Miracle-dz/UniFAR-FTUs huggingface dataset:
27 M ay 2 01 6 SNN : Stacked Neural Networks

It has been proven that transfer learning provides an easy way to achieve state-of-the-art accuracies on several vision tasks by training a simple classifier on top of feature s obtained from pre-trained neural networks. The goal of this project is to generate better features for transfer learning from multiple publicly available pre trained neural networks. To this end, we propose a novel architecture called Stacked Neural Networks which leverages the fast training time of transfer learning while simul taneously being much more accurate. We show that using a stacked NN architecture can result in up to 8% improvements in accuracy over state-of-the-art techniques u sing only one pre-trained network for transfer learning. A second aim of this project is to make network finetuning retain the generalizability of the base network to un seen tasks. To this end, we propose a new technique called “joint finetuning” that is able to give accuracies comparable to finetuning the same network individually over two datasets. We also show that a jointly finetuned network generalizes better to unseen tasks when compared to a network finetuned over a single task.
Explorer p 53 dependent centrosome clustering prevents multipolar mitosis in tetraploid cells

Background: p53 abnormality and aneuploidy often coexist in human tumors, and tetraploidy is considered as an intermediate between normal diploidy and aneuploidy. The purpose of this study was to investigate whether and how p53 influences the transformation from tetraploidy to aneuploidy. Principal Findings: Live cell imaging was performed to determine the fates and mitotic behaviors of several human and mouse tetraploid cells with different p53 status, and centrosome and spindle immunostaining was used to investigate centrosome behaviors. We found that p53 dominant-negative mutation, point mutation, or knockout led to a 2, 33-fold increase of multipolar mitosis in N/TERT1, 3T3 and mouse embryonic fibroblasts (MEFs), while mitotic entry and cell death were not significantly affected. In p53 tetraploid MEFs, the ability of centrosome clustering was compromised, while centrosome inactivation was not affected. Suppression of RhoA/ROCK activity by specific inhibitors in p53 tetraploid MEFs enhanced centrosome clustering, decreased multipolar mitosis from 38% to 20% and 16% for RhoA and ROCK, respectively, while expression of constitutively active RhoA in p53 tetraploid 3T3 cells increased the frequency of multipolar mitosis from 15% to 35%. Conclusions: p53 could not prevent tetraploid cells entering mitosis or induce tetraploid cell death. However, p53 abnormality impaired centrosome clustering and lead to multipolar mitosis in tetraploid cells by modulating the RhoA/ ROCK signaling pathway. Citation: Yi Q, Zhao X, Huang Y, Ma T, Zhang Y, et al. (2011) p53 Dependent Centrosome Clustering Prevents Multipolar Mitosis in Tetraploid Cells. PLoS ONE 6(11): e27304. doi:10.1371/journal.pone. 0027304 Editor: Mikhail V. Blagosklonny, Roswell Park Cancer Institute, United States of America Received September 15, 2011; Accepted October 13, 2011; Published November 4, 2011 Copyright: 2011 Yi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by grants from National Natural Science Foundation of China (Grants 30571030, 30671168 and 30725013), National High Technology Research and Development Program of China (2006AA02Z4B4), and Doctoral Fund of Ministry of Education of China (20070358022). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: qshi@ustc.edu. cn
( b ) Step 1 : FFT ( c ) Step 2 : Multiply ( d ) Step 3 : IFFT

