Query: A Protocol for Preventing Insider Attacks in Untrusted Infrastructure-as-a-Service Clouds

Relevant passages:
Terra: a virtual machine-based platform for trusted computing <=SEP=> We present a flexible architecture for trusted computing, called Terra, that allows applications with a wide range of security requirements to run simultaneously on commodity hardware. Applications on Terra enjoy the semantics of running on a separate, dedicated, tamper-resistant hardware platform, while retaining the ability to run side-by-side with normal applications on a general-purpose computing platform. Terra achieves this synthesis by use of a trusted virtual machine monitor (TVMM) that partitions a tamper-resistant hardware platform into multiple, isolated virtual machines (VM), providing the appearance of multiple boxes on a single, general-purpose platform. To each VM, the TVMM provides the semantics of either an "open box," i.e. a general-purpose hardware platform like today's PCs and workstations, or a "closed box," an opaque special-purpose platform that protects the privacy and integrity of its contents like today's game consoles and cellular phones. The software stack in each VM can be tailored from the hardware interface up to meet the security requirements of its application(s). The hardware and TVMM can act as a trusted party to allow closed-box VMs to cryptographically identify the software they run, i.e. what is in the box, to remote parties. We explore the strengths and limitations of this architecture by describing our prototype implementation and several applications that we developed for it.

Breaking up is hard to do: security and functionality in a commodity hypervisor <=SEP=> Cloud computing uses virtualization to lease small slices of large-scale datacenter facilities to individual paying customers. These multi-tenant environments, on which numerous large and popular web-based applications run today, are founded on the belief that the virtualization platform is sufficiently secure to prevent breaches of isolation between different users who are co-located on the same host. Hypervisors are believed to be trustworthy in this role because of their small size and narrow interfaces.
 We observe that despite the modest footprint of the hypervisor itself, these platforms have a large aggregate trusted computing base (TCB) that includes a monolithic control VM with numerous interfaces exposed to VMs. We present Xoar, a modified version of Xen that retrofits the modularity and isolation principles used in micro-kernels onto a mature virtualization platform. Xoar breaks the control VM into single-purpose components called service VMs. We show that this componentized abstraction brings a number of benefits: sharing of service components by guests is configurable and auditable, making exposure to risk explicit, and access to the hypervisor is restricted to the least privilege required for each component. Microrebooting components at configurable frequencies reduces the temporal attack surface of individual components. Our approach incurs little performance overhead, and does not require functionality to be sacrificed or components to be rewritten from scratch.

Principles of remote attestation <=SEP=> Remote attestation is the activity of making a claim about properties of a target by supplying evidence to an appraiser over a network. We identify five central principles to guide development of attestation systems. We argue that (i) attestation must be able to deliver temporally fresh evidence; (ii) comprehensive information about the target should be accessible; (iii) the target, or its owner, should be able to constrain disclosure of information about the target; (iv) attestation claims should have explicit semantics to allow decisions to be derived from several claims; and (v) the underlying attestation mechanism must be trustworthy. We illustrate how to acquire evidence from a running system, and how to transport it via protocols to remote appraisers. We propose an architecture for attestation guided by these principles. Virtualized platforms, which are increasingly well supported on stock hardware, provide a natural basis for our attestation architecture.

seL4: formal verification of an OS kernel <=SEP=> Complete formal verification is the only known way to guarantee that a system is free of programming errors.
 We present our experience in performing the formal, machine-checked verification of the seL4 microkernel from an abstract specification down to its C implementation. We assume correctness of compiler, assembly code, and hardware, and we used a unique design approach that fuses formal and operating systems techniques. To our knowledge, this is the first formal proof of functional correctness of a complete, general-purpose operating-system kernel. Functional correctness means here that the implementation always strictly follows our high-level abstract specification of kernel behaviour. This encompasses traditional design and implementation safety properties such as the kernel will never crash, and it will never perform an unsafe operation. It also proves much more: we can predict precisely how the kernel will behave in every possible situation.
 seL4, a third-generation microkernel of L4 provenance, comprises 8,700 lines of C code and 600 lines of assembler. Its performance is comparable to other high-performance L4 kernels.

