title: Compact Nonlinear Maps and Circulant Extensions

abstract: Kernel approximation via nonlinear random feature maps is widely used in speeding up kernel machines. There are two main challenges for the conventional kernel approximation methods. First, before performing kernel approximation, a good kernel has to be chosen. Picking a good kernel is a very challenging problem in itself. Second, high-dimensional maps are often required in order to achieve good performance. This leads to high computational cost in both generating the nonlinear maps, and in the subsequent learning and prediction process. In this work, we propose to optimize the nonlinear maps directly with respect to the classification objective in a data-dependent fashion. The proposed approach achieves kernel approximation and kernel learning in a joint framework. This leads to much more compact maps without hurting the performance. As a by-product, the same framework can also be used to achieve more compact kernel maps to approximate a known kernel. We also introduce Circulant Nonlinear Maps, which uses a circulant-structured projection matrix to speed up the nonlinear maps for high-dimensional data.


title: Elastosis and response to endocrine therapy in human breast cancer

abstract: Response to endocrine therapy in 51 patients with advanced breast cancer was compared with the amount of elastosis in histological sections from their primary tumours. There appeared to be an association between elastosis and response: tumours with no elastosis showed a lower rate of response than those with gross elastosis, indicating that this simple method might provide a useful predictive index for response to endocrine therapy. In addition, tumours with oestrogen-receptor activity (a feature associated with a high rate of response) but with no elastosis were unlikely to respond, suggesting that a combination of the 2 predictive indices might be more valuable than either taken alone.


title: 1 SD Codes: Erasure Codes Designed for How Storage Systems Really Fail

abstract: Traditionally, when storage systems employ erasure codes, they are designed to tolerate the failures of entire disks. However, the most common types of failures are latent sector failures, which only affect individual disk sectors, and block failures which arise through wear on SSD's. This paper introduces SD codes, which are designed to tolerate combinations of disk and sector failures. As such, they consume far less storage resources than traditional erasure codes. We specify the codes with enough detail for the storage practitioner to employ them, discuss their practical properties, and detail an open-source implementation.


title: Speech synthesis techniques. A survey

abstract: The goal of this paper is to provide a short but a comprehensive overview of Text-To-Speech synthesis by highlighting its digital signal processing component. First two rule-based synthesis techniques (formant synthesis and articulatory synthesis) are explained then the concatenative synthesis is explored. Concatenative synthesis is simpler than rule-based synthesis, since there is no need to determine speech production rules. However, it introduces the challenges of prosodic modification to speech units and resolving discontinuities at unit boundaries. Prosodic modification results in artifacts in the speech that make the speech sound unnatural. Unit selection synthesis, which is a kind of concatenative synthesis, solves this problem by storing numerous instances for each unit with varying prosodies. The unit that best matches the target prosody is selected and concatenated. To resolve mismatches speech synthesis system combines the unit-selection method with Harmonic plus Noise Model (HNM). This model represents speech signal as a sum of a harmonic and noise part. The decomposition of speech signal into these two parts enables more natural sounding modifications of the signal. Finally Hidden Markov model(HMM) synthesis combined with an HNM model is introduced in order to obtain a Text-To-Speech system that requires smaller development time and cost.


title: A soft robotic exoskeleton utilizing granular jamming

abstract: This paper presents the development and design of an exoskeleton for the hand, utilising a physical phenomena known as granular jamming, where pressure within a soft membrane is varied to cause a granular material to change from a solid to fluid-like state. Experimentation and simulation is performed to discover and analyse the relationship between chamber size and the generation of resistive forces or bending stiffness, and to observe the general behaviour of granular beam systems under bending forces. A prototype was developed and tested. It was found that increasing the chamber size of a jammed granular beam increases the generated resistive forces. There also appears to be two regions of bending for a jammed granular beam identified by a change in gradient on a graph of deflection versus weight. This indicates a change in the granular structure during bending and is theorised to be caused by two mechanisms, buckling and longitudinal strain. The prototype indicates that the exoskeleton is functional and is able to stabilise the hand, but fails to provide significant force. The work done in this project can be used as a foundation for further design and investigation.


title: A 22nm SoC platform technology featuring 3-D tri-gate and high-k/metal gate, optimized for ultra low power, high performance and high density SoC applications

abstract: A leading edge 22nm 3-D tri-gate transistor technology has been optimized for low power SoC products for the first time. Low standby power and high voltage transistors exploiting the superior short channel control, < 65mV/dec subthreshold slope and <40mV DIBL, of the Tri-Gate architecture have been fabricated concurrently with high speed logic transistors in a single SoC chip to achieve industry leading drive currents at record low leakage levels. NMOS/PMOS Idsat=0.41/0.37mA/um at 30pA/um Ioff, 0.75V, were used to build a low standby power 380Mb SRAM capable of operating at 2.6GHz with 10pA/cell standby leakages. This technology offers mix-and-match flexibility of transistor types, high-density interconnect stacks, and RF/mixed-signal features for leadership in mobile, handheld, wireless and embedded SoC products.


A Deep Learning Approach to Identify Diabetes


title: Anthelmintic Resistance in Haemonchus contortus: History, Mechanisms and Diagnosis.

abstract: Haemonchus contortus has shown a great ability to develop resistance to anthelmintic drugs. In many instances, resistance has appeared less than 10years after the introduction of a new drug class. Field populations of this species now show resistance to all major anthelmintic drug classes, including benzimidazoles (BZs), imidazothiazoles and macrocyclic lactones. In addition, resistance to the recently introduced amino-acetonitrile derivative class (monepantel) has already been reported. The existence of field populations showing resistance to all three major drug classes, and the early appearance of resistance to monepantel, threatens the sustainability of sheep and goat production systems worldwide. This chapter reviews the history of the development of resistance to the various anthelmintics in H. contortus and examines the mechanisms utilized by this species to resist the effects of these drugs. Some of these mechanisms are well understood, particularly for BZ drugs, while our knowledge and understanding of others are increasing. Finally, we summarize methods available for the diagnosis of resistance. While such diagnosis currently relies largely on the faecal egg count reduction test, which suffers from issues of expense and sensitivity, we describe past and current efforts to utilize cheaper and less laborious phenotypic assays with free-living life stages, and then describe progress on the development of molecular assays to provide sensitive resistance-detection tests.


title: A moving target DDoS defense mechanism

abstract: In this paper, we introduce a moving target defense mechanism that defends authenticated clients against Internet service DDoS attacks. Our mechanism employs a group of dynamic, hidden proxies to relay traffic between authenticated clients and servers. By continuously replacing attacked proxies with backup proxies and reassigning (shuffling) the attacked clients onto the new proxies, innocent clients are segregated from malicious insiders through a series of shuffles. To accelerate the process of insider segregation, we designed an efficient greedy algorithm which is proven to have near optimal empirical performance. In addition, the insider quarantine capability of this greedy algorithm is studied and quantified to enable defenders to estimate the resource required to defend against DDoS attacks and meet defined QoS levels under various attack scenarios. Simulations were then performed which confirmed the theoretical results and showed that our mechanism is effective in mitigating the effects of a DDoS attack. The simulations also demonstrated that the overhead introduced by the shuffling procedure is low.


The Facebook Paradox: Effects of Facebooking on Individuals’ Social Relationships and Psychological Well-Being


title: Design of the Artificial: lessons from the biological roots of general intelligence

abstract: Our desire and fascination with intelligent machines dates back to the antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought (syllogism) and Heron of Alexandria's mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.


title: Hippocampal Amnesia

abstract: This article reviews 147 cases of amnesia following damage including the hippocampus or fornix as reported in 179 publications. The aetiology, mnestic abilities and reference(s) are tabulated for each case. Consistent findings across cases include the association of bilateral hippocampal damage with a deficit in anterograde episodic memory combined with spared procedural and working memory. The limited nature of retrograde amnesia following lesions to the fornix is also noted. Less consistent and thus more controversial findings, include effects of lesion size or laterality, deficits in semantic memory or familiarity-based recognition and the extent of retrograde amnesia. The evidence concerning these issues is reviewed across cases.


title: Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan

abstract: The 2016 Kumamoto earthquake sequence was triggered by an M w 6.2 event at 21:26 on 14 April. Approximately 28 h later, at 01:25 on 16 April, an M w 7.0 event (the mainshock) followed. The epicenters of both events were located near the residential area of Mashiki and affected the region nearby. Due to very strong seismic ground motion, the earthquake produced extensive damage to buildings and infrastructure. In this paper, collapsed buildings were detected using a pair of digital surface models (DSMs), taken before and after the 16 April mainshock by airborne light detection and ranging (lidar) flights. Different methods were evaluated to identify collapsed buildings from the DSMs. The change in average elevation within a building footprint was found to be the most important factor. Finally, the distribution of collapsed buildings in the study area was presented, and the result was consistent with that of a building damage survey performed after the earthquake.


title: MalClassifier: Malware family classification using network flow sequence behaviour

abstract: Anti-malware vendors receive daily thousands of potentially malicious binaries to analyse and categorise before deploying the appropriate defence measure. Considering the limitations of existing malware analysis and classification methods, we present MalClassifier, a novel privacy-preserving system for the automatic analysis and classification of malware using network flow sequence mining. MalClassifier allows identifying the malware family behind detected malicious network activity without requiring access to the infected host or malicious executable reducing overall response time. MalClassifier abstracts the malware families' network flow sequence order and semantics behaviour as an n-flow. By mining and extracting the distinctive n-flows for each malware family, it automatically generates network flow sequence behaviour profiles. These profiles are used as features to build supervised machine learning classifiers (K-Nearest Neighbour and Random Forest) for malware family classification. We compute the degree of similarity between a flow sequence and the extracted profiles using a novel fuzzy similarity measure that computes the similarity between flows attributes and the similarity between the order of the flow sequences. For classifier performance evaluation, we use network traffic datasets of ransomware and botnets obtaining 96% F-measure for family classification. MalClassifier is resilient to malware evasion through flow sequence manipulation, maintaining the classifier's high accuracy. Our results demonstrate that this type of network flow-level sequence analysis is highly effective in malware family classification, providing insights on reoccurring malware network flow patterns.


