Miracle-dz/UniFAR-FTUs huggingface dataset:
First-order vs. Second-order Single Layer Recurrent Neural Networks

We examine the representational capabilities of rst-order and second-order Single Layer Recurrent Neural Networks (SLRNNs) with hard-limiting neurons. We show that a second-order SLRNN is strictly more powerful than a rst-order SLRNN. However, if the rst-order SLRNN is augmented with output layers of feedforward neurons, it can implement any nite-state recognizer, but only if state-splitting is employed. When a state is split, it is divided into two equivalent states. The judicious use of state-splitting allows for eecient implementation of nite-state recognizers using augmented rst-order SLRNNs.
Optimizing Large-scale Graph Analysis on Multithreaded, Multicore Platforms

The erratic memory access pattern of graph algorithms makes it hard to optimize on cache-based architectures. While multithreading hides memory latency, it is unclear how hardware threads combined with caches impact the performance of typical graph workload. As modern architectures strike different balances between caching and multithreading, it remains an open question whether the benefit of optimizing locality behavior outweighs the cost. We study parallel graph algorithms on two different multi-threaded, multi-core platforms, that is, IBM Power7 and Sun Niagara2. Our experiments first demonstrate their performance advantage over prior architectures. We find nonetheless the number of hardware threads in either platform is not sufficient to fully mask memory latency. Our cache-friendly scheduling of memory accesses improves performance by up to 2.6 times on Power7 and prior cache-based architectures, yet the same technique significantly degrades performance on Niagara2. Software prefetching and manipulating the storage of the input to improve spatial locality improve performance by up to 2.1 times and 1.3 times on both platforms. Our study reveals interesting interplay between architecture and algorithm.
Traffic Sign Recognition using Generative Model of Scale Invariant Feature Descriptors

This generative model o descriptors for th work, SIFT key providing rather referencing the des sign and thus th significantly impro the descriptor grou presented. Measur within the voting approach uses SI scaling, translation The approach sho images with diffe occlusions. This a traffic signs of d database used for consists of real w detection of traffic proved to be arou robust approach i that are dedicated
On Learning Submodular Functions A Preliminary Draft

Submodular functions are a central concept in combinatorial optimization. The wide collection of optimization problems involving submodular functions encompasses many important combinatorial problems, such as Min-Cut and Max-Cut in graphs, various plant location problems, etc. In the operations research literature, many heuristics, exact algorithms, and approximation algorithms have been developed for minimizing and maximizing submodular functions, under various side constraints. Motivating by the extensive literature on submodular function optimization, we investigate a fundamental question concerning the structure of submodular functions. We consider the problem of learning a submodular function on ground set of size n . The problem is stated as follows: Can we make a polynomial number of (value) queries to a submodular function, and then approximate the submodular function on any other set to within some factor? We answer this question by proving the following results. First, we show that one cannot ﬁnd a learning algorithm that achieves a factor better than Ω( √ n/ log n ), even if the queries are chosen adaptively, and even if f is monotone. Moreover, we show that any such algorithm must satisfy Ω( n/ log n ) if the queries are chosen non-adaptively, even if f is monotone, and this result is tight. That is, there exists an algorithm using non-adaptive queries which can approximate a monotone submodular function to within a factor O ( n/ log n ). For monotone functions, we prove that there exists a function using O ( n 2 ) space which approximates a monotone submodular function to within a factor √ n . Finally, we show a connection between our problem and an unresolved question in convex geometry.
A DATA MINING APPROACH FOR PREDICTION AND TREATMENT OFDIABETES DISEASE

The advancement in computers provided large amount of data. The task is to analyse the input data and obtain the required data which can be done by various data mining techniques. The diagnosis of diabetes is a significant and tedious task in medicine. So the present work focus on analysis of diabetes data by various data mining techniques which involve,Naive Bayes, J48(C4.5) JRip ,Neural networks, Decision trees, KNN, Fuzzy logic and Genetic Algorithms based on accuracy and time. The 9 selected attributes wereSex, Diastolic B.P, Plasma glucose, Skin fold thick, BMI, Diabetes Pedigree type, No. of times Pregnant, 2 hr Serum Insulin and Diabetes probability.J48(C4.5) reported simple, efficient classifier of diabetes data.
Biophysical Mapping of the Adenosine A2A Receptor

