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TRIM-TEX Choose Fast Edge for fast cornerbead install to get the job done on time and under budget, available with or without paper. Fast Edge offers the strength you can [...]

TRIM-TEX Choose Fast Edge for fast cornerbead install to get the job done on time and under budget. Fast Edge offers the strength you can feel confident in and the [...]

Avery Dennison Corporation Fastener Solution has developed a fully degradable apparel tag fasteners. The company claims that these completely degrade within 1 year of exposure to soil microorganism without leaving [...]

Byline: Beth Wright Avery Dennison Fastener Solutions has developed a new line of sustainable apparel tag fasteners made with post-consumer waste from recycled plastic bottles and carpets. The Ecotach range [...]

The article offers information on C-103 line of synthetic rubber coating materials from Thiokol Corp., sink-strainer liners from Harvey Paper Products Co. and Quicflex paper fasteners.

This is Hermione, who has a very rare genetic condition called hypertrichosis which causes excessive hair growth on portions of the body. It is sometimes referred to as 'werewolf syndrome'. [...]

Byline: Leonie Barrie Scovill Fasteners has named Craig Stoudt as its new chief executive officer, tasked with growing the firm's position as a supplier of fastener products and services. Stoudt [...]

India, March 6 -- Zenith Industrial Products (ZIP) has developed fasteners items which are useful for textile machinery manufacturing unit. Published by HT Syndication with permission from Indian Textile [...]

Linear parameter varying (LPV) subspace identification methods suffer from an exponential growth in number of parameters to estimate. This results in problems with ill-conditioning. In literature, attempts have been made to address the ill-conditioning by using regularization. Its effectiveness hinges on suitable a priori knowledge. In this paper, we propose using a novel, alternative regularization. That is, we first show that the LPV sub-Markov parameters can be organized into several tensors that are multilinear low rank by construction. Namely, their matricization along any mode is a low-rank matrix. Then, we propose a novel convex method with tensor nuclear norm regularization, which exploits this low-rank property. Simulation results show that the novel method can have higher performance than the regularized LPV-PBSID $_{\text{opt}}$ technique in terms of variance accounted for. [ABSTRACT FROM AUTHOR]

The truncated regular $L_{1}$ -loss support vector machine can eliminate the excessive number of support vectors (SVs); thus, it has significant advantages in robustness and scalability. However, in this paper, we discover that the associated state-of-the-art solvers, such as difference convex algorithm and concave–convex procedure, not only have limited sparsity promoting property for general truncated losses especially the $L_{2}$ -loss but also have poor scalability for large-scale problems. To circumvent these drawbacks, we present a general multistage scheme with explicit interpretation regarding SVs as well as outliers. In particular, we solve the general nonconvex truncated loss minimization through a sequence of associated convex subproblems, in which the outliers are removed in advance. The proposed algorithm can be regarded as a structural optimization attempt carefully considering sparsity imposed by the nonconvex truncated losses. We show that this general multistage algorithm offers sufficient sparsity especially for the truncated $L_{2}$ -loss. To further improve the scalability, we propose a linear multistep algorithm by employing a single iteration of coordinate descent to monotonically decrease the objective function at each stage and a kernel algorithm by using the Karush–Kuhn–Tucker conditions to cheaply find most part of the outliers for the next stage. Comparison experiments demonstrate that our methods have superiority in sparsity as well as efficiency in scalability. [ABSTRACT FROM AUTHOR]

Filed 9/30/2004. Issued 10/17/2009. A patent has been issued for foam-based fasteners. The article has a mechanical fastener. It is comprised of a body portion, which may include a fibrous [...]

Disposable Absorbent Garment With Elastic Inner Layer Having Multiple Fasteners: No. 7,329,794; Paul Van Gompel and Georgia Zehner, Larsen, WI, assignors to Kimberly-Clark Worldwide, Neenah, WI. Filed 12/31/03. A disposable [...]

