Query: Signal processing techniques for improving angular resolution performance in homodyne FMCW radar

Relevant passages:
System Design of a 77 GHz Automotive Radar Sensor with Superresolution DOA Estimation <=SEP=> This paper introduces a novel 77 GHz FMCW automotive long range radar (LRR) system concept. High resolution direction of arrival (DOA) estimation is an important requirement for the application in automotive safety systems. The challenges in system design regarding low cost and superresolution signal processing are discussed. Dominant interferences to the MUSIC DOA estimator are amplitude and phase mismatches due to inhomogeneous antenna patterns. System simulation results deliver design guidelines for the required signal-to-noise ratio (SNR) and the antenna design. Road traffic measurements with a demonstrator system show superior DOA resolution and demonstrate the feasibility of the design goals.

Automotive Radar – Status and Trends <=SEP=> The paper gives a brief overview of automotive radar. The status of the frequency regulation for short and long range radar is summarized because of its importance for car manufacturers and their sensor suppliers. Front end concepts and antenna techniques of 24 GHz and 77 GHz sensors are briefly described. Their impact on the sensor’s field of view and on the angular measurement capability is discussed. Esp. digital beamforming concepts are considered and promising results are presented.

Multiple Emitter Location and Signal Parameter-- Estimation <=SEP=> 

ESPRIT-estimation of signal parameters via rotational invariance techniques <=SEP=> High-resolution signal parameter estimation is a problem of significance in many signal processing applications. Such applications include direction-of-arrival (DOA) estimation, system identification, and time series analysis. A novel approach to the general problem of signal parameter estimation is described. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC.

Proposal of millimeter-wave holographic radar with antenna switching <=SEP=> This paper proposes a millimeter-wave holographic radar with a simple structure for automotive applications. The simplicity can be realized by switching both transmitting antennas and receiving antennas. Also, a super resolution technique is introduced for the detection of angular positions in the proposed radar. The radar developed experimentally has accomplished an azimuthal angular resolution of less than 2 degrees and an azimuthal field of view (FoV) of more than 20 degrees simultaneously.

Planning and Control for Collision-Free Cooperative Aerial Transportation <=SEP=> This paper presents planning and control synthesis for multiple aerial manipulators to transport a common object. Each aerial manipulator that consists of a hexacopter and a two-degree-of-freedom robotic arm is controlled by an augmented adaptive sliding mode controller based on a closed-chain robot dynamics. We propose a motion planning algorithm by exploiting rapidly exploring random tree star (RRT*) and dynamic movement primitives (DMPs). The desired path for each aerial manipulator is obtained by using RRT* with Bezier curve, which is designed to handle environmental obstacles, such as buildings or equipments. During aerial transportation, to avoid unknown obstacle, DMPs modify the trajectory based on the virtual leader–follower structure. By the combination of RRT* and DMPs, the cooperative aerial manipulators can carry a common object to keep reducing the interaction force between multiple robots while avoiding an obstacle in the unstructured environment. To validate the proposed planning and control synthesis, two experiments with multiple custom-made aerial manipulators are presented, which involve user-guided trajectory and RRT*-planned trajectory tracking in unstructured environments.Note to Practitioners—This paper presents a viable approach to autonomous aerial transportation using multiple aerial manipulators equipped with a multidegree-of-freedom robotic arm. Existing approaches for cooperative manipulation based on force decomposition or impedance-based control often require a heavy or expensive force/torque sensor. However, this paper suggests a method without using a heavy or expensive force/torque sensor based on closed-chain dynamics in joint space and rapidly exploring random tree star (RRT*) that generates the desired trajectory of aerial manipulators. Unlike conventional RRT*, in this paper, our method can also avoid an unknown moving obstacle during aerial transportation by exploiting RRT* and dynamic movement primitives. The proposed planning and control synthesis is tested to demonstrate performance in a lab environment with two custom-made aerial manipulators and a common object.