A new approach to generating information on ligand receptor interactions within the binding pocket of G protein-coupled receptors has been developed, called Biophysical Mapping (BPM). Starting from a stabilized receptor (StaR), minimally engineered for thermostability, additional single mutations are then added at positions that could be involved in small molecule interactions. The StaR and a panel of binding site mutants are captured onto Biacore chips to enable characterization of the binding of small molecule ligands using surface plasmon resonance (SPR) measurement. A matrix of binding data for a set of ligands versus each active site mutation is then generated, providing specific affinity and kinetic information (KD, kon, and koff) of receptor–ligand interactions. This data set, in combination with molecular modeling and docking, is used to map the small molecule binding site for each class of compounds. Taken together, the many constraints provided by these data identify key protein–ligand interactions and allow the shape of the site to be refined to produce a high quality three-dimensional picture of ligand binding, thereby facilitating structure based drug design. Results of biophysical mapping of the adenosine A2A receptor are presented.
Determinants of outward foreign direct investment by Chinese enterprises: An empirical study from institutional perspective

Purpose – The paper aims to investigate the determinants of outward foreign direct investment (OFDI) by Chinese enterprises during China's institutional and economic transition, specifically from the institutional perspective.Design/methodology/approach – An analytical framework was conceptualized by introducing several institutional‐based advantages peculiar to Chinese firms and a series of hypotheses developed regarding the influences of these advantages on the OFDI strategies of Chinese enterprises.Findings – The findings confirm the specific institutional‐based advantages in terms of home government support, investor's financing capacity and host ethnic Chinese resources have salient impacts on OFDI behavior of Chinese firms, as predicted by initial hypotheses. Evidence from sub‐samples tests indicates there are varied determinant mechanisms for trade‐oriented, produce‐oriented, and other types of OFDI, respectively.Originality/value – The paper offers two primary contributions. On one hand, by integr.
Evolution Meets Disease: Penetrance and Functional Epistasis of Mitochondrial tRNA Mutations

About half of the mitochondrial DNA (mtDNA) mutations causing diseases in humans occur in tRNA genes. Particularly intriguing are those pathogenic tRNA mutations than can reach homoplasmy and yet show very different penetrance among patients. These mutations are scarce and, in addition to their obvious interest for understanding human pathology, they can be excellent experimental examples to model evolution and fixation of mitochondrial tRNA mutations. To date, the only source of this type of mutations is human patients. We report here the generation and characterization of the first mitochondrial tRNA pathological mutation in mouse cells, an m.3739G>A transition in the mitochondrial mt-Ti gene. This mutation recapitulates the molecular hallmarks of a disease-causing mutation described in humans, an m.4290T>C transition affecting also the human mt-Ti gene. We could determine that the pathogenic molecular mechanism, induced by both the mouse and the human mutations, is a high frequency of abnormal folding of the tRNAIle that cannot be charged with isoleucine. We demonstrate that the cells harboring the mouse or human mutant tRNA have exacerbated mitochondrial biogenesis triggered by an increase in mitochondrial ROS production as a compensatory response. We propose that both the nature of the pathogenic mechanism combined with the existence of a compensatory mechanism can explain the penetrance pattern of this mutation. This particular behavior can allow a scenario for the evolution of mitochondrial tRNAs in which the fixation of two alleles that are individually deleterious can proceed in two steps and not require the simultaneous mutation of both.
Separating spatially correlated signals using first-order precoders

