Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Filter for Saliency

A method for analytical computing of finding objects of interest in images has been developed with multivariate normal distribution to improve the visual capability. Key requirement is the ability of significantly shifting attention to image region that is texture based in general case of real images. The visual attention evaluation is mainly involved for initial task of most visual applications including segmentation, gaze tracking and image re-targeting. To enhance the accuracy of saliency detection, we have to analyze the salient distinction of textured region by combining several techniques. As an initial step, the multivariate filters are designed for estimating local texture feature that is rotation invariant. Significant distinction of patches is then calculated to describe the possible interest regions. The final morphological operations bring fixation of objects of interest. On a test set which consists of ten thousands of images in several themes, the method provides a precision of 92%, recall of 83% and F-measure of 86%.
Tribological behavior of electroless Ni–B–W coating at room and elevated temperatures

Sodium borohydride reduced electroless Ni–B coatings possess high hardness, wear resistance, and low coefficient of friction. They are found to be suitable candidates for wear reduction of mechanical components. In a quest to achieve enhanced tribological behavior and high thermal stability, the present work reports the inclusion of W to Ni–B coatings. Electroless method is employed for Ni–B–W coating deposition on AISI 1040 steel specimens. Post deposition, the coatings are heat treated at 350 ℃, 400 ℃, and 450 ℃. Deposit characterization is carried out using energy-dispersive X-ray analysis, X-ray diffraction, and scanning electron microscopy. Inclusion of W leads to an increase in microhardness and thermal stability of Ni–B coatings. The tribological behavior of as-deposited and heat-treated Ni–B–W coatings are investigated at room and elevated temperatures (100 ℃, 300 ℃, and 500 ℃). Heat-treated coatings show lower wear rate at room temperature compared to as-deposited ones but the coefficient of friction increases. Tribological test results at elevated temperatures suggest an improvement in the wear resistance and coefficient of friction at 300 ℃ and 500 ℃ in comparison with 100 ℃. Phase transformation study post wear test indicate microstructural changes in the coating due to the in situ heat treatment at high temperature. The tribological behavior of the coatings at 100 ℃ and 300 ℃ is mainly governed by the loose wear debris and formation of debris patches, respectively. Whereas at 500 ℃, formation of protective tribo-oxide patches is also observed.
Information Flow Audit for Transparency and Compliance in the Handling of Personal Data

The adoption of cloud computing is increasing and its use is becoming widespread in many sectors. As the proportion of services provided using cloud computing increases, legal and regulatory issues are becoming more significant. In this paper we explore how an Information Flow Audit (IFA) mechanism, that provides key data regarding provenance, can be used to verify compliance with regulatory and contractual duty, and survey potential extensions. We explore the use of IFA for such a purpose through a smart electricity metering use case derived from a French Data Protection Agency recommendation.
RApID: A System for Real-time Analysis of Information Diffusion in Twitter

The advent of social media has facilitated the study of information diffusion, expressing information spreading and influence among users on social graphs. In this demo paper, we present a system for real-time analysis of information diffusion on Twitter; it constructs the so-called information cascades that capture how information is being propagated from user to user. We face the challenge of managing and presenting large and fast-evolving graph data. For this purpose, we have developed methods for computing and visualizing information flow dynamically, offering rich structural and temporal information. The interface offers the possibility to interact with the dynamic, evolving cascades and gives valuable insights in terms of how information propagates on real-time and how users are influenced from each other.
Genetic diversity of the Junin virus in Argentina: geographic and temporal patterns.

RNA was purified from 39 strains of cell-cultured Junin virus (JUN) from central Argentina, which included both human- and rodent-derived isolates (a total of 26 and 13, respectively), as well as from 2 laboratory JUN strains, XJ Cl3 and XJ #44. JUN-specific primers were used to amplify a 511-nucleotide (nt) fragment of the nucleocapsid protein gene and a 495-nt fragment of the glycoprotein 1 (GP1) gene. Genetic diversity among JUN strains studied was up to 13% at the nt level and up to 9% at the amino acid (aa) level for the GP1 gene and up to 9% (nt) and 4% (aa) for the NP gene. Phylogenetic analyses of both genes revealed three distinct clades. The first clade was composed of the JUN strains from the center of the endemic area and included the majority of JUN strains analyzed in the current study. The second clade contained 4 JUN strains isolated between 1963 and 1971 from Cordoba Province, the western-most edge of the known endemic area. The third clade contained 4 JUN strains that originated from Calomys musculinus trapped in Zarate, the northeastern edge of the known endemic area. Certain JUN sequences, which were obtained from GenBank and identified as XJ, XJ #44, and Candid #1 strains, appeared to form a separate clade. Over 400 nt of the GP1 and GP2 genes were additionally sequenced for 7 JUN strains derived from patients with different clinical presentations and outcomes of Argentine hemorrhagic fever. Analysis of the corresponding aa sequences did not allow us to attribute any particular genetic marker to the changing severity or clinical form of the human disease.
Intermediate desired value approach for continuous transition among multiple tasks of robots

