Query: Gated networks: an inventory

Relevant passages:
Deep Learning <=SEP=> Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search. Increasingly, these applications make use of a class of techniques called deep learning. Conventional machine-learning techniques were limited in their ability to process natural data in their raw form. For decades, constructing a pattern-recognition or machine-learning system required careful engineering and considerable domain expertise to design a feature extractor that transformed the raw data (such as the pixel values of an image) into a suitable internal representation or feature vector from which the learning subsystem, often a classifier, could detect or classify patterns in the input. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification. Deep-learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level. With the composition of enough such transformations, very complex functions can be learned. For classification tasks, higher layers of representation amplify aspects of the input that are important for discrimination and suppress irrelevant variations. An image, for example, comes in the form of an array of pixel values, and the learned features in the first layer of representation typically represent the presence or absence of edges at particular orientations and locations in the image. The second layer typically detects motifs by spotting particular arrangements of edges, regardless of small variations in the edge positions. The third layer may assemble motifs into larger combinations that correspond to parts of familiar objects, and subsequent layers would detect objects as combinations of these parts. The key aspect of deep learning is that these layers of features are not designed by human engineers: they are learned from data using a general-purpose learning procedure. Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years. It has turned out to be very good at discovering intricate structures in high-dimensional data and is therefore applicable to many domains of science, business and government. In addition to beating records in image recognition and speech recognition, it has beaten other machine-learning techniques at predicting the activity of potential drug molecules, analysing particle accelerator data, reconstructing brain circuits, and predicting the effects of mutations in non-coding DNA on gene expression and disease. Perhaps more surprisingly, deep learning has produced extremely promising results for various tasks in natural language understanding, particularly topic classification, sentiment analysis, question answering and language translation. We think that deep learning will have many more successes in the near future because it requires very little engineering by hand, so it can easily take advantage of increases in the amount of available computation and data. New learning algorithms and architectures that are currently being developed for deep neural networks will only accelerate this progress.

On multi-view feature learning <=SEP=> Sparse coding is a common approach to learning local features for object recognition. Recently, there has been an increasing interest in learning features from spatio-temporal, binocular, or other multi-observation data, where the goal is to encode the relationship between images rather than the content of a single image. We provide an analysis of multi-view feature learning, which shows that hidden variables encode transformations by detecting rotation angles in the eigenspaces shared among multiple image warps. Our analysis helps explain recent experimental results showing that transformation-specific features emerge when training complex cell models on videos. Our analysis also shows that transformation-invariant features can emerge as a by-product of learning representations of transformations.

Reducing the dimensionality of data with neural networks. <=SEP=> High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.

Learning Object Affordances: From Sensory--Motor Coordination to Imitation <=SEP=> Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.

Deep learning via Hessian-free optimization <=SEP=> We develop a 2nd-order optimization method based on the “Hessian-free” approach, and apply it to training deep auto-encoders. Without using pre-training, we obtain results superior to those reported by Hinton & Salakhutdinov (2006) on the same tasks they considered. Our method is practical, easy to use, scales nicely to very large datasets, and isn’t limited in applicability to autoencoders, or any specific model class. We also discuss the issue of “pathological curvature” as a possible explanation for the difficulty of deeplearning and how 2nd-order optimization, and our method in particular, effectively deals with it.

Hierarchical Singular Value Decomposition of Tensors <=SEP=> We define the hierarchical singular value decomposition (SVD) for tensors of order d ≥ 2. This hierarchical SVD has properties like the matrix SVD (and collapses to the SVD in d = 2), and we prove these. In particular, one can find low rank (almost) best approximations in a hierarchical format (H-Tucker) which requires only O((d − 1)k3 + dnk) parameters, where d is the order of the tensor, n the size of the modes and k the (hierarchical) rank. The H-Tucker format is a specialization of the Tucker format and it contains as a special case all (canonical) rank k tensors. Based on this new concept of a hierarchical SVD we present algorithms for hierarchical tensor calculations allowing for a rigorous error analysis. The complexity of the truncation (finding lower rank approximations to hierarchical rank k tensors) is in O((d−1)k4 +dnk2) and the attainable accuracy is just 2–3 digits less than machine precision.