This paper deals with blind separation of spatially correlated signals mixed by an instantaneous system. Taking advantage of the fact that the source signals are accessible in some man-made systems such as wireless communication systems, we preprocess the source signals in transmitters by a set of properly designed first-order precoders and then the coded signals are transmitted. At the receiving side, information about the precoders are utilized to perform signal separation. Compared with the existing precoder-based methods, the new method only employs the simplest first-order precoders, which reduces the delay in data transmission and is easier to implement in practical applications.
Mastering Financial Accounting Essentials: The Critical Nuts and Bolts

Preface. Acknowledgments. CHAPTER 1 Creating Ledger Accounting. Count Everything. The Beginnings of Double-Entry Accounting. Double-Entry Recording of Business Transactions. Handling Debits and Credits. Keeping Track of Data. A Mathematical Description of Double-Entry Conventions. Handling Income Items. Determining Profit in the Simple Accounting Model. Permanent Accounts Overview. Temporary Accounts Overview. Conclusion. Questions. CHAPTER 2 Accounting Conventions. Reasons Accountants Develop Conventions. Accounting Cycle. Classification. Comparability. Conservatism. Double-Entry. Full Disclosure. Focus on Addition. Generally Accepted Accounting Principles (GAAP). Going-Concern Value. Journal Entry. Matching. Materiality. Recognition. Understandability. Usefulness. Valuation. Verifiability. Conclusion. Questions. CHAPTER 3 Balance Sheet. Balance Sheet Contains Permanent Accounts. Time Line of Cash Flows. Types of Balance Sheet Accounts. Presenting the Classified Balance Sheet. Conclusion. Question. CHAPTER 4 Adding an Income Statement. Temporary Accounts. Using Temporary Accounts. Types of Transactions Involving Temporary Accounts. Income Accounts. Single-Step Income Statement. Multistep Income Statement. Conclusion. Questions. CHAPTER 5 Timing and Accrual Accounting. Journaling Accounting Transactions. Cash Basis Accounting. Accrual Basis Accounting. Conclusion. Questions. CHAPTER 6 The Statement of Cash Flows. Importance of Cash. An Intuitive Way to Track Cash. Standard Accounting Categories on the Statement of Cash Flows. Using the Indirect Method to Document Changes in the Cash Position. Using the Direct Method to Document Changes in the Cash Position. Producing a Statement of Cash Flows Using the Indirect Method. Producing a Statement of Cash Flows Using the Direct Method. Conclusion. Questions. CHAPTER 7 Ensuring Integrity. Internal Controls and Procedures. Independent Auditing. The Role of the User of Financial Statements. Conclusion. Questions. CHAPTER 8 Financial Statement Analysis. Restating Accounting Results. Ratio Analysis. Trend Analysis. Industry Analysis. Conclusion. Questions. Collected Questions. Answers. About the Author. Index.
Are Bayesian Fan Charts Useful for Central Banks? Uncertainty, Forecasting, and Financial Stability Stress Tests

This paper shows how fan charts generated from Bayesian vector autoregression (BVAR) models can be useful for assessing 1) the forecasting accuracy of central banksâ€™ prediction models and 2) the credibility of stress tests carried out to evaluate financial stability. Using unique data from the Czech National Bank (CNB), we compare our BVAR fan charts for inflation, GDP growth, interest rate and the exchange rate to those of the CNB, which are based on past forecasting errors. Our results suggest that in terms of the Kullback-Leibler Information Criterion, BVAR fan charts typically do not outperform those of the CNB, providing a useful cross-check of their accuracy. However, we show how BVAR fan charts can rigorously deal with the non-negativity constraint on the nominal interest rate and usefully complement the official fan charts. Finally, we put forward how BVAR fan charts can be useful for assessing financial stability and propose a simple method for evaluating whether the assumptions of banksâ€™ stress tests about the macroeconomic outlook are sufficiently adverse.
46 AN OVERVIEW OF THE 2014 NOAA HAZARDOUS WEATHER TESTBED SPRING FORECASTING EXPERIMENT

