title: State Duration and Interval Modeling in Hidden Semi-Markov Model for Sequential Data Analysis

abstract: Sequential data modeling and analysis have become indispensable tools for analyzing sequential data such as time-series data because a larger amount of sensed event data have become available. These methods capture the sequential structure of data of interest, such as input- output relationship and correlation among datasets. However, since most studies in this area are specialized or limited for their respective applications, rigorous requirement analysis on such a model has not been examined in a general point of view. Hence, we particularly examine the structure of sequential data, and extract the necessity of "state duration" and "state duration" of events for efficient and rich representation of sequential data. Specifically addressing the hidden semi-Markov model (HSMM) that represents such state duration inside a model, we attempt to newly add representational capability of state interval of events onto HSMM. To this end, we propose two extended models; one is interval state hidden semi-Markov model (IS-HSMM) to express the length of state interval with a special state node designated as "interval state node". The other is interval length probability hidden semi-Markov model (ILP-HSMM) which repre- sents the length of state interval with a new probabilistic parameter "interval length probability." From exhaustive simulations, we show superior performances of the proposed models in comparison with HSMM. To the best of our knowledge, our proposed models are the first extensions of HMM to support state interval representation as well as state duration representation.


title: Object Segmentation by Shape Matching with Wasserstein Modes

abstract: We gradually develop a novel functional for joint variational object segmentation and shape matching. The formulation, based on the Wasserstein distance, allows modelling of local object appearance, statistical shape variations and geometric invariance in a uniform way. For learning of class typical shape variations we adopt a recently presented approach and extend it to support inference of deformations during segmentation of new query images. The resulting way of describing and fitting trained shape variations is in style reminiscent of contour-based variational shape priors, but does not require an intricate conversion between the contour and the region representation of shapes. A well-founded hierarchical branch-and-bound scheme, based on local adaptive convex relaxation, is presented, that provably finds the global minimum of the functional.


Running head : FACTORS AND PERFORMANCE 1 External Factors and Athletic Performance


title: An Attribute-aware Neural Attentive Model for Next Basket Recommendation

abstract: Next basket recommendation is a new type of recommendation, which recommends a set of items, or a basket, to the user. Purchase in basket is a common behavior of consumers. Recently, deep neural networks have been applied to model sequential transactions of baskets in next basket recommendation. However, current methods do not track the user's evolving appetite for items explicitly, and they ignore important item attributes such as product category. In this paper, we propose a novel Attribute-aware Neural Attentive Model (ANAM) to address these problems. ANAM adopts an attention mechanism to explicitly model user's evolving appetite for items, and utilizes a hierarchical architecture to incorporate the attribute information. In specific, ANAM utilizes a recurrent neural network to model the user's sequential behavior over time, and relays the user's appetite toward items and their attributes to next basket through attention weights shared across baskets on the two different hierarchies. Experiment results on two public datasets (ie Ta-Feng and JingDong) demonstrate the effectiveness of our ANAM model for next basket recommendation.


title: Image tag completion and refinement by subspace clustering and matrix completion

abstract: Tag-based image retrieval (TBIR) has drawn much attention in recent years due to the explosive amount of digital images and crowdsourcing tags. However, the TBIR applications still suffer from the deficient and inaccurate tags provided by users. Inspired by the subspace clustering methods, we formulate the tag completion problem in a subspace clustering model which assumes that images are sampled from subspaces, and complete the tags using the state-of-the-art Low Rank Representation (LRR) method. And we propose a matrix completion algorithm to further refine the tags. Our empirical results on multiple benchmark datasets for image annotation show that the proposed algorithm outperforms state-of-the-art approaches when handling missing and noisy tags.


title: A medium-voltage DC (MVDC) with series active injection for shipboard power system applications

abstract: Modern electric ships utilize a medium-voltage zonal dc distribution system to meet the ever increasing power demand while keeping the power density as low as possible. Power electronics building blocks (PEBBs) composed of different DC/AC and DC/DC converters are used to convert the main DC bus voltage to appropriate dc and ac voltage levels suited for the loads in such a system. Due to the fact that most of these power electronic converters present a constant power load (CPL) with negative incremental impedance at the input terminal, the interaction of these converters under different system configurations with the main AC-DC rectifier of the MVDC system might induce voltage oscillations on the main DC-bus. In this paper, a novel method of series active injection on the dc-side is proposed to smooth out disturbances on the main DC-bus. This has resulted in much smoother DC-bus voltage enabling the system to use less amount of passive storage elements on dc-side which is very beneficial to ship's limited onboard space. The simulation results presented verify the operation and functionality of the proposed system.


title: Resisting structural re-identification in anonymized social networks

abstract: We identify privacy risks associated with releasing network data sets and provide an algorithm that mitigates those risks. A network consists of entities connected by links representing relations such as friendship, communication, or shared activity. Maintaining privacy when publishing networked data is uniquely challenging because an individual’s network context can be used to identify them even if other identifying information is removed. In this paper, we quantify the privacy risks associated with three classes of attacks on the privacy of individuals in networks, based on the knowledge used by the adversary. We show that the risks of these attacks vary greatly based on network structure and size. We propose a novel approach to anonymizing network data that models aggregate network structure and then allows samples to be drawn from that model. The approach guarantees anonymity for network entities while preserving the ability to estimate a wide variety of network measures with relatively little bias.


title: High-Frequency Modeling and Signal Integrity Analysis of a Silicone Rubber Socket for High-Performance Package

abstract: As a demand for electrical systems with a wide data bandwidth has increased, high-performance packages ensuring high data rates, such as low power double data rate series, have become common. The need for high-performance test sockets has also emerged to test these packages. However, a conventional pogo pin socket has a limited test bandwidth due to the parasitic components arising from its spring. On the other hand, a silicone rubber socket satisfies the wide bandwidth requirement because it has low parasitic components due to high-density conductive metal powders in an elastic silicone rubber. In this paper, we propose an RLGC equivalent circuit model of a silicone rubber socket and first experimentally verify it. The proposed model is experimentally verified in the frequency domain by comparing the insertion loss obtained from the proposed model to the measurement up to 20 GHz. The proposed model is experimentally verified in the time domain by comparing the eye diagrams obtained from the proposed model to the measurement at a data rate of 12.5 Gb/s. Also, the insertion loss of the sockets with varied height, diameter, and pitch is analyzed using the proposed model. The proposed model provides physical insight of a silicone rubber socket, and it allows to determine whether a socket is reliable for testing high performance packages in a short time. It also gives us the idea how to design a high-performance test socket. Furthermore, we discuss the current capacity and life cycle of the silicone rubber socket in terms of signal integrity as well.


title: Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals

abstract: As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals.


title: Augmented Reality Supported by Semantic Web Technologies

abstract: Augmented Reality applications are more and more widely used nowadays. With help of it the real physical environment could be extended by computer generated virtual elements. These virtual elements can be for example important context-aware information. With Semantic Web it is possible among others to handle data which come from heterogeneous sources. As a result we have the opportunity to combine Semantic Web and Augmented Reality utilizing the benefits of combination of these technologies. The obtained system may be suitable for daily use with wide range of applications in field of tourism, entertainment, navigation, ambient assisted living, etc. The purpose of my research is to develop a prototype of general framework which satisfies the above criteria.


title: Reorganization of motor and somatosensory cortex in upper extremity amputees with phantom limb pain

abstract: Phantom limb pain (PLP) in amputees is associated with reorganizational changes in the somatosensory system. To investigate the relationship between somatosensory and motor reorganization and phantom limb pain, we used focal transcranial magnetic stimulation (TMS) of the motor cortex and neuroelectric source imaging of the somatosensory cortex (SI) in patients with and without phantom limb pain. For transcranial magnetic stimulation, recordings were made bilaterally from the biceps brachii, zygomaticus, and depressor labii inferioris muscles. Neuroelectric source imaging of the EEG was obtained after somatosensory stimulation of the skin overlying face and hand. Patients with phantom limb pain had larger motor-evoked potentials from the biceps brachii, and the map of outputs was larger for muscles on the amputated side compared with the intact side. The optimal scalp positions for stimulation of the zygomaticus and depressor labii inferioris muscles were displaced significantly more medially (toward the missing hand representation) in patients with phantom limb pain only. Neuroelectric source imaging revealed a similar medial displacement of the dipole center for face stimulation in patients with phantom limb pain. There was a high correlation between the magnitude of the shift of the cortical representation of the mouth into the hand area in motor and somatosensory cortex and phantom limb pain. These results show enhanced plasticity in both the motor and somatosensory domains in amputees with phantom limb pain.


