Query: Comparison of Pooling Methods for Handwritten Digit Recognition Problem

Relevant passages:
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks <=SEP=> We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined with other regularization approaches, such as dropout and data augmentation. We achieve state-of-the-art performance on four image datasets, relative to other approaches that do not utilize data augmentation.

Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree <=SEP=> Abstract We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets; they are also easy to implement, and can be applied within various deep neural network architectures. These benefits come with only a light increase in computational overhead during training (ranging from 5% to 15% in timing experiments) and a very modest increase in the number of model parameters. For example, using 45 additional parameters, we improve AlexNet performance on ImageNet by 6% relative (top-5, single-view).

Fully convolutional networks for semantic segmentation <=SEP=> 

Reading Digits in Natural Images with Unsupervised Feature Learning <=SEP=> Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks.

Very Deep Convolutional Networks for Large-Scale Image Recognition <=SEP=> In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

A survey of frequent subgraph mining algorithms <=SEP=> Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining, and proposed solutions to address the main research issues.

Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition <=SEP=> This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.

Notions of Video Game Addiction and Their Relation to Self-Reported Addiction Among Players of World of Warcraft <=SEP=> In this study, 438 players of the online video game, World of Warcraft, completed a survey about video game addiction and answered an open-ended question about behaviors they considered characteristic of video game addiction. Responses were coded and correlated with players’ self-reports of being addicted to games and scores on a modified video game addiction scale. The behaviors most frequently mentioned as characteristic of addiction included playing a lot and games’ interfering with other activities, especially socializing or work. Few players mentioned such signs of addiction as withdrawal symptoms or tolerance, and some thought it was not possible to become addicted to video games. Self-reported addiction to video games correlated positively with perceptions that video game addiction involved playing a lot or playing to escape problems, and correlated negatively with perceptions that addiction involved games’ interfering with other activities or not being able to stop play. Implications for assessment are discussed.

Consideration of 10kW in-wheel type axial-gap motor using ferrite permanent magnets <=SEP=> In general, the in-wheel type permanent magnet synchronous motor (PMSM) for electric city commuters uses powerful rare earth permanent magnets (PMs). However, the employment of the rare earth PMs should be reduced due to high prices. Therefore, it is important to develop an in-wheel PMSM that does not use the rare earth PMs. We proposed 5kW in-wheel type axial gap motor that can generate high torque density and has features such as a coreless rotor structure with ferrite PMs and a reduction gearbox in the inner side of the stator. And we have presented details of experimental results on a prototype. So, in this paper, in order to achieve further higher power output, we considered a 10 kW in-wheel motor with the proposed structure. The motor with 10-kW output was examined by means of using 3D-FEM and experimental results in order to attain the further higher power as a rare earth free motor.

Sparse Non-rigid Registration of 3D Shapes <=SEP=> Non-rigid registration of 3D shapes is an essential task of increasing importance as commodity depth sensors become more widely available for scanning dynamic scenes. Non-rigid registration is much more challenging than rigid registration as it estimates a set of local transformations instead of a single global transformation, and hence is prone to the overfitting issue due to underdetermination. The common wisdom in previous methods is to impose an ℓ2-norm regularization on the local transformation differences. However, the ℓ2-norm regularization tends to bias the solution towards outliers and noise with heavy-tailed distribution, which is verified by the poor goodness-of-fit of the Gaussian distribution over transformation differences. On the contrary, Laplacian distribution fits well with the transformation differences, suggesting the use of a sparsity prior. We propose a sparse non-rigid registration (SNR) method with an ℓ1-norm regularized model for transformation estimation, which is effectively solved by an alternate direction method (ADM) under the augmented Lagrangian framework. We also devise a multi-resolution scheme for robust and progressive registration. Results on both public datasets and our scanned datasets show the superiority of our method, particularly in handling large-scale deformations as well as outliers and noise.

A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization <=SEP=> Text categorization has become a key research field in the NLP community. However, most works in this area are focused on Western languages ignoring other Semitic languages like Arabic. These languages are of immense political and social importance necessitating robust categorization techniques. In this paper, we present a novel three-stage technique to efficiently classify Arabic documents into different categories based on the words they contain. We leverage the significance of root-words in Arabic and incorporate a combination of Markov clustering and Deep Belief Networks to classify Arabic words into separate groups (clusters). Our approach is tested on two public datasets giving a F-Measure of 91.02%.