Glucose-6-phosphate dehydrogenase deficiency in Pakistani males.


title: A Novel Compact Printed Rectenna for Data Communication Systems

abstract: A novel design of a two-port printed microstrip rectenna with compact structure for communication systems is presented. An aperture-coupled dual polarization patch antenna is utilized as the receiving antenna. The vertical feed port receives the microwave energy and transfers it to the rectifying circuit for dc power generation, while the horizontal feed port is used for data communication, with high isolation between the two ports. This patch antenna includes harmonic suppression functionality, which is essential for high microwave—direct current (mw-dc) conversion efficiency. A co-simulation procedure using HFSS and ADS for the analysis of the rectenna and the rectifying circuit design is used. This design assumes low input power for the rectifying circuit to comply with safety standards. A mw-dc conversion efficient of 63% is measured with a 900 $\Omega$ load, 5.78 GHz operation frequency, and 25 mW receiving power. For the communication port, the measured reflection coefficient is $-$ 18 dB at 6.1 GHz center frequency, the gain is 7.0 dBi, and the cross polarization in the broadside direction is $-$ 15 dB.


Reinforcement Learning for Problems with Hidden State


title: Formalizing ETL Jobs forIncremental Loading of Data Warehouses

abstract: Extract-transform-load (ETL) tools are primarily designed for data ware- house loading, i.e. to perform physical data integration. When the operational data sources happen to change, the data warehouse gets stale. To ensure data timeliness, the data warehouse is refreshed on a periodical basis. The naive approach of simply reloading the data warehouse is obviously inefficient. Typically, only a small fraction of source data is changed during loading cycles. It is therefore desirable to capture these changes at the operational data sources and refresh the data warehouse incre- mentally. This approach is known as incremental loading. Dedicated ETL jobs are required to perform incremental loading. We are not aware of any ETL tool that helps to automate this task. In fact, incremental load jobs are handcrafted by ETL program- mers so far. The development is thus costly and error-prone. In this paper we present an approach to the automated derivation of incremental load jobs based on equational reasoning. We review existing Change Data Capture techniques and discuss limitations of different approaches. We further review existing loading facilities for data warehouse refreshment. We then provide transformation rules for the derivation of incremental load jobs. We stress that the derived jobs rely on existing Change Data Capture techniques, existing loading facilities, and existing ETL execution platforms.


title: Recent Developments in Total Variation Image Restoration

abstract: Since their introduction in a classic paper by Rudin, Osher and Fatemi [26], total variation minimizing models have become one of the most popular and successful methodology for image restoration. More recently, there has been a resurgence of interest and exciting new developments, some extending the applicabilities to inpainting, blind deconvolution and vector-valued images, while others offer improvements in better preservation of contrast, geometry and textures, in ameliorating the staircasing effect, and in exploiting the multiscale nature of the models. In addition, new computational methods have been proposed with improved computational speed and robustness. We shall review some of these recent developments.


title: Bubble rap: social-based forwarding in delay tolerant networks

abstract: In this paper we seek to improve our understanding of human mobility in terms of social structures, and to use these structures in the design of forwarding algorithms for Pocket Switched Networks (PSNs). Taking human mobility traces from the real world, we discover that human interaction is heterogeneous both in terms of hubs (popular individuals) and groups or communities. We propose a social based forwarding algorithm, BUBBLE, which is shown empirically to improve the forwarding efficiency significantly compared to oblivious forwarding schemes and to PROPHET algorithm. We also show how this algorithm can be implemented in a distributed way, which demonstrates that it is applicable in the decentralised environment of PSNs.


title: An EEG-based brain-computer interface for cursor control ☆

abstract: Abstract This study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8–12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distribution of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.


title: Combined Phase-Shift and Frequency Modulation of a Dual-Active-Bridge AC–DC Converter With PFC

abstract: This paper presents a combined phase-shift and frequency modulation scheme of a dual–active-bridge (DAB) ac–dc converter with power factor correction (PFC) to achieve zero voltage switching (ZVS) over the full range of the ac mains voltage. The DAB consists of a half bridge with bidirectional switches on the ac side and a full bridge on the dc side of the isolation transformer to accomplish single-stage power conversion. The modulation scheme is described by means of analytical formulas, which are used in an optimization procedure to determine the optimal control variables for minimum switch commutation currents. Furthermore, an ac current controller suitable for the proposed modulation scheme is described. A loss model and measurements on a 3.3-kW electric vehicle battery charger to connect to the 230 $\text{V}_\text{rms}$ / 50-Hz mains considering a battery voltage range of 280–420 V validate the theoretical analysis.


title: A 868MHz CMOS RF-DC power converter with −17dBm input power sensitivity and efficiency higher than 40% over 14dB input power range

abstract: In this paper we present a novel CMOS RF-DC converter circuit, operating at 868MHz, for RFID and remote powering applications. The novel reconfigurable architecture of the converter allows the circuit to operate over a very wide input power range with very high efficiency compared with previous art works. Prototypes realized in STM 0.130μm CMOS technology provide a regulated output voltage ∼2V with a −17dBm input power sensitivity. The circuit efficiency, higher than 40% over a 14dB input power range, peaks at 60%.


title: OS-Independent Live Migration Scheme for Bare-Metal Clouds

abstract: Bare-metal clouds are an emerging and attractive platform for cloud users who demand extreme computer performance. Bare-metal clouds lease physical machines rather than virtual machines, eliminating a virtualization overhead and providing maximum computer hardware performance. Therefore, bare-metal clouds are suitable for applications that require intensive, consistent, and predictable performance, such as big-data and high-performance computing applications. Unfortunately, existing bare-metal clouds do not support live migration because they lack virtualization layers. Live migration is an essential feature for bare-metal cloud vendors to perform proactive maintenance and fault tolerance that can avoid long user application downtime when underlying physical hardware is about to fail. Existing live migration approaches require either a virtualization overhead or OS-dependence and are therefore unsuitable for bare-metal clouds. This paper introduces an OS-independent live migration scheme for bare-metal clouds. We utilize a very thin hypervisor layer that does not virtualize hardware and directly exposes physical hardware to a guest OS. During live migration, the hypervisor carefully monitors and controls access to physical devices to capture, transfer, and restore the device states while the guest OS is still controlling the devices. After live migration, the hypervisor does almost nothing to eliminate the virtualization overhead and provide bare-metal performance for the guest OS. Experimental results confirmed that network performance of our system was comparable with that of bare-metal machines.


title: Adversarial synthesis learning enables segmentation without target modality ground truth

abstract: A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging modalities or segmenting abnormal organs from distinct disease cohorts. The manual efforts can be alleviated if one is able to reuse manual labels from one modality (e.g., MRI) to train a segmentation network for a new modality (e.g., CT). Previously, two stage methods have been proposed to use cycle generative adversarial networks (CycleGAN) to synthesize training images for a target modality. Then, these efforts trained a segmentation network independently using synthetic images. However, these two independent stages did not use the complementary information between synthesis and segmentation. Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT. The end-to-end EssNet achieved significantly higher median Dice similarity coefficient (0.9188) than the two stages strategy (0.8801), and even higher than canonical multi-atlas segmentation (0.9125) and ResNet method (0.9107), which used the CT manual labels.


title: Crop Selection Method to maximize crop yield rate using machine learning technique

abstract: Agriculture planning plays a significant role in economic growth and food security of agro-based country. Se- lection of crop(s) is an important issue for agriculture planning. It depends on various parameters such as production rate, market price and government policies. Many researchers studied prediction of yield rate of crop, prediction of weather, soil classification and crop classification for agriculture planning using statistics methods or machine learning techniques. If there is more than one option to plant a crop at a time using limited land resource, then selection of crop is a puzzle. This paper proposed a method named Crop Selection Method (CSM) to solve crop selection problem, and maximize net yield rate of crop over season and subsequently achieves maximum economic growth of the country. The proposed method may improve net yield rate of crops.