title: Provable Inductive Matrix Completion

abstract: Consider a movie recommendation system where apart from the ratings information, side information such as user's age or movie's genre is also available. Unlike standard matrix completion, in this setting one should be able to predict inductively on new users/movies. In this paper, we study the problem of inductive matrix completion in the exact recovery setting. That is, we assume that the ratings matrix is generated by applying feature vectors to a low-rank matrix and the goal is to recover back the underlying matrix. Furthermore, we generalize the problem to that of low-rank matrix estimation using rank-1 measurements. We study this generic problem and provide conditions that the set of measurements should satisfy so that the alternating minimization method (which otherwise is a non-convex method with no convergence guarantees) is able to recover back the {\em exact} underlying low-rank matrix. In addition to inductive matrix completion, we show that two other low-rank estimation problems can be studied in our framework: a) general low-rank matrix sensing using rank-1 measurements, and b) multi-label regression with missing labels. For both the problems, we provide novel and interesting bounds on the number of measurements required by alternating minimization to provably converges to the {\em exact} low-rank matrix. In particular, our analysis for the general low rank matrix sensing problem significantly improves the required storage and computational cost than that required by the RIP-based matrix sensing methods \cite{RechtFP2007}. Finally, we provide empirical validation of our approach and demonstrate that alternating minimization is able to recover the true matrix for the above mentioned problems using a small number of measurements.


title: Approximation Algorithms for the Directed k-Tour and k-Stroll Problems

abstract: We consider two natural generalizations of the Asymmetric Traveling Salesman problem: the k-Stroll and the k-Tour problems. The input to the k-Stroll problem is a directed n-vertex graph with nonnegative edge lengths, an integer k, as well as two special vertices s and t. The goal is to find a minimum-length s-t walk, containing at least k distinct vertices (including the endpoints s,t). The k-Tour problem can be viewed as a special case of k-Stroll, where s=t. That is, the walk is required to be a tour, containing some pre-specified vertex s. When k=n, the k-Stroll problem becomes equivalent to Asymmetric Traveling Salesman Path, and k-Tour to Asymmetric Traveling Salesman.Our main result is a polylogarithmic approximation algorithm for the k-Stroll problem. Prior to our work, only bicriteria (O(log2k),3)-approximation algorithms have been known, producing walks whose length is bounded by 3OPT, while the number of vertices visited is Ω(k/log2k). We also show a simple O(log2n/loglogn)-approximation algorithm for the k-Tour problem. The best previously known approximation algorithms achieved min(O(log3k),O(log2n⋅logk/loglogn)) approximation in polynomial time, and O(log2k) approximation in quasipolynomial time.


title: Editor’s Perspective AI in Space MAPGEN: Mixed-Initiative Planning and Scheduling for the

abstract: The Mars Exploration Rover mission is one of NASA's most ambitious science missions to date. Launched in the summer of 2003, each rover carries instruments for conducting remote and in site observations to elucidate the planet's past climate, water activity, and habitability. Science is MER's primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (mixed initiative activity plan generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool from the Jet Propulsion Laboratory and the Europa planning and scheduling system from NASA Ames Research Center. We discuss the issues arising from combining these tools in this mission's context. MAPGEN is the first AI-based system to control a space platform on another planet's surface.


title: Web workers unite! addressing challenges of online laborers

abstract: The ongoing rise of human computation as a means of solving computational problems has created an environment where human workers are often regarded as nameless, faceless computational resources. Some people have begun to think of online tasks as a "remote person call". In this paper, we summarize ethical and practical labor issues surrounding online labor, and offer a set of guidelines for designing and using online labor in ways that support more positive relationships between workers and requesters, so that both can gain the most benefit from the interaction.


title: Modified Hungarian Algorithm for Real-Time ALV Dispatching Problem in Huge Container Terminals

abstract: It is a fundamental decision making process in container terminals to allocate container transporting works among ALVs. Nowadays, container terminals tend to be larger in storage space and more efficient in handling. As a result, estimations of ALV travel times could be inaccurate, the scale of ALV work allocation could be quite large, and a fixed handling sequence could be hardly ensured beforehand. Hence, it is presented a real-time dispatching method, consisting of an allocation model for instantaneous ALV dispatching, and a set of events which trigger a new instantaneous dispatching. A modified Hungarian Algorithm is applied to solve the instantaneous dispatching model, and it is verified that the modified Algorithm outperforms the original one, even CPLEX, in solving these allocation problems


title: Real-time 3-D face tracking and modeling from awebcam

abstract: We first infer a 3-D face model from a single frontal image using automatically extracted 2-D landmarks and deforming a generic 3-D model. Then, for any input image, we extract feature points and track them in 2-D. Given these correspondences, sometimes noisy and incorrect, we robustly estimate the 3-D head pose using PnP and a RANSAC process. As the head moves, we dynamically add new feature points to handle a large range of poses. When the tracker gets lost, due to motion blur or occlusions, the system re-initializes by matching feature points to the reference frontal image feature points. Our system runs in real-time (>15Hz) on a standard CPU with a GPU card. We present results on stored video and will present a live demo, showing excellent tracking under large motion, fast movement, occlusion and facial expression variations. We also show comparative results with the ground truth BU head tracking dataset.


title: Nonlinear Antiswing Control for Crane Systems With Double-Pendulum Swing Effects and Uncertain Parameters: Design and Experiments

abstract: In practical applications, industrial cranes may exhibit double-pendulum swing effects, due to many factors, such as large payload scales and non-negligible hook masses. Currently, for double-pendulum cranes, most available methods are open-loop controllers designed based on linearized crane dynamics; even for existing closed-loop approaches, they are also mostly developed using linearized dynamics and require the exact knowledge of system parameters, which makes them sensitive to parametric uncertainties. To handle these issues, we present an adaptive antiswing control strategy for crane systems with double-pendulum swing effects and uncertain/unknown parameters, which can make the trolley accurately reach the target position with reduced overshoots and effectively eliminate the double-pendulum swing angles at the same time. A complete stability analysis, based upon the full nonlinear dynamics (i.e., without linearizing the dynamics), is included to support the theoretical derivations. We present hardware experimental results to demonstrate that the proposed controller achieves better performance than existing ones and exhibits good robustness. Note to Practitioners —This paper is motivated by the issue of controlling a crane system when double-pendulum swing effects are excited and present. The double-pendulum effects can happen in many practical scenarios and make the crane manual operation very challenging. Moreover, most existing crane control approaches are developed based upon single-pendulum crane models and they may not work normally in the presence of the double-pendulum phenomenon. In addition, usually, the model parameters, including rope length and trolley/hook/payload masses, are not exactly known in practice, which may badly degrade the performance of the control approaches requiring exact model knowledge. Toward this end, we suggest a new control method for cranes suffering from double-pendulum effects to achieve satisfactory performance. The presented control method is robust against parametric uncertainties and it can suppress the double-pendulum swing, reduce the trolley overshoots, and improve the efficiency. Preliminary physical experiments carried out on a double-pendulum crane hardware test bed indicate the effectiveness of the proposed method. In our future work, we will apply the suggested control approach to industrial crane systems to improve their working efficiency.


title: Fast-moving soft electronic fish

abstract: Soft robots driven by stimuli-responsive materials have unique advantages over conventional rigid robots, especially in their high adaptability for field exploration and seamless interaction with humans. The grand challenge lies in achieving self-powered soft robots with high mobility, environmental tolerance, and long endurance. We are able to advance a soft electronic fish with a fully integrated onboard system for power and remote control. Without any motor, the fish is driven solely by a soft electroactive structure made of dielectric elastomer and ionically conductive hydrogel. The electronic fish can swim at a speed of 6.4 cm/s (0.69 body length per second), which is much faster than previously reported untethered soft robotic fish driven by soft responsive materials. The fish shows consistent performance in a wide temperature range and permits stealth sailing due to its nearly transparent nature. Furthermore, the fish is robust, as it uses the surrounding water as the electric ground and can operate for 3 hours with one single charge. The design principle can be potentially extended to a variety of flexible devices and soft robots.


title: Improving Consistency and Correctness of Sequence Inpainting using Semantically Guided Generative Adversarial Network

abstract: Contemporary benchmark methods for image inpainting are based on deep generative models and specifically leverage adversarial loss for yielding realistic reconstructions. However, these models cannot be directly applied on image/video sequences because of an intrinsic drawback- the reconstructions might be independently realistic, but, when visualized as a sequence, often lacks fidelity to the original uncorrupted sequence. The fundamental reason is that these methods try to find the best matching latent space representation near to natural image manifold without any explicit distance based loss. In this paper, we present a semantically conditioned Generative Adversarial Network (GAN) for sequence inpainting. The conditional information constrains the GAN to map a latent representation to a point in image manifold respecting the underlying pose and semantics of the scene. To the best of our knowledge, this is the first work which simultaneously addresses consistency and correctness of generative model based inpainting. We show that our generative model learns to disentangle pose and appearance information; this independence is exploited by our model to generate highly consistent reconstructions. The conditional information also aids the generator network in GAN to produce sharper images compared to the original GAN formulation. This helps in achieving more appealing inpainting performance. Though generic, our algorithm was targeted for inpainting on faces. When applied on CelebA and Youtube Faces datasets, the proposed method results in a significant improvement over the current benchmark, both in terms of quantitative evaluation (Peak Signal to Noise Ratio) and human visual scoring over diversified combinations of resolutions and deformations.