Algorithms on strings, trees, and sequences <=SEP=> Linear-Time Construction of Suffix Trees We will present two methods for constructing suffix trees in detail, Ukkonen’s method and Weiner’s method. Weiner was the first to show that suffix trees can be built in linear time, and his method is presented both for its historical importance and for some different technical ideas that it contains. However, lJkkonen’s method is equally fast and uses far less space (i.e., memory) in practice than Weiner’s method Hence Ukkonen is the method of choice for most problems requiring the construction of a suffix tree. We also believe that Ukkonen’s method is easier to understand. Therefore, it will be presented first A reader who wishes to study only one method is advised to concentrate on it. However, our development of Weiner’s method does not depend on understanding Ukkonen’s algorithm, and the two algorithms can be read independently (with one small shared section noted in the description of Weiner’s method).

Use of assistance and therapy dogs for children with autism spectrum disorders: a critical review of the current evidence. <=SEP=> BACKGROUND
Autism spectrum disorders (ASD) are characterized by deficits in social reciprocity and communication, and by unusually restricted, repetitive behaviors. Intervention strategies based on the exploitation of the emotional aspects of human-dog relationships hold the potential to overcome the difficulty of subjects with ASD to relate and interact effectively with others, targeting core symptoms of this disorder.


METHODS
This review summarizes the results of six published studies on the effects of brief interactions with dogs and the effects of introducing dogs in families with a child diagnosed with ASD, with an emphasis on social behaviors and language use. Furthermore, the possible mechanisms responsible for the beneficial effects observed are discussed.


CONCLUSIONS
Although the studies described here are encouraging, further research with better designs and using larger samples is needed to strengthen translation of such interventions to the clinic. In addition, potential applications of analyzing child-dog interactions are highlighted to screen for early signs of the disorder.

Enterprise Software System Integration : An Architectural Perspective <=SEP=> The present thesis is concerned with enterprise software systems of companies in the Swedish electricity industry, an industry that for the past few years has been exposed to a fairly tumultuous change process as a consequence of legislative reforms. Previously, the business operations of electric utilities – as well as those of most companies in the computerized world – were supported by a number of isolated software systems performing specific tasks. In recent years, these systems have been extended, and more importantly, integrated into a company-wide system in its own right, in this thesis referred to as the enterprise software system. As enterprise software systems have evolved, so has a need for strategies, methods and techniques for their management. Enterprise software system management involves a number of concerns; of these concerns, software integration is one of the most prominent. The discipline of software architecture is concerned with the modeling of large-scale structures of software systems. While the generated models are employed for a number of purposes , their perhaps most significant function is to serve as a base for reasoning about the represented system. Several methods for analysis of architectures have been proposed in recent years, and although software architecture analysis has displayed considerable success for a number of systems, enterprise software systems have to a large extent been ignored. In the empirical context of the Scandinavian electricity industry, this thesis explores the applicability of software architecture analysis to enterprise software system integration. Two conceptually different architectural analysis methods – deduction-and induction-based approaches – are considered, as well as the engineering process in which architectural analysis is performed. As a result of the investigations, the thesis proposes a modified process for architectural analysis, presents an evaluation of deduction-based analysis methods , and proposes an adaptation of induction-based analysis methods to the enterprise software system context.

Wireless Sensor Network for Precise Agriculture Monitoring <=SEP=> Precision Agriculture Monitor System (PAMS) is an intelligent system which can monitor the agricultural environments of crops and provides service to farmers. PAMS based on the wireless sensor network (WSN) technique attracts increasing attention in recent years. The purpose of such systems is to improve the outputs of crops by means of managing and monitoring the growth period. This paper presents the design of a WSN for PAMS, shares our real-world experience, and discusses the research and engineering challenges in implementation and deployments.

Synthesis and Application of Monolayer Semiconductors (June 2015) <=SEP=> Recently, semiconducting monolayers, such as MoS2 and WSe2, have been highlighted for their spin-valley coupling, diverse band structures, bendability, and excellent optoelectronic performances. With a subnanometer thickness of atomic layers, the transition metal dichalcogenides (TMDc) atomic layers demonstrate a significant photoresponse, considerable absorption to incident sunlight and favorable transport performances, leading to applications in the electronic circuit requiring low stand-by power, diverse optoelectronic devices, and next-generation nanoelectronics. Therefore, the class of monolayer TMDc offers a burgeoning field in materials science, fundamental physics, and optoelectronics. A feasible synthetic process to realize controlled synthesis of large area and high quality of TMDc monolayers is in demands. In this review, we will introduce the progress on synthesis and applications of the TMDc atomic layers.