title: Promotion of plant growth by ACC deaminase-producing soil bacteria

abstract: Plant growth-promoting bacteria that contain the enzyme 1-aminocyclopropane-1-carboxylate (ACC) deaminase facilitate plant growth and development by decreasing plant ethylene levels, especially following a variety of environmental stresses. In this review, the physiological basis for this growth-promotion effect is examined in some detail. In addition, models are presented that endeavour to explain (i) the seemingly paradoxical effects of ethylene on a plant’s response to stress, (ii) how the expression of this enzyme is transcriptionally regulated in many bacterial strains and (iii) how ACC deaminase-containing plant growth-promoting bacteria alter plant gene expression and positively modulate plant growth.


title: Texture-based vanishing point voting for road shape estimation

abstract: Many rural roads lack sharp, smoothly curving edges and a homogeneous surface appearance, hampering traditional vision-based road-following methods. However, they often have strong texture cues parallel to the road direction in the form of ruts and tracks left by other vehicles. This paper describes an unsupervised algorithm for following ill-structured roads in which dominant texture orientations computed with Gabor wavelet filters vote for a consensus road vanishing point location. The technique is first described for estimating the direction of straight-road segments, then extended to curved and undulating roads by tracking the vanishing point indicated by a dierential “strip” of voters moving up toward the nominal vanishing line. Finally, the vanishing point is used to constrain a search for the road boundaries by maximizing texture- and color-based region discriminant functions. Results are shown for a variety of road scenes including gravel roads, dirt trails, and highways.


title: Constrained Low-Rank Learning Using Least Squares-Based Regularization

abstract: Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization. Most low-rank methods are incapable of capturing low-dimensional subspace for supervised learning tasks, e.g., classification and regression. This paper aims to learn both the discriminant low-rank representation (LRR) and the robust projecting subspace in a supervised manner. To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization. Naturally, the data label structure tends to resemble that of the corresponding low-dimensional representation, which is derived from the robust subspace projection of clean data by low-rank learning. Moreover, the low-dimensional representation of original data can be paired with some informative structure by imposing an appropriate constraint, e.g., Laplacian regularizer. Therefore, we propose a novel constrained LRR method. The objective function is formulated as a constrained nuclear norm minimization problem, which can be solved by the inexact augmented Lagrange multiplier algorithm. Extensive experiments on image classification, human pose estimation, and robust face recovery have confirmed the superiority of our method.


title: DropPruning for Model Compression

abstract: Deep neural networks (DNNs) have dramatically achieved great success on a variety of challenging tasks. However, most of the successful DNNs are structurally so complex, leading to much storage requirement and floating-point operation. This paper proposes a novel technique, named Drop Pruning, to compress the DNNs by pruning the weights from a dense high-accuracy baseline model without accuracy loss. Drop Pruning also falls into the standard iterative prune-retrain procedure, where a \emph{drop} strategy exists at each pruning step: \emph{drop out}, stochastic deleting some unimportant weights and \emph{drop in}, stochastic recovering some pruned weights. \emph{Drop out} and \emph{drop in} are supposed to handle the two drawbacks of the traditional pruning methods: local importance judgment and irretrievable pruning process, respectively. The suitable choosing of \emph{drop} probabilities can decrease the model size during pruning process and lead it to flow to the target sparsity. Drop Pruning also has some similar spirits with dropout, a stochastic algorithm in Integer Optimization and the Dense-Sparse-Dense training technique. Drop Pruning can significantly reducing overfitting while compressing the model. Experimental results demonstrates that Drop Pruning can achieve the state-of-the-art performance on many benchmark pruning tasks, about ${11.1\times}$ compression of VGG-16 on CIFAR10 and ${14.3\times}$ compression of LeNet-5 on MNIST without accuracy loss, which may provide some new insights into the aspect of model compression.


title: 1.5-V Operating fully-depleted amorphous oxide thin film transistors achieved by 63-mV/dec subthreshold slope

abstract: Thin film transistors (TFTs) with a small subthreshold slope (SS) are urgently required for low-voltage operating circuits on large-area and flexible substrates. Using InGaZnO deposited at room temperature (RT), we achieved 63 mV/dec, the smallest-ever SS reported for oxide semiconductor TFTs. To achieve the small SS as well as small Ioff, a fully-depleted off-state was employed by thinning the channel layer to 6 nm. Scalability down to L = 2 mum was confirmed. For Vg = 0 to 1.5 V operation, Ioff 108 were obtained.


title: A Machine Learning Model for Classifying Unsolicited IoT Devices by Observing Network Telescopes

abstract: The Internet of Things [IoT] promises to revolutionize the way we interact with our surroundings. Smart cars, smart cities, smart homes are now being realized with the help of various embedded devices that operate with little to no human interaction. However these embedded devices bring forth a plethora of security challenges as most manufacturers still assign higher importance to the three Ps (prototyping, production and performance) than security. This inherent flaw has manifested itself in the form of various Denial of Service (DoS) attacks orchestrated with the help of unsolicited IoT devices on the Internet. We are even seeing massive throughputs without the need for amplifications affecting large scale infrastructures on the Internet. Thus, understanding the nature of these attacks and quickly identifying infected devices becomes imperative to combat this situation. In this paper we present a model to classify unsolicited IoT devices in enterprises using machine learning (ML). Namely IP header information from darknet data is collected for analysis. We then consider multiple supervised ML algorithms to classify these Layer 3 headers. We evaluate these algorithms and compare their performances in terms of accurately identifying activities of malicious IoT devices on the Internet. Our results show that Random Forest and Gradient Boosting have high recall and precision scores whereas NaiveBayes has the worst performance. We believe our model can be used by enterprises as a part of their intrusion detection system to quickly identify infected IoT devices within their own environment as well as identify scanning activities directed towards them.


title: Going deeper than microscopy: the optical imaging frontier in biology

abstract: Optical microscopy has been a fundamental tool of biological discovery for more than three centuries, but its in vivo tissue imaging ability has been restricted by light scattering to superficial investigations, even when confocal or multiphoton methods are used. Recent advances in optical and optoacoustic (photoacoustic) imaging now allow imaging at depths and resolutions unprecedented for optical methods. These abilities are increasingly important to understand the dynamic interactions of cellular processes at different systems levels, a major challenge of postgenome biology. This Review discusses promising photonic methods that have the ability to visualize cellular and subcellular components in tissues across different penetration scales. The methods are classified into microscopic, mesoscopic and macroscopic approaches, according to the tissue depth at which they operate. Key characteristics associated with different imaging implementations are described and the potential of these technologies in biological applications is discussed.


title: Modeling dynamic social and psychological processes with fuzzy cognitive maps

abstract: A fuzzy cognitive map (FCM) is a graphical means of representing arbitrarily complex models of interrelated concepts, the implications of which can be calculated simply via matrix algebra. The paper discusses the use of FCMs for providing qualitative information about complex models in social and behavioral science research; specifically we show how FCMs can be used to reveal implications of models comprised of dynamic social and psychological processes. >


Monoidal Cut Strengthening and Generalized Mixed-Integer Rounding for Disjunctive Programs


title: Advanced Message Queuing Protocol

abstract: The advanced message queuing protocol (AMQP) working group's goal is to create an open standard for an interoperable enterprise-scale asynchronous messaging protocol. AMQP is finally addressing the lack of enterprise messaging interoperability standards. This relatively simple yet compellingly powerful enterprise messaging protocol is thus poised to open up a bright new era for enterprise messaging


title: Global Variational Method for Fingerprint Segmentation by Three-part Decomposition

abstract: Verifying an identity claim by fingerprint recognition is a commonplace experience for millions of people in their daily life, for example, for unlocking a tablet computer or smartphone. The first processing step after fingerprint image acquisition is segmentation, that is, dividing a fingerprint image into a foreground region which contains the relevant features for the comparison algorithm, and a background region. The authors propose a novel segmentation method by global three-part decomposition (G3PD). On the basis of global variational analysis, the G3PD method decomposes a fingerprint image into cartoon, texture and noise parts. After decomposition, the foreground region is obtained from the non-zero coefficients in the texture image using morphological processing. The segmentation performance of the G3PD method is compared with five state-of-the-art methods on a benchmark which comprises manually marked ground truth segmentation for 10,560 images. Performance evaluations show that the G3PD method consistently outperforms existing methods in terms of segmentation accuracy.


title: Event Specific Multimodal Pattern Mining for Knowledge Base Construction

abstract: Knowledge bases, which consist of a collection of entities, attributes, and the relations between them are widely used and important for many information retrieval tasks. Knowledge base schemas are often constructed manually using experts with specific domain knowledge for the field of interest. Once the knowledge base is generated then many tasks such as automatic content extraction and knowledge base population can be performed, which have so far been robustly studied by the Natural Language Processing community. However, the current approaches ignore visual information that could be used to build or populate these structured ontologies. Preliminary work on visual knowledge base construction only explores limited basic objects and scene relations. In this paper, we propose a novel multimodal pattern mining approach towards constructing a high-level "event" schema semi-automatically, which has the capability to extend text only methods for schema construction. We utilize a large unconstrained corpus of weakly-supervised image-caption pairs related to high-level events such as "attack" and "demonstration" to both discover visual aspects of an event, and name these visual components automatically. We compare our method with several state-of-the-art visual pattern mining approaches and demonstrate that our proposed method can achieve dramatic improvements in terms of the number of concepts discovered (33% gain), semantic consistence of visual patterns (52% gain), and correctness of pattern naming (150% gain).


title: Quantum support vector machine for big data classification

abstract: Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.