title: Malicious URL Detection using Machine Learning: A Survey

abstract: Malicious URL, a.k.a. malicious website, is a common and serious threat to cybersecurity. Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. We present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.). Further, this article provides a timely and comprehensive survey for a range of different audiences, not only for machine learning researchers and engineers in academia, but also for professionals and practitioners in cybersecurity industry, to help them understand the state of the art and facilitate their own research and practical applications. We also discuss practical issues in system design, open research challenges, and point out some important directions for future research.


title: Autonomous planning and control of soft untethered grippers in unstructured environments

abstract: The use of small, maneuverable, untethered and reconfigurable robots could provide numerous advantages in various micromanipulation tasks. Examples include microassembly, pick-and-place of fragile micro-objects for lab-on-a-chip applications, assisted hatching for in-vitro fertilization and minimally invasive surgery. This study assesses the potential of soft untethered magnetic grippers as alternatives or complements to conventional tethered or rigid micromanipulators. We demonstrate closed-loop control of untethered grippers and automated pick-and-place of biological material on porcine tissue in an unstructured environment. We also demonstrate the ability of the soft grippers to recognize and sort non-biological micro-scale objects. The fully autonomous nature of the experiments is made possible by the integration of planning and decision-making algorithms, as well as by closed-loop temperature and electromagnetic motion control. The grippers are capable of completing pick-and-place tasks of biological material at an average velocity of 1.8 ±0.71 mm/s and a drop-off error of 0.62 ±0.22 mm. Color-sensitive sorting of three micro-scale objects is completed at a velocity of 1.21 ±0.68 mm/s and a drop-off error of 0.85 ±0.41 mm. Our findings suggest that improved autonomous untethered grippers could augment the capabilities of current soft-robotic instruments especially in advancedtasks involving manipulation.


title: SoC CMOS UWB Pulse Radar Sensor for Contactless Respiratory Rate Monitoring

abstract: An ultra wideband (UWB) system-on-chip radar sensor for respiratory rate monitoring has been realized in 90 nm CMOS technology and characterized experimentally. The radar testchip has been applied to the contactless detection of the respiration activity of adult and baby. The field operational tests demonstrate that the UWB radar sensor detects the respiratory rate of person under test (adult and baby) associated with sub-centimeter chest movements, allowing the continuous-time non-invasive monitoring of hospital patients and other people at risk of obstructive apneas such as babies in cot beds, or other respiratory diseases.


title: Inferring 3D Object Pose in RGB-D Images

abstract: The goal of this work is to replace objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene using the approach from Gupta et al. [13]. We use a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel normals in images containing rendered synthetic objects. When tested on real data, it outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place the model that fits the best into the scene. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art [33], while being an order of magnitude faster at the same time.


title: Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control

abstract: Variations in neural circuitry, inherited or acquired, may underlie important individual differences in thought, feeling, and action patterns. Here, we used task-free connectivity analyses to isolate and characterize two distinct networks typically coactivated during functional MRI tasks. We identified a “salience network,” anchored by dorsal anterior cingulate (dACC) and orbital frontoinsular cortices with robust connectivity to subcortical and limbic structures, and an “executive-control network” that links dorsolateral frontal and parietal neocortices. These intrinsic connectivity networks showed dissociable correlations with functions measured outside the scanner. Prescan anxiety ratings correlated with intrinsic functional connectivity of the dACC node of the salience network, but with no region in the executive-control network, whereas executive task performance correlated with lateral parietal nodes of the executive-control network, but with no region in the salience network. Our findings suggest that task-free analysis of intrinsic connectivity networks may help elucidate the neural architectures that support fundamental aspects of human behavior.


title: Study on the classification of data streams with concept drift

abstract: Data streams mining has become a novel research topic of growing interest in knowledge discovery. Because of the high speed and huge size of data set in data streams, the traditional classification technologies are no longer applicable. In recent years a great deal of research has been done on this problem, most intends to efficiently solve the data streams mining problem with concept drift. This paper presents the state-of-the-art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a critical summary of existing approaches to the problem, including Stagger, FLORA, MetaL(B), MetaL(IB), CD3, CD4, CD5, OLIN, CVFDT and different ensemble classifiers. At last, this paper explores the challenges and future work in this field.


title: Dynamic remote data auditing for securing big data storage in cloud computing / Mehdi Sookhak

abstract: Nowadays, organizations produce a huge amount of sensitive data, such as personal information, financial data, and electronic health records. Consequently, the amount of ::: digital data produced has increased correspondingly and often overwhelmed the data storage capacity of many organizations. The management of such a large amount of data in local storage system is difficult and incurs high expenses because of high-capacity storage systems needed and the expert personnel to manage them. Although the cost of storage hardware has tremendously decreased in recent years, about 75% of the total ownership cost is still assigned tomanage data storage. It is not surprising, therefore, that cloud computing is now embraced as a key technology to provide a convenient, on-demand network ::: access to a shared pool of configurable computing resources, and demands minimum service provider interaction or management effort. Organizations now have an option to ::: outsource their data to cloud storage to decrease the burden on local data storage and also to reduce maintenance cost. Although the cloud offers tangible benefits to data owners, outsourcing data to a remote server and delegating management of data to an untrusted cloud service provider, can lead to loss of physical control over the data. To the clients, the cloud is inherently neither secure nor reliable and this poses new challenges to the confidentiality, integrity, and availability of data in cloud computing. Without a local copy of the data, traditional integrity verification techniques such as hash functions and signatures are inapplicable in the cloud storage. Also, it is impossible to download a large-size ::: file from the cloud storage. The situation is made worse when users access data using their mobile devices. In this context, a more efficient technique is required to remotely ::: verify the integrity of the outsourced data in the cloud. In this research, a new remote data auditing method is proposed for securing data storage in cloud computing based ::: on an algebraic signature. This signature allows the auditor to check data possession in cloud storage, and this incurs fewer computational overheads on the auditor and server in comparison to Homomorphic cryptosystem. Moreover, a new data structure – Divide and Conquer Table (D&CT) – is designed to efficiently update the outsourced data dynamically by performing insert, append, delete, and modify operations by the data owner. ::: Furthermore, the proposed method is implemented in the real environment to prove the security, justify the performance of our method, and compare with the most familiar and ::: the stat-of-the-art data auditing methods on the basis of computation and communication cost. It is found that by employing the proposed RDA method the computational ::: and communication costs of data integrity is reduced. D&CT data structure reduces the computation cost of data update for normal and large-scale files markedly. Hence, the ::: proposed RDA provides an efficient and secure solution for mobile cloud computing.


title: Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

abstract: Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets. In this paper, we introduced a CNN structure, namely deformable ConvNet, to address geometric modeling in object recognition. By adding offsets to the convolution layers, feature mapping of CNN can be applied to unfixed locations, enhancing CNNs’ visual appearance understanding. In our work, a deformable region-based fully convolutional networks (R-FCN) was constructed by substituting the regular convolution layer with a deformable convolution layer. To efficiently use this deformable convolutional neural network (ConvNet), a training mechanism is developed in our work. We first set the pre-trained R-FCN natural image model as the default network parameters in deformable R-FCN. Then, this deformable ConvNet was fine-tuned on very high resolution (VHR) remote sensing images. To remedy the increase in lines like false region proposals, we developed aspect ratio constrained non maximum suppression (arcNMS). The precision of deformable ConvNet for detecting objects was then improved. An end-to-end approach was then developed by combining deformable R-FCN, a smart fine-tuning strategy and aspect ratio constrained NMS. The developed method was better than a state-of-the-art benchmark in object detection without data augmentation.


title: 3D floor plan recovery from overlapping spherical images

abstract: We present a novel approach to automatically recover, from a small set of partially overlapping spherical images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. We introduce several improvements over previous approaches based on color and spatial reasoning exploiting Manhattan world priors. In particular, we introduce a new method for geometric context extraction based on a 3D facet representation, which combines color distribution analysis of individual images with sparse multi-view clues. We also introduce an efficient method to combine the facets from different viewpoints in a single consistent model, taking into the reliability of the facet information. The resulting capture and reconstruction pipeline automatically generates 3D multi-room environments in cases where most previous approaches fail, e.g., in the presence of hidden corners and large clutter, without the need for additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes. Our test data is available to allow further studies and comparisons.


title: Influence of domain and technology upon scope creep in software projects

abstract: Project management strategies play an influencing role in accomplishment of project success. A successful project alone can attain total customer satisfaction and withholding the industry in the market. Scope creep is deemed to be one of the several factors which influence the success of the project. Our research has proven that there are various factors which make an impact on scope creep. This paper put forth a case study carried out in leading software industries in order to realize the impact of domain and technology on scope creep. The investigation is carried out on various projects which are developed either in retail or pharmaceuticals domain. Retail projects are developed using Java and J2EE and the pharmaceutical projects are developed in Data Warehousing and Business Intelligence technology. Knowledge of impact of domain and technology upon scope creep certainly ensures one baseline their requirement specifications such that it reduces the scope creep associated overheads. It further accelerates quality of projects and productivity of the company.


