Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of Density-based Spike Classification for Neuroprosthetic Applications

Successful proof-of-concept laboratory experiments on cortically-controlled brain computer interface motivate continued development for neural prosthetic microsystems (NPMs). In order to improve the NPMs, one of the main issue is to realize the realtime spike sorting processors (SSPs). The S SP detects the spikes, extracts the features, and then perform s the classification algorithm to differentiate the spikes for different firing neurons. Several architectures have been designed fo r the spike detection and feature extraction. However, the hardware for classification is missing. To complete the SSP, a density based hardware-oriented clustering algorithm is adapted f or the hardware implementation for the classification. In the hardware architecture level, the concepts of convolution and data re use are adapted to further reduce the power consumption. The final implementation achieves 32. 6μW and 0.25 mm in 90nm lowleakage process.
Email Engagement Segmentation Using Bipartite Graph Co-clustering

In the industry of email marketing, it is important to send content relevant to the recipient. If the recipients are uninterested they may ignore the email or worse report it as spam. Such actions compromise the ability of the senders to deliver emails to the inboxes of other recipients and permanently harm their relationship with the uninterested recipients. Targeting highly engaged recipients with appropriate content helps senders maximize the value of their email marketing efforts. Traditional research on email recipient engagement has focused on identifying topic groups through text mining and sentiment analysis. In this paper, we investigate recipient engagement via co-clustering methods on graphs of sender-recipient engagement built from email opens and clicks on URL links. We study a real engagement graph from marketing emails sent over SendGrid in May of 2015. We find that the sender-recipient engagement graph has a self-similar or recurring dominant co-cluster structure and the minor co-clusters are paired as engagement contrasts. Additionally, we find that these co-clusters persist across subsequent months implying that this approach may be useful for predicting recipient engagement behavior within clusters.
Differentiation on Information Gathering Ability in Real and Virtual World

Information gathering ability in Virtual Environment (VE) refers to user's ability to learn information such as text, numbers, or annotations. To find the usability of a designed interaction technique, information gathering ability is broadly used as a criterion. However, determining which technique is effective in a comparative analysis based on a usability test is difficult. Also measuring user's information gathering ability in Information Rich Virtual Environment (IRVE) is difficult due to the higher cognitive load associated with travel. In this study, we measured one's information gathering ability with respect to textual information in different worlds in order to find a difference and set a minimum guide line for determining the usefulness of a designed interaction technique. The results indicate that although user's path of finding information is similar, one's information gathering ability differs between the real vs. the virtual world conditions.
An optimal energy efficient cooperative retransmission MAC scheme in wireless networks

While the benefits of cooperative diversity have been well studied in the literature, cooperative MAC protocol design has also attracted much attention recently. In the single-relay Cooperative Automatic Repeat reQuest (C-ARQ) protocol, the best relay node is selected in a distributed manner by relays using different backoff time before packet retransmission. However, this relay selection scheme does not work efficiently in a dense network scenario, due to possible high collision probability among different contending relays. In this paper, we propose an optimized relay selection scheme to maximize system energy efficiency by reducing collision probability. The energy efficiency performance by the proposed optimal relay selection scheme is verified by simulations.
An Evolutionary Video Assignment Optimization Technique for VOD System in Heterogeneous Environment

We investigate the video assignment problem of a hierarchical Video-on-Demand (VOD) system in heterogeneous environments where different quality levels of videos can be encoded using either replication or layering. In such systems, videos are delivered to clients either through a proxy server or video broadcast/unicast channels. The objective of our work is to determine the appropriate coding strategy as well as the suitable delivery mechanism for a specific quality level of a video such that the overall system blocking probability is minimized. In order to find a near-optimal solution for such a complex video assignment problem, an evolutionary approach based on genetic algorithm (GA) is proposed. From the results, it is shown that the system performance can be significantly enhanced by efficiently coupling the various techniques.
Exploring Social Networks: A Frequent Pattern Visualization Approach

Social network analysis and mining aims to search for implicit, previously unknown, and potentially useful relational information (e.g., social relationship) from social networks. A visual representation of the networks helps users to gain insights about the useful information mined from the networks. Many existing visualizers represent social networks as graphs. While the graphs depict pairwise connections between two social entities, they may not show the connection strength or the multi-entity relationship (e.g., coauthorship and collaboration frequency). In this paper, we propose a visualizer called SocialViz for providing users with frequency information on social relationship among multiple entities in the networks. SocialViz can serve as a standalone visualization tool, or as a complement to existing visualizers, for exploring social networks.
Potential Therapeutic Effects of Vitamin E and C on Placental Oxidative Stress Induced by Nicotine: An In Vitro Evidence