Kant and the Education of the Human Race


title: Grammatical Error Correction: Machine Translation and Classifiers

abstract: We focus on two leading state-of-the-art approaches to grammatical error correction – machine learning classification and machine translation. Based on the comparative study of the two learning frameworks and through error analysis of the output of the state-of-the-art systems, we identify key strengths and weaknesses of each of these approaches and demonstrate their complementarity. In particular, the machine translation method learns from parallel data without requiring further linguistic input and is better at correcting complex mistakes. The classification approach possesses other desirable characteristics, such as the ability to easily generalize beyond what was seen in training, the ability to train without human-annotated data, and the flexibility to adjust knowledge sources for individual error types. Based on this analysis, we develop an algorithmic approach that combines the strengths of both methods. We present several systems based on resources used in previous work with a relative improvement of over 20% (and 7.4 F score points) over the previous state-of-the-art.


title: Object Depth Estimation from a Single Image Using Fully Convolutional Neural Network

abstract: Convolutional Neural Network (CNN) has been used successfully in solving different computer vision tasks such as classification, detection, and segmentation. This paper addresses the problem of estimating object depth from a single RGB image. While stereo depth estimation is a straightforward task, predicting depth map of an object from a single RGB image is a more challenging task due to the lack of information from various image cues. Prior work focuses on exploiting geometric information or hand-crafted features. Also, they proposed the L2 norm to perform the optimization of a CNN during the training process. Using the L2 norm in regression tasks for optimization will bias the model. To address this problem, we propose a regression model with a fully convolutional neural network. To achieve robustness to outliers, we optimize the model using Tukey's biweight loss function, which is an M-estimator that is robust against outliers. The predictions are given by a single fully CNN without any post-processing techniques. In our experiments, we show that the quantitative and the qualitative results of using Tukey's biweight loss for optimization are better than of using L2 norm.


title: Wireless battery charging: E-bike application

abstract: Nowadays, Inductive Power Transfer (IPT) represents a widely investigated issue with respect to modern battery charging methods, by providing a wireless solution. IPT is applied across a large variety of applications, from Watt to kWatt power levels. Although IPT features great benefits in terms of safety and comfort, the most significant drawback consists of a relatively poor power conversion efficiency. In this paper, a 100W wireless charging equipment for E-bikes which improves efficiency is proposed. Complete magnetic structure design, as well as transmitter and receiver efficient architectures, are deeply exposed. The efficiency of the designed solution is shown by simulation results.


Losing One ’ s Mind Over Meaning : Analysing the Behaviour of English Possessive Idioms


title: Tamoxifen citrate for women with unexplained infertility

abstract: OBJECTIVE ::: To assess whether administration of tamoxifen citrate (TMX) to women with unexplained infertility is beneficial. The primary outcome was the clinical pregnancy rate. ::: ::: ::: STUDY DESIGN ::: In a prospective randomized trial, 66 consecutive women with unexplained infertility were recruited for the study. Thirty-six women received TMX at a 20-mg dosage and 30 women received no ovulation-induction drugs. ::: ::: ::: RESULTS ::: Fourteen patients in the TMX group stopped taking TMX, and observations were terminated because of antiestrogenic effects. The pregnancy rate (PR) per patient and the PR per cycle were significantly decreased (P < 0.005) in the TMX group than in the spontaneous group. Kaplan-Meier tests showed that the cumulative PR in the TMX group was significantly lower than in the spontaneous group (P < 0.05). Ten of 14 patients who had stopped taking TMX became pregnant in spontaneous cycles. ::: ::: ::: CONCLUSIONS ::: Administration of tamoxifen to women with unexplained infertility is not efficacious in terms of increasing the clinical PR.


title: Reconfigurable PIFA antenna using RF MEMS switches

abstract: This paper presents the simulations, fabrication and measurement of a tunable planar inverted-F antenna (PIFA). The proposed PIFA antenna uses RF MEMS switches to modify the electrical length and the behavior of the antenna. Measurements show a tuning range from 1.4 GHz to 1.1 GHz depending on the state of the MEMS switches. Matching better than −9.5 dB down to −19 dB was obtained over the entire tuning range. The antenna has also been tested versus power handling, through an original ACPR (Adjacent Channel Power Ratio) measurement setup, showing excellent linearity up to 40 dBm.


title: Psychotic disorder NOS with heterotopia

abstract: Background : Heterotopia are believed to result from faulty neuronal migration. Formerly considered to be extremely rare, with the availability of MRI reports they have appeared more frequently. Case details : We describe a 42 year old man with episodic disinhibition (violent and sexual) with disorganized thinking and behaviour, and a 20 year history of a diagnosis of schizophrenia. Soon after admission there were vague paranoid delusions. Initially, the cognitive state was impossible to access, but cognitive deficit was later demonstrated using the Mini Mental State Examination (MMSE). Although there was no clear evidence of delirium, the MMSE score subsequently improved, as did the disinhibited behaviour. Results : CT revealed no abnormality. Brain MRI, however, revealed multiple bilateral subependymal heterotopia (SEH). Conclusion : It is possible for SEH to be associated with a schizophrenia-like syndrome. CT is not sufficient and MRI is required to exclude SEH.


What Actually Wins Soccer Matches: Prediction of the2011-2012 Premier League for Fun and Profit


title: Genetic Fuzzy based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions

abstract: Breakthroughs in genetic fuzzy systems, most notably the development of the Genetic Fuzzy Tree methodology, have allowed fuzzy logic based Artificial Intelligences to be developed that can be applied to incredibly complex problems. The ability to have extreme performance and computational efficiency as well as to be robust to uncertainties and randomness, adaptable to changing scenarios, verified and validated to follow safety specifications and operating doctrines via formal methods, and easily designed and implemented are just some of the strengths that this type of control brings. Within this white paper, the authors introduce ALPHA, an Artificial Intelligence that controls flights of Unmanned Combat Aerial Vehicles in aerial combat missions within an extreme-fidelity simulation environment. To this day, this represents the most complex application of a fuzzy-logic based Artificial Intelligence to an Unmanned Combat Aerial Vehicle control problem. While development is on-going, the version of ALPHA presented withinwas assessed by Colonel (retired)Gene Lee who described ALPHA as “the most aggressive, responsive, dynamic and credible AI (he’s) seen-to-date.” The quality of these preliminary results in a problem that is not only complex and rife with uncertainties but also contains an intelligent and unrestricted hostile force has significant implications for this type of Artificial Intelligence. This work adds immensely to the body of evidence that this methodology is an ideal solution to a very wide array of problems.


title: Optimized Microstrip Antenna Arrays for Emerging Millimeter-Wave Wireless Applications

abstract: Two compact planar antennas operating in the unlicensed 60 GHz frequency band are presented based on the physical layer specifications of IEEE 802.15.3c and ECMA 387 standards for different classes of wireless applications. Each antenna is an array of 2 × 2 microstrip antennas covering at least two channels of the 60 GHz spectrum. The first antenna is optimized to achieve the highest gain, while the second antenna is optimized to give the largest beamwidth. The maximum measured radiation gain of the first antenna is 13.2 dBi. The measured beamwidth and gain of the second antenna are 76° and 10.3 dBi, respectively. The areas of these two antenna are only 0.25 and 0.16 cm2. The variation of radiation gain of each antenna over the frequency range of 57-65 GHz is less than 1 dB.


title: ASSESSING WEB SITE USABILITY MEASUREMENT

abstract: Web evaluation has been used in decade to validate the web site to see how it performs. When analysing a web site, typical factors to be considered are the way the information is organi zed and presented, and how to access and navigate t he informative structure [1]. Usability evaluations evaluate the ease of use of a web site functions and see either the user can per form their tasks efficiently. This paper review existing usability standards and model s in determining an appropriate model for evaluatin g the usability of web site. Previous research are reviewed and comparison and analysis of existing usability model and identificat ion of usability criteria and characteristics for web site is made to identify th e attribute or characteristic that should be used i n evaluating web site.This study proposed an extension of the QUIM model as a basic model for usability model for a web site. Therefore , a set of guidelines to assist in determining design and usability


title: A Semantic QA-Based Approach for Text Summarization Evaluation

abstract: Many Natural Language Processing and Computational Linguistics applications involves the generation of new texts based on some existing texts, such as summarization, text simplification and machine translation. However, there has been a serious problem haunting these applications for decades, that is, how to automatically and accurately assess quality of these applications. In this paper, we will present some preliminary results on one especially useful and challenging problem in NLP system evaluation: how to pinpoint content differences of two text passages (especially for large pas-sages such as articles and books). Our idea is intuitive and very different from existing approaches. We treat one text passage as a small knowledge base, and ask it a large number of questions to exhaustively identify all content points in it. By comparing the correctly answered questions from two text passages, we will be able to compare their content precisely. The experiment using 2007 DUC summarization corpus clearly shows promising results.