title: A Study of Layout, Rendering, and Interaction Methods for Immersive Graph Visualization

abstract: Information visualization has traditionally limited itself to 2D representations, primarily due to the prevalence of 2D displays and report formats. However, there has been a recent surge in popularity of consumer grade 3D displays and immersive head-mounted displays (HMDs). The ubiquity of such displays enables the possibility of immersive, stereoscopic visualization environments. While techniques that utilize such immersive environments have been explored extensively for spatial and scientific visualizations, contrastingly very little has been explored for information visualization. In this paper, we present our considerations of layout, rendering, and interaction methods for visualizing graphs in an immersive environment. We conducted a user study to evaluate our techniques compared to traditional 2D graph visualization. The results show that participants answered significantly faster with a fewer number of interactions using our techniques, especially for more difficult tasks. While the overall correctness rates are not significantly different, we found that participants gave significantly more correct answers using our techniques for larger graphs.


title: Scalable Data Partitioning Techniques for Parallel Sliding Window Processing over Data Streams

abstract: This paper proposes new techniques for e ciently parallelizing sliding window processing over data streams on a shared-nothing cluster of commodity hardware. Data streams are first partitioned on the fly via a continuous split stage that takes the query semantics into account in a way that respects the natural chunking (windowing) of the stream by the query. The split does not scale well enough when there is high degree of overlap across the windows. To remedy this problem, we propose two alternative partitioning strategies based on batching and pane-based processing, respectively. Lastly, we provide a continuous merge stage at the end that combines the results on the fly while meeting QoS requirements on ordered delivery. We implemented these techniques as part of the Borealis distributed stream processing system, and conducted experiments that show the scalability of our techniques based on the Linear Road Benchmark.


title: Generalized Identity Based and Broadcast Encryption Schemes

abstract: We provide a general framework for constructing identity-based and broadcast encryption systems. In particular, we construct a general encryption system called spatial encryption from which many systems with a variety of properties follow. The ciphertext size in all these systems is independent of the number of users involved and is just three group elements. Private key size grows with the complexity of the system. One application of these results gives the first broadcast HIBE system with short ciphertexts. Broadcast HIBE solves a natural problem having to do with identity-based encrypted email.


title: SmartLoc: push the limit of the inertial sensor based metropolitan localization using smartphone

abstract: We present SmartLoc, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, SmartLoc exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of SmartLoc in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90% of time while the known mean error of GPS is 42.22m.


title: Facade: Auto-generating Tactile Interfaces to Appliances

abstract: Digital keypads have proliferated on common appliances, from microwaves and refrigerators to printers and remote controls. For blind people, such interfaces are inaccessible. We conducted a formative study with 6 blind people which demonstrated a need for custom designs for tactile labels without dependence on sighted assistance. To address this need, we introduce Facade - a crowdsourced fabrication pipeline to make physical interfaces accessible by adding a 3D printed augmentation of tactile buttons overlaying the original panel. Blind users capture a photo of an inaccessible interface with a standard marker for absolute measurements using perspective transformation. Then this image is sent to multiple crowd workers, who work in parallel to quickly label and describe elements of the interface. These labels are then used to generate 3D models for a layer of tactile and pressable buttons that fits over the original controls. Users can customize the shape and labels of the buttons using a web interface. Finally, a consumer-grade 3D printer fabricates the layer, which is then attached to the interface using adhesives. Such fabricated overlay is an inexpensive ($10) and more general solution to making physical interfaces accessible.


title: Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models

abstract: Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood. Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood. Yet, GANs sidestep the characterization of an explicit density which makes quantitative evaluations challenging. To bridge this gap, we propose Flow-GANs, a generative adversarial network for which we can perform exact likelihood evaluation, thus supporting both adversarial and maximum likelihood training. When trained adversarially, Flow-GANs generate high-quality samples but attain extremely poor log-likelihood scores, inferior even to a mixture model memorizing the training data; the opposite is true when trained by maximum likelihood. Results on MNIST and CIFAR-10 demonstrate that hybrid training can attain high held-out likelihoods while retaining visual fidelity in the generated samples.


title: Noise-Induced Entrainment and Stochastic Resonance in Human Brain Waves

abstract: We present the first observation of stochastic resonance (SR) in the human brain's visual processing area. The novel experimental protocol is to stimulate the right eye with a subthreshold periodic optical signal and the left eye with a noisy one. The stimuli bypass sensory organs and are mixed in the visual cortex. With many noise sources present in the brain, higher brain functions, e.g., perception and cognition, may exploit SR.


Auditing Windows 7 Registry Keys to track the traces left out in copying files from system to external USB Device


title: Sex-specific differences in injury types among basketball players

abstract: The purpose of the present study was to investigate sex-specific differences in injury types among basketball players. According to our database, during the 20-year period between October 1991 and June 2011, 1,219 basketball players (640 males and 579 females) consulted our sports medicine clinic; in total, 1,414 injuries in basketball players (729 injuries in males and 685 injuries in females) were recorded. The mean age of patients was 19.6 years. The most common injury site was the knee, followed by the foot and ankle, lower back, and upper extremities. There was a higher proportion of female players presenting with a knee injury, compared with male players (50.4% vs 41.7%), and a lower proportion of female players presenting with an upper extremity injury (5.1% vs 9.7%). The proportion of anterior cruciate ligament injury in the 10–19-year-old age group was higher among female players than among male players (45.9% vs 22.1%), while the proportions of Osgood–Schlatter disease in the 10–19-year-old age group and jumper’s knee (patellar and femoral tendinopathy) in the 20–29-year-old age group were higher among male players than among female players (12.5% vs 1.8% and 14.6% vs 3.7%, respectively). However, the proportions of other injuries did not differ significantly between male and female players. The present observational study, which was performed using a retrospective case-series design, showed the existence of sex-specific differences in knee injuries sustained while participating in basketball.


title: An upper bound on the length of a Hamiltonian walk of a maximal planar graph

abstract: A Hamiltonian walk of a connected graph is a shortest closed walk that passes through every vertex at least once, and the length of a Hamiltonian walk is the total number of edges traversed by the walk. We show that every maximal planar graph with p(≥ 3) vertices has a Hamiltonian cycle or a Hamiltonian walk of length ≤ 3(p - 3)/2.


title: Graphene Mode-Locked Fiber Laser at 2.8 $\mu \text{m}$

abstract: Mid-infrared erbium (Er3+)-doped ZrF4–BaF2–LaF3–AlF3–NaF fiber laser mode-locked by multilayer graphene saturable absorber was demonstrated. Mode-locked pulses at 2.8 $\mu \text{m}$ with an average output power of 18 mW at a repetition rate of 25.4 MHz, corresponding to a pulse energy of 0.7 nJ, were obtained. The pulsewidth was measured to be $\sim 42$ ps by a home-made autocorrelator. Our experiment has validated the mode-locking capability of graphene in the 3- $\mu \text{m}$ wavelength region.


Laugier-Hunziker syndrome : A case report


title: A High-Gain Broadband Gradient Refractive Index Metasurface Lens Antenna

abstract: The design, simulation, and measurement results of a high-gain broadband gradient refractive index (GRIN) planar lens fed by an antipodal exponential taper slot antenna (ATSA) are presented. As a constituent part of this lens, a novel nonresonant metamaterial unit cell, composed of bilayer triple rectangular rings, is proposed and its equivalent circuit model is developed and described. It is shown that, by utilizing this element, stronger capacitive couplings between adjacent metallic layers are realized resulting in a large refractive index variation of about 2.5, and hence, a thin lens with a thickness of $0.38\lambda_{0}$ , where $\lambda_{0}$ is the wavelength at 9.5 GHz. In addition, since the unit cell is designed to resonate at higher frequencies, its refractive index response is smoothly increased over a broad frequency range and this considerably enhances the operating bandwidth of the lens. The achieved measured results demonstrate a broad matching and $-3\;{\rm dB}$ gain bandwidths of 52% (7–12 GHz) and 65% (7–13.2 GHz), respectively. Furthermore, this lens offers a high aperture efficiency of 50% (21.2 dB gain) at the center frequency, and its sidelobe and cross-polarization levels are less than $-20\;{\rm dB}$ and $-26\;{\rm dB}$ across the entire matched band, respectively.