Characterization of liquid crystal polymer (LCP) material and transmission lines on LCP substrates from 30 to 110 GHz <=SEP=> Liquid crystal polymer (LCP) is a material that has gained attention as a potential high-performance microwave substrate and packaging material. This investigation uses several methods to determine the electrical properties of LCP for millimeter-wave frequencies. Microstrip ring resonators and cavity resonators are measured in order to characterize the dielectric constant (/spl epsi//sub r/) and loss tangent (tan/spl delta/) of LCP above 30 GHz. The measured dielectric constant is shown to be steady near 3.16, and the loss tangent stays below 0.0049. In addition, various transmission lines are fabricated on different LCP substrate thicknesses and the loss characteristics are given in decibels per centimeter from 2 to 110 GHz. Peak transmission-line losses at 110 GHz vary between 0.88-2.55 dB/cm, depending on the line type and geometry. These results show, for the first time, that LCP has excellent dielectric properties for applications extending through millimeter-wave frequencies.

Contact-less Transfer of Energy by means of a Rotating Transformer <=SEP=> This paper examines the electrical properties of a rotating transformer used for contact-less transfer of energy to rotating equipment. Two winding layouts are analysed theoretically and experimentally. The reluctance modelling provides a deep understanding of how the geometry of the core and windings affect the electrical behaviour of the component. Theoretical calculations, measured results and finite element analysis are used to compare the proposed layouts. Basic design guidelines are given to adjust the leakage and magnetising inductances. The selection of a phaseshifted full bridge converter is suggested for applications at the power level of 1kW.

Anarchy, stability, and utopia: creating better matchings <=SEP=> Historically, the analysis of matching has centered on designing algorithms to produce stable matchings as well as on analyzing the incentive compatibility of matching mechanisms. Less attention has been paid to questions related to the social welfare of stable matchings in cardinal utility models. We examine the loss in social welfare that arises from requiring matchings to be stable, the natural equilibrium concept under individual rationality. While this loss can be arbitrarily bad under general preferences, when there is some structure to the underlying graph corresponding to natural conditions on preferences, we prove worst case bounds on the price of anarchy. Surprisingly, under simple distributions of utilities, the average case loss turns out to be significantly smaller than the worst-case analysis would suggest. Furthermore, we derive conditions for the existence of approximately stable matchings that are also close to socially optimal, demonstrating that adding small switching costs can make socially (near-)optimal matchings stable. Our analysis leads to several concomitant results of interest on the convergence of decentralized partner-switching algorithms, and on the impact of heterogeneity of tastes on social welfare.

Recovering human body configurations: combining segmentation and recognition <=SEP=> The goal of this work is to detect a human figure image and localize his joints and limbs along with their associated pixel masks. In this work we attempt to tackle this problem in a general setting. The dataset we use is a collection of sports news photographs of baseball players, varying dramatically in pose and clothing. The approach that we take is to use segmentation to guide our recognition algorithm to salient bits of the image. We use this segmentation approach to build limb and torso detectors, the outputs of which are assembled into human figures. We present quantitative results on torso localization, in addition to shortlisted full body configurations.

1 . 1 Definition of adjustable autonomy and human-centered autonomous <=SEP=> We expect a variety of autonomous systems, from rovers to life-support systems, to play a critical role in the success of manned Mars missions. The crew and ground support personnel will want to control and be informed by these systems at varying levels of detail depending on the situation. Moreover, these systems will need to operate safely in the presence of people and cooperate with them effectively. We call such autonomous systems human-centered in contrast with traditional Òblack-boxÓ autonomous systems. Our goal is to design a framework for human-centered autonomous systems that enables users to interact with these systems at whatever level of control is most appropriate whenever they so choose, but minimize the necessity for such interaction. This paper discusses on-going research at the NASA Ames Research Center and the Johnson Space Center in developing human-centered autonomous systems that can be used for a manned Mars mission.