Top- $k$ performance has recently received increasing attention in large data categories. Advances, like a top- $k$ multiclass support vector machine (SVM), have consistently improved the top- $k$ accuracy. However, the key ingredient in the state-of-the-art optimization scheme based upon stochastic dual coordinate ascent relies on the sorting method, which yields $O(d\log d)$ complexity. In this paper, we leverage the semismoothness of the problem and propose an optimized top- $k$ multiclass SVM algorithm, which employs semismooth Newton algorithm for the key building block to improve the training speed. Our method enjoys a local superlinear convergence rate in theory. In practice, experimental results confirm the validity. Our algorithm is four times faster than the existing method in large synthetic problems; Moreover, on real-world data sets it also shows significant improvement in training time. [ABSTRACT FROM AUTHOR]

Many practical applications such as gene expression analysis, multitask learning, image recognition, signal processing, and medical data analysis pursue a sparse solution for the feature selection purpose and particularly favor the nonzeros evenly distributed in different groups. The exclusive sparsity norm has been widely used to serve to this purpose. However, it still lacks systematical studies for exclusive sparsity norm optimization. This paper offers two main contributions from the optimization perspective: 1) we provide several efficient algorithms to solve exclusive sparsity norm minimization with either smooth loss or hinge loss (nonsmooth loss). All algorithms achieve the optimal convergence rate $O(1/k^{2})$. ($k$ is the iteration number.) To the best of our knowledge, this is the first time to guarantee such convergence rate for the general exclusive sparsity norm minimization and 2) when the group information is unavailable to define the exclusive sparsity norm, we propose to use the random grouping scheme to construct groups and prove that if the number of groups is appropriately chosen, the nonzeros (true features) would be grouped in the ideal way with high probability. Empirical studies validate the efficiency of the proposed algorithms, and the effectiveness of random grouping scheme on the proposed exclusive support vector machine formulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel adaptive piecewise linear model for dynamic system identification. It has four unique features. First, the model designs a new kind of basis function for function approximation. It maintains the uniform shape for each basis function, so as to achieve a satisfactory tradeoff between generalization ability and model complexity. Second, the model takes the structure of basis functions as decision variables to optimize the formulated identification problems instead of taking expansion coefficients as decision variables as proposed by many existing approaches. Third, we establish an incremental design strategy to solve the system identification problems. In each step of the identification, the selection of optimal basis function is a Lipschitz continuous optimization problem that is likely to be easily handled with some mature toolboxes. This incremental design strategy greatly reduces the estimation cost. Fourth, we introduce a smoothing mechanism to avoid overfitting, when the output of dynamic systems is disturbed by noise. Tests on several benchmark dynamic systems demonstrate the potential of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application. [ABSTRACT FROM AUTHOR]

Applying the pinball loss in a support vector machine (SVM) classifier results in pin-SVM. The pinball loss is characterized by a parameter $\tau $ . Its value is related to the quantile level and different $\tau $ values are suitable for different problems. In this paper, we establish an algorithm to find the entire solution path for pin-SVM with different $\tau $ values. This algorithm is based on the fact that the optimal solution to pin-SVM is continuous and piecewise linear with respect to $\tau $ . We also show that the nonnegativity constraint on $\tau $ is not necessary, i.e., $\tau $ can be extended to negative values. First, in some applications, a negative $\tau $ leads to better accuracy. Second, $\tau = -1$ corresponds to a simple solution that links SVM and the classical kernel rule. The solution for $\tau = -1$ can be obtained directly and then be used as a starting point of the solution path. The proposed method efficiently traverses $\tau $ values through the solution path, and then achieves good performance by a suitable $\tau $ . In particular, $\tau = 0$ corresponds to C-SVM, meaning that the traversal algorithm can output a result at least as good as C-SVM with respect to validation error. [ABSTRACT FROM AUTHOR]

