Miracle-dz/UniFAR-FTUs huggingface dataset:
Stabilization of a 2-DOF inverted pendulum by a low cost visual feedback

This paper presents a stabilization control system of a 2-DOF inverted pendulum. The challenge is to realize a control with a contact-less feedback including a low cost CCD camera. The global control system uses two feedback loops. The first one (inner loop) includes the delayed and sampled inverted pendulum's top coordinates, obtained from the CCD camera and the second one (outer loop) concerns the cart's position in continuous time (obtained from encoders). With this feedback scheme, we propose a specific control method by choosing an optimal balancing plane at each camera's sampling instant, in view of transforming the 2-DOF pendulum problem to a 1-DOF one. Then in this balancing plane, we realize two control loops: The inner loop, relative to the pendulum's angle, is controlled by a linearization and stabilization method based on an innovative observer called piecewise continuous reduced-order Luenberger observer (PCROLO). The outer loop uses a Lyapunov function based control scheme with slower internal dynamics compared to that of the pendulum, considering that the global controlled system (cart + pendulum) is an unstable non-minimum-phase system. Numerical simulations show that the stabilization control of the inverted pendulum on an x-y robot that are strongly non linear is successful.
Neural global router

A grid based neural network global router to route nets in presence of obstacles is presented. This algorithm can handle obstacles of equal height and different widths. It can be used for routing in gate arrays, FPGAs and standard cell layout. A neural model to avoid routing of wire through crowded region is suggested. This is used to rip up and reroute the wire which passes through the crowded region.
Effective document presentation with a locality-based similarity heuristic

The heuristics employed in information retrieval systems have traditionally been document-based, and have judged similarity holistically based upon entire documents. In this work we present a locality-based paradigm for information retrieval, in which every word location in each document is scored. The locality-based similarity heuristic provides retrieval e ectiveness as good as the documentbased technique, and has the additional advantage of allowing the matching section or sections of retrieved documents to be shown to the user when they are sifting the results of their query. This is a considerable improvement upon the conventional presentation mechanism, in which the user must manually search each document for the passage { if any such passage exists at all { that suggested to the retrieval mechanism that this document is an answer. We also describe an improved index representation that supports the required operations.
On finding skylines in external memory

We consider the skyline problem (a.k.a. the maxima problem), which has been extensively studied in the database community. The input is a set P of d-dimensional points. A point dominates another if the former has a lower coordinate than the latter on every dimension. The goal is to find the skyline, which is the set of points p ∈ P such that p is not dominated by any other data point. In the external-memory model, the 2-d version of the problem is known to be solvable in O((N/B)logM/B(N/B)) I/Os, where N is the cardinality of P, B the size of a disk block, and M the capacity of main memory. For fixed d ≥ 3, we present an algorithm with I/O-complexity O((N/B)logd-2/M/B(N/B)). Previously, the best solution was adapted from an in-memory algorithm, and requires O((N/B) logd-2/2(N/M)) I/Os.
Combining Declarative and Imperative Cloud Application Provisioning Based on TOSCA

The automation of application provisioning is one of the most important issues in Cloud Computing. The Topology and Orchestration Specification for Cloud Applications (TOSCA) supports automating provisioning by two different flavors: (i) declarative processing is based on interpreting application topology models by a runtime that infers provisioning logic whereas (ii) imperative processing employs provisioning plans that explicitly describe the provisioning tasks to be executed. Both flavors come with benefits and drawbacks. This paper presents a means to combine both flavors to resolve drawbacks and to profit from benefits of both worlds: we propose a standards-based approach to generate provisioning plans based on TOSCA topology models. These provisioning plans are workflows that can be executed fully automatically and may be customized by application developers after generation. We prove the technical feasibility of the approach by an end-to-end open source toolchain and evaluate its extensibility, performance, and complexity.
Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated.The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA) stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.
Image Feature Description by Frequent Patterns

The classification of image data becomes important, due to the increasing application of digital images, unsupervised classification technology with high capacity is necessary for processing digital images. In this paper, we propose an unsupervised approach of image pattern description and classification. In order to collect frequently appeared patterns in images, a compressibility feature space is built in an unsupervised manner. Based on this feature space the proposed approach transforms images to sequences, which are then divided into segments and replaced by characters. Finally, the similarities among compressibility vectors of texts are used for classification, instead of using texts themselves. Our experiments showed that the proposed approach is effective.
Stroke Risk Profile Predicts White Matter Hyperintensity Volume: The Framingham Study