GPS is one of the most widely used wireless systems. A GPS receiver has to lock on the satellite signals to calculate its position. The process of locking on the satellites is quite costly and requires hundreds of millions of hardware multiplications, leading to high power consumption. The fastest known algorithm for this problem is based on the Fourier transform and has a complexity of O(n logn), wheren is the number of signal samples. This paper presents the fastest GPS locking algorithm to date. The algorithm reduces the locking complexity to O(n √ logn). Further, if the SNR is above a threshold, the algorithm becomes linear, i.e.,O(n). Our algorithm builds on recent developments in the growing area of sparse recovery. It exploits the sparse nature of the synchronization problem, where only the correct alignment between the received GPS signal and the satellite code causes their cross-correlation to spike. We further show that the theoretical gain translates into empirical gains for GPS receivers. Specifically, we built a prototype of the design using software radios and tested it on two GPS datasets collected in the US and Europe. The results show that the new algorithm reduces the median number of multiplications by 2.2 × in comparison to the state of the art design, for real GPS signals.
Development of a Miniaturized Otoscope for Middle Ear Diagnostics by High Speed Holography

ii Significance . iii Acknowledgements . iv List of Figures . 3 List of Tables . 5 1 Project Scope . 6 1.1 Goal Statement . 6 1.2 Objectives . 6 2 Background . 7 2.1 The Ear. 7 2.1.1 The Process of Hearing. 7 2.1.2 Hearing Capabilities and Hearing Losses . 8 2.1. 3 The Tympanic Membrane . 9 2.2 Existing Diagnostic Technologies . 10 2.2.1 Otoscopes . 11 2.2.2 Endoscopes . 12 2.3 Interferometry . 13 2.3.1 Principles of Interferometry . 13 2.3.2 Holography . 14 2.3. 3 Current CHSLT Systems . 17 2.4 Design Specifications . 19 3 Methodology . 20 3.1 Preliminary Imaging . 20 3.1.1 Fiber Bundles . 20 3.1.2 Endoscopes . 23 3.1. 3 Otoscopes . 26 3.1. 4 Preliminary Imaging Results . 29 3.2 Optical Design . 30 3.2.1 Design of Optical Head . 30
Mixed segmentation: Combining audience segmentation, psychophysiology and textual content analysis. Mixed methodological research of triangulation and parallelization in a multivariate design

Citation for published version (APA): Hansen, A. G. , Bonde, A. , & Aagaard, M. (2017). Mixed segmentation: Combining audience segmentation, psychophysiology and textual content analysis. Mixed methodological research of triangulation and parallelization in a multivariate design. (1. ed.) Aalborg Universitetsforlag. e-bøger fra InDiMedia, No. 5 http://aauforlag.dk/Shop/skriftserier/e-boeger-fra-center-for-interaktive-digitale-m/default.aspx
SecureNN: 3-Party Secure Computation for Neural Network Training

Abstract Neural Networks (NN) provide a powerful method for machine learning training and inference. To effectively train, it is desirable for multiple parties to combine their data – however, doing so conflicts with data privacy. In this work, we provide novel three-party secure computation protocols for various NN building blocks such as matrix multiplication, convolutions, Rectified Linear Units, Maxpool, normalization and so on. This enables us to construct three-party secure protocols for training and inference of several NN architectures such that no single party learns any information about the data. Experimentally, we implement our system over Amazon EC2 servers in different settings. Our work advances the state-of-the-art of secure computation for neural networks in three ways: 1. Scalability: We are the first work to provide neural network training on Convolutional Neural Networks (CNNs) that have an accuracy of > 99% on the MNIST dataset; 2. Performance: For secure inference, our system outperforms prior 2 and 3-server works (SecureML, MiniONN, Chameleon, Gazelle) by 6×-113× (with larger gains obtained in more complex networks). Our total execution times are 2 − 4× faster than even just the online times of these works. For secure training, compared to the only prior work (SecureML) that considered a much smaller fully connected network, our protocols are 79× and 7× faster than their 2 and 3-server protocols. In the WAN setting, these improvements are more dramatic and we obtain an improvement of 553×! 3. Security: Our protocols provide two kinds of security: full security (privacy and correctness) against one semi-honest corruption and the notion of privacy against one malicious corruption [Araki et al. CCS’16]. All prior works only provide semi-honest security and ours is the first system to provide any security against malicious adversaries for the secure computation of complex algorithms such as neural network inference and training. Our gains come from a significant improvement in communication through the elimination of expensive garbled circuits and oblivious transfer protocols.
Testing for an Omitted Multiplicative Long-Term Component in GARCH Models