title: Solving inverse kinematics of delta robot using ANFIS

abstract: The paper propose a methodology using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to solve inverse kinematics problem of Delta parallel robot. A five layer neural network of ANFIS is used to adjust input and output parameters of membership function in a fuzzy logic controller. The hybrid algorithm is used for training this network. In this algorithm, the least square estimation method is applied for the tuning of linear output membership function parameters and the error backpropagation method is used to tune the nonlinear input membership function parameters, it is possible to predict position end-effector exactly against inverse kinematics and reduce mathematical representation of the system. Virtual Reality (VR) is used to simulation trajectory of robot. Computer simulations with Matlab ver. 2013b conducted on 3DOF robot Delta shows the effectiveness of the approach.


title: CyberSAVe: situational awareness visualization for cyber security of smart grid systems

abstract: We offer algorithms and visualization techniques for cyber trust in a Smart Grid system. Cyber trust is evaluated in terms of a mathematical model consisting of availability, detection and false alarm trust values, as well as a model of predictability. We develop a prototype Cyber Situational Awareness Visualization (CyberSAVe) tool to visualize cyber trust. We provide Operational Decision Aids (ODAs) displayed in context with a SCADA management information. We define cyber trust metrics, which are calculated and displayed in real-time in the Metric Assessment System (MAS) of CyberSAVe. We demonstrate the use of trust combined with visualization of trust to detect various types of attacks on the Smart Grid.


title: Measurement of a complete HV IGBT I-V-characteristic up to the breakdown point

abstract: This paper describes how to measure the complete output characteristic of a high-voltage IGBT non-destructively up to the breakdown point and beyond. Hereby, a deep knowledge of the IGBT behaviour at high voltages and saturation currents is gained. To construct the complete characteristic, short-circuit and curve-tracer measurements are combined. The results are compared and recapitulated with semiconductor simulations of IGBT models fitted to experimental characteristics.


title: Antimicrobial and mutagenic properties of the root tubers of Gloriosa superba Linn. (Kalihari).

abstract: The methanolic, aqueous and petroleum ether extracts of the root tubers of Gloriosa superba were studied for antibacterial, antifungal and mutagenic activities. Results showed that the petroleum ether extracts were highly active against the Gram-negative bacteria, Escherichia coli, Proteus vulgaris and Salmonella typhi at 50 µg/ml and active at 1000 µg/ml against Gram-positive bacteria. All extracts strongly inhibited the growth of Aspergillus niger and Mucor at 1000 µg/ml and the spore germination of all the fungi at 500 µg/ml. The tubers of G. superba were found to possess mutagenic properties by Ames Salmonella mutagenicity test due to the presence of the colchicines.


title: Ananta: cloud scale load balancing

abstract: Layer-4 load balancing is fundamental to creating scale-out web services. We designed and implemented Ananta, a scale-out layer-4 load balancer that runs on commodity hardware and meets the performance, reliability and operational requirements of multi-tenant cloud computing environments. Ananta combines existing techniques in routing and distributed systems in a unique way and splits the components of a load balancer into a consensus-based reliable control plane and a decentralized scale-out data plane. A key component of Ananta is an agent in every host that can take over the packet modification function from the load balancer, thereby enabling the load balancer to naturally scale with the size of the data center. Due to its distributed architecture, Ananta provides direct server return (DSR) and network address translation (NAT) capabilities across layer-2 boundaries. Multiple instances of Ananta have been deployed in the Windows Azure public cloud with combined bandwidth capacity exceeding 1Tbps. It is serving traffic needs of a diverse set of tenants, including the blob, table and relational storage services. With its scale-out data plane we can easily achieve more than 100Gbps throughput for a single public IP address. In this paper, we describe the requirements of a cloud-scale load balancer, the design of Ananta and lessons learnt from its implementation and operation in the Windows Azure public cloud.


title: Diagnosis of Parasitic Diseases: Old and New Approaches

abstract: Methods for the diagnosis of infectious diseases have stagnated in the last 20-30 years. Few major advances in clinical diagnostic testing have been made since the introduction of PCR, although new technologies are being investigated. Many tests that form the backbone of the "modern" microbiology laboratory are based on very old and labour-intensive technologies such as microscopy for malaria. Pressing needs include more rapid tests without sacrificing sensitivity, value-added tests, and point-of-care tests for both high- and low-resource settings. In recent years, research has been focused on alternative methods to improve the diagnosis of parasitic diseases. These include immunoassays, molecular-based approaches, and proteomics using mass spectrometry platforms technology. This review summarizes the progress in new approaches in parasite diagnosis and discusses some of the merits and disadvantages of these tests.


title: Facial Landmark Tracking by Tree-Based Deformable Part Model Based Detector

abstract: In this paper we describe a tracker of facial landmarks submitted to the 300 Videos in the Wild (300-VW) challenge. Our tracker is a straightforward extension of a well tuned tree-based DPM landmark detector originally developed for static images. The tracker is obtained by applying the static detector independently in each frame and using the Kalman filter to smooth estimates of the face positions as well as to compensate possible failures of the face detector. The resulting tracker provides a robust estimate of 68 landmarks running at 5 fps on an ordinary PC. We provide an open-source implementation of the proposed tracker at (http://cmp.felk.cvut.cz/~uricamic/clandmark/).


title: Conspiracy Theories: Evolved Functions and Psychological Mechanisms

abstract: Belief in conspiracy theories-such as that the 9/11 terrorist attacks were an inside job or that the pharmaceutical industry deliberately spreads diseases-is a widespread and culturally universal phenomenon. Why do so many people around the globe believe conspiracy theories, and why are they so influential? Previous research focused on the proximate mechanisms underlying conspiracy beliefs but ignored the distal, evolutionary origins and functions. We review evidence pertaining to two competing evolutionary hypotheses: (a) conspiracy beliefs are a by-product of a suite of psychological mechanisms (e.g., pattern recognition, agency detection, threat management, alliance detection) that evolved for different reasons, or (b) conspiracy beliefs are part of an evolved psychological mechanism specifically aimed at detecting dangerous coalitions. This latter perspective assumes that conspiracy theories are activated after specific coalition cues, which produce functional counterstrategies to cope with suspected conspiracies. Insights from social, cultural and evolutionary psychology provide tentative support for six propositions that follow from the adaptation hypothesis. We propose that people possess a functionally integrated mental system to detect conspiracies that in all likelihood has been shaped in an ancestral human environment in which hostile coalitions-that is, conspiracies that truly existed-were a frequent cause of misery, death, and reproductive loss.


title: TheArchitect: A Serverless-Microservices Based High-level Architecture Generation Tool

abstract: Software is ubiquitous in today’s systems and business operations. Most importantly the architecture of a software system determines its quality and longevity, because the development work related to the software system will be carried out to be in line with its architecture design. Hence, it’s highly important to structure the high-level software architecture accordingly to deliver the expected customer requirements while accounting for quality measures such as scalability, high availability and high performance. We propose TheArchitect, a serverless-microservices based high-level architecture generation tool, which will auto generate serverless-microservices based high-level architecture for a given business application, preserving the highlighted quality measures providing a tool based support for the software architect with respect to designing the high-level architecture. TheArchitect will provide any software developer to generate a proper architecture minimizing the involvement of an experienced software architect. Furthermore, the positives that microservices and serverless technologies has brought to the world of software engineering has made the software engineering community shift from the era of building large monolith applications containing overly complex designs, to microservices and serverless based technologies. Hence TheArchitect focuses on generating best fitted microservices and serverless based high-level architecture for a given application.


title: A precision agriculture management system based on Internet of Things and WebGIS

abstract: This thesis is based on the application of Internet of Things (IoT) and WebGIS in precision agriculture. Through analyzing the current development of precision agriculture in China and considering its advantages and shortcomings, we choose an ecology farm as an example to conduct a new precision agriculture management system (PAMS) based on the above two techniques. We designed the four architectures of PAMS: the spatial information infrastructure platform, the IoT infrastructure platform, the agriculture management platform and the mobile client. Users can monitor and manage the agriculture production by PAMS. What's more, module integration method and open source software can help us to reduce the development cost and to improve the system efficiency.