Fat grafting in facial rejuvenation. <=SEP=> Patients with significant facial atrophy and age-related loss of facial fat generally achieve suboptimal improvement from both surface treatments of facial skin and surgical lifts. Restoring lost facial volume by fat grafting is a powerful technique that is now acknowledged by most plastic surgeons and other physicians engaged in treating the aging face as one of the most important advances in aesthetic surgery. Properly performed, the addition of fat to areas of the face that have atrophied because of age or disease can produce a significant and sustained improvement in appearance that is unobtainable by other means.

A UWB based Localization System for Indoor Robot Navigation <=SEP=> For robots to become more popular for domestic applications, the short comings of current indoor navigation technologies have to be overcome. In this paper, we propose the use of UWB-IR for indoor robot navigation. Various parts of an actual implementation of a UWB-IR based robot navigation system such as system architecture, RF sub-system design, antennas and localization algorithms are discussed. It is shown that by properly addressing the various issues, a localization error of less than 25 cm can be achieved at all points within a realistic indoor localization space.

Graph Embedding Problem Settings Graph Embedding Input Graph Embedding Output Homogeneous Graph Heterogeneous Graph Graph with Auxiliary Information Graph Constructed from Non-relational Data Node Embedding Edge Embedding Hybrid Embedding Whole-Graph Embedding Graph Embedding Techniques Matrix Facto <=SEP=> Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.

Blockchain-Free Cryptocurrencies : A Framework for Truly Decentralised Fast Transactions <=SEP=> The “blockchain” distributed ledger pioneered by Bitcoin is effective at preventing double-spending, but inherently attracts (1) “user cartels” and (2) incompressible delays, as a result of linear verification and a winner-takes-all incentive lottery. We propose to forgo the “blocks” and “chain” entirely, and build a truly distributed ledger system based on a lean graph of cross-verifying transactions, which now become the main and only objects in the system. A fully distributed consensus mechanism, based on progressive proofs of work with predictable incentives, ensures rapid convergence even across a large network of unequal participants, who all get rewards working at their own pace. Graph-based affirmation fosters snappy response through automatic scaling, while application-agnostic design supports all modern cryptocurrency features such as multiple denominations, swaps, securitisation, scripting, smart contracts, etc. We prove theoretically, and experimentally verify, our proposal to show it achieves a crucial “convergence” property — meaning that any valid transaction entering the system will quickly become enshrined into the ancestry upon which all future transactions will rest.

Fuzzy Adaptive Sliding Mode Control of 6 DOF Parallel Manipulator with Electromechanical Actuators in Cartesian Space Coordinates <=SEP=> This paper proposes a fuzzy adaptive sliding mode controller in Cartesian space coordinates for trajectory tracking of the 6 DOF parallel manipulator with considering dynamics of electromechanical actuators. The 6-DOF sensors, may be very expensive or impossible to find at the desired accuracies; and also, especially at constant speeds, will contain errors; therefore, it is better to use LVDT position sensors and then, solving the forward kinematics problem using an iterative artificial neural network strategy, Instead of using the Numerical methods such as the Newton-Raphson method, that heavy computational load imposes to the system. This controller consists of a sliding mode control and adaptive learning algorithms to adjust uncertain parameters of system, that relax the requirement of bounding parameter values and not depending upon any parameter initialization conditions, and fuzzy approximators to estimate the plant’s unknown nonlinear functions, and robustifying control terms to compensate of approximation errors; so that closedloop stability and finite-time convergence of tracking errors can be guaranteed. Simulation results demonstrate that the proposed control strategy can achieve favorable control performance with regard to uncertainties, nonlinearities and external disturbances.

Spatial Planning: A Configuration Space Approach <=SEP=> This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.