There have been a few studies that examined the oxidative stress effects of nicotine during pregnancy and lactation. The adverse effect of prenatal smoking exposure on human fetal development and growth has been a major public health issue. Active or passive smoking during pregnancy can result in a wide variety of adverse outcomes, including intrauterine growth retardation (IUGR), prematurity, stillbirth, and the sudden infant death syndrome. Smoking in pregnancy has also been associated with an increased risk of attention deficit and learning problems in childhood. Some studies argued that as a principal component of tobacco smoke, nicotine alone is responsible for the majority of negative reproductive outcomes. Nicotine and its major metabolite cotinine can cross the placental barrier. The level of nicotine in fetal tissues was found to be equal to or greater than the plasma nicotine level in the mothers. The oxidative stress induce by nicotine has been increasingly postulated as a major contributor to endothelial dysfunction. A large body of research has investigated the potential role of antioxidant nutrients in the prevention of endothelial dysfunction in women. Therefore, the present study was undertaken to assess the potential benefit of antioxidant supplementation on markers of placental oxidative stress in an in vitro model of endothelial dysfunction induced by nicotine, since it was previously found that nicotine is able to trigger the placental secretion of stress molecules. In this regard, we evaluated the effects of vitamin C, vitamin E and N-acetylcysteine (NAC), alone or in combination, in placental villi culture after exposure to nicotine. The effect of antioxidant nutrients on trophoblast cells proliferation and vitality was also evaluated. The results obtained suggest that in a patho-physiological condition, such as endothelial dysfunction induced by nicotine, the deleterious effect of reactive oxygen species may be counteracted by an antioxidant therapy, and there is the need to investigate the optimum dosing and timing of antioxidants administration, since an inappropriate antioxidant treatment in pregnant women may have deleterious consequences, reducing placental cells proliferation until to cell death.
Scrutinizing Factors that Hold back Female Students’ from Speaking English in the Classroom at Mettu University in the case of first year Oromo folklore and literature students of 2008 E. C Entry

The purpose of this study was to scrutinize factors that hold back female students’ of Oromoo folklore and literature of Mettu University from speaking English in the classroom. To this end, the study employed descriptive survey design, which involves both quantitative and qualitative methods. Accordingly, Oromoo folklore and literature department was selected through purposive sampling. And the data were collected from 30 purposefully selected female students from Oromoo folklore and literature department for questionnaire and from two English teachers who were purposefully selected for interview. Thus, descriptive statistics using frequencies and percentages were employed in analyzing the quantitative data and the qualitative data were analyzed qualitatively. The result of the study revealed that almost all female students of Oromoo Folklore and Literature department at Mettu university were dominantly hold back from speaking English due to psychological, contextual, and linguistically factors. Finally, based on the findings, the researchers agreed to some recommendations that the most important one is to create conducive environment to support and encourage female students to speak and practice English frequently in the class.
Coping with Information Technology Challenges to Identity: A Theoretical Framework

Drawing on ideas from identity control theory and coping theory and on a diverse range of social psychology literature, we propose an integrative theoretical framework that unpacks and traces the processes by which information technology comes to affect users' identity. We define four types of strategies (acting on the situation, adjusting the self, cathartic practices and distancing) through which people cope with technological challenges to the self. We suggest that these strategies may lead to four individual-level outcomes, namely reinforced identity, redefined identity, ambivalent identity and anti-identity. The model is provided with a preliminary support through reference to real life situations, carefully selected from extant empirical IS enquiries.
An Architecture for Fail-Silent Operation of FPGAs and Configurable SoCs

We present an architecture for fail-silent operation of Field Programmable Gate Arrays (FPGAs) and configurable System-on-Chip (SoC) implementations. The architecture includes replication of the system function separated by a guard band region that guarantees that no single fault can allow interaction between the replicated system functions. The outputs of the replicated system functions are monitored such that, once an error is detected, the FPGA is shut down through a sequence of operations to prevent the transmission of erroneous data. This architecture can detect single event upsets (SEUs) in the configuration memory of FPGAs and FPGA cores in configurable SoCs for subsequent scrubbing operations. The guard band is also particularly effective in isolating operational regions of an FPGA to help to reduce erroneous operation due to SEUs in the configuration memory. 1
Teacher cognition in language teaching: A review of research on what language teachers think, know, believe, and do

This paper reviews a selection of research from the field of foreign and second language teaching into what is referred to here as teacher cognition – what teachers think, know, and believe and the relationships of these mental constructs to what teachers do in the language teaching classroom. Within a framework suggested by more general mainstream educational research on teacher cognition, language teacher cognition is here discussed with reference to three main themes: (1) cognition and prior language learning experience, (2) cognition and teacher education, and (3) cognition and classroom practice. In addition, the findings of studies into two specific curricular areas in language teaching which have been examined by teacher cognition – grammar teaching and literacy – are discussed. This review indicates that, while the study of teacher cognition has established itself on the research agenda in the field of language teaching and provided valuable insight into the mental lives of language teachers, a clear sense of unity is lacking in the work and there are several major issues in language teaching which have yet to be explored from the perspective of teacher cognition.
Automated trust negotiation in identity federations using OWL-based abduction of missing credentials