title: Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture

abstract: Information and communication technologies (ICT) can be instrumental in progressing towards smarter city environments, which improve city services, sustainability, and citizens’ quality of life. Smart City software platforms can support the development and integration of Smart City applications. However, the ICT community must overcome current technological and scientific challenges before these platforms can be widely adopted. This article surveys the state of the art in software platforms for Smart Cities. We analyzed 23 projects concerning the most used enabling technologies, as well as functional and non-functional requirements, classifying them into four categories: Cyber-Physical Systems, Internet of Things, Big Data, and Cloud Computing. Based on these results, we derived a reference architecture to guide the development of next-generation software platforms for Smart Cities. Finally, we enumerated the most frequently cited open research challenges and discussed future opportunities. This survey provides important references to help application developers, city managers, system operators, end-users, and Smart City researchers make project, investment, and research decisions.


title: EsdRank: Connecting Query and Documents through External Semi-Structured Data

abstract: This paper presents EsdRank, a new technique for improving ranking using external semi-structured data such as controlled vocabularies and knowledge bases. EsdRank treats vocabularies, terms and entities from external data, as objects connecting query and documents. Evidence used to link query to objects, and to rank documents are incorporated as features between query-object and object-document correspondingly. A latent listwise learning to rank algorithm, Latent-ListMLE, models the objects as latent space between query and documents, and learns how to handle all evidence in a unified procedure from document relevance judgments. EsdRank is tested in two scenarios: Using a knowledge base for web search, and using a controlled vocabulary for medical search. Experiments on TREC Web Track and OHSUMED data show significant improvements over state-of-the-art baselines.


title: Neurocytoma of the pituitary gland: A case report and literature review

abstract: Extraventricular neurocytomas (EVN) are rare central nervous system tumors, often located within the cerebral hemisphere. The present study reports a case of a 56-year-old male patient with bitemporal hemianopsia. Computed tomography and magnetic resonance imaging revealed a tumor in the sellar region. The tumor was totally excised. Postoperative histological examination of the tumor demonstrated that synaptophysin, chromogranin-A and neuron-specific enolase were positive, while luteinizing hormone, follicle-stimulating hormone, growth hormone, prolactin, adrenocorticotropic hormone, thyroid-stimulating hormone, glial fibrillary acidic protein, S-100, nestin and epithelial membrane antigen were negative, which were the main pathological features of neurocytomas. This is the fourth case of EVN located in the sellar region reported. The associated studies are also reviewed.


title: Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms

abstract: The paper analyzes the scalability of multiobjective estimation of distribution algorithms (MOEDAs) on a class of boundedly-difficult additively-separable multiobjective optimization problems. The paper illustrates that even if the linkage is correctly identified, massive multimodality of the search problems can easily overwhelm the nicher and lead to exponential scale-up. Facetwise models are subsequently used to propose a growth rate of the number of differing substructures between the two objectives to avoid the niching method from being overwhelmed and lead to polynomial scalability of MOEDAs.


A Multitask Approach for Named Entity Recognition in Social Media Data


title: Effect of dietary supplementation of fructooligosaccharide (FOS) on growth performance, survival, lactobacillus bacterial population and hemato-immunological parameters of stellate sturgeon (Acipenser stellatus) juvenile.

abstract: The dietary supplementation of fructooligosaccharide (FOS) in stellate sturgeon juvenile, Acipenser stellatus (with mean initial body weight of 30.16 ± 0.14 g) was evaluated for the effect on growth, autochthonous intestinal microbiata and hemato-immunological parameters for 11 weeks. FOS was added at a level of 0, 1% and 2% to the commercial pellet diet (BioMar). At the end of the experiment, growth parameters, survival rate, lactobacillus bacterial population, hematological and immunological parameters were determined. The fish fed on 1% FOS significantly showed higher final weight, WG%, SGR and PER and lower FCR compared to those of the control group (P < 0.05). Survival rate did not significantly differ between the treatments (P > 0.05). However, FOS administration resulted in lower survival. The serum lysozyme activity was significantly affected by dietary 1% FOS (P < 0.05), while respiratory burst activity was not significantly affected by dietary treatments (P > 0.05). In fish fed on the diet with 1% FOS showed a significant increase of total heterotrophic autochthonous bacterial and presumptive LAB levels (P < 0.05) compared with those fed on the diets supplemented with prebiotics. In addition to increase in WBC, RBC, MCV, hematocrit, hemoglobin and lymphocyte levels were observed in this group. These results indicated that dietary supplementation of FOS at a dose of 1% improved growth performance, beneficial intestinal microbiata and stimulate immune response of stellate sturgeon juvenile.


title: A simple algorithm for object location from a single image without camera calibration

abstract: In this paper we propose a method for locating a point (or object) in space without any camera calibration or object model. Instead we assume that the 2D location of 4 coplanar points is known and that the unknown point lay on this plane. This is similar to computing the fundamental matrix linking pixel position in the image plane with another plane position in 3D space. A concrete example could be the accurate location of a car on a highway from an airplane or helicopter camera knowing the location of four milestones or the location of a man in a city from a surveillance camera knowing the location of four block corners. Other applications in domains such as photogrammetry, remote sensing or military reconnaissance are also possible just to name a few. The algorithm is illustrated and tested on real images and shows errors less than 2%.


title: T-Linkage: A Continuous Relaxation of J-Linkage for Multi-model Fitting

abstract: This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers. The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J-linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets.


title: Effect of Information and Communication Technology-induced Multitasking on Academic Performance of University Students in Uganda

abstract: This article can be retrieved directly from the journal site at http://ijcat.com/archives/volume6/issue1/ijcatr06011005.pdf


title: Component-Based Recognition of Facesand Facial Expressions

abstract: Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. In this paper, we propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm which benefits from the idea of sparsity and morphological diversity. This entails building data-driven dictionaries for neutral and expressive components. The DCS algorithm then uses these dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition. Experiments on publicly available expression and face data sets show the effectiveness of our method.


title: Probabilistic Inference on Twitter Data to Discover Suspicious Users and Malicious Content

abstract: While the power of social media on the Internet is undeniable, it has become a major weapon for launching cyberattacks against an organization and its people. Today, there is a growing number of cyberattacks being launched through social media such as posting of false content from hacked accounts, posting malicious URLs to spread malware, and others. In this paper, we present a simple and flexible unified framework called SocialKB for modeling social media posts and reasoning about them to ascertain their veracity, a first step towards discovering emerging cyber threats. SocialKB is based on Markov Logic Networks (MLNs), a popular representation in statistical relational learning. It learns a knowledge base (KB) on the social media posts and users' behavior in a unified manner. By conducting probabilistic inference on the KB, SocialKB can identify suspicious users and malicious content. In this work, we specifically focus on tweets posted by users on Twitter. Finally, we report an evaluation of SocialKB on 20,000 tweets and discuss our early inference results.


title: Fingerprint matrices: uncovering the dynamics of social networks in prose literature

abstract: In prose literature often complex dynamics of interpersonal relationships can be observed between the different characters. Traditionally, node-link diagrams are used to depict the social network of a novel. However, static graphs can only visualize the overall social network structure but not the development of the networks over the course of the story, while dynamic graphs have the serious problem that there are many sudden changes between different portions of the overall social network. In this paper we explore means to show the relationships between the characters of a plot and at the same time their development over the course of a novel. Based on a careful exploration of the design space, we suggest a new visualization technique called Fingerprint Matrices. A case study exemplifies the usage of Fingerprint Matrices and shows that they are an effective means to analyze prose literature with respect to the development of relationships between the different characters.


title: Lower-limb exoskeletons: Research trends and regulatory guidelines in medical and non-medical applications

abstract: With the recent progress in personal care robots, interest in wearable exoskeletons has been increasing due to the demand for assistive technologies generally and specifically to meet the concerns in the increasing ageing society. Despite this global trend, research focus has been on load augmentation for soldiers/workers, assisting trauma patients, paraplegics, spinal cord injured persons and for rehabilitation purposes. Barring the military-focused activities, most of the work to date has focused on medical applications. However, there is a need to shift attention towards the growing needs of elderly people, that is, by realizing assistive exoskeletons that can help them to stay independent and maintain a good quality of life. Therefore, the present article covers the rapidly evolving area of wearable exoskeletons in a holistic manner, for both medical and non-medical applications, so that relevant current developments and future issues can be addressed; this includes how the physical assistance/rehabil...


title: A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system.

abstract: BACKGROUND ::: Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. ::: ::: ::: MATERIALS AND METHODS ::: In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. ::: ::: ::: RESULTS ::: In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results. ::: ::: ::: CONCLUSION ::: The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.


title: Risk of venous thromboembolism with the angiogenesis inhibitor bevacizumab in cancer patients: a meta-analysis.