Design and Describe REST API without Violating REST: A Petri Net Based Approach <=SEP=> As REST architectural style gains popularity in the web service community, there is a growing concern and debate on how to design Restful web services (REST API) in a proper way. We attribute this problem to lack of a standard model and language to describe a REST API that respects all the REST constraints. As a result, many web services that claim to be REST API are not hypermedia driven as prescribed by REST. This situation may lead to REST APIs that are not as scalable, extensible, and interoperable as promised by REST. To address this issue, this paper proposes REST Chart as a model and language to design and describe REST API without violating the REST constraints. REST Chart models a REST API as a special type of Colored Petri Net whose topology defines the REST API and whose token markings define the representational state space of user agents using that API. We demonstrate REST Chart with an example REST API. We also show how REST Chart can support efficient content negotiation and reuse hybrid representations to broaden design choices. Furthermore, we argue that the REST constraints, such as hypermedia driven and statelessness, can either be enforced naturally or checked automatically in REST Chart.

Compact Rep-Rate GW Pulsed Generator Based on Forming Line With Built-In High-Coupling Transformer <=SEP=> In this paper, a compact rep-rate GW pulsed power generator is developed. First, its three key subsystems are theoretically analyzed, engineering designed, and experimentally investigated, respectively. The emphases are put on these four problems: the theoretical analysis of the voltage distribution across the conical secondary windings of the high-coupling transformer, the investigation of the high energy storage density dielectric used in the pulse forming line, the choice of the gas flow velocity of the gas blowing system, and theoretical analysis of the passive stability of the pulsed power generator operated in rep-rate mode. Second, the developed pulsed power generator is described in detail. It has a 0.2-m diameter, a 1.0-m length, and a 20- Ω wave impedance. Across a 100- Ω resistive dummy load, it can steadily operate at a 300-kV output voltage in 50-Hz rep-rate and 250 kV in 150 Hz without gas blowing system. The average power is ~ 1 kW. Finally, the pulsed power generator is applied to drive a relativistic backward-wave oscillator, generating a high-power microwave with peak output power of 200 MW and duration (full-width at half-maximum) of 5 ns in 150-Hz rep-rate. These efforts set a good foundation for the development of a compact rep-rate pulsed power generator and show a promising application for the future.

Near-optimal hybrid analog and digital precoding for downlink mmWave massive MIMO systems <=SEP=> Millimeter wave (mmWave) massive MIMO can achieve orders of magnitude increase in spectral and energy efficiency, and it usually exploits the hybrid analog and digital precoding to overcome the serious signal attenuation induced by mmWave frequencies. However, most of hybrid precoding schemes focus on the full-array structure, which involves a high complexity. In this paper, we propose a near-optimal iterative hybrid precoding scheme based on the more realistic subarray structure with low complexity. We first decompose the complicated capacity optimization problem into a series of ones easier to be handled by considering each antenna array one by one. Then we optimize the achievable capacity of each antenna array from the first one to the last one by utilizing the idea of successive interference cancelation (SIC), which is realized in an iterative procedure that is easy to be parallelized. It is shown that the proposed hybrid precoding scheme can achieve better performance than other recently proposed hybrid precoding schemes, while it also enjoys an acceptable computational complexity.

Power electronics for renewable energy systems: Wind turbine and photovoltaic systems <=SEP=> The use of renewable energy sources are increased because of the depletion of natural resources and the increasing pollution level from energy production. The wind energy and the solar energy are most widely used among the renewable energy sources. Power electronics is needed in almost all kinds of renewable energy system. It controls the renewable source and interfaces with the load effectively, which can be grid-connection or working in stand-alone mode. In this paper, overview of wind and photovoltaic energy systems are introduced. Next, the power electronic circuits behind the most common wind and photovoltaic configurations are discussed. Finally, their controls and important requirements for grid connection are explained.