Background and Purpose— Previous studies of cardiovascular risk factors and white matter hyperintensity (WMH) on brain MRI have been limited by the failure to exclude symptomatic cerebrovascular disease and dementia or by the use of semiquantitative rather than quantitative methods to measure WMH volume (WMHV). We examined the relationship between Framingham Stroke Risk Profile (FSRP) and WMHV measured quantitatively in a stroke and dementia-free subset of the Framingham Offspring Cohort. Methods— Brain MRI was performed in 1814 members of the Framingham Offspring Cohort. Pixel-based quantitative measures of WMHV corrected for head size were obtained using a semiautomated algorithm. WMHV was not normally distributed and therefore was log-transformed (LWMHV). The FSRP and its component risk factors measured a mean of 7.5 years before MRI were related to both continuous measures of LWMHV and to the presence of large volumes of LWMHV (LWMHV-large). All analyses were adjusted for age and sex. Results— FSRP was strongly associated with LWMHV and LWMHV-large. Age, smoking, history of cardiovascular disease, hypertension, and left ventricular hypertrophy by electrocardiogram were all significantly related to LWMHV or LWMHV-large. Conclusions— FSRP and several cardiovascular risk factors were related to both WMHV measured continuously and to a categorical designation of large volumes of WMH. These findings provide strong evidence of a vascular basis for WMH.
On Lower Bounds for Statistical Learning Theory

In recent years, tools from information theory have played an increasingly prevalent role in statistical machine learning. In addition to developing efficient, computationally feasible algorithms for analyzing complex datasets, it is of theoretical importance to determine whether such algorithms are “optimal” in the sense that no other algorithm can lead to smaller statistical error. This paper provides a survey of various techniques used to derive information-theoretic lower bounds for estimation and learning. We focus on the settings of parameter and function estimation, community recovery, and online learning for multi-armed bandits. A common theme is that lower bounds are established by relating the statistical learning problem to a channel decoding problem, for which lower bounds may be derived involving information-theoretic quantities such as the mutual information, total variation distance, and Kullback–Leibler divergence. We close by discussing the use of information-theoretic quantities to measure independence in machine learning applications ranging from causality to medical imaging, and mention techniques for estimating these quantities efficiently in a data-driven manner.
Spectral Angle Based Kernels for the Classification of Hyperspectral Images Using Support Vector Machines

Support vector machines (SVM) have been extensively used for classification purposes in a broad range of applications. These learning machines base their classification on the Euclidean distance of the data vectors or their dot products. These measures do not account for the spectral signature information that can be achieved from remote sensing images. Given the high value of this information, integrating it into the SVM algorithm is a reasonable suggestion. This paper utilizes the spectral angle (SA) function as a measure for classification of a hyperspectral image. The SA function is joined together with the radial basis function (RBF) to form a spectral angle based RBF function. Experimentation results are promising and confirm that this approach can compete with existing classification methods.
High Resolution Characterization of Myosin IIC Protein Tailpiece and Its Effect on Filament Assembly

Background: NMII-C tailpiece positively charged region binds to the coiled-coil rod, inducing filament assembly. Results: Positive and aromatic residues within the tailpiece are important for filament assembly, and the tailpiece binding sites in the rod were identified. Conclusion: Specific amino acid residues within the tailpiece interact with multiple sites in the rod. Significance: Understanding NMII filament assembly mechanism is crucial for exploring NMII based-cellular processes. The motor protein nonmuscle myosin II (NMII) must undergo dynamic oligomerization into filaments to perform its cellular functions. A small nonhelical region at the tail of the long coiled-coil region (tailpiece) is a common feature of all dynamically assembling myosin II proteins. This tailpiece is a key regulatory domain affecting NMII filament assembly properties and is subject to phosphorylation in vivo. We previously demonstrated that the positively charged region of the tailpiece binds to assembly-incompetent NMII-C fragments, inducing filament assembly. In the current study, we investigated the molecular mechanisms by which the tailpiece regulates NMII-C self-assembly. Using alanine scan, we found that specific positive and aromatic residues within the positively charged region of the tailpiece are important for inducing NMII-C filament assembly and for filament elongation. Combining peptide arrays with deletion studies allowed us to identify the tailpiece binding sites in the coiled-coil rod. Elucidation of the mechanism by which the tailpiece induces filament assembly permitted us further investigation into the role of tailpiece phosphorylation. Sedimentation and CD spectroscopy identified that phosphorylation of Thr1957 or Thr1960 inhibited the ability of the tailpiece to bind the coiled-coil rod and to induce NMII-C filament formation. This study provides molecular insight into the role of specific residues within the NMII-C tailpiece that are responsible for shifting the oligomeric equilibrium of NMII-C toward filament assembly and determining its morphology.
M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification

Our approach is among the three best to tackle the M2CAI Workflow challenge. The latter consists in recognizing the operation phase for each frames of endoscopic videos. In this technical report, we compare several classification models and temporal smoothing methods. Our submitted solution is a fine tuned Residual Network-200 on 80% of the training set with temporal smoothing using simple temporal averaging of the predictions and a Hidden Markov Model modeling the sequence.
Who Determines When You Retire? Peer Effects and Retirement

We study the factors that affect the choice of retirement date for a large sample of retirement-eligible Oregon state employees between 1992 and 2003. Given the complexities of the Oregon Public Employees Retirement System, we hypothesize that individuals will infer their optimal retirement behavior, in part, from the retirement behavior of their coworkers. Indeed, controlling for individual retirement incentives and characteristics, we find that individuals are economically significantly more likely to retire in months when more of their coworkers retire. The facts that this positive correlation is robust to the inclusion of numerous fixed effects and controls and survives estimation using two distinct sets of instrumental variables lead us to conclude that it reflects peer effects in the choice of retirement date. With respect to the possible welfare consequences of peer effects, our preliminary evidence is mixed, but points to modest costs and benefits. Interestingly, we find little evidence of peer effects amongst employees whose salaries are in the bottom 25th percentile, where financial literacy is likely to be the lowest. Working paper -this version September, 2008 * This research was supported by the U.S. Social Security Administration through grant #10-P98363-1-05 to the National Bureau of Economic Research as part of the SSA Retirement Research Consortium. The findings and conclusions expressed are solely those of the author(s) and do not represent the views of SSA, any agency of the Federal Government, or the NBER. We thank employees from the Oregon Public Employees Retirement System who provided invaluable assistance to us in understanding Oregon's pension system and then collecting and interpreting the data. We also caution readers that we describe the PERS’ mechanics as of the time period from which our data are gathered. The PERS system underwent major Legislative changes in late 2003 which change many of the fundamental characteristics of the system. As a result, our descriptions should not be viewed as up-to-date descriptions of the PERS system. We thank Guy Tauer from the Oregon Employment Department for his assistance in collecting data. We have received valuable comments from Martin Gruber and Andrew Biggs. The Securities and Exchange Commission, as a matter of policy, disclaims responsibility for any private publication or statement by any of its employees. The views expressed herein are those of the author and do not necessarily reflect the views of the Commission or of the author’s colleagues upon the staff of the Commission.
Similarity of "core" structures in two different glycans of tyrosine-linked eubacterial S-layer glycoproteins

Previously, the repeating-unit structure of the S-layer glycoprotein from the eubacterium Bacillus alvei CCM 2051 has been determined to be [-->3)-beta-D-Galp-(1-->4)-[alpha-D-Glcp-(1-->6)-]-beta-D-ManpNAc- (1-->]n (E. Altman, J.-R. Brisson, P. Messner, and U. B. Sleytr, Biochem. Cell Biol. 69:72-78, 1991). Nuclear magnetic resonance spectroscopic reexamination of this glycan reveals that the O-antigen-like domain of the polysaccharide is [see text] connected with the S-layer polypeptide through the "core" structure -->3)-alpha-L-Rhap-(1-->3)-alpha-L-Rhap-(1-->3)-alpha-L-R hap-(1-->3)-beta-D-Galp-(1-->O)-Tyr. Except for the substitution in position 4 of the nonreducing rhamnose with the modified glyceric acid phosphate residue GroA-2-->OPO2-->4-beta-D-ManpNAc-(1-->, this core is identical to the core of the tyrosine-linked glycan from the S-layer glycoprotein of Thermoanaerobacter thermohydrosulfuricus L111-69 (K. Bock, J. Schuster-Kolbe, E. Altman, G. Allmaier, B. Stahl, R. Christian, U. B. Sleytr, and P. Messner, J. Biol. Chem. 269:7137-7144, 1994).
Atmospheric and laser propagation

This paper reviews three phenomena that affect the propagation of electro-optical radiation through the atmosphere: absorption and scattering, refraction and turbulence. The net effect on imaging or laser systems is a net reduction of the effective range, or a degradation of the information contained in the electro-optical radiation. Recent advances in techniques to assess the concentration and composition of atmospheric aerosols, and to assess the strength of optical turbulence are discussed in more detail.
Using a Mixture of N-Best Lists from Multiple MT Systems in Rank-Sum-Based Confidence Measure for MT Outputs