abstract: CONTEXT ::: Venous thromboembolism is one of the leading causes of morbidity and mortality in patients with cancer. Concerns have arisen regarding the risk of venous thromboembolism with the novel antiangiogenic agent bevacizumab, a recombinant humanized monoclonal antibody to vascular endothelial growth factor that is widely used in cancer treatment. Currently, the role of bevacizumab in venous thromboembolism is controversial. ::: ::: ::: OBJECTIVE ::: To assess the overall risk of venous thromboembolism associated with the use of bevacizumab, a systematic review and meta-analysis of published randomized controlled trials was performed. ::: ::: ::: DATA SOURCES ::: The databases of PubMed and Web of Science were searched for articles published in the English language from January 1966 until January 2008 and abstracts presented at American Society of Clinical Oncology conferences held between January 2000 and January 2008 were searched to identify relevant clinical trials. ::: ::: ::: STUDY SELECTION AND DATA EXTRACTION ::: Eligible studies included prospective randomized controlled trials in which standard antineoplastic therapy was used with and without bevacizumab and data on venous thromboembolism were available. Summary incidence rates, relative risks (RRs), and 95% confidence intervals (CIs) were calculated using random-effects or fixed-effects models based on the heterogeneity of included studies. ::: ::: ::: DATA SYNTHESIS ::: A total of 7956 patients with a variety of advanced solid tumors from 15 randomized controlled trials were identified and included for analysis. Among those patients receiving bevacizumab, the summary incidences of all-grade and high-grade venous thromboembolism were 11.9% (95% CI, 6.8%-19.9%) and 6.3% (95% CI, 4.8%-8.3%), respectively. Patients treated with bevacizumab had a significantly increased risk of venous thromboembolism with an RR of 1.33 (95% CI, 1.13-1.56; P < .001) compared with controls. The risk was significantly increased for both all-grade and high-grade venous thromboembolism. In addition, the risk was similarly increased for bevacizumab at 2.5 mg/kg per week (low dose; RR, 1.31 [95% CI, 1.08-1.60]; P = .007) and 5 mg/kg per week (high dose; RR, 1.31 [95% CI, 1.02-1.68]; P = .04). ::: ::: ::: CONCLUSION ::: The use of bevacizumab was significantly associated with an increased risk of developing venous thromboembolism in cancer patients receiving this drug.


title: Convex feasible set algorithm for constrained trajectory smoothing

abstract: Trajectory smoothing is an important step in robot motion planning, where optimization methods are usually employed. However, the optimization problem for trajectory smoothing in a clustered environment is highly non-convex, and is hard to solve in real time using conventional non-convex optimization solvers. This paper discusses a fast online optimization algorithm for trajectory smoothing, which transforms the original non-convex problem to a convex problem so that it can be solved efficiently online. The performance of the algorithm is illustrated in various cases, and is compared to that of conventional sequential quadratic programming (SQP). It is shown that the computation time is greatly reduced using the proposed algorithm.


title: A validated facial grading scale: the future of facial ageing measurement tools?

abstract: AbstractValidated, standardized scales for measurement of ageing and response to cosmetic procedures are lacking. Numerous scales have been published and, while they allow intra-study comparisons, almost all remain non-validated, and their heterogeneity makes comparisons across studies impossible. A set of validated, objective, quantitative scales has been developed in association with experts from different specialties within aesthetic medicine. These scales allow evaluation of the key signs of ageing that cause individuals to seek cosmetic procedures. Each scale is a five-point photonumeric scale based on computer-simulated photographs incorporating each aspect to be evaluated in a stepwise manner. Validation studies show that these scales have good intra- and inter-rater reliability, with intra-class correlation coefficients generally in the range 0.85–0.95, and similar test–retest correlations.


title: Mixed Methods Sampling : A Typology With Examples

abstract: This article presents a discussion of mixed methods (MM) sampling techniques. MM sampling involves combining well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs. Several issues germane to MM sampling are presented including the differences between probability and purposive sampling and the probability-mixed-purposive sampling continuum. Four MM sampling prototypes are introduced: basic MM sampling strategies, sequential MM sampling, concurrent MM sampling, and multilevel MM sampling. Examples of each of these techniques are given as illustrations of how researchers actually generate MM samples. Finally, eight guidelines for MM sampling are presented.


title: Retroperitoneal Castleman's tumors of hyaline vascular type: imaging study. Case report.

abstract: We report two retroperitoneal Castleman's tumors (giant lymph node hyperplasia) of hyaline vascular type. One was a large tumor situated in the pararenal region, the other was located in the pancreas head. On computed tomography (CT), both tumors were solid and moderately enhanced after administration of contrast material. On angiography, both tumors were hypervascular with dilated feeding arteries, and showed capillary blush. In an appropriate clinical setting, a retroperitoneal tumor showing these findings should include Castleman's tumor in the differential diagnosis.


title: Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration

abstract: In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's capabilities and partially adapt to the robot, i.e., they might change their actions based on the observed outcomes and the robot's actions, without replicating the robot's policy. We present a game-theoretic model of human partial adaptation to the robot, where the human responds to the robot's actions by maximizing a reward function that changes stochastically over time, capturing the evolution of their expectations of the robot's capabilities. The robot can then use this model to decide optimally between taking actions that reveal its capabilities to the human and taking the best action given the information that the human currently has. We prove that under certain observability assumptions, the optimal policy can be computed efficiently. We demonstrate through a human subject experiment that the proposed model significantly improves human-robot team performance, compared to policies that assume complete adaptation of the human to the robot.


title: Neural Variational Inference for Text Processing

abstract: Recent advances in neural variational inference have spawned a renaissance in deep latent variable models. In this paper we introduce a generic variational inference framework for generative and conditional models of text. While traditional variational methods derive an analytic approximation for the intractable distributions over latent variables, here we construct an inference network conditioned on the discrete text input to provide the variational distribution. We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering. Our neural variational document model combines a continuous stochastic document representation with a bag-of-words generative model and achieves the lowest reported perplexities on two standard test corpora. The neural answer selection model employs a stochastic representation layer within an attention mechanism to extract the semantics between a question and answer pair. On two question answering benchmarks this model exceeds all previous published benchmarks.


title: Wearable augmented reality system using gaze interaction

abstract: Undisturbed interaction is essential to provide immersive AR environments. There have been a lot of approaches to interact with VEs (virtual environments) so far, especially in hand metaphor. When the userpsilas hands are being used for hand-based work such as maintenance and repair, necessity of alternative interaction technique has arisen. In recent research, hands-free gaze information is adopted to AR to perform original actions in concurrence with interaction. [3, 4]. There has been little progress on that research, still at a pilot study in a laboratory setting. In this paper, we introduce such a simple WARS (wearable augmented reality system) equipped with an HMD, scene camera, eye tracker. We propose dasiaAgingpsila technique improving traditional dwell-time selection, demonstrate AR gallery - dynamic exhibition space with wearable system.


title: Robust Tracking Control of Optical Disk Recording System based on Sudden Disturbance Observer

abstract: In optical recording system, the tracking servo system must follow the track of optical disk against the influence of the track eccentricity and the force disturbance. The influence of the track eccentricity becomes periodic disturbance. The influence of force disturbance caused by shaking is treated as sudden disturbance. The conventional tracking servo system is constructed by using the only feedback control structure, Therefore, it is difficult to suppress the influence of both periodic disturbance and sudden disturbance. This paper proposes a new structure of robust tracking servo system, which suppresses the both periodic disturbance and the sudden disturbance, respectively. In order to suppress the periodic disturbance quickly, this paper designs the ZPET feedforward servo system with prediction of tracking error. The multi rate sudden disturbance observer estimates the only sudden disturbance, more accurately. The experimental results point out that the proposed tracking servo system has a precise tracking response against both periodic disturbance and sudden disturbance.


title: Asthma and child behavioral skills: does family socioeconomic status matter?

abstract: Asthma is associated with poorer behavioral and psychological outcomes in children, yet little is known about whether and how the social stratification process affects the impacts of asthma on children's outcomes. Using data from the Early Childhood Longitudinal Study-Birth Cohort, this study considered the role of socioeconomic status in shaping the developmental consequences of children's asthma. Results showed that asthma was negatively associated with attention and social competence and positively associated with externalizing problem behaviors for children with low-educated mothers and children who lived in poor households. However, the adverse consequences of asthma disappeared for children with high-educated mothers and children who did not experience poverty. Additionally, the socioeconomic disparities were not fully explained by healthcare resources, family process, and exposure to environment risks and the disparities were found for both mild and severe cases. These findings suggest that, to fully understand the developmental consequences of illness in children, it is important to place socioeconomic status at the center of investigation.


