Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Quadrature Sequence and Sparsifying Methodology for Mean-Field Variational Inference

This work proposes a quasirandom sequence of quadratures for high-dimensional mean-ﬁeld variational inference and a related sparsifying methodology. Each iterate of the sequence contains two evaluations points that combine to correctly integrate all univariate quadratic functions, as well as univariate cubics if the mean-ﬁeld factors are symmetric. More importantly, averaging re-sults over short subsequences achieves periodic exactness on a much larger space of multivariate polynomials of quadratic total degree. This framework is devised by ﬁrst considering stochastic blocked mean-ﬁeld quadratures, which may be useful in other contexts. By replacing pseudorandom sequences with quasirandom sequences, over half of all multivariate quadratic basis functions integrate exactly with only 4 function evaluations, and the exactness dimension increases for longer subsequences. Analysis shows how these eﬃcient integrals characterize the dominant log-posterior contributions to mean-ﬁeld variational approximations, including diagonal Hessian approximations, to support a robust sparsifying methodology in deep learning algorithms. A numerical demonstration of this approach on a simple Convolutional Neural Network for MNIST retains high test accuracy, 96 . 9%, while training over 98 . 9% of parameters to zero in only 10 epochs, bearing potential to reduce both storage and energy requirements for deep learning models.
The Secure Sockets Layer (SSL) Protocol Version 3.0

This document specifies Version 3.0 of the Secure Sockets Layer (SSL V3.0) protocol, a security protocol that provides communications privacy over the Internet. The protocol allows client/server applications to communicate in a way that is designed to prevent eavesdropping, tampering, or message forgery.
Thermally assisted OSL: A potent tool for improvement in minimum detectable dose and extension of dose range of Al2O3:C

The influence of electron-phonon interaction on the shape of the optically stimulated luminescence decay curve of Al2O3:C has been studied using thermally assisted optically stimulated luminescence (TA-OSL). The minimum detectable dose (MDD) of a phosphor depends on the standard deviation of the background signal which affects the signal-to-noise ratio. The standard deviation of the background signal reduces at lower stimulation light intensity while the readout time increases. Further, measurement at higher temperature enhances the OSL signal with faster decay due to the temperature dependence of photo-ionization cross-section. To achieve the same decay constant and more signal, the temperature of measurement was raised. As a result of lowering the stimulation in-tensity at higher temperature (85°C) the overall MDD of α-Al2O3:C was found to improve by 1.8 times. For extension of dose linearity in higher range, deeper traps were studied by simultaneous application of CW-OSL and thermal stimulation up to 400°C, using a linear heating rate of 4K/s. By using this method, two well defined peaks at 121°C and 232°C were observed. These TA-OSL peaks have been correlated with two deeper defects which can be thermally bleached at 650°C and 900°C respectively. These deeper defects are stable up to 500°C, so they can store absorbed dose information even if the sample is inadvertently exposed to light or heat. The dose vs. TA-OSL response from deep traps of α-Al2O3:C was found to be linear up to 10 kGy, thus extending its application for high dose dosimetry.
Simulating the heterogeneity in braided channel belt deposits: 2. Examples of results and comparison to natural deposits

In part 1 of this paper (Ramanathan et al., 2010b) we presented a methodology and a code for modeling the hierarchical sedimentary architecture in braided channel belt deposits. Here in part 2, the code was used to create a digital model of this architecture and the corresponding spatial distribution of permeability. The simulated architecture was compared to the real stratal architecture observed in an abandoned channel belt. The comparisons included assessments of similarity which were both qualitative and quantitative. The qualitative comparisons show that the geometries of unit types within the synthetic deposits are generally consistent with field observations. The unit types in the synthetic deposits would generally be recognized as representing their counterparts in nature, including cross stratasets, lobate and scroll bar deposits, and channel fills. Furthermore, the synthetic deposits have a hierarchical spatial relationship among these units consistent with observations from field exposures and geophysical images. In quantitative comparisons the proportions and the length, width, and height of unit types at different scales, across all levels of the stratal hierarchy, compare well between the synthetic and the natural deposits. A number of important attributes of the synthetic channel belt deposits are shown to be influenced by more than one level within the hierarchy of stratal architecture. First, the high‐permeability open‐framework gravels connected across all levels and thus formed preferential flow pathways; open‐framework gravels are known to form preferential flow pathways in natural channel belt deposits. The nature of a connected cluster changed across different levels of the stratal hierarchy, and as a result of the geologic structure, the connectivity occurs at proportions of open‐framework gravels below the theoretical percolation threshold for random infinite media. Second, when the channel belt model was populated with permeability distributions by lowest‐level unit type, the composite permeability semivariogram contained structures that were identifiable at more than one scale, and each of these structures could be directly linked to unit types of different scales existing at different levels within the hierarchy of strata. These collective results are encouraging with respect to our goal that this model be relevant for testing ideas in future research on flow and transport in aquifers and reservoirs with multiscale heterogeneity.
Towards 3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair

The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for the need of 3D alignments between inserted devices and aortic branches. The navigation commonly via 2D fluoroscopic images, lacks anatomical information, resulting in longer operation hours and radiation exposure. In this paper, a skeleton instantiation framework of Abdominal Aortic Aneurysm (AAA) from a single 2D fluoroscopic image is introduced for real-time 3D robotic path planning. A graph matching method is proposed to establish the correspondences between the 3D preoperative and 2D intra-operative AAA skeletons, and then the two skeletons are registered by skeleton deformation and regularization in respect to skeleton length and smoothness. Furthermore, deep learning was used to segment 3D preoperative AAA from Computed Tomography (CT) scans to facilitate the framework automation. Simulation, phantom and patient AAA data sets have been used to validate the proposed framework. 3D distance error of 2mm was achieved in the phantom setup. Performance advantages were also achieved in terms of accuracy, robustness and time-efficiency.
An overview of reservoir quality in producing Cretaceous strata of the Middle East

ABSTRACT A compilation of average porosity and permeability data for Cretaceous petroleum reservoirs of the Middle East reveals important differences between the two main tectonic provinces. The Arabian Platform is characterized by inverse correlation of average porosity with burial depth in both carbonates and sandstones, whereas the Zagros Fold Belt (almost exclusively carbonates) has distinctly lower porosity and no depth correlation. These contrasts are suggested to reflect the fact that Arabian Platform strata are mostly near their maximum burial depth, whereas Zagros strata have experienced varying uplift and erosion following maximum burial in mid-Tertiary time. The carbonate reservoirs show no correlation between average porosity and average permeability, probably because of wide differences in the dominant pore types present, and permeabilities tend to be much higher for sandstones than for carbonates. Existence of the Arabian Platform porosity–depth correlation, despite an apparently wide diversity of depositional settings and early diagenetic porosity modifications among the individual reservoirs, illustrates and confirms some fundamental generalities about how burial diagenesis controls the overall porosity evolution of reservoir rock bodies. Although porosity commonly shows enormous small-scale heterogeneity in both carbonates and sandstones, the average pre-burial porosity of larger stratigraphic intervals tends to be very high. Burial diagenesis progressively destroys this porosity by chemical compaction and associated (stylolite-sourced) cementation. Thus, all portions of the affected rock body move toward the zero limit as depth increases, although the rates of porosity occlusion vary greatly, depending on rock fabric and early diagenesis. Average reservoir porosity therefore tends to correlate inversely with maximum burial depth, regardless of initial lithological heterogeneity.
Réponse sismique de la croûte terrestre à un changement de contrainte : application aux doublets de séismes

Nous presentons une analyse des variations du taux de sismicite liees aux doublets de forts seismes. Cette etude se concentre sur la zone des repliques du 1er seisme, et cherche a estimer l'impact du 2nd choc principal sur cette activite. Nous nous sommes penches sur deux cas de doublets de seismes : la sequence turque des seismes d'Izmit et de Duzce en 1999, et le doublet sud-islandais de juin 2000. Ces deux exemples nous apprennent que la reactivation de sismicite induite par le 2nd seisme d'un doublet n'est pas forcement correlee avec les regions d'augmentation de la contrainte statique, et qu'elle s'etablit avec predilection sur les sites d'une activite geothermale. Sa decroissance temporelle n'est pas systematiquement conforme a la loi d'Omori, puisque des apaisements de ce taux peuvent apparaitre avec un delai de quelques jours a quelques mois. Nous avons egalement investigue les proprietes des doublets de seismes a l'echelle globale, et nous montrons que ceux-ci peuvent etre simplement decrits par les lois habituelles gouvernant la sismicite, la loi d'Omori-Utsu et la loi de Gutenberg-Richter. Enfin, contrairement aux resultats obtenus a partir de catalogues regionaux, il semblerait que la production moyenne de repliques consecutive aux 2nds seismes des doublets soit equivalente a la superposition des productions de deux seismes isoles de magnitudes similaires. Cette derniere remarque merite toutefois d'etre etayee par une analyse plus fine des catalogues regionaux, au vu du manque de resolution d'un tel catalogue global pour les seismes de petite magnitude.
Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information

MOTIVATION Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Alzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence. RESULTS In this paper, we develop a method based on support vector machines (SVMs) that starting from the protein sequence information can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans. Using a dataset of 21 185 single point mutations, 61% of which are disease-related, out of 3587 proteins, we show that our predictor can reach more than 74% accuracy in the specific task of predicting whether a single point mutation can be disease related or not. Our method, although based on less information, outperforms other web-available predictors implementing different approaches. AVAILABILITY A beta version of the web tool is available at http://gpcr.biocomp. unibo.it/cgi/predictors/PhD-SNP/PhD-SNP.cgi
Resource Allocation in Wireless Control Systems via Deep Policy Gradient