Journey to Data Quality <=SEP=> 

Reliable Client Accounting for P2P-Infrastructure Hybrids <=SEP=> Content distribution networks (CDNs) have started to adopt hybrid designs, which employ both dedicated edge servers and resources contributed by clients. Hybrid designs combine many of the advantages of infrastructurebased and peer-to-peer systems, but they also present new challenges. This paper identifies reliable client accounting as one such challenge. Operators of hybrid CDNs are accountable to their customers (i.e., content providers) for the CDN’s performance. Therefore, they need to offer reliable quality of service and a detailed account of content served. Service quality and accurate accounting, however, depend in part on interactions among untrusted clients. Using the Akamai NetSession client network in a case study, we demonstrate that a small number of malicious clients used in a clever attack could cause significant accounting inaccuracies. We present a method for providing reliable accounting of client interactions in hybrid CDNs. The proposed method leverages the unique characteristics of hybrid systems to limit the loss of accounting accuracy and service quality caused by faulty or compromised clients. We also describe RCA, a system that applies this method to a commercial hybrid content-distribution network. Using trace-driven simulations, we show that RCA can detect and mitigate a variety of attacks, at the expense of a moderate increase in logging overhead.

A survey of appearance models in visual object tracking <=SEP=> Visual object tracking is a significant computer vision task which can be applied to many domains, such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models.
 To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic.
 The contributions of this survey are fourfold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source codes and video datasets) are examined in this survey.

The Sixth PASCAL Recognizing Textual Entailment Challenge <=SEP=> This paper presents the Fifth Recognizing Textual Entailment Challenge (RTE5). Following the positive experience of the last campaign, RTE-5 has been proposed for the second time as a track at the Text Analysis Conference (TAC). The structure of the RTE-5 Main Task remained unchanged, offering both the traditional two-way task and the threeway task introduced in the previous campaign. Moreover, a pilot Search Task was set up, consisting of finding all the sentences in a set of documents that entail a given hypothesis. 21 teams participated in the campaign, among which 20 in the Main Task (for a total of 54 runs) and 8 in the Pilot Task (for a total of 20 runs). Another important innovation introduced in this campaign was mandatory ablation tests that participants had to perform for all major knowledge resources employed by their systems.

Polarization imaging reflectometry in the wild <=SEP=> We present a novel approach for on-site acquisition of surface reflectance for planar, spatially varying, isotropic samples in uncontrolled outdoor environments. Our method exploits the naturally occurring linear polarization of incident and reflected illumination for this purpose. By rotating a linear polarizing filter in front of a camera at three different orientations, we measure the polarization reflected off the sample and combine this information with multi-view analysis and inverse rendering in order to recover per-pixel, high resolution reflectance and surface normal maps. Specifically, we employ polarization imaging from two near orthogonal views close to the Brewster angle of incidence in order to maximize polarization cues for surface reflectance estimation. To the best of our knowledge, our method is the first to successfully extract a complete set of reflectance parameters with passive capture in completely uncontrolled outdoor settings. To this end, we analyze our approach under the general, but previously unstudied, case of incident partial linear polarization (due to the sky) in order to identify the strengths and weaknesses of the method under various outdoor conditions. We provide practical guidelines for on-site acquisition based on our analysis, and demonstrate high quality results with an entry level DSLR as well as a mobile phone.

Product platform design and customization: Status and promise <=SEP=> In an effort to improve customization for today’s highly competitive global marketplace, many companies are utilizing product families and platform-based product development to increase variety, shorten lead times, and reduce costs. The key to a successful product family is the product platform from which it is derived either by adding, removing, or substituting one or more modules to the platform or by scaling the platform in one or more dimensions to target specific market niches. This nascent field of engineering design has matured rapidly in the past decade, and this paper provides a comprehensive review of the flurry of research activity that has occurred during that time to facilitate product family design and platform-based product development for mass customization. Techniques for identifying platform leveraging strategies within a product family are reviewed along with metrics for assessing the effectiveness of product platforms and product families. Special emphasis is placed on optimization approaches and artificial intelligence techniques to assist in the process of product family design and platform-based product development. Web-based systems for product platform customization are also discussed. Examples from both industry and academia are presented throughout the paper to highlight the benefits of product families and product platforms. The paper concludes with a discussion of potential areas of research to help bridge the gap between planning and managing families of products and designing and manufacturing them.