Linux kernel integrity measurement using contextual inspection <=SEP=> This paper introduces the Linux Kernel Integrity Monitor (LKIM) as an improvement over conventional methods of software integrity measurement. LKIM employs contextual inspection as a means to more completely characterize the operational integrity of a running kernel. In addition to cryptographically hashing static code and data in the kernel, dynamic data structures are examined to provide improved integrity measurement. The base approach examines structures that control the execution flow of the kernel through the use of function pointers as well as other data that affect the operation of the kernel. Such structures provide an efficient means of extending the kernel operations, but they are also a means of inserting malicious code without modifying the static parts. The LKIM implementation is discussed and initial performance data is presented to show that contextual inspection is practical

Person Re-Identification by Iterative Re-Weighted Sparse Ranking <=SEP=> In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.

Convolutional LSTM Networks for Video-based Person Re-identification <=SEP=> In this paper, we study the problem of video-based person re-identification. This is more challenging, and of greater practical interest, than conventional image-based person re-identification. To address this problem, we propose the use of convolutional Long Short Term Memory (LSTM) based networks to learn a video-based representation for person re-identification. To this end, we propose to jointly leverage deep Convolutional Neural Networks (CNNs) and LSTM networks. Given sequential video frames of a person, the spatial information encoded in the frames is first extracted by a set of CNNs. An encoderdecoder framework derived from LSTMs is employed to encode the resulting temporal of CNN outputs. This approach leads to a refined feature representation that is able to explicitly model the video as an ordered sequence, while preserving the spatial information. Comparative experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose.

A Review of Ensemble Methods in Bioinformatics <=SEP=> Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is two-fold. First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed.

Integration of independent component analysis and neural networks for ECG beat classification <=SEP=> In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG. 2007 Elsevier Ltd. All rights reserved.

MSc THESIS Hardware Acceleration of BWA-MEM Genome Mapping Application <=SEP=> Faculty of Electrical Engineering, Mathematics and Computer Science CE-MS-2014 Next Generation Sequencing technologies have had a tremendous impact on our understanding of DNA and its role in living organisms. The cost of DNA sequencing has decreased drastically over the past decade, leading the way for personalised genomics. Sequencers provide millions of fragments of DNA (termed short reads), which have to be aligned aginst a reference genome to reconstruct the full information of the DNA molecule under study. Processing short reads requires large computational resources, with many specialised computing platforms now being used to accelerate software aligners. We take up the challenge of accelerating a well know sequence alignment tool called Burrows Wheeler aligner on the Convey Hybrid Computing platform, which has FPGAs as co-processors. The focus of the research is to accelerate the BWA-MEM algorithm of the Burrows Wheeler aligner on the Convey HC-2 platform. The implementation was carried out using the Vivado HLS tool. The architectures proposed are targeted to overcome the memory bottleneck of the application. Two architectures are proposed, the Base architecture and the Batch architecture meant to address the memory bottleneck. Simulations were performed for the intended platform and it was found that, the Batch architecture is 18% faster than the Base architecture for reads with similar run time characteristics. The architectures provide possibilities of further pipelining and implementation of more cores, which is expected to provide better performance than the current implementation. Hardware Acceleration of BWA-MEM Genome Mapping Application

Union simulation on lower limbs rehabilitation robot based on MATLAB and ADAMS <=SEP=> For people who suffers the lower limbs motor dysfunction, using lower limbs exoskeleton rehabilitation devices is a very effective method to restore the motor ability of lower limbs. However, it works only if the device has a suitable mechanical structure and appropriate design parameters for the specific individual. To get this work completed, we carry out the modeling and simulation of a new proposed lower limbs rehabilitation robot with joint help of two kinds of software, i.e. the MATLAB and the ADAMS. Firstly, the three-dimensional model of the robot is created with the SolidWorks software. After importing the model into ADAMS, some essential kinematic pairs are added into the movable joints. Meanwhile, the control system model is established using the MATLAB/SIMULINK software. Then, the union simulation of the lower limbs rehabilitation robot is carried out by using the interface modular ADAMS/CONTROL. The results show that the proposed rehabilitation robot has a good kinematic performance, which means that the device can be used in the actual rehabilitation process. Besides, the results also indicate that the control method is of good trajectory tracking ability. These simulated results will be significant for the development of physical prototype of lower limbs rehabilitation robot.

Efficient and Consistent Flow Update for Software Defined Networks <=SEP=> Software defined network (SDN) provides flexible and scalable routing by separating control plane and data plane. With centralized control, SDN has been widely used in traffic engineering, link failure recovery, and load balancing. This work considers the flow update problem, where a set of flows need to be migrated or rearranged due to change of network status. During flow update, efficiency and consistency are two main challenges. Efficiency refers to how fast these updates are completed, while consistency refers to prevention of blackholes, loops, and network congestions during updates. This paper proposes a scheme that maintains all these properties. It works in four phases. The first phase partitions flows into shorter routing segments to increase update parallelism. The second phase generates a global dependency graph of these segments to be updated. The third phase conducts actual updates and then adjusts dependency graphs accordingly. The last phase deals with deadlocks, if any, and then loops back to phase three if necessary. Through simulations, we validate that our scheme not only ensures freedom of blackholes, loops, congestions, and deadlocks during flow updates, but is also faster than existing schemes.

Realization of three-dimensional walking of a cheetah-modeled bio-inspired quadruped robot <=SEP=> Adaptability of quadruped animals is not solely reached by brain control, but by the interaction between its body, environment, and control. Especially, morphology of the body is supposed to contribute largely to the adaptability. We have tried to understand quadrupedal locomotion by building a bio-inspired quadruped robot named “Pneupard”, which has a feline-like muscular-skeletal structure. In our previous study, we successfully realized alternative gait of hindlimbs by reflex control based on the sole touch information, which is called an unloading rule, and that of forelimbs as well. In this paper, we finally connect forelimbs and hindlimbs by a rigid spine, and conduct 3D walking experiments only with the simple unloading rule. Through several preliminary experiments, we realize that the touch information on the sole is the most critical for stable 3D walking.

Development of an exoskeleton haptic interface for virtual task training <=SEP=> An exoskeleton haptic interface is developed for functional training in virtual environments. A composite control scheme enables a variety of tasks to be implemented, and a “Qt” graphics library is used to generate the virtual environment for the haptic interface at the hand and graphical user interfaces for input and telemetry. Inter-process communications convert telemetry from the exoskeleton into motion commands for objects in the virtual environment. A second haptic interface at the upper arm is used to control the elbow orbit self-motion of the arm during tasks. Preliminary results are reviewed for a wall-painting task in which the virtual wall stiffness and viscosity are generated using an admittance controller.

Superior thermal conductivity of single-layer graphene. <=SEP=> We report the measurement of the thermal conductivity of a suspended single-layer graphene. The room temperature values of the thermal conductivity in the range approximately (4.84+/-0.44)x10(3) to (5.30+/-0.48)x10(3) W/mK were extracted for a single-layer graphene from the dependence of the Raman G peak frequency on the excitation laser power and independently measured G peak temperature coefficient. The extremely high value of the thermal conductivity suggests that graphene can outperform carbon nanotubes in heat conduction. The superb thermal conduction property of graphene is beneficial for the proposed electronic applications and establishes graphene as an excellent material for thermal management.

Centrifugal fan design for permanent magnet synchronous motor in a traction application <=SEP=> The requirements of high torque density and high efficiency, which are particularly pronounced in electric traction applications, often result in substantial thermal loading of electric machines for driving trams, electric multiple units (EMU) or electric cars. Permanent magnet synchronous machines are suitable candidates for traction applications due to their inherently high torque density and high efficiency. At the same time they are sensitive to temperature rise, especially in permanent magnets, highlighting the need for implementation of efficient cooling system. The performance of the cooling system and its ability to remove heat directly affect the attainable torque and efficiency of the electric machine. In this paper, the selection and sizing of the cooling system for an interior permanent magnet motor designed to drive a low-floor tram is presented. The procedure for selecting the basic dimensions of the centrifugal fan according to the analytical formulas in combination with computational fluid dynamics (CFD) analysis are explained. In addition to the geometry of the centrifugal fan itself, the geometry of the passive system components (e.g. air flow router) which have a significant impact on the performance of the cooling system, are also considered. The results of computer aided CFD analysis, which is taken as a benchmark of system performance in the design stage of the cooling system, have been confirmed with measurements on the machine prototype.

Sentiment Analysis using Deep Convolutional Neural Networks with Distant Supervision Master Thesis <=SEP=> This thesis addresses the problem of predicting message-level sentiments of English micro-blog messages from Twitter. Convolutional neural networks (CNN) have shown great promise in the task of sentiment classification. Here we expand the CNN proposed by [31, 32] and perform an in-depth analysis to deepen the understanding of these systems. In a first step we compare the performance of different architectures with focus on deeper architectures. We show how different choices of hyper-parameters impact the results and present the benefits and challenges of using deeper architectures. Second, we introduce the procedure for achieving a high quality initialization of the CNN and show the massive impact it has on its predictive capabilities. This procedure consists of creating custom word embeddings followed by pre-training the network using 90M tweets where the polarity of a tweet is inferred by noisy labels. Third, we present an in-depth analysis of the supervised phase. We show the impact of the pre-training on the supervised phase. We show how the choice of the optimization algorithm and the step size impact the variance of the score during the supervised phase. The analysis is concluded by an inspection of the errors made by the system. This gives us insights into the inner workings of the CNN and hints as to what is needed for further improvement. The CNN presented in this thesis is part of an ensemble which ranked 1st in the SemEval-2016 competition, Task 4a [22]. The task consists of classifying a given tweet as either positive, negative or neutral. This further demonstrates the potential these systems have in the field of sentiment analysis.