In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to packet dropouts, proper allocation of limited resources, e.g. transmission power, across plants is critical for maintaining reliable operation. In this paper, we formulate the design of an optimal resource allocation policy that uses current plant states and wireless channel conditions to assign resources used to send control actuation information back to plants. While this problem is challenging due to its infinite dimensionality and need for explicit system model and state knowledge, we propose the use of deep reinforcement learning techniques to find data-driven resource allocation policies. In particular, we use model-free policy gradient methods to directly learn continuous power allocation policies without knowledge of plant dynamics or communication models. Numerical simulations demonstrate the strong performance of learned policies relative to baseline resource allocation methods.
A Slingshot Approach to Learning in Monotone Games

In this paper, we address the problem of computing equilibria in monotone games. The traditional Follow the Regularized Leader algorithms fail to converge to an equilibrium even in two-player zero-sum games. Although optimistic versions of these algorithms have been proposed with last-iterate convergence guarantees, they require noiseless gradient feedback. To overcome this limitation, we present a novel framework that achieves last-iterate convergence even in the presence of noise. Our key idea involves perturbing or regularizing the payoffs or utilities of the games. This perturbation serves to pull the current strategy to an anchored strategy, which we refer to as a slingshot strategy. First, we establish the convergence rates of our framework to a stationary point near an equilibrium, regardless of the presence or absence of noise. Next, we introduce an approach to periodically update the slingshot strategy with the current strategy. We interpret this approach as a proximal point method and demonstrate its last-iterate convergence. Our framework is comprehensive, incorporating existing payoff-regularized algorithms and enabling the development of new algorithms with last-iterate convergence properties. Finally, we show that our algorithms, based on this framework, empirically exhibit faster convergence.
Transition extra-tropicale d'ouragans en Atlantique Nord et impact sur la prévisibilité d'événements extrêmes en Méditerranée

La transition extra-tropicale est la transformation d'un cyclone tropical en cyclone extra-tropical lors de sa migration vers les latitudes moyennes. Au cours de son interaction avec son nouvel environnement barocline, le cyclone peut se reintensifier par des processus dynamiques et diabatiques complexes. Il peut egalement avoir un impact en aval par le renforcement d'un train d'ondes de Rossby, qui se propage rapidement et dont le deferlement est souvent a l'origine d'evenements extremes. La complexite des processus dynamiques et diabatiques de la transition extra-tropicale conduit alors a une reduction de la previsibilite en aval. Les ouragans Florence et Helene en Atlantique Nord ont ainsi contribue au declenchement des evenements extremes de septembre 2006 en Mediterranee, respectivement un episode de precipitations intenses et un phenomene plus rare, un mediterragan (ouragan mediterraneen). Helene s'est distinguee de Florence par des processus diabatiques preponderants au cours de sa reintensification sur un ocean plus chaud, ce qui lui a permis de conserver des caracteristiques tropicales. Ses trois reintensifications en trois jours induites par l'etirement de trois filaments de tourbillon potentiel, ajoutees au developpement du mediterragan en aval, font d'Helene un cas exceptionnel. Les deux evenements extremes en Mediterranee etaient absents de la prevision deterministe a moyen terme. L'incertitude dans la representation des processus diabatiques a ete examinee car ceux-ci controlent l'impact des ouragans sur les ondes de Rossby. Pour la premiere fois, des simulations ont ete realisees avec le modele Meso-NH sur un grand domaine avec une resolution explicite de la convection profonde, tirant parti de la parallelisation massive du modele. Une faible sensibilite a la resolution horizontale du modele a ete trouvee dans les precipitations des ouragans, donc dans leur impact sur les ondes de Rossby et sur les evenements extremes en Mediterranee. La trajectoire d'Helene a par contre montre une forte sensibilite a sa synchronisation avec le train d'ondes de Rossby, influencee par la resolution horizontale du modele. Cette forte sensibilite a ete retrouvee dans les previsions d'ensemble du Centre Europeen pour la Prevision Meteorologique a Moyen Terme, ou le developpement du mediterragan a requis la synchronisation d'Helene avec le train d'ondes de Rossby. La perturbation ciblee des conditions initiales autour d'Helene et du thalweg en amont a suffi a prevoir le mediterragan a une echeance de 108 h dans une simulation Meso-NH. Ces resultats montrent l'impact possible de transitions extra-tropicales en Atlantique Nord, qui surviennent a la meme saison que la plupart des episodes de precipitations intenses en Mediterranee. La reduction de l'incertitude dans la prevision de la transition extra-tropicale est donc un des enjeux de l'amelioration de la prevision d'evenements extremes d'automne en Mediterranee.
A quadratically convergent iterative scheme for locating conical degeneracies in the spectra of parametric self-adjoint matrices