WebWitness: Investigating, Categorizing, and Mitigating Malware Download Paths <=SEP=> Most modern malware download attacks occur via the browser, typically due to social engineering and driveby downloads. In this paper, we study the “origin” of malware download attacks experienced by real network users, with the objective of improving malware download defenses. Specifically, we study the web paths followed by users who eventually fall victim to different types of malware downloads. To this end, we propose a novel incident investigation system, named WebWitness. Our system targets two main goals: 1) automatically trace back and label the sequence of events (e.g., visited web pages) preceding malware downloads, to highlight how users reach attack pages on the web; and 2) leverage these automatically labeled in-the-wild malware download paths to better understand current attack trends, and to develop more effective defenses. We deployed WebWitness on a large academic network for a period of ten months, where we collected and categorized thousands of live malicious download paths. An analysis of this labeled data allowed us to design a new defense against drive-by downloads that rely on injecting malicious content into (hacked) legitimate web pages. For example, we show that by leveraging the incident investigation information output by WebWitness we can decrease the infection rate for this type of drive-by downloads by almost six times, on average, compared to existing URL blacklisting approaches.

Speech recognition in adverse conditions : A review <=SEP=> Speech recognition in adverse conditions: A review Sven L. Mattys a , Matthew H. Davis b , Ann R. Bradlow c & Sophie K. Scott d a Department of Psychology, University of York, York, UK b Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK c Department of Linguistics, Northwestern University, Evanston, IL, USA d Institute of Cognitive Neuroscience, University College London, London, UK

3D free-form surface registration and object recognition <=SEP=> A new technique to recognise 3D free-form objects via registration is proposed. This technique attempts to register a free-form surface, represented by a set of % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaaGOmamaala% aabaGaaGymaaqaaiaaikdaaaGaamiraaaa!38F8!\[2\frac{1}{2}D\] sensed data points, to the model surface, represented by another set of % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaaGOmamaala% aabaGaaGymaaqaaiaaikdaaaGaamiraaaa!38F8!\[2\frac{1}{2}D\] model data points, without prior knowledge of correspondence or view points between the two point sets. With an initial assumption that the sensed surface be part of a more complete model surface, the algorithm begins by selecting three dispersed, reliable points on the sensed surface. To find the three corresponding model points, the method uses the principal curvatures and the Darboux frames to restrict the search over the model space. Invariably, many possible model 3-typles will be found. For each hypothesized model 3-tuple, the transformation to match the sensed 3-tuple to the model 3-tuple can be determined. A heuristic search is proposed to single out the optimal transformation in low order time. For realistic object recognition or registration, where the two range images are often extracted from different view points of the model, the earlier assumption that the sensed surface be part of a more complete model surface cannot be relied on. With this, the sensed 3-tuple must be chosen such that the three sensed points lie on the common region visible to both the sensed and model views. We propose an algorithm to select a minimal non-redundant set of 3-tuples such that at least one of the 3-tuples will lie on the overlap. Applying the previous algorithm to each 3-tuple within this set, the optimal transformation can be determined. Experiments using data obtained from a range finder have indicated fast registration for relatively complex test cases. If the optimal registrations between the sensed data (candidate) and each of a set of model data are found, then, for 3D object recognition purposes, the minimal best fit error can be used as the decision rule.

Multi-Prediction Deep Boltzmann Machines <=SEP=> We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MPDBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.1

A Modified CFOA and Its Applications to Simulated Inductors, Capacitance Multipliers, and Analog Filters <=SEP=> In this paper, using a minimum number of passive components, i.e., new grounded and floating inductance simulators, grounded capacitance multipliers, and frequency-dependent negative resistors (FDNRs) based on one/two modified current-feedback operational amplifiers (MCFOAs), are proposed. The type of the simulators depends on the passive element selection used in the structure of the circuit without requiring critical active and passive component-matching conditions and/or cancellation constraints. In order to show the flexibility of the proposed MCFOA, a single-input three-output (SITO) voltage-mode (VM) filter, two three-input single-output (TISO) VM filters, and an SITO current-mode (CM) filter employing a single MCFOA are reported. The layout of the proposed MCFOA is also given. A number of simulations using the SPICE program and some experimental tests are performed to exhibit the performance of the introduced structures.