The LightCycler: a microvolume multisample fluorimeter with rapid temperature control. <=SEP=> Experimental and commercial microvolume fluorimeters with rapid temperature control are described. Fluorescence optics adopted from flow cytometry were used to interrogate 1-10-microL samples in glass capillaries. Homogeneous temperature control and rapid change of sample temperatures (10 degrees C/s) were obtained by a circulating air vortex. A prototype 2-color, 32-sample version was constructed with a xenon arc for excitation, separate excitation and emission paths, and photomultiplier tubes for detection. The commercial LightCycler, a 3-color, 24-sample instrument, uses a blue light-emitting diode for excitation, paraxial epi-illumination through the capillary tip and photodiodes for detection. Applications include analyte quantification and nucleic acid melting curves with fluorescent dyes, enzyme assays with fluorescent substrates and techniques that use fluorescence resonance energy transfer. Microvolume capability allows analysis of very small or expensive samples. As an example of one application, rapid cycle DNA amplification was continuously monitored by three different fluorescence techniques, Which included using the double-stranded DNA dye SYBR Green I, a dual-labeled 5'-exonuclease hydrolysis probe, and adjacent fluorescein and Cy5z-labeled hybridization probes. Complete amplification and analysis requires only 10-15 min.

The Internet of things. <=SEP=> Keynote David Rose The New Vanguard for Business: Connectivity, Design, and the Internet of Things The Internet of Things is the hottest topic of the moment a shift predicted to be as momentous as the impact of the internet itself. The internet has allowed us to share ideas and data largely input by humans. What about a world where data from objects as diverse as umbrellas, fridges, and gas tanks all flows through the internet?

Kinetic Energy of Wind-Turbine Generators for System Frequency Support <=SEP=> As wind power penetration increases and fossil plants are retired, it is feared that there will be insufficient kinetic energy (KE) from the plants to support the system frequency. This paper shows the fear is groundless because the high inertias (H cong 4 seconds) of wind turbine-generators (WTGs) can be integrated to provide frequency support during generation outage.

Enhancing financial performance with social media: An impression management perspective <=SEP=> The growing plethora of social media outlets have sparked both opportunity and concern in how organizations manage their corporate image. While previous research has examined the various problems associated with negative, word-of-mouth transference of information occurring simultaneously throughout many networks in social media, this paper seeks to address social media usage in impression management (IM). Specifically, we seek to answer two questions: Do IM direct-assertive strategies in social media impact a firm’s financial performance? And which social media strategies impact a firm’s financial performance? To analyze these questions in depth, we use text mining to collect and analyze text from a variety of social network platforms, including blogs, forums, and corporate websites, to assess how such IM strategies impact financial performance. Our results provide text mining validation that social media have a 1 Corresponding author positive impact on IM. We also provide further understanding of how social media strengthens organizations’ communication with various internal and external stakeholders. Lastly, we provide future research ideas concerning social media’s usage in IM.

Low power/low voltage high speed CMOS differential track and latch comparator with rail-to-rail input <=SEP=> A new CMOS differential latched comparator suitable for low voltage, low-power application is presented. The circuit consists of a constant-gm rail-to-rail common-mode operational transconductance amplifier followed by a regenerative latch in a track and latch configuration to achieve a relatively constant delay. The use of a track and latch minimizes the total number of gain stages required for a given resolution. Potential offset from the constant-gm differential input stage, estimated as the main source of offset, can be minimized by proper choice of transistors sizes. Simulation results show that the circuit requires less than 86 μA with a supply voltage of 1.65 V in a standard CMOS 0.18 μm digital process. The average delay is less than 1 ns and is approximately independent of the common-mode input voltage.

From semantics to syntax and back again: Argument structure in the third year of life <=SEP=> An essential part of the human capacity for language is the ability to link conceptual or semantic representations with syntactic representations. On the basis of data from spontaneous production, suggested that young children acquire such links on a verb-by-verb basis, with little in the way of a general understanding of linguistic argument structure. Here, we suggest that a receptive understanding of argument structure--including principles linking syntax and conceptual/semantic structure--appears earlier. In a forced-choice pointing task we have shown that toddlers in the third year of life can map a single scene (involving a novel causative action paired with a novel verb) onto two distinct syntactic frames (transitive and intransitive). This suggests that even before toddlers begin generalizing argument structure in their own speech, they have some representation of conceptual/semantic categories, syntactic categories, and a system that links the two.