Human skin as data transmission medium for improved privacy and usability in wearable electronics <=SEP=> Wearable electronics have multiple benefits in medical applications, such as feedback, monitoring and prevention. The number of these devices in circulation is rapidly growing, emphasizing increasing problems in their use caused by one of two standard communication types - wireless communications and wired communications. These problems include obtrusiveness and inconvenient usage, limited bandwidth, privacy and security issues and relatively high energy consumption. Wireless communications are less secure, with a more limited bandwidth but more unobtrusive and convenient than wired ones. On the other hand, wired communications don’t need so many batteries, thereby devices can be charged more rarely and provide more privacy. A third, emerging technology for wearable sensor network communication using the body itself as communication medium tries to solve these problems. The body coupled communications benefit from the communication privacy of the wired networks, while not requiring obtrusive wiring. Unfortunately this type of communication requires complex signal processing for reliable communications as the signal changes depending on the body parts connected. To solve this problem and allow more rapid adoption of body coupled communications in wearable electronics we develop a transfer function for calculating the parameters of human skin as data transmission medium, allowing more usable and privacy oriented communications between the wearable nodes. Additionally this has the potential of reducing the energy consumption of wearable devices making them more practical to use for a prolonged period of time as well as opening new applications as secure personal data exchange using touch.

Gender and Smile Classification Using Deep Convolutional Neural Networks <=SEP=> Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute-specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods.

Evidence-Based Psychological Assessment. <=SEP=> In recent years there has been increasing emphasis on evidence-based practice in psychology (EBPP), and as is true in most health care professions, the primary focus of EBPP has been on treatment. Comparatively little attention has been devoted to applying the principles of EBPP to psychological assessment, despite the fact that assessment plays a central role in myriad domains of empirical and applied psychology (e.g., research, forensics, behavioral health, risk management, diagnosis and classification in mental health settings, documentation of neuropsychological impairment and recovery, personnel selection and placement in organizational contexts). This article outlines the central elements of evidence-based psychological assessment (EBPA), using the American Psychological Association's tripartite definition of EBPP as integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences. After discussing strategies for conceptualizing and operationalizing evidence-based testing and evidence-based assessment, 6 core skills and 3 meta-skills that underlie proficiency in psychological assessment are described. The integration of patient characteristics, culture, and preferences is discussed in terms of the complex interaction of patient and assessor identities and values throughout the assessment process. A preliminary framework for implementing EBPA is offered, and avenues for continued refinement and growth are described.

Very low-voltage fully differential amplifier for switched-capacitor applications <=SEP=> A fully differential opamp suitable for very-low voltage switched-capacitor circuits in standard CMOS technologies is introduced. The proposed two stage opamp needs a simple low voltage CMFB switched-capacitor circuit only for the second stage. Due to the reduced supply voltage, the CMFB circuit is implemented using boot-strapped switches. Minor modifications allow to use chop-per stabilization for flicker noise reduction. Two different compensation schemes are discussed and compared using an example for 1V operation of the amplifier.

Optimization of 6Slots-7Poles & 12Slots-14Poles flux-switching permanent magnet machines for plug-in HEV <=SEP=> Plug-in hybrid electric vehicle (PHEV) is independent of the internal combustion engine but acts as the auxiliary unit mostly on the electric drive system. Main while, the Flux-switching permanent magnet machine (FSPMM) has been studied in terms of structure with its operation principle been analyzed. To achieve a higher torque with a corresponding high power density and lower torque ripple, FSPMM with 6Slots-7Poles and 12Slots-14Poles, according investigations, are optimized based on their objective functions. Moreover, it analyzes several typical performance curves, such as cogging torque, flux linkage and back-EMF. Finally, the torque and corresponding power, efficiency and rotor mechanical strength analysis of optimized designed are obtained. The results indicate that FSPMM is a viable candidate for PHEV and also has good mechanical robustness with strong flux-weakening ability.

Magnetic Equivalent Circuit Modeling of the AC Homopolar Machine for Flywheel Energy Storage <=SEP=> This paper develops a magnetic equivalent circuit model suitable to the design and optimization of the synchronous ac homopolar machine. The ac homopolar machine is of particular interest in the application of grid-based flywheel energy storage, where it has the potential to significantly reduce self-discharge associated with magnetic losses. The ac homopolar machine features both axial and radial magnetizing flux paths, which requires finite element analysis to be conducted in 3-D. The computation time associated with 3-D finite element modeling is highly prohibitive in the design process. The magnetic equivalent circuit model developed in this paper is shown to be a viable alternative for calculating several design performance parameters and has a computation time which is orders of magnitude less than that of 3-D finite element analysis. Results obtained from the developed model are shown to be in good agreement with finite element and experimental results for varying levels of saturation.