A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization


Architecture of the Hyperledger Blockchain Fabric


title: Multiview learning of word embeddings via cca

abstract: Recently, there has been substantial interest in using large amounts of unlabeled data to learn word representations which can then be used as features in supervised classifiers for NLP tasks. However, most current approaches are slow to train, do not model the context of the word, and lack theoretical grounding. In this paper, we present a new learning method, Low Rank Multi-View Learning (LR-MVL) which uses a fast spectral method to estimate low dimensional context-specific word representations from unlabeled data. These representation features can then be used with any supervised learner. LR-MVL is extremely fast, gives guaranteed convergence to a global optimum, is theoretically elegant, and achieves state-of-the-art performance on named entity recognition (NER) and chunking problems.


title: Compact UWB Bandpass Filter Using Stub-Loaded Multiple-Mode Resonator

abstract: A compact microstrip-line ultra-wideband (UWB) bandpass filter (BPF) using the proposed stub-loaded multiple-mode resonator (MMR) is presented. This MMR is formed by loading three open-ended stubs in shunt to a simple stepped-impedance resonator in center and two symmetrical locations, respectively. By properly adjusting the lengths of these stubs, the first four resonant modes of this MMR can be evenly allocated within the 3.1-to-10.6 GHz UWB band while the fifth resonant frequency is raised above 15.0GHz. It results in the formulation of a novel UWB BPF with compact-size and widened upper-stopband by incorporating this MMR with two interdigital parallel-coupled feed lines. Simulated and measured results are found in good agreement with each other, showing improved UWB bandpass behaviors with the insertion loss lower than 0.8dB, return loss higher than 14.3dB, and maximum group delay variation less than 0.64ns in the realized UWB passband


title: 187 REVIEW Circadian clocks: regulators of endocrine and metabolic rhythms

abstract: Daily and seasonal rhythms in the endocrine system are co-ordinated by a hypothalamic pacemaker, the suprachiasmatic nuclei (SCN) that is synchronised to solar time by direct retinal afferents. Individual SCN neurons are circadian clocks, their intrinsic oscillator consisting of a series of interlinked autoregulatory transcriptional/post-translational feedback loops incorporating Period (Per) and Cryptochrome (Cry) genes. Mutations that alter the rate of transcription of Per and Cry genes or the stability of Per and Cry proteins affect clock speed. Molecular timekeeping in SCN neurons is synchronised and sustained by interneuronal neuropeptidergic signals. A molecular clock mechanism comparable to that of the SCN is present in most major organ systems. These tissue clocks are synchronised by endocrine, autonomic and behavioural cues that are dependent on the SCN, and in turn they drive the circadian expression of local transcriptomes, thereby co-ordinating circadian metabolism and physiology. Rhythmic glucocorticoid signalling is a prominent mediator of SCN output and internal synchroniser. The role of local SCN-synchronised clocks in controlling vital processes, including xenobiotic detoxification, cell division and nutrient metabolism, is essential to health, and disturbances to circadian timing arising from modern working schedules are becoming recognised as an increasingly relevant factor in major systemic illness. Moreover, the newly identified molecular components of circadian control systems provide novel avenues for therapeutic intervention.


The Haplolethal Region at the 16 F Gene Cluster of Drosophila melanogaster : Structure and Function


title: Advanced signal processing method for ground penetrating radar feature detection and enhancement

abstract: This paper focuses on new signal processing algorithms customized for an air coupled Ultra-Wideband (UWB) Ground Penetrating Radar (GPR) system targeting highway pavements and bridge deck inspections. The GPR hardware consists of a high-voltage pulse generator, a high speed 8 GSps real time data acquisition unit, and a customized field-programmable gate array (FPGA) control element. In comparison to most existing GPR system with low survey speeds, this system can survey at normal highway speed (60 mph) with a high horizontal resolution of up to 10 scans per centimeter. Due to the complexity and uncertainty of subsurface media, the GPR signal processing is important but challenging. In this GPR system, an adaptive GPR signal processing algorithm using Curvelet Transform, 2D high pass filtering and exponential scaling is proposed to alleviate noise and clutter while the subsurface features are preserved and enhanced. First, Curvelet Transform is used to remove the environmental and systematic noises while maintain the range resolution of the B-Scan image. Then, mathematical models for cylinder-shaped object and clutter are built. A two-dimension (2D) filter based on these models removes clutter and enhances the hyperbola feature in a B-Scan image. Finally, an exponential scaling method is applied to compensate the signal attenuation in subsurface materials and to improve the desired signal feature. For performance test and validation, rebar detection experiments and subsurface feature inspection in laboratory and field configurations are performed.


title: A Methodology for Introducing Competitive Anxiety and Pressure in VR Sports Training

abstract: Athletes' performances are influenced by internal and external factors, including their psychological state and environmental factors, especially during competition. As a consequence, current training programs include stress management. In this paper, we explore whether highly immersive systems can be used for such training programs. First, we propose methodological guidelines to design sport training scenarios both on considering the elements that a training routine must have, and how external factors might influence the participant. The proposed guidelines are based on Flow and social-evaluative threat theories. Second, to illustrate and validate our methodology, we designed an experimental setup reproducing a ten meter Olympic pistol shooting. We analyzed whether changes in the environment are able to induce changes in user performance, physiological responses and the subjective perception of the task. The simulation included stressors in order to raise a social-evaluative threat, such as aggressive public behavior or unforced errors, increasing the pressure while performing the task. The results showed significant differences in their subjective impressions, trends in the behavioral and physiological data were also observed. Taken together, our results suggest that highly immersive systems could be further used for training in sports.


title: Extremely parallel memristor crossbar architecture for convolutional neural network implementation

abstract: This paper presents a simulated memristor crossbar based Convolutional Neural Network (CNN). Deep networks implemented on GPU clusters have become the state of the art in providing excellent classification ability, at the cost of a more complex data manipulation process. In this work we show that once deep networks are trained, the analog crossbar circuits in this paper can parallelize the recognition phase of a CNN algorithm. With the massively parallel structure proposed in this work, we are able to generate multiple output feature maps in a single processing cycle. We show the proposed system is capable of operating with a negligible loss in classification accuracy if the memristors utilized are able to store at least 16 unique values (essentially acting as 4-bit devices). Furthermore, we quantify the amount of classification accuracy lost due to analog amplification in the signal path. To the best of our knowledge, this is the first paper that presents a memristor based circuit that is able to completely parallelize the CNN recognition process.


title: Beyond Cybersecurity Awareness: Antecedents and Satisfaction

abstract: Organizations develop technical and procedural measures to protect information systems. Relying only on technical based security solutions is not enough. Organizations must consider technical security solutions along with social, human, and organizational factors. The human element represents the employees (insiders) who use the information systems and other technology resources in their day-to-day operations. ISP awareness is essential to protect organizational information systems. This study adapts the Innovation Diffusion Theory to examine the antecedents of ISP awareness and its impact on the satisfaction with ISP and security practices. A sample of 236 employees in universities in the United States is collected to evaluate the research model. Results indicated that ISP quality, self-efficacy, and technology security awareness significantly impact ISP awareness. The current study presents significant contributions toward understanding the antecedents of ISP awareness and provides a starting point toward including satisfaction aspect in information security behavioral domain.


title: Fusion-based skin detection using image distribution model

abstract: Skin colour detection under poor or varying illumination condition is a big challenge for various image processing and human-computer interaction applications. In this paper, a novel skin detection method utilizing image pixel distribution in a given colour space is proposed. The pixel distribution of an image can provide a better localization of the actual skin colour distribution of an image. Hence, a local skin distribution model (LSDM) is derived using the image pixel distribution model and its similarity with the global skin distribution model (GSDM). Finally, a fusion-based skin model is obtained using both the GSDM and the LSDM. Subsequently, a dynamic region growing method is employed to improve the overall detection rate. Experimental results show that proposed skin detection method can significantly improve the detection accuracy in presence of varying illumination conditions.


title: Thrombosis and hemorheology

abstract: The potential importance of hemorheology in altering blood flow and oxygen delivery has been considered in Chapter 5 of this book. In Chapter 6, the contribution of rheological factors to ischemia of the heart, brain and limbs was discussed. The present chapter reviews the contribution of hemorheology to thrombosis. This is a young field, for only in recent years have rheology and thrombology come together. However, it is already apparent that rheological phenomena are potentially of considerable importance in thrombogenesis and in the pathophysiology of the tissue infarction which follows arterial thrombosis.


title: Cyber-Physical Systems: A New Frontier

abstract: The report of the President's Council of Advisors on Science and Technology (PCAST) has placed CPS on the top of the priority list for federal research investment [6]. This article first reviews some of the challenges and promises of CPS, followed by an articulation of some specific challenges and promises that are more closely related to the sensor networks, ubiquitous and trustworthy computing conference.


title: Interactive Ambiguity Resolution of Named Entities in Fictional Literature

abstract: Named entity recognition NER denotes the task to detect entities and their corresponding classes, such as person or location, in unstructured text data. For most applications, state of the art NER software is producing reasonable results. However, as a consequence of the methodological limitations and the well-known pitfalls when analyzing natural language data, the NER results are likely to contain ambiguities. In this paper, we present an interactive NER ambiguity resolution technique, which enables users to create post-processing rules for named entity recognition data based on the content and entity context of the analyzed documents. We specifically address the problem that in use-cases where ambiguities are problematic, such as the attribution of fictional characters with traits, it is often unfeasible to train models on custom data to improve state of the art NER software. We derive an iterative process model for improving NER results, show an interactive NER ambiguity resolution prototype, illustrate our approach with contemporary literature, and discuss our work and future research.