Otto Rohwedder reinvented bread when he created the first machine to slice it. Your name for the venerable CMU probably depends upon where you live, but cinder block, breeze block, and hollow block all refer to an 8by8by16inch brick with two or three internal voids. Your name for the venerable CMU probably depends upon where you live, but cinder block, breeze block, and hollow block all refer to an 8by8by16inch brick with two or three internal voids. The Piper J-3 Cub was a cheap, simple, and quick machine with two seats placed one behind the other inside a tubular steel frame wrapped in cotton fabric. [Extracted from the article]

• Obtaining the natural frequency of the Fastened Spherical-Conical-Conical Shells (FSCCSs). • Analyzing 12 different geometry types related to the FSCCSs. • Employing four new nanocomposite sandwiches composed of agglomerated CNT face layers and GNP core to reinforce the FSCCSs. • Determining the primary relationships of the FSCCS shell segments by using the FSDT and Donnell's shell theory and discretizing the governing differential equations of the FSCCSs by using the meshless GDQ method. • Applying the artificial springs technique to provide elastic boundary conditions related to the FSCCS. The following paper is prepared to compute the Natural Frequency Parameters (NFPs) linked to the Fastened Spherical-Conical-Conical Shell (FSCCS) structures composted of Nanocomposite Sandwich (NS) materials under the effects of springs as Boundary Conditions (BCs). To explain, the NS material implemented here is made of three layers covering Top Face Layer (TFL), Bottom Face Layer (BFL), and Core Layer (CL). In addition, the TFL and BFL are composed of Carbon Nano Tube (CNT) nano-fillers with agglomeration characteristics, while the CL benefits from Graphene Nano Platelet (GNP) nano-materials. For more information, the mechanical values related to the Agglomerated Carbon Nano-Tube Nanocomposite (ACNTN) material composer the TFL and BFL (ACNTN-TFL and ACNTN-BFL) are carried out by the Eshelby-Mori-Tanaka Approach (EMTA). At the same time, the Halpin-Tsai Approach (HTA) is used to extract these values for GNP-CL. To add more, four different sandwich models are implemented by considering the compatibility between layers. Moreover, the First Order Shear Deformation Theory (FOSDT) and Donnell's Shell Theory (DST) are exploited to obtain the primary relationships of the FSCCS segments. Hamilton's strategy is also operated to derive the Governing Differential Equations (GDEs) of the FSCCS's components. Further, the well-systematized computational meshless method tagged the Generalized Differential Quadrature (GDQ) program is carried out to discrete the GDEs coupled with the structure's segments. Furthermore, the calculation of the eigenvalues is implemented to compute the NFPs of the FSCCS structure. After that, to credit the submitted program, the outputs related to the FSCCS calculated by the present framework are analogized with the responses linked to the FE-based commercial software. In the last, multiple examples are styled and computed to point to the influences of the different material, geometric, and BCs forms on the NFPs of the NS-FSCCS structures, including 12 different geometry types related to the NS-FSCCS. [ABSTRACT FROM AUTHOR]

Multitask learning (MTL) aims at improving the generalization performance of multiple tasks by exploiting the shared factors among them. An important line of research in the MTL is the robust MTL (RMTL) methods, which use trace-norm regularization to capture task relatedness via a low-rank structure. The existing algorithms for the RMTL optimization problems rely on the accelerated proximal gradient (APG) scheme that needs repeated full singular value decomposition (SVD) operations. However, the time complexity of a full SVD is O(\min (md^2,m^2d)) for an RMTL problem with $m$ tasks and $d$ features, which becomes unaffordable in real-world MTL applications that often have a large number of tasks and high-dimensional features. In this paper, we propose a scalable solution for large-scale RMTL, with either the least squares loss or the squared hinge loss, by a divide-and-conquer method. The proposed method divides the original RMTL problem into several size-reduced subproblems, solves these cheaper subproblems in parallel by any base algorithm (e.g., APG) for RMTL, and then combines the results to obtain the final solution. Our theoretical analysis indicates that, with high probability, the recovery errors of the proposed divide-and-conquer algorithm are bounded by those of the base algorithm. Furthermore, in order to solve the subproblems with the least squares loss or the squared hinge loss, we propose two efficient base algorithms based on the linearized alternating direction method, respectively. Experimental results demonstrate that, with little loss of accuracy, our method is substantially faster than the state-of-the-art APG algorithms for RMTL. [ABSTRACT FROM PUBLISHER]