ABSTRACT We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative, there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte Carlo simulation. Our test also covers the mixed-frequency case in which the returns are observed at a higher frequency than the explanatory variable. The usefulness of our procedure is illustrated by empirical applications to S&P 500 return data. Supplementary materials for this article are available online.
Robotic Cutting of Solids: Fracture Mechanics Based Modeling Using the FEM

Cutting skills are important for robots to acquire not only because of a need from kitchen automation and home robotics, but also because of the technical challenge for robotic manipulation. Modeling of fracture and deformation during a cutting action, often based on the finite element method (FEM), provides the force and shape information needed for knife control to implement a skill such as slice, chop, or dice. A 3D mesh model can be computationally prohibitive for achieving a desired accuracy since tiny elements must be used surrounding the knife’s moving edge. To address this issue, we represent the object as evenly spaced slices normal to the cutting plane. Cutting of each slice undergoes 2D modeling which affords the use of tiny elements in the fractured region. Fracture and force can be then interpolated between every two adjacent slices. Experiment with an Adept arm and a force/torque sensor has demonstrated reasonable accuracy in force and shape modeling.
Major Qualifying Projects ( All Years ) Major Qualifying Projects April 2018 Characterizing IQGAP 1-C Domain Binding to Phosphoinositides

4 Introduction . 5 Background . 6 History . 6 Scaffolding Proteins . 6 IQGAP Protein Family . 8 IQGAPs in Yeast Cells . 8 Mammalian IQGAPs . 9 IQGAP1 . 9 Domains . 9 Expression . 10 Location within the Cell . 10 Cellular Function . 11 Physiological Function . 13 IQGAP Functions Relevant to Cancer . 15 IQGAP1 and Phosphoinositide 3-Kinase (PI3K)/Akt Signaling . 18 Scientific Techniques . 20 Fluorescence Correlation Spectroscopy (FCS) . 20 Fluorescence Resonance Energy Transfer (FRET) . 20 Circular Dichroism . 21 Proposed Research and Aims. 22 Aims . 22 Aim 1Purify full length IQGAP1 protein as well as IQGAP1 domains . 22 Aim 2Characterize the binding between IQGAP1 and PIP2 and PIP3 using LUVs . 22 Research Outline . 22 Aim 1Purify full length IQGAP1 protein as well as IQGAP1 domains . 22 Aim 2Characterize the binding between IQGAP1 and PIP2 and PIP3 using LUVs . 24 Methodology . 25 Protein Expression. 25 Protein Purification . 25
Calibrating Ensemble Forecasts to Produce More Reliable Probabilistic Extreme Weather Forecasts

Accurate predictions of severe weather events are extremely important for society, economy, and environment. Due to the fact that weather forecasts are inherently uncertain, it is required to give information about forecast uncertainty to all users providing weather forecasts in probabilistic terms utilizing ensemble forecasts. Since ensemble forecasts tend to be under dispersive and biased, they need to be calibrated with statistical methods. This paper presents a method for the calibration of temperature forecasts using Gaussian regression, and the calibration of wind gust forecasts with a box-cox t-distribution method. Statistical calibration was made for the operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (ENS) forecasts for lead times from 3 to 360 hours. The verification results showed that calibration improved both temperature and wind gust ensemble forecasts. The probabilistic temperature forecasts were better after calibration over whole lead time scale, but the probabilistic wind gust forecasts up to 240 hours.
Consensus opinion model in online social networks based on the impact of influential users. (Modèle d'avis de consensus dans les réseaux sociaux en ligne basé sur l'impact des utilisateurs influents)