title: Performance of a QRS detector on self-collected database using a handheld two-electrode ECG

abstract: This paper presents a development of a handheld device for single-channel ambulatory ECG measurements. Our ECG device is designed for easy-to-use. ECG signal will be recorded for 1 minute by placing both thumbs on two dry electrodes and we have used this device to collect ECG signals from 50 volunteers. An ECG database is created from the collected ECG signals with self-annotated locations of QRS complex. This process is indispensable for developing the algorithm and for testing the performance of ECG analysis. The self-collected database has been used to evaluate the performance of a simple slope-based QRS detection algorithm, which had a relatively high accuracy on MIT-BIH Arrhythmia Database from our previous work without preprocessing filtering. As a result, the performance of the same QRS detector on our 50 short-term ECG records has an excellent sensitivity of 99.74%, but a moderate positive predictivity of 94.25% because of high level of EMG and movement artefacts from thumbs. After applying preprocessing filter (band pass 9–20 Hz), the positive predictivity is significantly improved by about 4%. This work showed that for the development of QRS detector for a handheld two-electrode ECG device in our case, the detection algorithm should be optimized by using a device-specific database because of some particular noise characteristics of ECG signals recorded from the handheld ECG device.


title: Multi-mode surround view for ADAS vehicles

abstract: Advanced driver's assist systems for optional self-driving vehicles require highly reliable navigation capabilities to ensure dependable autonomous function of vehicle safety and driver assistance. Radars, lidars and cameras have found widespread application in nearly all vehicle brands for this purpose. In this paper we present the implementation of these sensors for constructing a 360-degree field of view of the vehicle surrounding. A stereo vision system is complemented by several long, mid and short-range radar sensors. The data gathered from the advanced sensors is prioritized and fused to create a model of the surrounding. The system is being tested on an autonomous car vehicle. In particular, this paper discusses three clustering algorithms that were tested.


title: Gender Differences in Criminal Sentencing: Do Effects Vary Across Violent, Property, and Drug Offenses?

abstract: Many studies find that females benefit from their gender in sentencing decisions. Few researchers, however, address whether the gender-sentencing association might be stronger for some crimes, such as minor nonviolent offending, and weaker for other offenses, such as serious violent crime. Copyright (c) 2006 by the Southwestern Social Science Association.


title: Cooperative forest monitoring and fire detection using a team of UAVs-UGVs

abstract: This paper investigates forest monitoring and fire detection strategies using a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). UAVs have great advantages in forest fire detection and fighting. However they have limited running time and payload capabilities, therefore UGVs are paired with UAVs to avoid their demerits. First, UGVs are used to transport the UAVs to the nearest location to their assigned search area. The UAVs will take-off and start the monitoring and search mission. When one of the UAVs detects the fire, it sends the fire spot coordinates to the leader UGV and ground fire management personnel. Then, the leader UGV that has a powerful computing and image processing capabilities will generate the reference trajectory for UAVs to follow in order to detect and continuously monitor the fire spread. Simulation results are presented in order to demonstrate the effectiveness of the proposed algorithm.


title: Enclosed: a component-centric interface for designing prototype enclosures

abstract: This paper explores the problem of designing enclosures (or physical cases) that are needed for prototyping electronic devices. We present a novel interface that uses electronic components as handles for designing the 3D shape of the enclosure. We use the .NET Gadgeteer platform as a case study of this problem, and implemented a proof-of-concept system for designing enclosures for Gadgeteer components. We show examples of enclosures designed and fabricated with our system.


title: Effectiveness of fundamental frequency (F0) and strength of excitation (SOE) for spoofed speech detection

abstract: Current countermeasures used in spoof detectors (for speech synthesis (SS) and voice conversion (VC)) are generally phase-based (as vocoders in SS and VC systems lack phaseinformation). These approaches may possibly fail for non-vocoder or unit-selection-based spoofs. In this work, we explore excitation source-based features, i.e., fundamental frequency (F0) contour and strength of excitation (SoE) at the glottis as discriminative features using GMM-based classification system. We use F0 and SoE1 estimated from speech signal through zero frequency (ZF) filtering method. Further, SoE2 is estimated from negative peaks of derivative of glottal flow waveform (dGFW) at glottal closure instants (GCIs). On the evaluation set of ASVspoof 2015 challenge database, the F0 and SoEs features along with its dynamic variations achieve an Equal Error Rate (EER) of 12.41%. The source features are fused at score-level with MFCC and recently proposed cochlear filter cepstral coefficients and instantaneous frequency (CFCCIF) features. On fusion with MFCC (CFCCIF), the EER decreases from 4.08% to 3.26% (2.07% to 1.72%). The decrease in EER was evident on both known and unknown vocoder-based attacks. When MFCC, CFCCIF and source features are combined, the EER further decreased to 1.61%. Thus, source features captures complementary information than MFCC and CFCCIF used alone.


title: Music-genre classification system based on spectro-temporal features and feature selection

abstract: An automatic classification system of the music genres is proposed. Based on the timbre features such as mel-frequency cepstral coefficients, the spectro-temporal features are obtained to capture the temporal evolution and variation of the spectral characteristics of the music signal. Mean, variance, minimum, and maximum values of the timbre features are calculated. Modulation spectral flatness, crest, contrast, and valley are estimated for both original spectra and timbre-feature vectors. A support vector machine (SVM) is used as a classifier where an elaborated kernel function is defined. To reduce the computational complexity, an SVM ranker is applied for feature selection. Compared with the best algorithms submitted to the music information retrieval evaluation exchange (MIREX) contests, the proposed method provides higher accuracy at a lower feature dimension for the GTZAN and ISMIR2004 databases.


Gradient-Based Learning Algorithms for Recurrent Connectionist Networks


title: Architectural support for hypervisor-secure virtualization

abstract: Virtualization has become a standard part of many computer systems. A key part of virtualization is the all-powerful hypervisor which manages the physical platform and can access all of its resources, including memory assigned to the guest virtual machines (VMs). Continuing releases of bug reports and exploits in the virtualization software show that defending the hypervisor against attacks is very difficult. In this work, we present hypervisor-secure virtualization - a new research direction with the goal of protecting the guest VMs from an untrusted hypervisor. We also present the HyperWall architecture which achieves hypervisor-secure virtualization, using hardware to provide the protections. HyperWall allows a hypervisor to freely manage the memory, processor cores and other resources of a platform. Yet once VMs are created, our new Confidentiality and Integrity Protection (CIP) tables protect the memory of the guest VMs from accesses by the hypervisor or by DMA, depending on the customer's specification. If a hypervisor does become compromised, e.g. by an attack from a malicious VM, it cannot be used in turn to attack other VMs. The protections are enabled through minimal modifications to the microprocessor and memory management units. Whereas much of the previous work concentrates on protecting the hypervisor from attacks by guest VMs, we tackle the problem of protecting the guest VMs from the hypervisor.


title: SDFog: A Software Defined Computing Architecture for QoS Aware Service Orchestration over Edge Devices

abstract: Cloud computing revolutionized the information technology (IT) industry by offering dynamic and infinite scaling, on-demand resources and utility-oriented usage. However, recent changes in user traffic and requirements have exposed the shortcomings of cloud computing, particularly the inability to deliver real-time responses and handle massive surge in data volumes. Fog computing, that brings back partial computation load from the cloud to the edge devices, is envisioned to be the next big change in computing, and has the potential to address these challenges. Being a highly distributed, loosely coupled and still in the emerging phase, standardization, quality-of-service management and dynamic adaptability are the key challenges faced by fog computing research fraternity today. This article aims to address these issues by proposing a service-oriented middleware that leverages the convergence of cloud and fog computing along with software defined networking (SDN) and network function virtualization (NFV) to achieve the aforementioned goals. The proposed system, called"Software Defined Fog"(SDFog), abstracts connected entities as services and allows applications to orchestrate these services with end-to-end QoS requirements. A use-case showing the necessity of such a middleware has been presented to show the efficacy of the SDN-based QoS control over the Fog. This article aims at developing an integrated system to realize the software-defined control over fog infrastructure.


title: Mobile Edge Computing: Survey and Research Outlook

abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.