There are countless colourful things out there to pick up, toy with, and perhaps pause to admire. During the Nazi occupation, Norwegians wore paper clips on their lapels as a sign of national unity and opposition to the Germans, an act that could result in arrest. The first member of the Alessi family to craft metal was Giovanni Alessi, who founded his company by the shores of Lake Orta. Design became an increasingly important element, while stainless steel took over as the primary metal being used, Alessi now exports 65 per cent of all it manufactures. The first food processors were cumbersome and designed to be used in commercial kitchens, but miniaturisation soon saw them entering the domestic sphere. The Kenwood Chef appeared in Great Britain in 1950. Soon after, Sunbeam's sleek, chrome-plated Mixmaster drew heavily on automotive design influences.

This document, written by Tim Van Milligan, provides a checklist for dual deployment in high power rocketry. It outlines the mandatory items needed for dual deployment, such as a dual deployment rocket kit, altimeter, igniters, ejection charges, chutes, motor, battery, and mounting hardware. Optional components, including shear pins, fasteners, and deployment bags, are also discussed, with caution advised due to increased complexity and cost. The document emphasizes the importance of seeking guidance from a mentor and gaining a solid understanding of the basics before incorporating additional features. It further explains the benefits of using deployment bags and discusses the use of recovery harnesses, swivels, and quick links in the rocket's recovery system. The document concludes by stating that dual deployment requires careful planning and preparation but can lead to significant enjoyment of the hobby. [Extracted from the article]

The article offers step-by-step instructions for making a piggy bank using paper and metal fastener.

The article presents information on several commercial products including silent radio by Dictograph Products, jet-black rustproof finish by Glidden and Parker Rust Proof, and Fastnrite paper fastener by Progressive Mechanical.

A patent has been issued to Essity Hygiene and Health Aktiebolag for an absorbent article with an improved fastening system. The patent describes a method of manufacturing the article, which includes folding layers of material to create surface protrusions that can be fastened to a landing zone. The invention aims to provide an absorbent article that is easy to adjust and does not cause discomfort to the wearer. The patent includes detailed claims and descriptions of the article's construction. [Extracted from the article]

Online learning algorithms in a reproducing kernel Hilbert space associated with convex loss functions are studied. We show that in terms of the expected excess generalization error, they can converge comparably fast as corresponding kernel-based batch learning algorithms. Under mild conditions on loss functions and approximation errors, fast learning rates and finite sample upper bounds are established using polynomially decreasing step-size sequences. For some commonly used loss functions for classification, such as the logistic and the p -norm hinge loss functions with p\in [{1,2}] , the learning rates are the same as those for Tikhonov regularization and can be of order O(T^{- {(1 / 2)}} \log T)$ , which are nearly optimal up to a logarithmic factor. Our novelty lies in a sharp estimate for the expected values of norms of the learning sequence (or an inductive argument to uniformly bound the expected risks of the learning sequence in expectation) and a refined error decomposition for online learning algorithms. [ABSTRACT FROM AUTHOR]

Twin support-vector machine (TSVM), which generates two nonparallel hyperplanes by solving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single larger-sized QPP, works faster than the standard SVM, especially for the large-scale data sets. However, the traditional TSVM adopts hinge loss which easily leads to its sensitivity of the noise and instability for resampling. To enhance the performance of the TSVM, we present a novel TSVM with the pinball loss (Pin-TSVM) which deals with the quantile distance and is less sensitive to noise points. We further investigate its properties, including the noise insensitivity, between-class distance maximization, and within-class scatter minimization. In addition, we compare our Pin-TSVM with the twin parametric-margin SVM and the SVM with the pinball loss in theory. Numerical experiments on a synthetic data set and 14 benchmark data sets with different noises demonstrate the feasibility and validity of our proposed method. [ABSTRACT FROM AUTHOR]

The article reports on the possible penalty to be received by U.S. aircraft manufacturer Boeing from the Federal Aviation Administration (FAA) for using non-conforming fasteners on its model 777 aircrafts. It is inferred that a civil penalty letter was sent to the company for its failure to comply with agency-approved procedures. The prioritization of the FAA of safety and quality control system is noted.