Physical Discipline and Behavior Problems in African American , European American , and Hispanic Children : Emotional Support as a Moderator <=SEP=> Using data collected over a 6-year period on a sample of 1,039 European American children, 550 African American children, and 401 Hispanic children from the children of the National Longitudinal Survey of Youth, this study assessed whether maternal emotional support of the child moderates the relation between spanking and behavior problems. Children were 4–5 years of age in the first of 4 waves of data used (1988, 1990, 1992, 1994). At each wave, mothers reported their use of spanking and rated their children’s behavior problems. Maternal emotional support of the child was based on interviewer observations conducted as part of the Home Observation for Measurement of the Environment. For each of the 3 racial-ethnic groups, spanking predicted an increase in the level of problem behavior over time, controlling for income-needs ratio and maternal emotional support. Maternal emotional support moderated the link between spanking and problem behavior. Spanking was associated with an increase in behavior problems over time in the context of low levels of emotional support, but not in the context of high levels of emotional support. This pattern held for all 3 racial-ethnic groups.

Depressive symptomatology among university students in Denizli, Turkey: prevalence and sociodemographic correlates. <=SEP=> AIM
To determine overall and subgroup prevalence of depressive symptomatology among university students in Denizli, Turkey during the 1999-2000 academic year, and to investigate whether sociodemographic factors were associated with depressive symptoms in university students.


METHODS
A stratified probability sample of 504 Turkish university students (296 male, 208 female) was used in a cross-sectional study. Data were obtained by self-administered questionnaire, including questions on sociodemographic characteristics and problem areas. The revised Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores 17 or higher were categorized as depressive for logistic regression analysis. Student t-test and linear regression were used for continuous data analysis.


RESULTS
Out of all participants, 26.2% had a BDI score 17 or higher. The prevalence of depressive symptoms increased to 32.1% among older students, 34.7% among students with low socioeconomic status, 31.2% among seniors, and 62.9% among students with poor school performance. The odds ratio of depressive symptoms was 1.84 (95% confidence interval [CI], 1.03-3.28) in students with low socioeconomic status and 7.34 (95% CI, 3.36-16.1) in students with poor school performance in the multivariate logistic model. The participants identified several problem areas: lack of social activities and shortage of facilities on the campus (69.0%), poor quality of the educational system (54.8%), economic problems (49.3%), disappointment with the university (43.2%), and friendship problems (25.9%).


CONCLUSIONS
Considering the high frequency of depressive symptoms among Turkish university students, a student counseling service offering mental health assistance is necessary. This service should especially find the way to reach out to poor students and students with poor school performance.

Momentum Investment Strategies of Mutual Funds , Performance Persistence , and Survivorship Bias <=SEP=> We show that the persistent use of momentum investment strategies by mutual funds has important implications for the performance persistence and survivorship bias controversies. Using mutual fund portfolio holdings from a database free of survivorship bias, we  ̄nd that the best performing funds during one year are the best performers during the following year, with the exception of 1981, 1983, 1988, and 1989. This pattern corresponds exactly to the pattern exhibited by the momentum e®ect in stock returns,  ̄rst documented by Jegadeesh and Titman (1993) and recently studied by Chan, Jegadeesh, and Lakonishok (1996). Our evidence points not only to the momentum e®ect in stock returns, but to the persistent, active use of momentum strategies by mutual funds as the reasons for performance persistence. Moreover, essentially no persistence remains after controlling for the one-year momentum e®ect in stock returns. We also explain why most recent studies have found that survivorship bias is a relatively small concern. Funds that were the best performers during one year are the worst performers during the following year whenever the momentum e®ect in stock returns is absent, and these funds tend to disappear with a frequency not appreciably lower than that of consistently poor performers. Therefore, the pool of non-surviving funds is more representative of the cross-section of all funds than previously thought. Speci ̄cally, we  ̄nd a di®erence of only 20 basis points per year in risk-adjusted pre-expense returns between the average fund and the average surviving fund.