Analysis of brushless permanent magnet synchronous motors <=SEP=> A brief design review of the Permanent Magnet Synchronous Motors has been presented. A procedure has been developed to predict the steady state and dynamic performances of a brushless permanent magnet synchronous motor. Finite element analysis has been combined with a lumped parameter circuit model in order to provide satisfactory engineering information. To this end, two coordinated steps are involved. One is to develop a unified lumped parameter circuit model for both steady state and dynamic analysis. The second step is to extract the individual lumped parameters from finite element solutions based on corresponding equivalent circuits, each with a pre-determined topology. The proposed techniques have been experimentally verified in a laboratory permanent magnet synchronous motor.

Mail versus Mall: A Strategic Analysis of Competition between Direct Marketers and Conventional Retailers <=SEP=> Consumers now purchase several offerings from direct sellers, including catalog and Internet marketers. These direct channels exist in parallel with the conventional retail stores. The availability of multiple channels has significant implications for the performance of consumer markets. The literature in marketing and economics has, however, been dominated by a focus on the conventional retail sector. This paper is an effort toward modeling competition in the multiplechannel environment from a strategic viewpoint. At the outset, a parsimonious model that accommodates the following consumer and market characteristics is introduced. First, the relative attractiveness of retail shopping varies across consumers. Second, the fit with the direct channel varies across product categories. Third, the strength of existing retail presence in local markets moderates competition. Fourth, in contrast with the fixed location of the retail store that anchors its localized market power, the location of the direct marketer is irrelevant to the competitive outcome. The model is first applied in a setting where consumers have complete knowledge of product availability and prices in all channels. In the resulting equilibrium, the direct marketer acts as a competitive wedge between retail stores. The direct presence is so strong that each retailer competes against the remotely located direct marketer, rather than against neighboring retailers. This outcome has implications for the marketing mix of retailers, which has traditionally been tuned to attract consumers choosing between retail stores. In the context of market entry, conditions under which a direct channel can access a local market in retail entry equilibrium are derived. Our analysis suggests that the traditional focus on retail entry equilibria may not yield informative or relevant findings when direct channels are a strong presence. Next, the role of information in multiple-channel markets is modeled. This issue is particularly relevant in the context of direct marketing where the seller can typically control the level of information in the marketplace, sometimes on a customer-by-customer basis (e.g., by deciding on the mailing list for a catalog campaign). When a certain fraction of consumers does not receive information from the direct marketer, the retailers compete with each other for that fraction of the market. The retailer’s marketing mix has to be tuned, in this case, to jointly address direct and neighboring retail competition. The level of information disseminated by the direct marketer is shown to have strategic implications, and the use of market coverage as a lever to control competition is described. Even with zero information costs, providing information to all consumers may not be optimal under some circumstances. In particular, when the product is not well adapted to the direct channel, the level of market information about the direct option should ideally be lowered. The only way to compete with retailers on a larger scale with a poorly adapted product is by lowering direct prices, which lowers profits. Lowering market information levels and allowing retailers to compete more with each other facilitates a higher equilibrium retail price. In turn, this allows a higher direct price to be charged and improves overall direct profit. On the other hand, when the product is well adapted, increasing direct market presence and engaging in greater competition with the retail sector yields higher returns. The finding that high market coverage may depress profits raises some issues for further exploration. First, implementing the optimal coverage is straightforward when the seller controls the information mechanism, as in the case of catalog marketing. The Internet, in contrast, is an efficient mechanism to transmit information, but does not provide the sellers with such control over the level of market information. A key reason is that the initiative to gather information on the Internet lies largely with consumers. The design and implementation of mechanisms to control aggregate information levels in electronic markets can, therefore, be an important theme for research and managerial interest. Second, direct marketers have traditionally relied on the statistical analysis of customer records to decide on contact policies. The analysis in this paper reveals that these policies can have significant strategic implications as well. Research that integrates the statistical and strategic aspects could make a valuable contribution. The paper concludes with a discussion of issues for future research in multiple-channel markets, including avenues to model competition in settings with multiple direct marketers. (Channels of Distribution; Catalog Marketing; Direct Marketing; Electronic Marketing; Internet Marketing; Competitive Strategy; Game Theory)