Several letters to the editor are presented in response to previous issues such as the Adel clamp fastening trick and about the journal's free index

To deal with ambiguities in partial label learning (PLL), the existing PLL methods implement disambiguations, by either identifying the ground-truth label or averaging the candidate labels. However, these methods can be easily misled by the false-positive labels in the candidate label set. We find that these ambiguities often originate from the noise caused by highly correlated or overlapping candidate labels, which leads to the difficulty in identifying the ground-truth label on the first attempt. To give the trained models more tolerance, we first propose the top- $k$ partial loss and convex top- $k$ partial hinge loss. Based on the losses, we present a novel top- $k$ partial label machine (TPLM) for partial label classification. An efficient optimization algorithm is proposed based on accelerated proximal stochastic dual coordinate ascent (Prox-SDCA) and linear programming (LP). Moreover, we present a theoretical analysis of the generalization error for TPLM. Comprehensive experiments on both controlled UCI datasets and real-world partial label datasets demonstrate that the proposed method is superior to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Nonparametric kernel learning (NPKL) is a flexible approach to learn the kernel matrix directly without assuming any parametric form. It can be naturally formulated as a semidefinite program (SDP), which, however, is not very scalable. To address this problem, we propose the combined use of low-rank approximation and block coordinate descent (BCD). Low-rank approximation avoids the expensive positive semidefinite constraint in the SDP by replacing the kernel matrix variable with \mathbfV^\top \mathbfV , where \mathbfV is a low-rank matrix. The resultant nonlinear optimization problem is then solved by BCD, which optimizes each column of \mathbfV sequentially. It can be shown that the proposed algorithm has nice convergence properties and low computational complexities. Experiments on a number of real-world data sets show that the proposed algorithm outperforms state-of-the-art NPKL solvers. [ABSTRACT FROM AUTHOR]

1sourcevend, a provider of inventory control solutions, has added drawer cabinet units to its inventory delivery options, according to a press release. The drawer units, complete with touchscreen access, offer improved organization and tracking of fasteners, tools and small parts.... [ABSTRACT FROM AUTHOR]

In classification, the use of 0–1 loss is preferable since the minimizer of 0–1 risk leads to the Bayes optimal classifier. However, due to the nonconvexity of 0–1 loss, this optimization problem is NP-hard. Therefore, many convex surrogate loss functions have been adopted. Previous works have shown that if a Bayes-risk consistent loss function is used as a surrogate, the minimizer of the empirical surrogate risk can achieve the Bayes optimal classifier as the sample size tends to infinity. Nevertheless, the comparison of convergence rates of minimizers of different empirical surrogate risks to the Bayes optimal classifier has rarely been studied. Which characterization of the surrogate loss determines its convergence rate to the Bayes optimal classifier? Can we modify the loss function to achieve a faster convergence rate? In this article, we study the convergence rates of empirical surrogate minimizers to the Bayes optimal classifier. Specifically, we introduce the notions of consistency intensity and conductivity to characterize a surrogate loss function and exploit this notion to obtain the rate of convergence from an empirical surrogate risk minimizer to the Bayes optimal classifier, enabling fair comparisons of the excess risks of different surrogate risk minimizers. The main result of this article has practical implications including: 1) showing that hinge loss (SVM) is superior to logistic loss (Logistic regression) and exponential loss (Adaboost) in the sense that its empirical minimizer converges faster to the Bayes optimal classifier and 2) guiding the design of new loss functions to speed up the convergence rate to the Bayes optimal classifier with a data-dependent loss correction method inspired by our theorems. [ABSTRACT FROM AUTHOR]