title: Platooning With IVC-Enabled Autonomous Vehicles: Strategies to Mitigate Communication Delays, Improve Safety and Traffic Flow

abstract: Intraplatoon information management strategies for dealing with safe and stable operation are proposed in this paper. New algorithms to mitigate communication delays are presented, and Matlab/Simulink-based simulation results are reported. We argue that using anticipatory information from both the platoon's leader and the followers significantly impacts platoon string stability. The obtained simulation results suggest that the effects of communication delays may be almost completely canceled out. The platoon presents a very stable behavior, even when subjected to strong acceleration patterns. When the communication channel is subjected to a strong load, proper algorithms may be selected, lowering network load and maintaining string stability. Upon emergency occurrences, the platoon's timely response may be ensured by dynamically increasing the weight of the platoons' leaders data over the behavior of their followers. The simulation results suggest that the algorithms are robust under several demanding scenarios. To assess if current intervehicle communication technology can cope with the proposed information-updating schemes, research into its operation was conducted through a network simulator.


title: Smart RFID based Interactive Kiosk cart using wireless sensor node

abstract: Now days it is common to see people getting enthusiast in online shopping through e-commerce websites but still the shopping centers are popular. We come across many types of carts used for shopping in malls and shopping centers. We are proposing an Interactive Kiosk based cart which uses the RFID technology to identify the products details which is already available in the database. We propose to have facility to browse the available products list onscreen in the display connected to beagle bone black cart. The cart is interacting with the Main Server and it will have the facility to generate the bill for all the products added into the cart. The proposed system will be helpful for avoiding queues in shopping malls for billing. With the proposed design customers will have an ease to shop by avoiding conventional queue system for billing generation and hence the shopping becomes easy and enjoyable.


title: Selection and identification of physical parameters from passive observation. Application to a winding process

abstract: This paper deals with parameter selection and estimation of large and complex simulation models. This estimation problem is addressed in the case of passive observation, i.e. when no controlled experiment is possible. Given the lack of information in the data, an appropriate methodology is proposed to select and estimate some physical parameters of the model. Its implementation is based on a new software: Diffedge© which makes it possible to symbolically determine model output sensitivity functions of block diagrams. An application to a winding prototype is developed to illustrate the effectiveness of such an approach in practice.


title: Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimizing Global Loss Functions

abstract: Recent innovations in training deep convolutional neural network (ConvNet) models have motivated the design of new methods to automatically learn local image descriptors. The latest deep ConvNets proposed for this task consist of a siamese network that is trained by penalising misclassification of pairs of local image patches. Current results from machine learning show that replacing this siamese by a triplet network can improve the classification accuracy in several problems, but this has yet to be demonstrated for local image descriptor learning. Moreover, current siamese and triplet networks have been trained with stochastic gradient descent that computes the gradient from individual pairs or triplets of local image patches, which can make them prone to overfitting. In this paper, we first propose the use of triplet networks for the problem of local image descriptor learning. Furthermore, we also propose the use of a global loss that minimises the overall classification error in the training set, which can improve the generalisation capability of the model. Using the UBC benchmark dataset for comparing local image descriptors, we show that the triplet network produces a more accurate embedding than the siamese network in terms of the UBC dataset errors. Moreover, we also demonstrate that a combination of the triplet and global losses produces the best embedding in the field, using this triplet network. Finally, we also show that the use of the central-surround siamese network trained with the global loss produces the best result of the field on the UBC dataset. Pre-trained models are available online at this https URL


2017 Trojaning Attack on Neural Networks


title: Evolution and Innovation in 5G Cellular Communication System and Beyond: A Study

abstract: Since the last few years there has been a phenomenal growth in the wireless industry. Widespread wireless technologies, increasing variety of user-friendly and multimedia- enabled terminals and wider availability of open source tools for content generation has lead encouraged user-centric networks resulting in a need for efficient network design. The objective of this paper is comprehensive study related to 5G technology of mobile communication. Existing research work in mobile communication is related to 5G technology. The major contribution of this study is the key provisions of 5G (Fifth Generation) technology of mobile communication, which is seen as consumer oriented. In 5G technology, the mobile consumer has given utmost priority compared to others. In this context, the existing and highly demanded technologies for 5G technologies has beed studied extensively. Open challenges are highlighted for researcher for further study of the emerging 5G systems.


title: High performance nano-titania photocatalytic paper composite. Part I: Experimental design study for TiO2 composite sheet using a natural zeolite microparticle system and its photocatalytic property

abstract: Nanosized titanium dioxide (TiO2) photocatalytic papers were successfully prepared using a natural zeolite microparticle retention aid in papermaking. Applying a factorial experimental design, natural zeolite has been demonstrated to be the most significant and interactive factor for increasing the TiO2 retention rate in paper stock. The photocatalysis of as-prepared sheets was evaluated with toluene as one of the representative volatile organic compounds (VOCs). Under UV irradiation, it was shown to be effective in removing gaseous toluene by photodecomposition, assisted by adsorption. It was revealed that natural zeolite plays an important role in both increasing retention rate of TiO2 nanoparticles and enhancing photocatalytic efficiency of the paper. The photocatalytic paper can be potentially applied for environmental purification.


title: Visual Recognition in RGB Images and Videos by Learning from RGB-D Data

abstract: In this work, we propose a framework for recognizing RGB images or videos by learning from RGB-D training data that contains additional depth information. We formulate this task as a new unsupervised domain adaptation (UDA) problem, in which we aim to take advantage of the additional depth features in the source domain and also cope with the data distribution mismatch between the source and target domains. To handle the domain distribution mismatch, we propose to learn an optimal projection matrix to map the samples from both domains into a common subspace such that the domain distribution mismatch can be reduced. Such projection matrix can be effectively optimized by exploiting different strategies. Moreover, we also use different ways to utilize the additional depth features. To simultaneously cope with the above two issues, we formulate a unified learning framework called domain adaptation from multi-view to single-view (DAM2S). By defining various forms of regularizers in our DAM2S framework, different strategies can be readily incorporated to learn robust SVM classifiers for classifying the target samples, and three methods are developed under our DAM2S framework. We conduct comprehensive experiments for object recognition, cross-dataset and cross-view action recognition, which demonstrate the effectiveness of our proposed methods for recognizing RGB images and videos by learning from RGB-D data.


title: Innovative engineering education in the cooperative VR environment

abstract: In the scope of the article, there is the presentation of the cyber-higher educational environment feasible by the technical innovations appearing rapidly and the investigation of the methods which can be applied successfully in the educational scene of the future. My goal was to create an educational scene suiting to the digital life of the CE generation students-called Z generation and alpha generation in the traditional generation theories, but clearly called CE and the CCE generation in the CogInfoCom theory — presently studying in the higher education of engineering and meeting the students requirements set against the learning environment. The educational method presented in the article and the research aiming at defining the strategy suit to the science of CogInfoCom supplementing it with an additional pedagogical aspect. The study shows an example for presenting the realization of how we can teach the future technologies to the the future engineers with the help of the present and modern technologies by applying innovative educational methods so that the the human and the ICT merges during work in the coopertative VR scene suiting mostly to the CE genetations digital life while the development of the future technologies is happenning. The cooperative VR learning environment presented in the article ensures an efficient educational scene for the higher education of engineering due to its project-based educational opportunity and at the same time it allows the education of the disruptive technologies appearing more and more extensively, even immediately after the appearance of the technology.


title: [Notes on cases of carcinoma of the uterine body observed in the S. Anna Hospital, Turin, between 1954-1969 and the Piedmont and Aosta Valley areas in the period 1965-1969].

abstract: An increase in the frequency of corpus cancer over the period 1954-1969 and 1965-1969 in records for the S. Anna Hospital, Turin, and the Piedmont and Aosta Valley areas respectively is reported. Attention is drawn to the fact that more than 30% of patients were in the 66-80 age group (i.e. over the usual age of 56 to 60). A higher frequency of infertility and delayed menopause in patients with such tumours has been proved statistically significant. Diabetes, obesity and hypertension are pathogenetically linked to adenocarcinoma of the endometrium and not to casual associations. The findings are in line with the view that this neoplasia is hormone-dependent and a public health programme for its prevention is proposed.


title: Enhancing particle swarm optimization using generalized opposition-based learning

abstract: Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this problem. GOBL can provide a faster convergence, and the Cauchy mutation with a long tail helps trapped particles escape from local optima. The proposed approach uses a similar scheme as opposition-based differential evolution (ODE) with opposition-based population initialization and generation jumping using GOBL. Experiments are conducted on a comprehensive set of benchmark functions, including rotated multimodal problems and shifted large-scale problems. The results show that GOPSO obtains promising performance on a majority of the test problems.


title: Webcam-based Attention Tracking in Online Learning: A Feasibility Study

abstract: A main weakness of the open online learning movement is retention: a small minority of learners (on average 5-10%, in extreme cases


title: Towards Bi-Directional Communication in Human-Swarm Teaming: A Survey

abstract: Swarm systems consist of large numbers of robots that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from search-and-rescue situations to Cyber defence. The two decision making cycles of swarms and humans operate on two different time-scales, where the former is normally orders of magnitude faster than the latter. Closing the loop at the intersection of these two cycles will create fast and adaptive human-swarm teaming networks. This paper brings desperate pieces of the ground work in this research area together to review this multidisciplinary literature. We conclude with a framework to synthesize the findings and summarize the multi-modal indicators needed for closed-loop human-swarm adaptive systems.