Online Social Networks are increasing and piercing our lives such that almost every person in the world has a membership at least in one of them. Among famous social networks, there are online shopping websites such as Amazon, eBay and other ones which have members and the concepts of social networks apply to them. This thesis is particularly interested in the online shopping websites and their networks. According to the statistics, the attention of people to use these websites is growing due to their reliability. The consumers refer to these websites for their need (which could be a product, a place to stay, or home appliances) and become their customers. One of the challenging issues is providing useful information to help the customers in their shopping. Thus, an underlying question the thesis seeks to answer is how to provide comprehensive information to the customers in order to help them in their shopping. This is important for the online shopping websites as it satisfies the customers by this useful information and as a result increases their customers and the benefits of both sides. To overcome the problem, three specific connected studies are considered: (1) Finding the influential users, (2) Opinion Propagation and (3) Opinion Aggregation. In the first part, the thesis proposes a methodology to find the influential users in the network who are essential for an accurate opinion propagation. To do so, the users are ranked based on two scores namely optimist and pessimist. In the second part, a novel opinion propagation methodology is presented to reach an agreement and maintain the consistency among users which subsequently, makes the aggregation feasible. The propagation is conducted considering the impacts of the influential users and the neighbors. Ultimately, in the third part, the opinion aggregation is proposed to gather the existing opinions and present it as the valuable information to the customers regarding each product of the online shopping website. To this end, the weighted averaging operator and fuzzy techniques are used. The thesis presents a consensus opinion model in signed and unsigned networks. This solution can be applied to any group who needs to find a plenary opinion among the opinions of its members. Consequently, the proposed model in the thesis provides an accurate and appropriate rate for each product of the online shopping websites that gives precious information to their customers and helps them to have a better insight regarding the products.
The Digital Crow's Nest: A Framework for Proactive Disaster Informatics & Resilience by Open Source Intelligence

The research on technology-assisted crisis management has primarily existed for two decades since 9/11. Although, the focus of technology research has been centered around tools to assist the response phase after a disaster. There has been a lack of emphasis on the role and design of technologies to assist the other phases of the crisis management cycle, particularly preparedness and mitigation phases to lead towards the vision of building resilient communities. In this paper, we first identify resilience characteristics of a community from the prior literature. We then analyze a co-occurrence network of concepts in the ISCRAM publications to validate the gap in relating technologies to resilience and conduct an indicator analysis of factors for proactive disaster informatics via a case study of recent disaster. Our analysis leads us to propose a conceptual framework “ Digital Crow’s Nest ” based on Open Source Intelligence to improve the technology design for community resilience.
IBM POWER9 circuit design and energy optimization for 14-nm technology

Modern multicore microprocessors require attentive development to energy requirements when maximizing power-performance efficiency and ensuring reliable plus scalable functionality. IBM POWER9 relies on extensive modeling to identify representative workloads used when analyzing thermal design power and regulator design power against product requirements. Compounding benefits of circuit optimizations applied to the diverse subcomponents of the chip results in lower power cores, caches, and memory/IO interconnect. Specific dc- and ac-current analyses ensure proper definition of chip specifications for system voltage and current delivery. Finally, a systematic exploration of microbenchmarks on intermediate and final POWER9 hardware provides insight into processor core requirements while validating model accuracy.
Evaluation results for a Social Media Analyst Responding Tool

We take a human-centered design approach to develop a fully functional prototype, SMART (“Social Media Analyst Responding Tool”), informed by emergency practitioners. The prototype incorporates machine learning techniques to identify relevant information during emergencies. In this paper, we report the result of a user study to gather qualitative feedback on SMART. The evaluation results offer recommendations into the design of social media analysis tools for emergencies. The evaluation findings show the interest of emergency practitioners into designing such solutions; it reflects their need to not only identify relevant information but also to further perceive the outcome of their actions in social media. We found out there is a notable emphasis on the sentiment from these practitioners and social media analysis tools need to do a better job of handling negative sentiment within the emergency concept.
Legible Visualization of Semi-Transparent Objects using Light Transport. (Visualisation d'objets semi-transparents basée sur le transport lumineux)