As the performance criteria for plated fasteners have increased, directives to meet strict environmental considerations has forced sweeping changes to surface finishes. The resulting field performance has been as varied as the alternatives. This paper will compare zinc-nickel alloy plating and dip spin coating technology in regards to various critical performance characteristics for automotive fastener applications. Corrosion resistance, total friction coefficient and contact with dissimilar metals among other key properties will be discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigated the interaction of a piezoelectric screw dislocation with a nonuniformly coated circular inclusion in an unbounded piezoelectric matrix subjected to remote antiplane shear and electric fields. In addition to having a discontinuous displacement and a discontinuous electric potential across the slip plane, the dislocation is subjected to a line force and a line charge at the core. The alternating technique in conjunction with the method of analytical continuation is applied to derive the general solutions in an explicit series form. This approach has a clear advantage in deriving the solution to the heterogeneous problem in terms of the solution for the corresponding homogeneous problem. The presented series solutions have rapid convergence which is guaranteed numerically. The image force acting on the piezoelectric screw dislocation is calculated by using the generalized Peach–Koehler formula. The numerical results show that the varying thickness of the interphase layer will exert a significant influence on the shear stress and electric field within the circular inclusion, and on the direction and magnitude of the image force. This solution can be used as Green’s function for the analysis of the corresponding piezoelectric matrix cracking problem. [Copyright &y& Elsevier]

The author offers suggestions on avoiding breaking fasteners to make fastening into hardwood and mentions wrapping Teflon tape around threads to lubricate the thread not leaving residue behind.

The article evaluates process heating equipment which include the Mix-Proof Valve for Dairy Applications from SPX Flow, the Sanitary Steam Heater for Starch Cooking from Komax Systems Inc., and the Flow Control Ball from Valve Asahi/America Inc.

We cut the tenons on a tablesaw and cleaned up the tenon cheeks with a block plane, a chisel, a rasp, and a sanding block until they slipped into the mortises without binding. The technique I use to notch the beams also works for lap joints in the purlins and joists. We clamped the beams tight to the cross beams and set the 3x6 purlins in place over the beams. We used 8-in. screws to fasten the purlins to the beams and 5-in. screws for the joists. [Extracted from the article]

A wide variety of sparsity-inducing feature selection methods have been developed in recent years. Most of the loss functions of these approaches are built upon regression since it is general and easy to optimize, but regression is not well suitable for classification. In contrast, the hinge loss (HL) of support vector machines has proved to be powerful to handle classification tasks, but a model with existing multiclass HL and sparsity regularization is difficult to optimize. In view of that, we propose a new loss, called smooth and robust HL, which gathers the merits of regression and HL but overcome their drawbacks, and apply it to our sparsity regularized feature selection model. To optimize the model, we present a new variant of accelerated proximal gradient (APG) algorithm, which boosts the discriminative margins among different classes, compared with standard APG algorithms. We further propose an efficient optimization technique to solve the proximal projection problem at each iteration step, which is a key component of the new APG algorithm. We theoretically prove that the new APG algorithm converges at rate $O({1}/{k^{2}})$ if it is convex ($k$ is the iteration counter), which is the optimal convergence rate for smooth problems. Experimental results on nine publicly available data sets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The article informs that there's nothing like drywall repair to get the creative juices flowing. The article discusses various techniques for drywall repair. Cut a square piece of drywall to cover the hole, and then on the back of the patch remove a thin strip of drywall along each edge without removing the paper. Done correctly, this forms a paper flange on the patch's perimeter. Apply compound to the paper and press the patch in place. There is no tape, no backing board, no screws, no finding studs; Install a hinge-pin doorstop or a baseboard stop to prevent future doorknob damage. And use an 8-in.-wide drywall knife, as opposed to a 6-in., for a smoother repair.

The article reviews new plastic rivets as of August 1, 1944.