title: A survey of cross-lingual embedding models.

abstract: Cross-lingual embedding models allow us to project words from different languages into a shared embedding space. This allows us to apply models trained on languages with a lot of data, e.g. English to low-resource languages. In the following, we will survey models that seek to learn cross-lingual embeddings. We will discuss them based on the type of approach and the nature of parallel data that they employ. Finally, we will present challenges and summarize how to evaluate cross-lingual embedding models.


title: Attention-Based End-to-End Speech Recognition in Mandarin

abstract: Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. In this paper, we explore the use of attention-based encoder-decoder model for Mandarin speech recognition and to the best of our knowledge, achieve the first promising result. We reduce the source sequence length by skipping frames and regularize the weights for better generalization and convergence. Moreover, we investigate the impact of varying attention mechanism (convolutional attention and attention smoothing) and the correlation between the performance of the model and the width of beam search. On the MiTV dataset, we achieve a character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% without using any lexicon or language model. While together with a trigram language model, we reach 2.81% CER and 5.77% SER.


title: Fast Parallel Algorithm for Enumerating All Chordless Cycles in Graphs

abstract: Finding chordless cycles is an important theoretical problem in the Graph Theory area. It also can be applied to practical problems such as discover which predators compete for the same food in ecological networks. Motivated by the problem of theoretical interest and also by its significant practical importance, we present in this paper a parallel algorithm for enumerating all the chordless cycles in undirected graphs, which was implemented in OpenCL.


title: Relationships between sprinting, agility, and jump ability in female athletes

abstract: Abstract The aim of this study was to assess the relationships between various field tests in female athletes. Altogether, 83 high school soccer, 51 college soccer, and 79 college lacrosse athletes completed tests for linear sprinting, countermovement jump, and agility in a single session. Linear sprints (9.1, 18.3, 27.4, and 36.6 m) and agility tests (Illinois and pro-agility) were evaluated using infrared timing gates, while countermovement jump height was assessed using an electronic timing mat. Pearson's product – moment correlation coefficients (r) were used to determine the strength and directionality of the relationship between tests and coefficients of determination (r 2) were used to examine the amount of explained variance between tests. All of the performance scores were statistically correlated with each other; however, the coefficients of determination were low, moderate, and high depending on the test pairing. Linear sprint split times were strongly correlated with each other (r = 0.775 to 0...


Linguistic Metadata Augmented Classifiers at the CLEF 2017 Task for Early Detection of Depression


title: AAindex: Amino Acid index database

abstract: AAindex is a database of amino acid indices and amino acid mutation matrices. An amino acid index is a set of 20 numerical values representing various physico--chemical and biochemical properties of amino acids. An amino acid mutation matrix is generally 20 x 20 numerical values representing similarity of amino acids. AAindex consists of two sections: AAindex1 for the collection of published amino acid indices and AAindex2 for the collection of published amino acid mutation matrices. Each entry of either AAindex1 or AAindex2 consists of the definition, the reference information, a list of related entries in terms of the correlation coefficient and the actual data. The database may be accessed through the DBGET/LinkDB system at GenomeNet (http://www. genome.ad.jp/aaindex/ ) or may be downloaded by anonymous FTP (ftp://ftp.genome.ad.jp/db/genomenet/aaindex/ ).


title: FlashKV: Accelerating KV Performance with Open-Channel SSDs

abstract: As the cost-per-bit of solid state disks is decreasing quickly, SSDs are supplanting HDDs in many cases, including the primary storage of key-value stores. However, simply deploying LSM-tree-based key-value stores on commercial SSDs is inefficient and induces heavy write amplification and severe garbage collection overhead under write-intensive conditions. The main cause of these critical issues comes from the triple redundant management functionalities lying in the LSM-tree, file system and flash translation layer, which block the awareness between key-value stores and flash devices. Furthermore, we observe that the performance of LSM-tree-based key-value stores is improved little by only eliminating these redundant layers, as the I/O stacks, including the cache and scheduler, are not optimized for LSM-tree’s unique I/O patterns. To address the issues above, we propose FlashKV, an LSM-tree based key-value store running on open-channel SSDs. FlashKV eliminates the redundant management and semantic isolation by directly managing the raw flash devices in the application layer. With the domain knowledge of LSM-tree and the open-channel information, FlashKV employs a parallel data layout to exploit the internal parallelism of the flash device, and optimizes the compaction, caching and I/O scheduling mechanisms specifically. Evaluations show that FlashKV effectively improves system performance by 1.5× to 4.5× and decreases up to 50% write traffic under heavy write conditions, compared to LevelDB.


title: The Sharing Economy in Computing: A Systematic Literature Review

abstract: The sharing economy has quickly become a very prominent subject of research in the broader computing literature and the in human--computer interaction (HCI) literature more specifically. When other computing research areas have experienced similarly rapid growth (e.g. human computation, eco-feedback technology), early stage literature reviews have proved useful and influential by identifying trends and gaps in the literature of interest and by providing key directions for short- and long-term future work. In this paper, we seek to provide the same benefits with respect to computing research on the sharing economy. Specifically, following the suggested approach of prior computing literature reviews, we conducted a systematic review of sharing economy articles published in the Association for Computing Machinery Digital Library to investigate the state of sharing economy research in computing. We performed this review with two simultaneous foci: a broad focus toward the computing literature more generally and a narrow focus specifically on HCI literature. We collected a total of 112 sharing economy articles published between 2008 and 2017 and through our analysis of these papers, we make two core contributions: (1) an understanding of the computing community's contributions to our knowledge about the sharing economy, and specifically the role of the HCI community in these contributions (i.e.what has been done) and (2) a discussion of under-explored and unexplored aspects of the sharing economy that can serve as a partial research agenda moving forward (i.e.what is next to do).


HURRICANE TRAJECTORY PREDICTION VIA A SPARSE RECURRENT NEURAL NETWORK


title: Current and Future Research Trends in Substrate Integrated Waveguide Technology

abstract: Substrate Integrated Waveguide (SIW) tech- nology is the most promising candidate for the implemen- tation of millimeter-wave (mm-wave) integrated circuits and systems for the next decade. Based on planar dielectric substrates with top and bottom metal layers perforated with metalized holes, SIW structures offer a compact, low loss, flexible, and cost-effective solution for integrating active circuits, passive components and radiating elements on the same substrate. This paper presents an overview of the current status and future trends of academic and in- dustrial research on SIW technology. The historical devel- opment of SIW components and circuits is briefly outlined, and the current research topics are discussed: they include the development of numerical techniques for the modeling and design of SIW components, the investigation of novel compact and broadband interconnects, the determination of design solutions for loss minimization. Future research trends are also discussed: they mainly aim at the imple- mentation of SIW components at higher frequency (60-350 GHz) and the integration of complete Systems-on-Substrate (SoS).


title: Global versus local search: the impact of population sizes on evolutionary algorithm performance

abstract: In the field of Evolutionary Computation, a common myth that "An Evolutionary Algorithm (EA) will outperform a local search algorithm, given enough runtime and a large-enough population" exists. We believe that this is not necessarily true and challenge the statement with several simple considerations. We then investigate the population size parameter of EAs, as this is the element in the above claim that can be controlled. We conduct a related work study, which substantiates the assumption that there should be an optimal setting for the population size at which a specific EA would perform best on a given problem instance and computational budget. Subsequently, we carry out a large-scale experimental study on 68 instances of the Traveling Salesman Problem with static population sizes that are powers of two between $$(1+2)$$(1+2) and $$({262144}+{524288})$$(262144+524288) EAs as well as with adaptive population sizes. We find that analyzing the performance of the different setups over runtime supports our point of view and the existence of optimal finite population size settings.


title: Essentially No Barriers in Neural Network Energy Landscape

abstract: Training neural networks involves finding minima of a high-dimensional non-convex loss function. Knowledge of the structure of this energy landscape is sparse. Relaxing from linear interpolations, we construct continuous paths between minima of recent neural network architectures on CIFAR10 and CIFAR100. Surprisingly, the paths are essentially flat in both the training and test landscapes. This implies that neural networks have enough capacity for structural changes, or that these changes are small between minima. Also, each minimum has at least one vanishing Hessian eigenvalue in addition to those resulting from trivial invariance.


title: A Compact Low-loss Magic-T using Microstrip-Slotline Transitions

abstract: The design of a compact low-loss magic-T is proposed. The planar magic-T incorporates the compact microstrip-slotline tee junction and small microstrip-slotline transition area to reduce slotline radiation. The experimental results show that the magic-T produces broadband in-phase and out-of-phase power combiner/divider responses, has an average in-band insertion loss of 0.3 dB and small in-band phase and amplitude imbalance of less than plusmn 1.6deg and plusmn 0.3 dB, respectively.