This paper addressees the problem of eliminating unsatisfactory outputs from machine translation (MT) systems. The authors intend to eliminate unsatisfactory MT outputs by using confidence measures. Confidence measures for MT outputs include the rank-sum-based confidence measure (RSCM) for statistical machine translation (SMT) systems. RSCM can be applied to non-SMT systems but does not always work well on them. This paper proposes an alternative RSCM that adopts a mixture of the N-best lists from multiple MT systems instead of a single-system's N-best list in the existing RSCM. In most cases, the proposed RSCM proved to work better than the existing RSCM on two non-SMT systems and to work as well as the existing RSCM on an SMT system.
Application of Optical Fiber Sensor on Fermentation Monitoring

The development of robust and feasible sensors for assessing biochemical processes is crucial to improve the productivity and to reduce the time and costs in industries. In this paper, the utilization of an optical fiber reflectometer for measuring fermentation reactions is demonstrated. The single-mode fiber probe is placed in the tested sample, so the reflected light is analyzed in terms of average intensity and dynamic light scattering. Firstly, the system was validated by assessing the concentration in a biomass solution, yielding 1.89×104 L.s−1.g−1 sensitivity. Next, the sensor was utilized for dynamically monitoring the alcoholic and the oxidative fermentations, with the further comparison to pH measurements. It was possible to retrieve the theoretical reaction curves from the intensity and light scattering data, including the growth and stationary phases, making it possible to refine mathematical models on the optimization of biochemical processes control loops.
A Review on Parametric Analysis of Magnetic Abrasive Machining Process

The magnetic abrasive machining (MAM) process is a highly developed unconventional machining process. It is frequently used in manufacturing industries for nanometer range surface finishing of workpiece with the help of Magnetic abrasive particles (MAPs) and magnetic force applied in the machining zone. It is precise and faster than conventional methods and able to produce defect free finished components. This paper provides a comprehensive review on the recent advancement of MAM process carried out by different researcher till date. The effect of different input parameters such as rotational speed of electromagnet, voltage, magnetic flux density, abrasive particles size and working gap on the performances of Material Removal Rate (MRR) and surface roughness (Ra) have been discussed. On the basis of review, it is observed that the rotational speed of electromagnet, voltage and mesh size of abrasive particles have significant impact on MAM process.
Distributed algorithms for ensuring fair interprocess communicatons

Message passing is one of the primary methods of information exchange between communicating processes. Many programming languages (e.g., CSP, ADA) provide interprocess communication through a <underline>rendezvous</underline> in which a sender (receiver) process waits until the receiver (sender) is ready to receive (send) a message; thus there is no buffering of messages. Many of these languages also allow non-deterministic constructs by which a process may wait to communication with <underline>any</underline> of a set of other processes. In this paper we consider the problem of ensuring different <underline>fairness</underline> properties in such a system of processes. In a natural model we prove a simple lower bound on the time complexity to ensure <underline>weak fairness</underline> and present near optimal algorithms in special cases. We also give new efficient algorithms to ensure <underline>strong fairness</underline>. In this paper we introduce a formal model for processes and consider two different fairness properties: <underline>weak fairness and strong fairness</underline>. We investigate different distributed scheduling algorithms for ensuring the various fairness properties in a model where neighboring schedulers interact with each other using shared variables. We use the time complexity measure as defined in [Ly80].
Elongation Factor G Is a Critical Target during Oxidative Damage to the Translation System of Escherichia coli*

Background: Elongation factor G of Escherichia coli is sensitive to oxidation. Results: Elongation factor G is inactivated via the formation of an intramolecular disulfide bond. Conclusion: Elongation factor G is a critical target during oxidative damage to the translation system. Significance: Oxidation of elongation factor G suggests a novel mechanism for the redox regulation of translation. Elongation factor G (EF-G), a key protein in translational elongation, is known to be particularly susceptible to oxidation in Escherichia coli. However, neither the mechanism of the oxidation of EF-G nor the influence of its oxidation on translation is fully understood. In the present study, we investigated the effects of oxidants on the chemical properties and function of EF-G using a translation system in vitro derived from E. coli. Treatment of EF-G with 0.5 mm H2O2 resulted in the complete loss of translational activity. The inactivation of EF-G by H2O2 was attributable to the oxidation of two specific cysteine residues, namely, Cys114 and Cys266, and subsequent formation of an intramolecular disulfide bond. Replacement of Cys114 by serine rendered EF-G insensitive to oxidation and inactivation by H2O2. Furthermore, generation of the translation system in vitro with the mutated EF-G protected the entire translation system from oxidation, suggesting that EF-G might be a primary target of oxidation within the translation system. Oxidized EF-G was reactivated via reduction of the disulfide bond by thioredoxin, a ubiquitous protein that mediates dithiol-disulfide exchange. Our observations indicate that the translational machinery in E. coli is regulated, in part, by the redox state of EF-G, which might depend on the balance between the supply of reducing power and the degree of oxidative stress.