Climbing gaits of a modular biped climbing robot <=SEP=> For high-rise work in fields such as agriculture, forestry and architecture, and inspired by the climbing motion of inchworms, chimpanzees, and sloths, we have developed a biped climbing robot - Climbot. Consisting of five 1-DOF joint modules in series and two special grippers at the ends, Climbot is capable of grasping objects and climbing poles, trees and trusses. In this paper, we first introduce this novel robot, and then present three climbing gaits. We perform simulation to illustrate, verify and compare the proposed climbing gaits.

Efficient algorithm ? <=SEP=> 

A Quantitative Comparison of Calibration Methods for RGB-D Sensors Using Different Technologies <=SEP=> RGB-D (Red Green Blue and Depth) sensors are devices that can provide color and depth information from a scene at the same time. Recently, they have been widely used in many solutions due to their commercial growth from the entertainment market to many diverse areas (e.g., robotics, CAD, etc.). In the research community, these devices have had good uptake due to their acceptable levelofaccuracyformanyapplicationsandtheirlowcost,butinsomecases,theyworkatthelimitof their sensitivity, near to the minimum feature size that can be perceived. For this reason, calibration processes are critical in order to increase their accuracy and enable them to meet the requirements of such kinds of applications. To the best of our knowledge, there is not a comparative study of calibration algorithms evaluating its results in multiple RGB-D sensors. Speciﬁcally, in this paper, a comparison of the three most used calibration methods have been applied to three different RGB-D sensors based on structured light and time-of-ﬂight. The comparison of methods has been carried out by a set of experiments to evaluate the accuracy of depth measurements. Additionally, an object reconstruction application has been used as example of an application for which the sensor works at the limit of its sensitivity. The obtained results of reconstruction have been evaluated through visual inspection and quantitative measurements.

Interactive ramp merging planning in autonomous driving: Multi-merging leading PGM (MML-PGM) <=SEP=> Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. The challenge of cooperative driving is estimating other vehicles' intentions. In this paper, we present a novel method to estimate other human-driven vehicles' intentions with the aim of achieving a natural and amenable cooperative driving behavior, without using wireless communication. The new approach allows the autonomous vehicle to cooperate with multiple observable merging vehicles on the ramp with a leading vehicle ahead of the autonomous vehicle in the same lane. To avoid calculating trajectories, simplify computation, and take advantage of mature Level-3 components, the new method reacts to merging cars by determining a following target for an off-the-shelf distance keeping module (ACC) which governs speed control of the autonomous vehicle. We train and evaluate the proposed model using real traffic data. Results show that the new approach has a lower collision rate than previous methods and generates more human driver-like behaviors in terms of trajectory similarity and time-to-collision to leading vehicles.

Religious meaning and subjective well-being in late life. <=SEP=> OBJECTIVES
The purpose of this study is to examine the relationship between religious meaning and subjective well-being. A major emphasis is placed on assessing race differences in the relationship between these constructs.


METHODS
Interviews were conducted with a nationwide sample of older White and older Black adults. Survey items were administered to assess a sense of meaning in life that is derived specifically from religion. Subjective well-being was measured with indices of life satisfaction, self-esteem, and optimism.


RESULTS
The findings suggest that older adults who derive a sense of meaning in life from religion tend to have higher levels of life satisfaction, self-esteem, and optimism. The data further reveal that older Black adults are more likely to find meaning in religion than older White adults. In addition, the relationships among religious meaning, life satisfaction, self-esteem, and optimism tend to be stronger for older African Americans persons than older White persons.


DISCUSSION
Researchers have argued for some time that religion may be an important source of resilience for older Black adults, but it is not clear how these beneficial effects arise. The data from this study suggest that religious meaning may be an important factor.