Explorer et comprendre des donnees volumetriques ou surfaciques est un des nombreux enjeux du domaine de l'informatique graphique. L'apparence de telles donnees peut etre modelisee et visualisee en utilisant la theorie du transport lumineux. Afin de rendre une telle visualisation comprehensible, le recours a des materiaux transparents est tres repandu. Si des solutions existent pour simuler correctement la propagation de la lumiere et ainsi afficher des objets semi-transparents, offrir une visualisation comprehensible reste un sujet de recherche ouvert. Le but de cette these est double. Tout d'abord, une analyse approfondie du modele optique pour le transport de la lumiere et ses implications sur la generation d'images par ordinateur doit etre effectuee. Ensuite, cette connaissance pourra etre utilisee pour proposer des solutions efficaces et fiables pour visualiser des milieux transparents et semi-transparents. Dans ce manuscrit, premierement, nous presentons le modele optique communement utilise pour modeliser le transport de la lumiere dans des milieux participatifs, sa simplification si l'on reduit la situation a des surfaces et la maniere dont ces modeles sont utilises en informatique graphique pour generer des images. Deuxiemement, nous presentons une solution pour ameliorer la representation des formes dans le cas particulier des surfaces. La technique proposee utilise le transport lumineux comme base pour modifier le processus d'eclairage et modifier l'apparence et l'opacite des materiaux. Troisiemement, nous nous concentrons sur la problematique de l’utilisation de donnees volumetriques au lieu du cas simplifie des surfaces. Dans ce cas, le fait de ne modifier que les proprietes du materiau a un impact limite. Nous etudions donc comment le transport lumineux peut etre utilise pour fournir des informations utiles a la comprehension de milieux participatifs. Enfin, nous presentons notre modele de transport lumineux pour les milieux participatifs, qui vise a explorer une region d'interet d’un volume.
Online clustering and classification for real-time event detection in Twitter

Event detection from social media is a challenging task due to the volume, the velocity and the variety of user-generated data requiring real-time processing. Despite recent works on this subject, a generalized and scalable approach that could be applied across languages and topics has not been consolidated, yet. In this paper, we propose a methodology for real-time event detection from Twitter data that allows users to select a topic of interest by deﬁning a simple set of keywords and a matching rule. We implement the proposed methodology and evaluate it with real data to detect diﬀerent types of events.
Damage Identification in Social Media Posts using Multimodal Deep Learning

Social media has recently become a digital lifeline used to relay information and locate survivors in disaster situations. Currently, officials and volunteers scour social media for any valuable information; however, this approach is implausible as millions of posts are shared by the minute. Our goal is to automate actionable information extraction from social media posts to efficiently direct relief resources. Identifying damage and human casualties allows first responders to efficiently allocate resources and save as many lives as possible. Since social media posts contain text, images and videos, we propose a multimodal deep learning framework to identify damage related information. This framework combines multiple pretrained unimodal convolutional neural networks that extract features from raw text and images independently, before a final classifier labels the posts based on both modalities. Experiments on a home-grown database of labeled social media posts showed promising results and validated the merits of the proposed approach.
The Sound of Silence: Exploring How Decreases in Tweets Contribute to Local Crisis Identification

Recent research has identified a correlation between increasing Twitter activity and incurred damage in disasters. This research, however, fails to account for localized emergencies occurring in areas in which people have lost power, otherwise lack internet connectivity, or are uncompelled to Tweet during a disaster. In this paper, we analyze the correlation between daily Tweet counts and FEMA Building Level Damage Assessments during Hurricane Harvey. We find that the absolute deviation of Tweet counts from steady state is a potentially useful tool for the evolving information needs of emergency responders. Our results show this to be a more consistent and persistent metric for flood damage across the full temporal extent of the disaster. This shows that, when considering the varied information needs of emergency responders, social media tools that seek to identify emergencies need to consider both where Tweet counts are increasing and where they are dropping off.