title: A study on evolution of email spam over fifteen years

abstract: With the increasing dedication and sophistication of spammers, email spam is a persistent problem even today. Popular social network sites such as Facebook, Twitter, and Google+ are not exempt from email spam as they all interface with email systems. While some report predicts that email spam business is dying due to the decreasing volume of email spam. Whether email spam business is really dying is an interesting question. In this paper, we analyze email spam trends on Spam Archive dataset, which contains 5.5 million spam emails over 15 years (1998 – 2013). We statistically analyze emails contents including header information (e.g. content type) and embedded items (e.g. URL links). Also, we investigate topic drift using topic modeling technique. Moreover, we perform network analysis on sender-to-receiver IP routing networks. Our study shows the dynamic nature of email spam over one and a half decades and demonstrate that the email spam business is not dying but more capricious. Received on 21 January 2014; accepted on 11 April 2014; published on 27 May 2014


title: The effects of personality type on engineering student performance and attitudes

abstract: The Myers-Briggs Type Indicator ® (MBTI) was administered to a group of 116 students taking the introductory chemical engineering course at North Carolina State University. That course and four subsequent chemical engineering courses were taught in a manner that emphasized active and cooperative learning and inductive presentation of course material. Type differences in various academic performance measures and attitudes were noted as the students progressed through the curriculum. The observations were generally consistent with the predictions of type theory, and the experimental instructional approach appeared to improve the performance of MBTI types (extraverts, sensors, and feelers) found in previous studies to be disadvantaged in the engineering curriculum. The conclusion is that the MBTI is a useful tool for helping engineering instructors and advisors to understand their students and to design instruction that can benefit students of all types.


title: Learning and Reasoning in Cognitive Radio Networks

abstract: Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.


title: Fault Diagnosis with Missing Data Based on Hopfield Neural Networks

abstract: Most of the existing artificial neural network models use a significant amount of information for their training. The need for such information could be an inconvenience for its application in fault diagnosis in industrial systems, where the information, due to different factors such as data losses in the data acquisition systems, is scarce or not verified. In this chapter, a diagnostic system based on a Hopfield neural network is proposed to overcome this inconvenience. The proposal is tested using the development and application of methods for the actuator diagnostic in industrial control systems (DAMADICS) benchmark, with successful performance.


title: Abstract Data mining for yield enhancement in semiconductor manufacturing and an empirical study

abstract: During wafer fabrication, process data, equipment data, and lot history will be automatically or semi-automatically recorded and accumulated in database for monitoring the process, diagnosing faults, and managing manufacturing. However, in high-tech industry such as semiconductor manufacturing, many factors that are interrelated affect the yield of fabricated wafers. Engineers who rely on personal domain knowledge cannot find possible root causes of defects rapidly and effectively. This study aims to develop a framework for data mining and knowledge discovery from database that consists of a Kruskal-Wallis test, K-means clustering, and the variance reduction splitting criterion to investigate the huge amount of semiconductor manufacturing data and infer possible causes of faults and manufacturing process variations. The extracted information and knowledge is helpful to engineers as a basis for trouble shooting and defect diagnosis. We validated this approach with an empirical study in a semiconductor foundry company in Taiwan and the results demonstrated the practical viability of this approach.


title: A survey of techniques and open-source tools for processing streams of spatio-temporal events

abstract: With the rise of location-aware IoT devices, there is an increased desire to process data streams in a real-time manner. Responding to such streams may require processing data from multiple streams to inform decisions. There are many uses cases for putting the location data from the sensors or an analytic derivative on a map for a live view of sensors or other assets. Here we describe an architecture which relies solely on free and open-source components to provide streaming spatio-temporal event processing, analysis, and near-real-time visualization.


title: An FPGA Design Framework for CNN Sparsification and Acceleration

abstract: Convolutional neural networks (CNNs) have recently broken many performance records in image recognition and object detection problems. The success of CNNs, to a great extent, is enabled by the fast scaling-up of the networks that learn from a huge volume of data. The deployment of big CNN models can be both computation-intensive and memory-intensive, leaving severe challenges to hardware implementations. In recent years, sparsification techniques that prune redundant connections in the networks while still retaining the similar accuracy emerge as promising solutions to alliterate the computation overheads associated with CNNs [1]. However, imposing sparsity in CNNs usually generates random network connections and thus, the irregular data access pattern results in poor data locality. The low computation efficiency of the sparse networks, which is caused by the incurred unbalance in computing resource consumption and low memory bandwidth usage, significantly offsets the theocratical reduction of the computation complexity and limits the execution scalability of CNNs on general- purpose architectures [2]. For instance, as an important computation kernel in CNNs – the sparse convoluation, is usually accelerated by using data compression schemes where only nonzero elements of the kernel weights are stored and sent to multiplication-accumulation computations (MACs) at runtime. However, the relevant executions on CPUs and GPUs reach only 0.1% to 10% of the system peak performance even designated software libraries are applied (e.g., MKL library for CPUs and cuSPARSE library for GPUs). Field programmable gate arrays (FPGAs) have been also extensively studied as an important hardware platform for CNN computations [3]. Different from general-purpose architectures, FPGA allows users to customize the functions and organization of the designed hardware in order to adapt various resource needs and data usage patterns. This characteristic, as we identified in this work, can be leveraged to effectively overcome the main challenges in the execution of sparse CNNs through close coordinations between software and hardware. In particular, the reconfigurability of FPGA helps to 1) better map the sparse CNN onto the hardware for improving computation parallelism and execution efficiency and 2) eliminate the computation cost associated with zero weights and enhance data reuse to alleviate the adverse impacts of the irregular data accesses. In this work, we propose a hardware-software co-design framework to address the above challenges in sparse CNN accelerations. First, we introduce a data locality-aware sparsification scheme that optimizes the structure of the sparse CNN during training phase to make it friendly for hardware mapping. Both memory allocation and data access regularization are considered in the optimization process. Second, we develop a distributed architecture composed of the customized processing elements (PEs) that enables high computation parallelism and data reuse rate of the compressed network. Moreover, a holistic sparse optimization is introduced to our design framework for hardware platforms with different requirement. We evaluate our proposed frame- work by executing AlexNet on Xilinx Zynq ZC706. Our FPGA accelerator obtains a processing power of 71.2 GOPS, corresponding to 271.6 GOPS on the dense CNN model. On average, our FPGA design runs 11.5× faster than a well- tuned CPU implementation on Intel Xeon E5-2630, and has 3.2× better energy efficiency over the GPU realization on Nvidia Pascal Titan X. Compared to state-of-the-art FPGA designs [4], our accelerator reduces the classification time by 2.1×, with


title: HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics

abstract: As data sets grow and conventional processor performance scaling slows, data analytics move towards heterogeneous architectures that incorporate hardware accelerators (notably GPUs) to continue scaling performance. However, existing GPU-based databases fail to deal with big data applications efficiently: their execution model suffers from scalability limitations on GPUs whose memory capacity is limited; existing systems fail to consider the discrepancy between fast GPUs and slow storage, which can counteract the benefit of GPU accelerators. ::: ::: In this paper, we propose HippogriffDB, an efficient, scalable GPU-accelerated OLAP system. It tackles the bandwidth discrepancy using compression and an optimized data transfer path. HippogriffDB stores tables in a compressed format and uses the GPU for decompression, trading GPU cycles for the improved I/O bandwidth. To improve the data transfer efficiency, HippogriffDB introduces a peer-to-peer, multi-threaded data transfer mechanism, directly transferring data from the SSD to the GPU. HippogriffDB adopts a query-over-block execution model that provides scalability using a stream-based approach. The model improves kernel efficiency with the operator fusion and double buffering mechanism. ::: ::: We have implemented HippogriffDB using an NVMe SSD, which talks directly to a commercial GPU. Results on two popular benchmarks demonstrate its scalability and efficiency. HippogriffDB outperforms existing GPU-based databases (YDB) and in-memory data analytics (MonetDB) by 1-2 orders of magnitude.


title: Anxiety Reduction through Detachment: Subjective, Physiological, and Neural Effects

abstract: The ability to volitionally regulate emotions helps to adapt behavior to changing environmental demands and can alleviate subjective distress. We show that a cognitive strategy of detachment attenuates subjective and physiological measures of anticipatory anxiety for pain and reduces reactivity to receipt of pain itself. Using functional magnetic resonance imaging, we locate the potential site and source of this modulation of anticipatory anxiety in the medial prefrontal/anterior cingulate and anterolateral prefrontal cortex, respectively.


title: How to Build Complete Creatures Rather than Isolated Cognitive Simulators

abstract: Artificial Intelligence as a discipline has gotten bogged down in subproblems of intelligence. These subproblems are the result of applying reductionist methods to the goal of creating a complete artificial thinking mind. In Brooks (1987) 1 have argued that these methods will lead us to solving irrelevant problems; interesting as intellectual puzzles, but useless in the long run for creating an artificial being.


title: Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective

abstract: This paper addresses the task of dense non-rigid structure-front-motion (NRSfM) using multiple images. State-of-the-art methods to this problem are often hurdled by scalability, expensive computations, and noisy measurements. Further, recent methods to NRSfM usually either assume a small number of sparse feature points or ignore local non-linearities of shape deformations, and thus cannot reliably model complex non-rigid deformations. To address these issues, in this paper, we propose a new approach for dense NRSfM by modeling the problem on a Grassmann manifold. Specifically, we assume the complex non-rigid deformations lie on a union of local linear subspaces both spatially and temporally. This naturally allows for a compact representation of the complex non-rigid deformation over frames. We provide experimental results on several synthetic and real benchmark datasets. The procured results clearly demonstrate that our method, apart from being scalable and more accurate than state-of-the-art methods, is also more robust to noise and generalizes to highly nonlinear deformations.