Miracle-dz/UniFAR-FTUs huggingface dataset:
Channels with Synchronization/Substitution Errors and Computation of Error Control Codes

We introduce the concept of an \ff-maximal error-detecting block code, for some parameter \ff{} between 0 and 1, in order to formalize the situation where a block code is close to maximal with respect to being error-detecting. Our motivation for this is that constructing a maximal error-detecting code is a computationally hard problem. We present a randomized algorithm that takes as input two positive integers $N,\ell$, a probability value \ff, and a specification of the errors permitted in some application, and generates an error-detecting, or error-correcting, block code having up to $N$ codewords of length $\ell$. If the algorithm finds less than $N$ codewords, then those codewords constitute a code that is \ff-maximal with high probability. The error specification (also called channel) is modelled as a transducer, which allows one to model any rational combination of substitution and synchronization errors. We also present some elements of our implementation of various error-detecting properties and their associated methods. Then, we show several tests of the implemented randomized algorithm on various channels. A methodological contribution is the presentation of how various desirable error combinations can be expressed formally and processed algorithmically.
Tripeptidyl Peptidase II Mediates Levels of Nuclear Phosphorylated ERK1 and ERK2*

Tripeptidyl peptidase II (TPP2) is a serine peptidase involved in various biological processes, including antigen processing, cell growth, DNA repair, and neuropeptide mediated signaling. The underlying mechanisms of how a peptidase can influence this multitude of processes still remain unknown. We identified rapid proteomic changes in neuroblastoma cells following selective TPP2 inhibition using the known reversible inhibitor butabindide, as well as a new, more potent, and irreversible peptide phosphonate inhibitor. Our data show that TPP2 inhibition indirectly but rapidly decreases the levels of active, di-phosphorylated extracellular signal-regulated kinase 1 (ERK1) and ERK2 in the nucleus, thereby down-regulating signal transduction downstream of growth factors and mitogenic stimuli. We conclude that TPP2 mediates many important cellular functions by controlling ERK1 and ERK2 phosphorylation. For instance, we show that TPP2 inhibition of neurons in the hippocampus leads to an excessive strengthening of synapses, indicating that TPP2 activity is crucial for normal brain function.
A property of the minimum vectors of a regularizing functional defined by means of the absolute norm

We consider a regularizing functional defined by means of the l/sub 1/ norm, where the regularization is obtained using first differences; as is well-known, such a functional can be put in relation with recursive median filters of appropriate window length. We show that at least one of the minima is reached at a vector, whose components have values over the same discrete set of the given signal. This suggests a simple method to refine the approximate solution to the regularization problem, which can be obtained with recursive median filters of increasing order. We also report an example of application, where the refinement method is employed for a signal detection problem.
3D reconstruction and model acquisition of objects in real world scenes using stereo imagery

Recently, the use of three dimensional computer models has greatly increased, in part due to the availability of fast, inexpensive hardware and technologies like VRML-ready Internet browsers. These models often represent objects in real world scenes and are typically built by hand using CAD software, an error-prone and time consuming process. The paper outlines a simple and efficient method based on passive stereo imaging techniques by which these models may be acquired, processed and utilized with little effort. The described method extracts the 3D geometrical data from the stereo images, for the purpose of creating realistic 3D models. The approach uses calibrated cameras
Inference on Low-Rank Data Matrices with Applications to Microarray Data.

Probe-level microarray data are usually stored in matrices, where the row and column correspond to array and probe, respectively. Scientists routinely summarize each array by a single index as the expression level of each probe-set (gene). We examine the adequacy of a uni-dimensional summary for characterizing the data matrix of each probe-set. To do so, we propose a low-rank matrix model for the probe-level intensities, and develop a useful framework for testing the adequacy of uni-dimensionality against targeted alternatives. This is an interesting statistical problem where inference has to be made based on one data matrix whose entries are not i.i. d. We analyze the asymptotic properties of the proposed test statistics, and use Monte Carlo simulations to assess their small sample performance. Applications of the proposed tests to GeneChip data show that evidence against a uni-dimensional model is often indicative of practically relevant features of a probe-set.
Autonomous task execution within NAO robot scouting mission framework