As the capability of robots is getting improved, more various tasks are expected to be performed by the robots. Complex operation of the robots can be composed of many different tasks. These tasks are executed sequentially, simultaneously, or in a combined way of both. This paper discusses the transition issue among multiple tasks on how the transition can be effectively and smoothly achieved. The proposed approach is to compose intermediate desired values to smooth the transitions rather than to modify control laws. The approach can be practically used on robotic systems without modification on their specific control algorithms. In this paper, multi-points control and joint limit avoidance are performed as applications of the proposed approach.
Embedded JIT Compilation with CACAO on YARI

Java is one of the most popular programming languages for thedevelopment of portable workstation and server applications availabletoday. Because of its clean design and typesafety, it is alsobecoming attractive in the domain of embedded systems. Unfortunately,the dynamic features of the language and its rich class library causeconsiderable overhead in terms of runtime and memory consumption.Efficient techniques to implement Java virtual machines that aresuitable for use in resource constrained environments are thusneeded. In this work we present a solution for very restrictedenvironments based on CACAO. CACAO is a just-in-time compilingvirtual machine implementation, combining high speed and small size.We have modified the original version of CACAO to run without anunderlying operating system within only 1~MB of memory. In additionwe present a new technique to selectively compile methods during theinitialization phase of real-time Java applications to preventunwanted interaction between dynamic compilation and critical tasks.Furthermore we present the YARI soft-core as the execution platformof CACAO within an field-programmable gate array. We compare ourimplementation with two well known Java processors, JOP and Sun'spicoJava-II, on the same technology. Although JOP achieves a higherclock frequency and picoJava-II occupies nearly 4 times the resourceof YARI, our solution is capable to outperform both of them by afactor of up to 2.8 and 2.2 respectively.
268 14 Towards a Mechanistic Philosophy of Neuroscience

Neuroscience is an interdisciplinary research community united by the goal of understanding, predicting and controlling the functions and malfunctions of the central nervous system (CNS). The philosophy of neuroscience is the subfi eld of the philosophy of science concerned with the goals and standards of neuroscience, its central explanatory concepts, and its experimental and inferential methods.1 Neuroscience is especially interesting to philosophers of science for at least three reasons. First, neuroscience is immature in comparison to physics, chemistry and much of biology and medicine. It has no unifying theoretical framework or common vocabulary for its myriad subfi elds. Many of its basic concepts, techniques and exemplars of success are under revision simultaneously. Neuroscience thus exemplifi es a form of scientifi c progress in the absence of an overarching paradigm (Kuhn 1970). Second, neuroscience is a physiological science. Philosophers of biology have tended to neglect physiology (though see Schaff ner 1993; Wimsa 2007). Physiological sciences study the parts of organisms, how they are organized together into systems, how they work and how they break. Its generalities are not universal in scope. Its theories intermingle concepts from several levels. Neuroscience thus off ers an opportunity to refl ect on the structure of physiological science more generally. Finally, unlike other physiological sciences, neuroscientists face the challenges of relating mind to brain. The question arises whether the explanatory resources of physiological science can be extended into the domains involving consciousness, rationality and agency, or whether such phenomena call out for distinctive explanatory resources. The philosophy of neuroscience should also interest neuroscientists. Because neuroscience is so young, and because it faces unique explanatory
A General Formula for the Stationary Distribution of the Age of Information and Its Application to Single-Server Queues

This paper considers the stationary distribution of the age of information (AoI) in information update systems. We first derive a general formula for the stationary distribution of the AoI, which holds for a wide class of information update systems. The formula indicates that the stationary distribution of the AoI is given in terms of the stationary distributions of the system delay and the peak AoI. To demonstrate its applicability and usefulness, we analyze the AoI in single-server queues with four different service disciplines: first-come first-served (FCFS), preemptive last-come first-served (LCFS), and two variants of non-preemptive LCFS service disciplines. For the FCFS and the preemptive LCFS service disciplines, the GI/GI/1, M/GI/1, and GI/M/1 queues are considered, and for the non-preemptive LCFS service disciplines, the M/GI/1 and GI/M/1 queues are considered. With these results, we further show comparison results for the mean AoI’s in the M/GI/1 and GI/M/1 queues under those service disciplines.
Dental Materials Effect in Neutron Contamination: Electron Mode of a Linac