The 2014 Spring Forecasting Experiment (SFE2014) was conducted from 5 May – 6 June by the Experimental Forecast Program (EFP) of the NOAA/Hazardous Weather Testbed (HWT). SFE2014 was organized by the Storm Prediction Center (SPC) and National Severe Storms Laboratory (NSSL) with participation from numerous forecasters, researchers, and developers from around the world to test emerging concepts and technologies designed to improve the prediction of hazardous convective weather. SFE2014 aimed to address several primary goals:
Microfluidic Synthesis of Rigid Nanovesicles for Hydrophilic Reagents Delivery

We present a hollow-structured rigid nanovesicle (RNV) fabricated by a multi-stage microfluidic chip in one step, to effectively entrap various hydrophilic reagents inside, without complicated synthesis, extensive use of emulsifiers and stabilizers, and laborious purification procedures. The RNV contains a hollow water core, a rigid poly (lactic-co-glycolic acid) (PLGA) shell, and an outermost lipid layer. The formation mechanism of the RNV is investigated by dissipative particle dynamics (DPD) simulations. The entrapment efficiency of hydrophilic reagents such as calcein, rhodamine B and siRNA inside the hollow water core of RNV is ≈90 %. In comparison with the combination of free Dox and siRNA, RNV that co-encapsulate siRNA and doxorubicin (Dox) reveals a significantly enhanced anti-tumor effect for a multi-drug resistant tumor model.
Bifurcation analysis in an autonomous chaotic system

We investigated an autonomous chaotic system in detail by varying three control parameters. More precisely, the dynamical behavior of this system is further studied, including equilibrium points and stability, various attractors, bifurcations, and Lyapunov-exponent spectrum by means of nonlinear dynamics theory. The existence of Hopf bifurcation is investigated by choosing the appropriate bifurcation parameter. Finally, numerical simulation is given to illustrate the theoretical analysis.
Synthesis and Thermophysical Properties of Ether‐Functionalized Sulfonium Ionic Liquids as Potential Electrolytes for Electrochemical Applications

Abstract During this work, a novel series of hydrophobic room temperature ionic liquids (ILs) based on five ether functionalized sulfonium cations bearing the bis{(trifluoromethyl)sulfonyl}imide, [NTf2]− anion were synthesized and characterized. Their physicochemical properties, such as density, viscosity and ionic conductivity, electrochemical window, along with thermal properties including phase transition behavior and decomposition temperature, have been measured. All of these ILs showed large liquid range temperature, low viscosity, and good conductivity. Additionally, by combining DFT calculations along with electrochemical characterization it appears that these novel ILs show good electrochemical stability windows, suitable for the potential application as electrolyte materials in electrochemical energy storage devices.
Enhanced multipath LEACH protocol for increasing network life time and minimizing overhead in MANET

Energy utilization is one of the important issues in MANET, efficient routing is necessary to utilize energy consumption and enhance the network performance. A mobile Ad hoc network (MANET) is autonomous, self-organizing and self-configuring network with the capability of rapid deployment in response to application needs. LEACH is an energy based protocol which works on the cluster base mechanism to utilize the energy consumption. The cluster formation is completely depends on the remaining energy of mobile nodes. The cluster has always having more energy than cluster members. In this paper we analyses the performance of LEACH protocol with reactive on demand multipath AOMDV protocol to efficiently utilize the energy constraint in network. Here proposed scheme are not compare with any previous existing scheme. The performance of proposed MAX energy based LEACH-AOMDV is compared with normal LEACH-AOMDV routing and observes that the energy utilization is more in proposed LEACH. In Our experiments show that nodes have at best imprecise state information, especially under high traffic rates. LEACH generates cluster and gives information about energy of each cluster belonging to zone and if energy of the node is higher so LEACH selects that particular node for data transmission that increases the reliable communication. In this paper we also analyse the result in the form of network parameter like throughput, packet delivery ratio, energy consumption via node and routing overhead.
Current Products and Practices OrthoCAD™: Digital Models for a Digital Era