Creating ontologies and access control policies have one thing in common - a lot of work is spent on creating precise definitions like “who is a full time student” and answering difficult questions like “Are unpaid teachers employees? ”. Thus ontologies written in OWL and SWRL are a good match for access control policies. However the research in this area always assumed that the semantic information about a user is readily available. But such assumption can be satisfied only in a centralized system, not in decentralized systems enabled by the recent formation of identity federations, in which organizations can authenticate users from other organizations, and make authorization decisions about access to their resources based on user information provided by the other organizations.
Supplementary material for “ Provable Subspace Clustering : When LRR meets SSC ”

The supplementary material is organized as follows. In Section A and B, we provide the detailed proof of respectively the deterministic and randomized guarantee for LRSSC. In Section C, we derive the fast Alternating Direction Methods of Multipliers (ADMM) algorithm for LRSSC and NoisyLRSSC and verify its convergence guarantee. In Section D, additional numerical experiments of LRSSC are provided, including the real data experiments on Hopkins155 motion segmentation dataset[16]. In Section E, we provide in-depth discussions on the Minimax Subspace Incoherence Property, illustrating its main difference to Subspace Incoherence Property in [14] with intuitive examples and proofs. Also in this section, we formalize the concept of “sufficiently independent” that appeared in Example 3 of the main paper. In Section F, the proof to some stand-alone claims in the paper are given, including the graph connectivity of LRR and the computational tractability of the singular value condition. Finally, for readers’ easy reference, we attach a table of symbols and notations at the very end of the supplementary material.
Key Challenges in Online Social networks Analysis: A Survey

Online Social Networks (OSNs), an emerging multidisciplinary research field has become the important element of information society. OSNs provide a basis for maintaining social relationships, finding users with similar interests, and locating content and knowledge that has been contributed or endorsed by other users. The key aspect of many of the OSNs is that they are rich in data, and provide unprecedented challenges and opportunities from the perspective of knowledge discovery and data mining. This survey paper reviews the current state-of-art on the selected key challenges in OSNs such as data gathering techniques, heterogeneity, scalability and missing data, which helps the research community and also suggests that significant further research is required in this area.
Wnt4 Enhances Murine Hematopoietic Progenitor Cell Expansion Through a Planar Cell Polarity-Like Pathway

Background While the role of canonical (β-catenin-mediated) Wnt signaling in hematolymphopoiesis has been studied extensively, little is known of the potential importance of non-canonical Wnt signals in hematopoietic cells. Wnt4 is one of the Wnt proteins that can elicit non-canonical pathways. We have previously shown that retroviral overexpression of Wnt4 by hematopoietic cells increased thymic cellularity as well as the frequency of early thymic progenitors and bone marrow hematopoietic progenitor cells (HPCs). However, the molecular pathways responsible for its effect in HPCs are not known. Methodology/Principal Findings Here we report that Wnt4 stimulation resulted in the activation of the small GTPase Rac1 as well as Jnk kinases in an HPC cell line. Jnk activity was necessary, while β-catenin was dispensable, for the Wnt4-mediated expansion of primary fetal liver HPCs in culture. Furthermore, Jnk2-deficient and Wnt4 hemizygous mice presented lower numbers of HPCs in their bone marrow, and Jnk2-deficient HPCs showed increased rates of apoptosis. Wnt4 also improved HPC activity in a competitive reconstitution model in a cell-autonomous, Jnk2-dependent manner. Lastly, we identified Fz6 as a receptor for Wnt4 in immature HPCs and showed that the absence of Wnt4 led to a decreased expression of four polarity complex genes. Conclusions/Significance Our results establish a functional role for non-canonical Wnt signaling in hematopoiesis through a pathway involving Wnt4, Fz6, Rac1 and Jnk kinases.
Fairness and Optimality in Trading

