees with high confidence that the Hoeffding decision tree is asymptotically identical to a batch-learning one. The high dimensional statistics, required for evaluating splits, are stored as sparse matrices in main memory across the Spark cluster. These statistics are instantly updated, when new training instances are available. Furthermore, distributed computations are performed in order to identify the optimal split and assess whether the splitting criterion is satisfied. The generated model is used in order to make color classification based on the spectral signature of each color. Each color has a different chemical composition, and as a consequence a different spectral signature.
Vasodilation of rat skeletal muscle arteries by the novel BK channel opener GoSlo is mediated by the simultaneous activation of BK and Kv7 channels

BK channels play important roles in various physiological and pathophysiological processes and thus have been the target of several drug development programmes focused on creating new efficacious BK channel openers, such as the GoSlo‐SR compounds. However, the effect of GoSlo‐SR compounds on vascular smooth muscle has not been studied. Therefore, we tested the hypothesis that GoSlo‐SR compounds dilate arteries exclusively by activating BK channels.
Thymoquinone reverses nonalcoholic fatty liver disease (NAFLD) associated with experimental hypothyroidism.

OBJECTIVES To assess the efficacy of thymoquinone (TQ), the most active constituent in Nigella sativa, which is a medicinal plant from the Ranunculaceae family, in restoring the normal liver structure after 6-propyl-2-thiouracil (PTU)-induced hypothyroidism and explore the mechanism behind this. MATERIALS AND METHODS Hypothyroidism was induced in rats by injection of PTU [6 mg∕kg body weight (b.w.)] for six weeks. Twenty-four adult male Wistar rats were divided into four groups; the control, TQ-treated at the dose 400 mg∕kg b.w. , untreated hypothyroidism and TQ-treated hypothyroid groups. Serum levels of thyroid hormones and antioxidant profile were measured. Real-time polymerase chain reaction was used to assess gene expression of catalase (CAT). Liver was histopathologically examined using routine and immunohistochemical techniques. RESULTS Livers of rats with hypothyroidism displayed nonalcoholic fatty liver disease (NAFLD) in the form of steatosis as well as nonalcoholic steatohepatitis (NASH). Moreover, there was an intralobular inflammatory reaction associated with significant (p<0.05) increases in the density of resident hepatic macrophages [cluster of differentiation 68 (CD68)+ cells], as well as in activated hepatic stellate cells, alpha-smooth muscle actin (α-SMA) index in livers with hypothyroidism. Resolution of hypothyroid NAFLD was observed in livers after treatment with TQ. The significantly increased (p<0.05) steatosis, lobular inflammation, NAFLD activity scores, α-SMA index as well as CD68+ cells induced by hypothyroidism were corrected after TQ administration. Up-regulation of the CAT gene in livers with hypothyroidism after treatment with TQ supported our hypothesis of its antioxidant mechanistic hepatoprotective action. CONCLUSIONS TQ efficiently restores the normal liver histology in hypothyroid rats with up-regulation of the antioxidant CAT gene.
Translation, Cross-cultural Adaptation and Psychometric Properties of the Hausa Versions of the Numerical Pain Rating Scale and Global Rating of Change Scale in a Low-literate Population with Chronic Low Back Pain.

STUDY DESIGN Translation, cross-cultural adaptation and psychometric testing. OBJECTIVE To translate, cross-culturally adapt and validate the Numeric Pain Rating Scale (NPRS) and the Global Rating of Change Scale (GRCS) into Hausa language. SUMMARY OF BACKGROUND DATA The NPRS and GRCS are commonly used patient-reported outcome measures in a variety of pain-related conditions including low back pain. To date, neither the NPRS nor GROC are available in Hausa language. METHODS The Hausa versions of the NPRS (NPRS-H) and GRCS (GRCS-H) were developed using recommended guidelines. The final versions were then administered to 120 patients to access their psychometric properties. Reliability assessment included calculations of intraclass correlation coefficient (ICC) and minimal detectable change (MDC) among the stable group. Construct validity and concurrent validity were assessed using the Spearman rank correlation coefficient. Internal responsiveness was assessed using mean change scores, standardized effect size and standard response mean. Receiver operating characteristic curves were plotted to determine the external responsiveness of the NPRS-H using the area under the curve (AUC), and minimum important change (MIC) for small, medium and large improvements. Outcome measures consisted of the Visual Analogue Scale for pain (VAS-pain) and Oswestry Disability Index (ODI). RESULTS The NPRS-H and GRCS-H were successfully developed. High test-retest reliability was demonstrated for both the NPRS-H (ICC = 0.95) and GRCS-H (ICC = 0.94) with MDC points of 1.0 and 1.2 respectively. Both the NPRS-H and GRCS-H correlated moderately with VAS-pain and ODI. The mean change of the NPRS-H scores correlated moderately with the GRCS-H. Both measures demonstrated good internal responsiveness. External responsiveness of the NPRS-H was demonstrated at three levels with AUC = 0.875-0.972, and MIC = 2.5-3.5. CONCLUSIONS The NPRS and GRCS were successfully adapted into Hausa language with acceptable reliability, validity and responsiveness. These measures are appropriate for clinical and research use among Hausa-speaking patients. LEVEL OF EVIDENCE 3.