How to derive priorities in AHP: a comparative study <=SEP=> A heated discussion has arisen over the “best” priorities derivation method. Using a Monte Carlo simulation this article compares and evaluates the solutions of four AHP ratio scaling methods: the right eigenvalue method, the left eigenvalue method, the geometric mean and the mean of normalized values. Matrices with different dimensions and degree of impurities are randomly constructed. We observe a high level of agreement between the different scaling techniques. The number of ranking contradictions increases with the dimension of the matrix and the inconsistencies. However these contradictions affect only close priorities.

An unusual case of asymptomatic spontaneous umbilical endometriosis treated with skin-sparing excision <=SEP=> Spontaneous umbilical endometriosis is a rare extrapelvic manifestation of endometriosis. Very few such cases have been previously reported, almost always associated with a variety of symptoms, usually during menstruation. We present a case of asymptomatic umbilical endometriosis treated with skin-sparing excision. Differential diagnoses relevant to the clinician, as well as treatment options, are also presented. Surgeons should always consider umbilical endometriosis in their diagnostic approach when confronted with atypical umbilical nodules, regardless of whether they are symptomatic or not.

Bayesian Nonparametric Methods for Learning Markov Switching Processes <=SEP=> In this article, we explored a Bayesian nonparametric approach to learning Markov switching processes. This framework requires one to make fewer assumptions about the underlying dynamics, and thereby allows the data to drive the complexity of the inferred model. We began by examining a Bayesian nonparametric HMM, the sticky HDPHMM, that uses a hierarchical DP prior to regularize an unbounded mode space. We then considered extensions to Markov switching processes with richer, conditionally linear dynamics, including the HDP-AR-HMM and HDP-SLDS. We concluded by considering methods for transferring knowledge among multiple related time series. We argued that a featural representation is more appropriate than a rigid global clustering, as it encourages sharing of behaviors among objects while still allowing sequence-specific variability. In this context, the beta process provides an appealing alternative to the DP.

Cetus: A Source-to-Source Compiler Infrastructure for Multicores <=SEP=> The Cetus tool provides an infrastructure for research on multicore compiler optimizations that emphasizes automatic parallelization. The compiler infrastructure, which targets C programs, supports source-to-source transformations, is user-oriented and easy to handle, and provides the most important parallelization passes as well as the underlying enabling techniques.

Turret: A Platform for Automated Attack Finding in Unmodified Distributed System Implementations <=SEP=> Security and performance are critical goals for distributed systems. The increased design complexity, incomplete expertise of developers, and limited functionality of existing testing tools often result in bugs and vulnerabilities that prevent implementations from achieving their design goals in practice. Many of these bugs, vulnerabilities, and misconfigurations manifest after the code has already been deployed making the debugging process difficult and costly. In this paper, we present Turret, a platform for automatically finding performance attacks in unmodified implementations of distributed systems. Turret does not require the user to provide any information about vulnerabilities and runs the implementation in the same operating system setup as the deployment, with an emulated network. Turret uses a new attack finding algorithm and several optimizations that allow it to find attacks in a matter of minutes. We ran Turret on 5 different distributed system implementations specifically designed to tolerate insider attacks, and found 30 performance attacks, 24 of which were not previously reported to the best of our knowledge.

A NEW GENERALIZATION OF FIBONACCI SEQUENCE AND EXTENDED BINET’S FORMULA <=SEP=> Consider the Fibonacci sequence {Fn}n=0 with initial conditions F0 = 0, F1 = 1 and recurrence relation Fn = Fn−1 + Fn−2 (n ≥ 2). The Fibonacci sequence has been generalized in many ways, some by preserving the initial conditions, and others by preserving the recurrence relation. In this article, we study a new generalization {qn}, with initial conditions q0 = 0 and q1 = 1, which is generated by the recurrence relation qn = aqn−1 + qn−2 (when n is even) or qn = bqn−1 + qn−2 (when n is odd), where a and b are nonzero real numbers. Some well-known sequences are special cases of this generalization. The Fibonacci sequence is a special case of {qn} with a = b = 1. Pell’s sequence is {qn} with a = b = 2 while k-Fibonacci sequence has a = b = k. We produce an extended Binet’s formula for {qn} and, thereby, identities such as Cassini’s, Catalan’s, d’Ocagne’s, etc.