A polygon for movement and task execution by the humanoid robot NAO was defined in the form of a 2D map. A series of tasks was designed for the robot to accomplish during its scouting mission. A localization algorithm using markers and the robot's camera was developed, as well as algorithms for navigation, path planning, and robot motion. A GUI for mission definition, supervision, and control, along with manual robot tele-operation, was developed. A finite automaton was defined which enables switching between autonomous, semi-autonomous, and manual operation modes. The developed modules were integrated with modules for audio and visual perception and the complete system was tested on a chosen scouting mission.
Solving the Third-Shift Problem in IC Piracy With Test-Aware Logic Locking

The increasing IC manufacturing cost encourages a business model where design houses outsource IC fabrication to remote foundries. Despite cost savings, this model exposes design houses to IC piracy as remote foundries can manufacture in excess to sell on the black market. Recent efforts in digital hardware security aim to thwart piracy by using XOR-based chip locking, cryptography, and active metering. To counter direct attacks and lower the exposure of unlocked circuits to the foundry, we introduce a multiplexor-based locking strategy that preserves test response allowing IC testing by an untrusted party before activation. We demonstrate a simple yet effective attack against a locked circuit that does not preserve test response, and validate the effectiveness of our locking strategy on IWLS 2005 benchmarks.
Supporting Software Process Decisions Using Bi-Directional Simulation

In this paper, we present a "forward-looking" decision support framework that integrates timely metrics data with a simulation based defect model of the software development process in order to support software project management decisions regarding product quality. These predictions are evaluated using Outcome Based Control Limits (OBCLs) and Bi-Directional (both forward and reverse) simulation models of the software development process. The Bi-Directional models provide useful guidance to the program manager when evaluating possible corrective actions on a project that has gone outside the OBCLs. The forward model predicts the potential performance impacts of the proposed corrective actions. The reverse simulation models are new and take the desired system outcome as the starting point and represent the system as it evolves backward in time to identify the necessary starting point to achieve the desired outcome. The approach is presented using an illustrative example.
Learning Structured Outputs via Kernel Dependency Estimation and Stochastic Grammars

We focus on graph-valued outputs in supervised learning and propose a novel solution to the pre-image problem in the kernel dependency estimation framework. Output structures are generated by a stochastic grammar and the output feature space is directly associated with the set of productions for the grammar. The regression estimation step learns to map input examples into a feature vector that counts the number of applications of each production rule. A max-propagation algorithm finally builds the predicted output according to the normalized counts. We test our method on a ambiguous context free grammar (CFG) parse tree reconstruction problem. We show on an artificial dataset that mimics the prepositional attachment problem how learning the number of applications of each production rule on a per example base allows CFG parser to better tackle ambiguity issues.
Estimating block accesses in database organizations: a closed noniterative formula

Authors' Present Addresses: Kyu-Young Whang and Gio Wiederbold, Computer Systems Laboratory, Stanford University, Stanford, CA 94305; and Daniel Sagalowicz, Artificial Intelligence Center, SRI International, Menlo Park, CA 94025. This work was supported by the Defense Advanced Research Project Agency under the KBMS Project, Contract N39-80-G-0132. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. © 1983 ACM 0001-0782/83/1100-0940 75¢ 1. I N T R O D U C T I O N In eva lua t ing the access cost o f a q u e r y for a da tabase organiza t ion in w h i c h records are g rouped into b locks in s e c o n d a r y storage [16], one m u s t o f ten es t ima te t he n u m b e r of b lock assesses r equ i r ed to re t r ieve t h e records se lec ted by t h e query . Var ious fo rmulas h a v e b e e n p roposed for this pu rpose [2, 3, 8-13, 17, 19]. In part icular , Yao [17] p r e s e n t e d the following theo rem: 1
Automatic detection of social rhythms in bipolar disorder

OBJECTIVE To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones. METHODS Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app. RESULTS We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86). CONCLUSIONS Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.
Fragment‐based similarity searching with infinite color space