Background: Neutron contamination is produced in electron beams of linac when tooth or dental materials are located in the path of beam. Objective: This study aims to determine the neutron dose contamination from different dental restoration materials in electron mode of a linac. Material and Methods: In this experimental study, the neutron dose contamination was calculated in the presence of tooth and tooth restored by Ceramco C3 veneer, Eclipse or amalgam. The electron mode included 8, 12, and 14 MeV electron beams of Siemens Primus linac at different depths before and after tooth. MCNPX code was used to simulate the linear accelerator and dental restoration materials. Tooth and tooth restoration materials were located in the beams’ central axis and the neutron dose was scored in 3 × 3 × 1 cm3 voxels at different depths before and after the tooth. Results: The highest neutron dose contamination was observed for the combination of the tooth and Eclipse in 12 and 14 MeV beams and the maximum calculated relative neutron dose was 1.53 for tooth and Eclipse for 14 MeV electron beam. Conclusion: Tooth and dental materials lead to neutron dose contamination production, therefore, in order to avoid having harmful effects on normal tissues due to the neutron beam in head and neck cancer, it is recommended that treatment planning performed should not place tooth with dental restoration materials in the path of the beam and lower energy electron beams be used.
Two-Staged Algorithm for Eyebrows Segmentation

Eyebrows segmentation technique has been rapidly evolved these years, yet the precision and efficiency of the segmentation algorithm cannot be simultaneously balanced. Instead of treating the segmentation task as an independent problem, we investigate the possibility of balancing segmentation robustness with computationally efficiency through a two stage solution. In this paper, we start from achieving a rough localization of eyebrows based on a facial landmark detection. Next, we generate a dataset based on Helen dataset using this rough localization algorithm, and present an optimized U-Net convolutional network to accomplish the precise segmentation inside of the localized region. Our two-staged model achieved a greater accuracy than traditional facial landmark detection, and a better efficiency than global segmentation task over the whole picture.
Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network

We study the problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population. In particular, we consider the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial biased opinion for the following phase. Using an extension of the popular DeGroot-Friedkin model, we formulate the utility functions of the camps, and show that they involve what can be interpreted as multiphase Katz centrality. Focusing on two phases, we analytically derive Nash equilibrium investment strategies, and the extent of loss that a camp would incur if it acted myopically. Our simulation study affirms that nodes attributing higher weightage to initial biases necessitate higher investment in the first phase, so as to influence these biases for the terminal phase. We then study the setting in which a camp's influence on a node depends on its initial bias. For single camp, we present a polynomial time algorithm for determining an optimal way to split the budget between the two phases. For competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time.
Noise-Driven Excursions from Consensus : A Theoretical Review and Simulation for Simple Graphs with Common Edge Weights

In this report, a review of fundamental results from Markov theory is provided to motivate the discussion of a linear averaging process, the consensus iteration. The consensus iteration, ubiquitous in distributed control algorithms, is introduced and its behavior is analyzed using graph theory and several key theorems for ergodic Markov chains. In particular, necessary and sufficient conditions for convergence to consensus are provided. Noise is later added to the iteration, and it is shown that the convergence property is destroyed as the additive noise continually drives excursions from consensus. The size of the individual deviations from consensus tend to meander about zero, but converge in the meansquare sense. A theorem, from [Xiao et al., 2007], relating the limit of the mean-square deviation from consensus to the spectrum of a symmetric transition matrix for the noiseless iteration is reproduced here along with its proof, which is drawn out in detail. A generalization of this theorem due to [Jadbabaie and Olshevsky, 2015], is provided in the following section. The mean-square deviation, or disagreement, is computed for a set of simple graphs and common edge weights, together forming a set of reversible matrices. The MATLAB code used to run the simulations is included in the appendices, and may be of some use to others interested in Markov chains, graph theory, and consensus protocols.
Multiplexing High‐Content Flow (HCF) and Quantitative High‐Throughput Screening (qHTS) to Identify Compounds Capable of Decreasing Cell Viability, Activating Caspase 3/7, Expressing Annexin V, and Changing Mitochondrial Membrane Integrity