Data Structures for Traveling Salesmen <=SEP=> The choice of data structure for tour representation plays a critical role in the efficiency of local improvement heuristics for the Traveling Salesman Problem. The tour data structure must permit queries about the relative order of cities in the current tour and must allow sections of the tour to be reversed. The traditional array-based representation of a tour permits the relative order of cities to be determined in small constant time, but requires worst-case Ω(N) time (where N is the number of cities) to implement a reversal, which renders it impractical for large instances. This paper considers alternative tour data structures, examining them from both a theoretical and experimental point of view. The first alternative we consider is a data structure based on splay trees, where all queries and updates take amortized time O( logN). We show that this is close to the best possible, because in the cell probe model of computation any data structure must take worst-case amortized time Ω( logN /log logN) per operation. Empirically (for random Euclidean instances), splay trees overcome their large constant-factor overhead and catch up to arrays by N = 10 , 000, pulling ahead by a factor of 4-10 (depending on machine) when N = 100 , 000. Two alternative tree-based data structures do even better in this range, however. Although both are asymptotically inferior to the splay tree representation, the latter does not appear to pull even with them until N ∼ 1 , 000 , 000. ________________ 1 Rutgers University, New Brunswick, NJ 08903, and University of California at San Diego, La Jolla, CA 92093 2 Room 2D-150, AT&T Bell Laboratories, Murray Hill, NJ 07974 3 Department of Mathematics and Computer Science, Amherst College, Amherst, MA 01002 4 Department of Mathematics, Rutgers University, New Brunswick, NJ 08903 * A preliminary version of this paper appeared under the same title in Proceedings 4th Ann. ACM-SIAM Symp. on Discrete Algorithms (1993), 145-154.

An Operating System for the Home <=SEP=> Network devices for the home such as remotely controllable locks, lights, thermostats, cameras, and motion sensors are now readily available and inexpensive. In theory, this enables scenarios like remotely monitoring cameras from a smartphone or customizing climate control based on occupancy patterns. However, in practice today, such smarthome scenarios are limited to expert hobbyists and the rich because of the high overhead of managing and extending current technology. We present HomeOS, a platform that bridges this gap by presenting users and developers with a PC-like abstraction for technology in the home. It presents network devices as peripherals with abstract interfaces, enables cross-device tasks via applications written against these interfaces, and gives users a management interface designed for the home environment. HomeOS already has tens of applications and supports a wide range of devices. It has been running in 12 real homes for 4–8 months, and 42 students have built new applications and added support for additional devices independent of our efforts.

How do training and competition workloads relate to injury? The workload-injury aetiology model. <=SEP=> Injury aetiology models that have evolved over the previous two decades highlight a number of factors which contribute to the causal mechanisms for athletic injuries. These models highlight the pathway to injury, including (1) internal risk factors (eg, age, neuromuscular control) which predispose athletes to injury, (2) exposure to external risk factors (eg, playing surface, equipment), and finally (3) an inciting event, wherein biomechanical breakdown and injury occurs. The most recent aetiological model proposed in 2007 was the first to detail the dynamic nature of injury risk, whereby participation may or may not result in injury, and participation itself alters injury risk through adaptation. However, although training and competition workloads are strongly associated with injury, existing aetiology models neither include them nor provide an explanation for how workloads alter injury risk. Therefore, we propose an updated injury aetiology model which includes the effects of workloads. Within this model, internal risk factors are differentiated into modifiable and non-modifiable factors, and workloads contribute to injury in three ways: (1) exposure to external risk factors and potential inciting events, (2) fatigue, or negative physiological effects, and (3) fitness, or positive physiological adaptations. Exposure is determined solely by total load, while positive and negative adaptations are controlled both by total workloads, as well as changes in load (eg, the acute:chronic workload ratio). Finally, we describe how this model explains the load-injury relationships for total workloads, acute:chronic workload ratios and the training load-injury paradox.