Fragment‐based searching and abstract representation of molecular features through reduced graphs have separately been used for virtual screening. Here, we combine these two approaches and apply the algorithm RedFrag to virtual screens retrospectively and prospectively. It uses a new type of reduced graph that does not suffer from information loss during its construction and bypasses the necessity of feature definitions. Built upon chemical epitopes resulting from molecule fragmentation, the reduced graph embodies physico‐chemical and 2D‐structural properties of a molecule. Reduced graphs are compared with a continuous‐similarity‐distance‐driven maximal common subgraph algorithm, which calculates similarity at the fragmental and topological levels. The performance of the algorithm is evaluated by retrieval experiments utilizing precompiled validation sets. By predicting and experimentally testing ligands for endothiapepsin, a challenging model protease, the method is assessed in a prospective setting. Here, we identified five novel ligands with affinities as low as 2.08 μM. © 2015 Wiley Periodicals, Inc.
Effects of Sampling on Twitter Trend Detection

Much research has focused on detecting trends on Twitter, including health-related trends such as mentions of Influenza-like illnesses or their symptoms. The majority of this research has been conducted using Twitter’s public feed, which includes only about 1% of all public tweets. It is unclear if, when, and how using Twitter’s 1% feed has affected the evaluation of trend detection methods. In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection. We focus on using health-related trends to estimate the prevalence of Influenza-like illnesses based on tweets. We use ground truth obtained from the CDC and Google Flu Trends to explore how the prevalence estimates degrade when moving from a 100% to a 1% sample. We find that using the 1% sample is unlikely to substantially harm ILI estimates made at the national level, but can cause poor performance when estimates are made at the city level.
Induction clamping of high-revolution tools by rotation in a system of unmovable permanent magnets

An alternative way of induction clamping is described, consisting in heating of the sleeve by its rotation in static magnetic field generated by suitably arranged permanent magnets. The most important dimensions of the heating system are optimised with the aim to reach the required increase of the bore of the clamping head at the highest possible efficiency and shortest time of heating.
Highly-Concurrent Doubly-Linked Lists

As file systems are increasingly being deployed on ever larger systems with many cores and multi-gigabytes of memory, scaling the internal data structures of file systems has taken greater importance and urgency. A doubly-linked list is a simple and very commonly used data structure in file systems but it is not very friendly to multi-threaded use. While special cases of lists, such as queues and stacks, have lock-free versions that scale reasonably well, the general form of a doubly-linked list offers no such solution. Using a mutex to serialize all operations remains the de-facto method of maintaining a doubly linked list. This severely limits the scalability of the list and developers must resort to ad-hoc workarounds that involve using multiple smaller lists (with individual locks) and deal with the resulting complexity of the system. In this paper, we present an approach to building highly concurrent data structures, with special focus on the implementation of highly concurrent doubly-linked lists. Dubbed "advanced doubly-linked list" or "adlist" for short, our list allows iteration in any direction, and insert/delete operations over non-overlapping nodes to execute in parallel. Operations with common nodes get serialized so as to always present a locally consistent view to the callers. An adlist node needs an additional 8 bytes of space for keeping synchronization information. The Data Domain File System makes extensive use of adlists which has allowed for significant scaling of the system without sacrificing simplicity.
Study on Creep Behavior of Semi-Solid A356 Alloy Produced by the GISS Process

The A356 alloy is a class of aluminum alloy widely applied in automotive industries because of its high strength, good heat treatability, and excellent castability. Gas Induced Semi-Solid (GISS) process is a kind of semi-solid preparation process which has been developed by a Thai and US researcher team at the Department of Mining and Materials Engineering, Prince of Songkla University, Thailand. In the GISS, non-dendrite, globular, semi-solid metal slurry can be obtained via using flowing gas bubbles through a porous graphite diffuser into the molten metal at a temperature between its liquidus point and solidus point to get semi-solid slurry with 10~70% solid fraction before cast into the molds in order to get less gas porosity, smaller solid shrinkage, better heat treatability and higher ductility with shorter cycle times. Nowadays there are many parts of automotive and aeroplane are replaced by aluminum alloys to reduce weight and save energy, however many of them have to expose at high temperature and endure high stress conditions, where time-dependent plastic deformation and fracture process always take place, this phenomenon was first referred to as stretch by Philips and viscous flow by Andrade, later became known as creep. Therefore, it is vital to learn creep properties before design for high temperature application of those aluminum alloys. In this study, a tensile creep test machine has been designed and fabricated by the author and his team. Primary A356 alloy pieces were produced by squeeze casting with and without the GISS process, and further improved by the T6 heat treatment. Tensile and creep properties of the A356 alloy with different cast process and T6 heat treatment were investigated, microstructures of the alloys were observed by OM, SEM and TEM, and mechanical composition of them were tested
A computer model of context-dependent perception in a very simple world