High‐content flow (HCF) screening systems, such as the iQue Screener and HTFC Screening System from IntelliCyt, have facilitated the implementation of flow cytometry assays for high‐throughput screening. HCF screening systems enable the use of smaller sample volumes and multiplexed assays to simultaneously assess different cellular parameters from a single well. This becomes invaluable when working with cells or compounds that are available in limited quantities or when conducting large‐scale screens. When assays can be miniaturized to a 384‐ or 1536‐well microplate format, it is possible to implement dose‐response‐based high‐throughput screens, also known as quantitative HTS or qHTS. This article describes how qHTS at the new National Center for Advancing Translational Science (NCATS) has been systematically coupled with the HTFC Screening System and Multimetric Apoptosis Screening Kit from IntelliCyt to biologically validate active compounds from primary cell proliferation screens using a model of diffuse large B cell lymphoma (DLBCL). Curr. Protoc. Chem. Biol. 5:195‐212 © 2013 by John Wiley & Sons, Inc.
Building a Bw-Tree Takes More Than Just Buzz Words

In 2013, Microsoft Research proposed the Bw-Tree (humorously termed the "Buzz Word Tree''), a lock-free index that provides high throughput for transactional database workloads in SQL Server's Hekaton engine. The Buzz Word Tree avoids locks by appending delta record to tree nodes and using an indirection layer that allows it to atomically update physical pointers using compare-and-swap (CaS). Correctly implementing this techniques requires careful attention to detail. Unfortunately, the Bw-Tree papers from Microsoft are missing important details and the source code has not been released. This paper has two contributions: First, it is the missing guide for how to build a lock-free Bw-Tree. We clarify missing points in Microsoft's original design documents and then present techniques to improve the index's performance. Although our focus here is on the Bw-Tree, many of our methods apply more broadly to designing and implementing future lock-free in-memory data structures. Our experimental evaluation shows that our optimized variant achieves 1.1--2.5× better performance than the original Microsoft proposal for highly concurrent workloads. Second, our evaluation shows that despite our improvements, the Bw-Tree still does not perform as well as other concurrent data structures that use locks.
Experts' Boasting in Trainable Fusion Rules

We consider the trainable fusion rule design problem when the expert classifiers provide crisp outputs and the behavior space knowledge method is used to fuse local experts' decisions. If the training set is utilized to design both the experts and the fusion rule, the experts' outputs become too self-assured. In small sample situations, "optimistically biased" experts' outputs bluffs the fusion rule designer. If the experts differ in complexity and in classification performance, then the experts' boasting effect and can severely degrade the performance of a multiple classification system. Theoretically-based and experimental procedures are suggested to reduce the experts' boasting effect.
Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part I: Methodologies

We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The framework explicitly combines multi-stage feedback policies with any forecasting method and historical forecast error data. The objective is to determine power scheduling policies for controllable devices in a power network to balance operational cost and conditional value-at-risk of device and network constraint violations. These decisions include both nominal power schedules and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming that the uncertainties across the networks follow prescribed probability distributions, we consider ambiguity sets of distributions centered around a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real unknown data-generating distribution, we formulate a multi-stage distributionally robust OPF problem to compute control policies that are robust to both forecast errors and sampling errors inherent in the dataset. Two specific data-based distributionally robust stochastic OPF problems are proposed for distribution networks and transmission systems.
New Techniques for Continuous Optimization and Fast Algorithms for Flow

Author(s): Sherman, Jonah | Advisor(s): Vazirani, Umesh | Abstract: Spielman and Teng's nearly linear time algorithm for solving Laplacian systems was a breakthrough in combining techniques from discrete and continuous optimization. In this dissertation we adapt their approach to solve more general flow problems, overcoming long-standing barriers in continuous optimization. As a result we solve longstanding open questions in optimization by presenting nearly-linear time approximation algorithms for the undirected versions of several network flow problems, including \emph{maximum flow}, \emph{optimal transportation}, \emph{maximum concurrent multi-commodity flow}, and \emph{sparsest cut} problems. Our contributions include two general tools. The first is a scheme for boosting the performance of iterative solvers for continuous optimization problems. These boosters use recursive composition to achieve residual error that decreases exponentially in the number of iterations of the solver.The second is a technique for designing iterative solvers that bypasses the infamous $\ell_{\infty}$ barrier for strongly convex regularization. Instead we introduce the notion of an area convex regularizer, and show that such regularizers suffice in designing good iterative solvers. Moreover, we show how to design regularizers satisfying this weaker property for the problem of approximately solving $A X \leq B$ over right stochastic $X$, which in turn implies the algorithm for maximum concurrent multi-commodity flow.