Comparing and Aligning Process Representations <=SEP=> Processes within organizations can be highly complex chains of inter-related steps, involving numerous stakeholders and information systems. Due to this complexity, having access to the right information is vital to the proper execution and effective management of an organization’s business processes. A major challenge in this regard is that information on a single process is often spread out over numerous models, documents, and systems. This phenomenon results from efforts to provide a variety of process stakeholders with the information that is relevant to them, in a suitable format. However, this disintegration of process information also has considerable disadvantages for organizations. In particular, it can lead to severe maintenance issues, reduced execution efficiency, and negative effects on the quality of process results. Against this background, this doctoral thesis focuses on the spread of process information in organizations and, in particular, on the mitigation of the negative aspects associated with this phenomenon. The main contributions of this thesis are five techniques that focus on the alignment and comparison of process information from different informational artifacts. Each of these techniques tackles a specific scenario involving multiple informational artifacts that contain process information in different representation formats. Among others, we present automated techniques for the detection of inconsistencies between process models and textual process descriptions, the alignment of process performance measurements to process models, conformance-checking in the context of uncertainty, and the matching of process models through the analysis of event-log information. We demonstrate the efficacy and usefulness of these techniques through quantitative evaluations involving data obtained from real-world settings. Altogether, the presented work provides important contributions for the analysis, comparison, and alignment of process information in various representation formats through the development of novel concepts and techniques. The contributions, furthermore, provide a means for organizations to improve the efficiency and quality of their processes.

Variability of Worked Examples and Transfer of Geometrical Problem-Solving Skills : A Cognitive-Load Approach <=SEP=> Four computer-based training strategies for geometrical problem solving in the domain of computer numerically controlled machinery programming were studied with regard to their effects on training performance, transfer performance, and cognitive load. A lowand a high-variability conventional condition, in which conventional practice problems had to be solved (followed by worked examples), were compared with a lowand a high-variability worked condition, in which worked examples had to be studied. Results showed that students who studied worked examples gained most from high-variability examples, invested less time and mental effort in practice, and attained better and less effort-demanding transfer performance than students who first attempted to solve conventional problems and then studied work examples.

Comparison of the Hardware Performance of the AES Candidates Using Reconfigurable Hardware <=SEP=> The results of implementations of all five AES finalists using Xilinx Field Programmable Gate Arrays are presented and analyzed. Performance of four alternative hardware architectures is discussed and compared. The AES candidates are divided into three classes depending on their hardware performance characteristics. Recommendation regarding the optimum choice of the algorithms for AES is provided.

Alzheimer's disease: genes, proteins, and therapy. <=SEP=> Rapid progress in deciphering the biological mechanism of Alzheimer's disease (AD) has arisen from the application of molecular and cell biology to this complex disorder of the limbic and association cortices. In turn, new insights into fundamental aspects of protein biology have resulted from research on the disease. This beneficial interplay between basic and applied cell biology is well illustrated by advances in understanding the genotype-to-phenotype relationships of familial Alzheimer's disease. All four genes definitively linked to inherited forms of the disease to date have been shown to increase the production and/or deposition of amyloid beta-protein in the brain. In particular, evidence that the presenilin proteins, mutations in which cause the most aggressive form of inherited AD, lead to altered intramembranous cleavage of the beta-amyloid precursor protein by the protease called gamma-secretase has spurred progress toward novel therapeutics. The finding that presenilin itself may be the long-sought gamma-secretase, coupled with the recent identification of beta-secretase, has provided discrete biochemical targets for drug screening and development. Alternate and novel strategies for inhibiting the early mechanism of the disease are also emerging. The progress reviewed here, coupled with better ability to diagnose the disease early, bode well for the successful development of therapeutic and preventative drugs for this major public health problem.

FA*IR: A Fair Top-k Ranking Algorithm <=SEP=> In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n » k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria.
 Our ranked group fairness definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above or indistinguishable from a given minimum. Utility is operationalized in two ways: (i) every candidate included in the top-k should be more qualified than every candidate not included; and (ii) for every pair of candidates in the top-k, the more qualified candidate should be ranked above.
 An efficient algorithm is presented for producing the Fair Top-k Ranking, and tested experimentally on existing datasets as well as new datasets released with this paper, showing that our approach yields small distortions with respect to rankings that maximize utility without considering fairness criteria. To the best of our knowledge, this is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.