A New Rectenna With All-Polarization-Receiving Capability for Wireless Power Transmission <=SEP=> This letter presents a new rectenna with all-polarization-receiving capability for wireless power transmission applications. By utilizing a dual linearly-polarized antenna, the incident wave of arbitrary polarization can be totally collected at its two ports. To be connected with the antenna as a rectenna, a dual-input rectifier is designed so that the RF power supplied by the two ports of the antenna can be efficiently rectified. With a proper polarization of the incident wave, the proposed rectenna exhibits a maximum efficiency of 78% under the input power density of 295.3 μW/cm2. Moreover, under the same power density, the rectenna's efficiency can always stay higher than 61% regardless of the incident wave's polarization.

Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles <=SEP=> Precise trajectory tracking is a crucial property for Micro Air Vehicles (MAVs) to operate in cluttered environment or under disturbances. In this paper we present a detailed comparison between two state-of-the-art model-based control techniques for MAV trajectory tracking. A classical Linear Model Predictive Controller (LMPC) is presented and compared against a more advanced Nonlinear Model Predictive Controller (NMPC) that considers the full system model. In a careful analysis we show the advantages and disadvantages of the two implementations in terms of speed and tracking performance. This is achieved by evaluating hovering performance, step response, and aggressive trajectory tracking under nominal conditions and under external wind disturbances.

Extraction of Multi-word Expressions from Small Parallel Corpora <=SEP=> M.Sc. in Computer Science Cum Laude, advised by Shuly Wintner. 2007-2010 • Research topics: semi-supervised multiword expression extraction from parallel and monolingual corpora. • Thesis: “Extraction of Multi-word Expressions from Small Parallel Corpora”. • Awards: Gutwirth award for excellence (awarded top 50 graduate students in Israel); excellence scholarship in honor of Captain Uri Akavia.

Optically transparent conductive polymer RFID meandering dipole antenna <=SEP=> In this paper, we present optically transparent flexible conductive polymer antennas for radio frequency identification systems. The designs for these antennas are presented along with simulated and measured results of antenna radiating properties. These conductive polymer antennas are compared to antennas with the same design fabricated out of copper. Finally, we include an analysis of the optical transparency of the conductive polymer antennas.

Cryptonite: a programmable crypto processor architecture for high bandwidth applications <=SEP=> Cryptographic methods are widely used within networking and digital rights management. Numerous algorithms exist, e.g. spanning VPNs or distributing sensitive data over a shared network infrastructure. While these algorithms can be run with moderate performance on general purpose processors, such processors do not meet typical embedded systems requirements (e.g. area, cost and power consumption). Instead, specialized cores dedicated to one or a combination of algorithms are typically used. These cores provide very high bandwidth data transmission and meet the needs of embedded systems. However, with such cores changing the algorithm is not possible without replacing the hardware. This paper describes a fully programmable processor architecture which has been tailored for the needs of a spectrum of cryptographic algorithms and has been explicitly designed to run at high clock rates while maintaining a significantly better performance/area/power tradeoff than general purpose processors. Both the architecture and instruction set have been developed to achieve a bits-per-clock rate of greater than one, even with complex algorithms. This performance will be demonstrated with standard cryptographic algorithms (AES and DES) and a widely used hash algorithm (MD5).

Global refinement of random forest <=SEP=> Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniques, global refinement and global pruning, to improve a pre-trained random forest. The proposed global refinement jointly relearns the leaf nodes of all trees under a global objective function so that the complementary information between multiple trees is well exploited. In this way, the fitting power of the forest is significantly enhanced. The global pruning is developed to reduce the model size as well as the over-fitting risk. The refined model has better performance and smaller storage cost, as verified in extensive experiments.

Potentials and Challenges of C-RAN Supporting Multi-RATs Toward 5G Mobile Networks <=SEP=> This paper presents an overview of the cloud radio access network (C-RAN), which is a key enabler for future mobile networks in order to meet the explosive capacity demand of mobile traffic, and reduce the capital and operating expenditure burden faced by operators. We start by reviewing the requirements of future mobile networks, called 5G, followed by a discussion on emerging network concepts for 5G network architecture. Then, an overview of C-RAN and related works are presented. As a significant scenario of a 5G system, the ultra dense network deployment based on C-RAN is discussed with focuses on flexible backhauling, automated network organization, and advanced mobility management. Another import feature of a 5G system is the long-term coexistence of multiple radio access technologies (multi-RATs). Therefore, we present some directions and preliminary thoughts for future C-RAN-supporting Multi-RATs, including joint resource allocation, mobility management, as well as traffic steering and service mapping.

Depth-Based Hand Pose Estimation: Methods, Data, and Challenges <=SEP=> Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.