This thesis proposes a novel approach to address the issues of efficiency and fairness when multiple portfolios are rebalanced simultaneously. A fund manager who rebalances multiple portfolios needs to not only optimize the total efficiency, i.e., maximize net risk-adjusted return, but also guarantee that trading costs are fairly split among the clients. The existing approaches in the literature, namely the Social Welfare and the Competitive Equilibrium schemes, do not compromise efficiency and fairness effectively. To this end, we suggest an approach that utilizes popular and well-accepted resource allocation ideas from the field of communications and economics, such as Max-Min fairness, Proportional fairness and a-fairness. We incorporate in our formulation a quadratic model of market impact cost to reflect the cumulative effect of trade pooling. Total trading costs are split fairly among accounts using the so-called pro rata scheme. We solve the resulting multi-objective optimization problem by adopting the Max-Min fairness, Proportional fairness and a-fairness schemes. Under these schemes, the resulting optimization problems have non-convex objectives and non-convex constraints, which are NP-hard in general. We solve these problems using a local search method based on linearization techniques. The efficiency of this approach is discussed when we compare it with a deterministic global optimization method on small size optimization problems that have similar structure to the aforementioned problems. We present computational results for a small data set (2 funds, 73 assets) and a large set (6 funds, 73 assets). These results suggest that the solution obtained from our model provides a better compromise between efficiency and fairness than existing approaches. An important implication of our work is that given a level of fairness that we want to maintain, we can always find Pareto-efficient trade sets. Thesis Supervisor: Dimitris Bertsimas Title: Boeing Professor of Operations Research
An Ionosphere Estimation Algorithm for WAAS Based on Kriging

GPS alone cannot provide the integrity needed for air navigation. Several error sources deteriorate the precision of the position estimate. One of the largest and more unpredictable sources of error for single frequency users is the ionosphere. For this reason, ionospheric behavior drives the performance of the Wide Area Augmentation System (WAAS). At any given time, the only information we have of the ionosphere is a limited amount of Total Electron Content (TEC) measurements. As a consequence, in order to estimate the ionospheric delay and get a confidence bound on such an estimate, we need to understand the spatial structure of the ionosphere over the region of interest. Using the thin shell model framework, where each TEC measurement is identified as a location on the thin shell, labeled the Ionospheric Pierce Point (IPP), the problem is reduced to a 2-dimensional problem. Once we have a good description of a nominal ionosphere, there are two questions that need to be answered before estimating the delay at a given IPP: Are the IPP measurements compatible with the assumed nominal model of the ionosphere? How relevant are the IPP measurements to the location we need to estimate? To answer the first one, an accurate characterization of the ionosphere in nominal conditions is needed. The large observed stationarity violations make this latter question very difficult. A worst case based method to determine the spatial structure of the nominal ionosphere in terms of the variogram, or, equivalently, the covariance is presented. The technique called "kriging" produces at each location an estimate and a confidence bound on the estimate, the kriging variance. The particular behavior of the kriging variance at the edge of coverage allows us to intuitively define the "well sampled" region. We show that a carefully designed estimation algorithm based on kriging could provide confidence bounds on the ionospheric delay corrections allowing WAAS to meet the GNSS Landing System requirements.
Combining incremental Hidden Markov Model and Adaboost algorithm for anomaly intrusion detection

Traditional Hidden Markov Model (HMM) has been successfully applied to anomaly intrusion detection. Incremental HMM (IHMM) further improves the training time of HMM. However, both HMM and IHMM still have the problem of high false positive rate. In this paper, we propose an Adaboost-IHMM to combine IHMM and adaboost for anomaly intrusion detection. As adaboost firstly uses many IHMMs to collectively classify samples then decides the results of samples' classifications, the Adaboost-IHMM can improve the accurate rate of classifications. Experimental results with Stide datasets show that the proposed method can significantly improve the false positive rate by 70% without decreasing detection rate. Besides, we also propose a method to adjust the normal profile for avoiding erroneous detection caused by changes of normal behavior. We perform with experiments with realistic datasets extracted from the use of popular browsers. Compared with traditional HMM method, our method can improve the training time by 90% to build a new normal profile.
The body as mediator of music in the Emotion Light

This paper describes the development of the Emotion Light, an interactive biofeedback artwork where the user listens to a piece of electronic music whilst holding a semi-transparent sculpture that tracks his/her bodily responses and translates these into changing light patterns that emerge from the sculpture. The context of this work is briefly described and the questions it poses are derived from interviews held with audience members.
Optimal point estimates for multi-target states based on kernel distances

Almost all multi-target tracking systems have to generate point estimates for the targets, e.g., for displaying the tracks. The novel idea in this paper is to consider point estimates for multi-target states that are optimal according to a kernel distance measure. Because the kernel distance is a metric on point sets and ignores the target labels, shortcomings of Minimum Mean Squared Error (MMSE) estimates for multi-target states can be avoided. We show how the calculation of these point estimates can be casted as an optimization problem and it turns out that it corresponds to the problem of reducing the Probability Hypothesis Density (PHD) function to a Dirac mixture density. Finally, we discuss a generalization of the kernel distance called LCD distance, which does not require to choose a specific kernel width. The presented methods are evaluated in a Multiple-Hypotheses Tracker (MHT) setting with up to ten targets.