Lower limb rehabilitation robot <=SEP=> This paper describes a new prototype of lower limb rehabilitation robot (for short: LLRR), including its detailed structure, operative principles, manipulative manual and control mode which give considerate protection to patients. It implements the programmable process during the course of the limbs rehabilitation, furthermore, renders variable and predetermined step posture and force sensing. LLRR could assist patient in-- simulating normal people's footsteps, exercising leg muscles, gradually recovering the neural control toward walking function and finally walking in normal way utterly. Such robot is comprised with steps posture controlling system and weight alleviation controlling mechanism.

The Air Traffic Flow Management Problem: An Integer Optimization Approach <=SEP=> In this paper, we present a new Integer Program (IP) for the Air Traffic Flow Management (ATFM) problem. The model we propose provides a complete representation of all the phases of each flights, i.e., the phase of taking-off, of cruising and of landing; suggesting all the actions to be implemented to achieve the goal of safe, efficient, and expeditious aircraft movement. The distinctive feature of the model is that it allows rerouting decisions. These decisions are formulated by means of “local” conditions, which allow us to represent such decisions in a very compact way by only introducing new constraints. Moreover, to strengthen the polyhedral structure of the underlying relaxation, we also present three classes of valid inequalities. We report short computational times (less than 15 minutes) on instances of the size of the US air traffic control system that make it realistic that our approach can be used as the main engine of managing air traffic in the US.

An Improved Neural Segmentation Method Based on U-NET <=SEP=> 摘要:局部麻醉技术作为现代社会最为常见的麻醉技 术,具有安全性高,副作用小等优势。通过分析超声 图像,分割图像中的神经区域,有助于提升局部麻醉 手术的成功率。卷积神经网络作为目前最为高效的图 像处理方法之一,具有准确性高,预处理少等优势。 通过卷积神经网络来对超声图像中的神经区域进行分 割,速度更快,准确性更高。目前已有的图像分割网 络结构主要有U-NET[1],SegNet[2]。U-NET网络训练 时间短,训练参数较少,但深度略有不足。SegNet 网 络层次较深,训练时间过长,但对训练样本需求较多 由于医学样本数量有限,会对模型训练产生一定影响。 本文我们将采用一种改进后的 U-NET 网络结构来对超 声图像中的神经区域进行分割,改进后的 U-NET 网络 结构加入的残差网络(residual network)[3],并对每一层 结果进行规范化(batch normalization)处理[4]。实验结 果表明,与传统的U-NET网络结构相比,改进后的UNET 网络分割效果具有显著提升,训练时间略有增加。 同时,将改进后的 U-NET 网络与 SegNet 网络结构进 行对比,发现改进后的 U-net 无论从训练速度还是从 分割效果均高于 SegNet 网络结构。改进后的 U-net 网 络结构在神经识别方面具有很好的应用场景。

Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels <=SEP=> Covariate shift is an unconventional learning scenario in which training and testing data have different distributions. A general principle to solve the problem is to make the training data distribution similar to that of the test domain, such that classifiers computed on the former generalize well to the latter. Current approaches typically target on sample distributions in the input space, however, for kernel-based learning methods, the algorithm performance depends directly on the geometry of the kernel-induced feature space. Motivated by this, we propose to match data distributions in the Hilbert space, which, given a pre-defined empirical kernel map, can be formulated as aligning kernel matrices across domains. In particular, to evaluate similarity of kernel matrices defined on arbitrarily different samples, the novel concept of surrogate kernel is introduced based on the Mercer’s theorem. Our approach caters the model adaptation specifically to kernel-based learning mechanism, and demonstrates promising results on several real-world applications. Proceedings of the 30 th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013. JMLR: W&CP volume 28. Copyright 2013 by the author(s).

Massive Social Network Analysis: Mining Twitter for Social Good <=SEP=> Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software and hardware. We present GraphCT, a Graph Characterization Toolkit for massive graphs representing social network data. On a 128-processor Cray XMT, GraphCT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes and a real-world graph (Kwak, et al.) with 61.6 million vertices and 1.47 billion edges in 105 minutes. We use GraphCT to analyze public data from Twitter, a microblogging network. Twitter's message connections appear primarily tree-structured as a news dissemination system. Within the public data, however, are clusters of conversations. Using GraphCT, we can rank actors within these conversations and help analysts focus attention on a much smaller data subset.