A simple iterative scheme is proposed for locating the parameter values for which a 2-parameter family of real symmetric matrices has a double eigenvalue. The convergence is proved to be quadratic. An extension of the scheme to complex Hermitian matrices (with 3 parameters) and to location of triple eigenvalues (5 parameters for real symmetric matrices) is also described. Algorithm convergence is illustrated in several examples: a real symmetric family, a complex Hermitian family, a family of matrices with an "avoided crossing" (no covergence) and a 5-parameter family of real symmetric matrices with a triple eigenvalue.
Evaluating Computer Intrusion Detection Systems

The evaluation of computer intrusion detection systems (which we refer to as intrusion detection systems) is an active research area. In this article, we survey and systematize common practices in the area of evaluation of such systems. For this purpose, we define a design space structured into three parts: workload, metrics, and measurement methodology. We then provide an overview of the common practices in evaluation of intrusion detection systems by surveying evaluation approaches and methods related to each part of the design space. Finally, we discuss open issues and challenges focusing on evaluation methodologies for novel intrusion detection systems.
Extensions to the Proximal Distance of Method of Constrained Optimization

The current paper studies the problem of minimizing a loss $f(\boldsymbol{x})$ subject to constraints of the form $\boldsymbol{D}\boldsymbol{x} \in S$, where $S$ is a closed set, convex or not, and $\boldsymbol{D}$ is a fusion matrix. Fusion constraints can capture smoothness, sparsity, or more general constraint patterns. To tackle this generic class of problems, we combine the Beltrami-Courant penalty method of optimization with the proximal distance principle. The latter is driven by minimization of penalized objectives $f(\boldsymbol{x})+\frac{\rho}{2}\text{dist}(\boldsymbol{D}\boldsymbol{x},S)^2$ involving large tuning constants $\rho$ and the squared Euclidean distance of $\boldsymbol{D}\boldsymbol{x}$ from $S$. The next iterate $\boldsymbol{x}_{n+1}$ of the corresponding proximal distance algorithm is constructed from the current iterate $\boldsymbol{x}_n$ by minimizing the majorizing surrogate function $f(\boldsymbol{x})+\frac{\rho}{2}\|\boldsymbol{D}\boldsymbol{x}-\mathcal{P}_S(\boldsymbol{D}\boldsymbol{x}_n)\|^2$. For fixed $\rho$ and convex $f(\boldsymbol{x})$ and $S$, we prove convergence, provide convergence rates, and demonstrate linear convergence under stronger assumptions. We also construct a steepest descent (SD) variant to avoid costly linear system solves. To benchmark our algorithms, we adapt the alternating direction method of multipliers (ADMM) and compare on extensive numerical tests including problems in metric projection, convex regression, convex clustering, total variation image denoising, and projection of a matrix to one that has a good condition number. Our experiments demonstrate the superior speed and acceptable accuracy of the steepest variant on high-dimensional problems. Julia code to replicate all of our experiments can be found at this https URL.
On Fundamental Bounds on Failure Identifiability by Boolean Network Tomography

Boolean network tomography is a powerful tool to infer the state (working/failed) of individual nodes from path-level measurements obtained by edge-nodes. We consider the problem of optimizing the capability of identifying network failures through the design of monitoring schemes. Finding an optimal solution is NP-hard and a large body of work has been devoted to heuristic approaches providing lower bounds. Unlike previous works, we provide upper bounds on the maximum number of identifiable nodes, given the number of monitoring paths and different constraints on the network topology, the routing scheme, and the maximum path length. These upper bounds represent a fundamental limit on identifiability of failures via Boolean network tomography. Our analysis provides insights on how to design topologies and related monitoring schemes to achieve the maximum identifiability under various network settings. Through analysis and experiments we demonstrate the tightness of the bounds and efficacy of the design insights for engineered as well as real networks.
Influence of exception handling on the support of cooperative office work

Cooperative work on office tasks can be understood as consisting of steps to be carried out by persons acting in organizational functions. In many cases, neither the number of steps nor the persons involved are known in advance. Often, deviations from predefined cooperation procedures and exception handling become necessary. Moreover, many tasks are completely unformalized. Thus, support of exception handling has become a crucial requirement. ProMlnanD represents office tasks by means of Electronic Circulation Folders which migrate automatically through an office organization transferring arbitrary kinds of documents. A migration specification describes the formalized part of office tasks. The user requirements on cooperation support and their impact on the design of ProMlnanD are presented.