This article describes the use of OrthoCAD™—a digital study model capture, assessment and storage system. It is estimated that approximately 10% of orthodontists in USA and Canada now utilize digital study models, and improving technology is making it increasingly popular worldwide. The technology behind digital study models is briefly reviewed. The OrthoCAD™ system is described, and the advantages and disadvantages of using digital study models are highlighted.
Eight Obstacles to Overcome in the Theory Testing Genre

Theory testing work is popular in information systems (IS), with many studies using questionnaires, experiments, or other methods to gather quantitative data and test hypotheses with statistical techniques. This editorial note highlights some of the obstacles that theory testing researchers face, and assists authors so that their papers will not be rejected outright on submission, nor slowed unnecessarily in the review process. In particular, we identify three obstacles relating to theorizing and five related to methods. We provide guidance on how authors can deal with each obstacle, and include examples of studies that have successfully addressed the obstacle. We hope that our editorial will encourage authors to take better note of these obstacles and to give further consideration to avoiding them when they plan and conduct their research studies.
Proportionate affine projection algorithm based on coefficient difference

This paper proposes a proportionate affine projection algorithm based on coefficient difference (DPAPA). Its adaptation gain for each tap is proportional to the absolute value of the difference between the current tap weight estimate and the previous tap weight estimate. In addition, a simple version is introduced to decrease the amount of calculation. The simulation results show that the proposed algorithm has better performance than the other algorithms, e.g. the proportionate affine projection algorithm (PAPA) and improved proportionate affine projection algorithm (IPAPA).
Human Mitochondrial Thioredoxin

Thioredoxins (Trx) are a class of small multifunctional redox-active proteins found in all organisms. Recently, we reported the cloning of a mitochondrial thioredoxin, Trx2, from rat heart. To investigate the biological role of Trx2 we have isolated the human homologue, hTrx2, and generated HEK-293 cells overexpressing Trx2 (HEK-Trx2). Here, we show that HEK-Trx2 cells are more resistant toward etoposide. In addition, HEK-Trx2 are more sensitive toward rotenone, an inhibitor of complex I of the respiratory chain. Finally, overexpression of Trx2 confers an increase in mitochondrial membrane potential, ΔΨm. Treatment with oligomycin could both reverse the effect of rotenone and decrease the membrane potential suggesting that Trx2 interferes with the activity of ATP synthase. Taken together, these results suggest that Trx2 interacts with specific components of the mitochondrial respiratory chain and plays an important role in the regulation of the mitochondrial membrane potential.
Looking Away and Catching Up: Dealing with Brief Attentional Disconnection in Synchronous Groupware

Awareness information in shared-workspace groupware is often ephemeral, and when users are interrupted or look away from the system, they can miss important information about others' activities. These attentional disconnections are brief (generally less than ten seconds), are frequent in many work environments, and can occur for many reasons. In this paper, we provide visualization techniques that can help groupware users recover from these short absences. First, we provide a simple attention monitor that informs groupware systems whether a user is looking at the screen and whether the workspace is visible. Second, we provide a framework of catch-up techniques for visualizing missed activity -- including highlighting, motion traces, and replay. We implemented these techniques in three applications that cover different communication methods and interaction speeds, and explored the use of the effects in three studies. Participants clearly saw the value of catch-up techniques, and although different techniques were preferred in different situations, nearly all users strongly preferred the catch-up techniques. Our work shows that helping users deal with periods of attentional disconnection can substantially improve the usability of real-time groupware systems.
A decentralised variable structure model following controller for robot manipulators

An algorithm for the trajectory control of robot manipulators be decentralized feedback is proposed. A decentralized model following controller is used that bypasses the complexities posed by the nonlinear, uncertain dynamics of manipulator, dealing instead with the simpler actuator dynamics and treating the joint torques as disturbances. The controller is simple computationally easy, and robust to parametric uncertainties and payload variations. <<ETX>>