Abstract We propose the foundations of a computer model of scientific discovery that takes into account certain psychological aspects of human observation of the world. To this end, we simulate two main components of such a system. The first is a dynamic microworld in which physical events take place, and the second is an observer that visually perceives entities and events in the microworld. For reason of space, this paper focuses only on the starting phase of discovery, which is the relatively simple visual inputs of objects and collisions.
Application of 3D Morphable Models to faces in video images

The 3D Morphable Face Model (3DMM) has been used for over a decade for creating 3D models from single images of faces. This model is based on a PCA model of the 3D shape and texture generated from a limited number of 3D scans. The goal of fitting a 3DMM to an image is to find the model coefficients, the lighting and other imaging variables from which we can remodel that image as accurately as possible. These coefficients can without further processing be used in verification and recognition experiments. In this paper, we investigate the potential benefits of using multiple images from the same person to fit a 3DMM. Lighting and imaging variables can differ from image to image, but the PCA coefficients will remain the same, assuming no change in expression between the images. We expect using multiple images could result in a more accurate fit.
Information Systems and Healthcare XXVIII: The Information Needs of Family Carers in Collaborative Healthcare

While the important role of family carers has been increasingly recognized in healthcare service provision, particularly for patients with acute or chronic illnesses, the family carer’s information needs have not been well understood or adequately supported by health information systems. In this study, we explore the information needs of a family carer by analyzing the extensive online diary of a Vietnamese family carer supporting his wife, who was a lung cancer patient. The study provides a deep understanding of the information needs of the family carer and suggests a four-stage information journey model including identification, searching, interpretation and information sharing, and collaboration. A number of themes emerge from the study including the key role of the carer, information filtering by the carer, information sharing and collaboration, and the influence of Vietnamese culture. The paper concludes with a discussion of the requirements for health information systems that meet the needs of family carers.
The effect of elevated temperature on the mechanical properties and failure modes of GFRP face sheets and PET foam cored sandwich beams

The influence of elevated temperatures on stiffness and strength of composite face sheet and polyethylene terephthalate foam cored sandwich beam has been experimentally investigated. Standard test methods and analytical failure models were used to determine the effect of elevated temperatures. The authors examined E-glass/epoxy cross-ply face laminates, polyethylene terephthalate foam, and sandwich beams consisting of glass/epoxy face laminates and polyethylene terephthalate foam core loaded in four-point flexure. The tensile properties of the face laminate were examined over a temperature range from 25 to 175°C. Compression and shear tests on the face laminate, polyethylene terephthalate foam, and sandwich beams were performed at temperatures up to 100°C. The face laminates exhibited moderate reductions of Young’s modulus and tensile strength, while the compressive strength, shear modulus, and shear strength substantially decreased at elevated temperatures. Similarly, the compressive and shear moduli as well as the compressive strength of the polyethylene terephthalate foam decreased substantially by exposure to a temperature of 100°C. The failure mode of the sandwich panels was observed to be highly dependent on temperature, distinguishing three basic failure modes, viz. core shear failure, indentation failure, and face wrinkling. The failure loads associated to these failure modes were calculated using models available in the literature. The failure loads were found to be consistent with the failure predictions and failure modes.
Digital Wildfires: Propagation, Verification, Regulation, and Responsible Innovation

Social media platforms provide an increasingly popular means for individuals to share content online. Whilst this produces undoubted societal benefits, the ability for content to be spontaneously posted and reposted creates an ideal environment for rumour and false/malicious information to spread rapidly. When this occurs it can cause significant harm and can be characterised as a “digital wildfire. ” In this article, we demonstrate that the propagation and regulation of digital wildfires form important topics for research and conduct an overview of existing work in this area. We outline the relevance of a range of work from the computational and social sciences, including a series of insights into the propagation of rumour and false/malicious information. We argue that significant research gaps remain—for instance, there is an absence of systematic studies on the effects of digital wildfires and there is a need to combine empirical research with a consideration of how the responsible governance of social media can be determined. We propose an agenda for research that establishes a methodology to explore in full the propagation and regulation of unverified content on social media. This agenda promotes high-quality interdisciplinary research that will also inform policy debates.
Structural Matching by Discrete Relaxation

This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we focus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations of the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter.