Miracle-dz/UniFAR-FTUs huggingface dataset:
Inspection of Pole-Like Structures Using a Visual-Inertial Aided VTOL Platform with Shared Autonomy

This paper presents an algorithm and a system for vertical infrastructure inspection using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structures such as light and power distribution poles is a difficult task that is time-consuming, dangerous and expensive. Recently, micro VTOL platforms (i.e., quad-, hexa- and octa-rotors) have been rapidly gaining interest in research, military and even public domains. The unmanned, low-cost and VTOL properties of these platforms make them ideal for situations where inspection would otherwise be time-consuming and/or hazardous to humans. There are, however, challenges involved with developing such an inspection system, for example flying in close proximity to a target while maintaining a fixed stand-off distance from it, being immune to wind gusts and exchanging useful information with the remote user. To overcome these challenges, we require accurate and high-update rate state estimation and high performance controllers to be implemented onboard the vehicle. Ease of control and a live video feed are required for the human operator. We demonstrate a VTOL platform that can operate at close-quarters, whilst maintaining a safe stand-off distance and rejecting environmental disturbances. Two approaches are presented: Position-Based Visual Servoing (PBVS) using an Extended Kalman Filter (EKF) and estimator-free Image-Based Visual Servoing (IBVS). Both use monocular visual, inertia, and sonar data, allowing the approaches to be applied for indoor or GPS-impaired environments. We extensively compare the performances of PBVS and IBVS in terms of accuracy, robustness and computational costs. Results from simulations and indoor/outdoor (day and night) flight experiments demonstrate the system is able to successfully inspect and circumnavigate a vertical pole.
Charles A. Dice Center for Research in Financial Economics Excess Volatility of Corporate Bonds

This paper examines the connection between the return volatilities of corporate bonds, equities, and Treasuries under the Merton model with stochastic interest rates. Constructing empirical volatilities using bond returns over daily, weekly, and monthly horizons, we find that empirical bond volatilities are too high to be explained by equity and Treasury volatilities. Furthermore, the results are robust to using credit default swaps rather than corporate bonds to measure volatility in the credit market. At the daily return horizon, the excess volatility of corporate bonds is related to known liquidity proxies. However, this relation disappears at the monthly horizon even though corporate bonds continue to be excessively volatile. Thus, there appears to be a disconnect between corporate bonds and equities that goes beyond the illiquidity of corporate bonds. ∗Bao is at the Fisher College of Business, Ohio State University, bao 40@fisher.osu. edu. Pan is at the MIT Sloan School of Management and NBER, junpan@mit.edu. We have benefited from discussions with Sreedhar Bharath (AFA Discussant), Hayne Leland, Jiang Wang, and seminar participants at the AFA 2009 meetings, Berkeley, Boston University (Economics), Chung Hsing University, Cheng Kung University, the MIT Finance Lunch, National Taiwan University, and the FM program at Stanford. We thank Duncan Ma for assistance in gathering the Bloomberg data and financial support from the J.P. Morgan Outreach Program. Bao also thanks the Dice Center for financial support.
Learning Predictive Linguistic Features for Alzheimer’s Disease and related Dementias using Verbal Utterances

Early diagnosis of neurodegenerative disorders (ND) such as Alzheimer’s disease (AD) and related Dementias is currently a challenge. Currently, AD can only be diagnosed by examining the patient’s brain after death and Dementia is diagnosed typically through consensus using specific diagnostic criteria and extensive neuropsychological examinations with tools such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA). In this paper, we use several Machine Learning (ML) algorithms to build diagnostic models using syntactic and lexical features resulting from verbal utterances of AD and related Dementia patients. We emphasize that the best diagnostic model distinguished the AD and related Dementias group from the healthy elderly group with 74% FMeasure using Support Vector Machines (SVM). Additionally, we perform several statistical tests to indicate the significance of the selected linguistic features. Our results show that syntactic and lexical features could be good indicative features for helping to diagnose AD and related Dementias.
The differential expression of microRNA-143,145 in endometriosis

Background: Recent studies showed that inappropriate expression of microRNAs (miRNAs) is strongly associated with tumor-related processes in humans (2-9,11-17). Objective: To understand the changes of miRNAs in endometriosis. Materials and Methods: With real-time RT-PCR, we investigated the miR-143 and miR-145 expression in eutopic (EU, n=2) and ectopic endometrium (EC, n=11) (from women with endometriosis) (as well as EU+EC, n=11), along with the normal endometrium (EN, n=22) (from women without endometriosis, but with leiomyoma). Results: We did not find that the expression of miR-143 and/or miR-145 in EN or EC changed with menstrual cycle. But our results showed the miR-143 was up-regulated in EC (p=0.000) compared to EN. The miR-143 was also up-regulated in EU, but the difference did not reach statistically significance (p=0.053). Compared to EU, the expression of miR-143 in EC was up-regulated (p=0.016). MiR-145 had the similar characteristic to miR-143. The miR-145 was up-regulated in both EU (p=0.004) and EC (p=0.000) in compared to EN group. When compared with EU, the miR-145 in EC was up-regulated (p=0.008). Conclusion: In conclusion, the miR-143 and miR-145 may play a certain role in the development and progression of endometriosis.
Pseudo-random Pulse Sequences Aided UWB-TR Communication System

Ultra-wideband (UWB) transmitted reference (TR) scheme has been recently applied in many wireless location applications to achieve data communication due to its low complexity. Its major drawback is serious performance degradation for a very noisy template. In this paper, a novel pseudo-random (PN) pulse sequences aided UWB-TR communication system is presented by replacing the traditional repetitive reference pulses with multiple short PN sequences reference pulses. The receiver uses them to estimate the channel impulse response and based on this to form an improved template which removes the parts mainly consist of noise and gives the multipath components that have better signal-to-noise ratio higher weights. The proposed UWB-TR system is described and its performance in a multipath channel is analyzed and simulated. The result shows that it has a better bit-error-ratio performance than that of an averaged TR system.
Tag Similarity in Folksonomies

Folksonomies - collections of user-contributed tags, proved to be efficient in reducing the inherent semantic gap when retrieving web contents. To get best use of folksonomies, tag clustering was proposed to address the problems implied by free-style user tagging, such as lexical variations, tag split, multilingualism, etc. In this paper, we propose a novel approach for identifying similar tags in folksonomies. It is based on the idea that in folksonomies, the most frequent tags can be used to identify groups of semantically related tags. For this purpose, frequent tags are identified and their co-occurrence statistics are used to create a probability distribution for each tag. After that, the frequent tags are clustered based on the distance between their co-occurrence probability distributions. Next, probability distributions for the less frequent tags are generated based on the co-occurrence with the clusters of most frequent tags. Finally, similar tags are identified by calculating the distance between the corresponding probability distributions. To that end, we propose an extension for Jensen-Shannon Divergence which is sensitive for the size of the sample from which the co-occurrence probability distributions are calculated. We evaluated our approach by applying it on folksonomies obtained from Flickr. Additionally, we compared our results to that which were produced by a traditional method for tag clustering. The adversary method identifies similar tags by calculating the cosine similarity between the co-occurrence vectors of the tags. The evaluation shows promising results and emphasizes the advantage of our approach.
The Role of Self-Esteem in the Relationship Between Sexual Minority Status and Depressive Symptoms

Title of thesis: THE ROLE OF SELF-ESTEEM IN THE RELATIONSHIP BETWEEN SEXUAL MINORITY STATUS AND DEPRESSIVE SYMPTOMS Diane Ng, Master of Public Health, 2015 Thesis directed by: Professor Sunmin Lee Department of Epidemiology and Biostatistics Sexual minority (SM) youth have been found to experience higher rates of depression and depressive symptoms compared with heterosexual youth. It has been suggested that there are mediators in the pathway between stigma-related stress and psychopathology, such as self-esteem. This study was interested in investigating whether self-esteem is a mediator between SM status by romantic attraction and the outcome depressive symptoms during the transition from adolescence to adulthood, and whether sex moderated this mediation. Results showed that those who were both-sex attracted had significantly higher depressive symptoms than their opposite-sex attracted counterparts (β=0.04,p=0.049). Further, findings showed that self-esteem is a mediator in the pathway between both-sex attraction and depressive symptoms (p=0.007). Although females were found to have higher depressive symptoms than males, no significant interaction with sexual minority status was found. These results can have implications for possible interventions to reduce depressive symptomatology for sexual minority groups transitioning into adulthood.
Improve round-trip time measurement quality via clustering in inter-domain traffic engineering

For multi-homed networks, inter-domain traffic engineering (TE) consists in selecting the best path via available transit providers, so that the transmission quality is improved in front of network events, such as congestion and fail-over. In practice, this choice bases on end-to-end (e2e) measurements toward destination networks. These measurements, especially Round-Trip Time (RTT), are expected to offer an faithful view on inter-domain path properties. Hosts in destination networks with open ports are deliberately discovered for active measurement. RTT traces so obtained can be influenced by host-local factors that are not relevant to inter-domain routing and eventually mislead route decisions. We data-mined the RTT time-series between two ASes with unsupervised learning method - clustering, on a set of statistic features. Achieved results showed that our method was capable of improving data quality, by excluding less reliable traces. Moreover, we considered traceroute measurements. Early results suggested that most variations of e2e delay actually occured in access networks. We thus believe that the proposed scheme can improve the accuracy and stability of the route selection for multi-homed networks.
Improved Particle Approximations to the Joint Smoothing Distribution Using Markov Chain Monte Carlo

Particle filtering and smoothing algorithms approximate posterior state distributions with a set of samples drawn from those distributions. Conventionally, samples from the joint smoothing distribution are generated by sequentially resampling from the particle filter results. If the number of filtering particles is high, this process is limited by computational complexity. In addition, the support of the smoothing distribution is restricted to the values which appear in the filtering approximation. In this paper, a Metropolis-Hastings sampling procedure is used to improve the efficiency of the particle smoother, achieving comparable error performance but with a lower execution time. In addition, an algorithm for approximating the joint smoothing distribution without limited support is presented, which achieves simultaneous improvements in both execution time and error. These algorithms also provide a greater degree of flexibility over existing methods, allowing a trade-off between execution time and error, controlled by the length of the Markov chains.
Mechanism of Induction of NK Activation by 2B4 (CD244) via Its Cognate Ligand

We have previously shown that coincubation of purified B cells with IL-2–propagated NK cells can result in the induction of IL-13 mRNA and that the induction requires the presence of CD48 on B cells and 2B4 on NK cells. Because both of these molecules are expressed on NK cells, it is surprising that very little IL-13 mRNA can be detected in the absence of B cells. We have now found that incubation of NK cells on plates containing immobilized anti-CD48 Abs results in the clustering of CD48 and colocalization with 2B4 on the same cell. This colocalization, together with the requirement for SAP, the signal transducer for 2B4, is necessary for the induction of IL-13 mRNA expression. Activation of NK cell via CD48 on another cell may require a similar ability to alter the configuration of 2B4 to activate downstream signaling. By the use of double CD2/2B4 knockout mice, we have also shown that the induction of NK cell activation by anti-CD48 or by B cells is not due to the release of inhibitory effects of 2B4.
A Hybrid Optimization Model for Green Cloud Computing

This paper presents a hybrid optimization model that allows a cloud service provider to establish virtual machine (VM) placement strategies for its data centers in such a way that energy efficiency and network quality of service are jointly optimized. Usually, VM placement is an activity not fully integrated with network operations. As such, the VM placement strategy does not take into account the impact it produces on the network performance in terms of quality of service parameters such as packet losses and traffic delays. The proposed strategy allows cloud providers to reach a balance between the energy efficiency of their infrastructures and the network quality of service they offer to their customers. The proposed strategy has high potential for parallelism making it feasible for the energetic optimization of large cloud infrastructures. In addition, the strategy allows network operations practices such as constraint-based routing be incorporated into the VM placement process.
LINQits: big data on little clients

We present LINQits, a flexible hardware template that can be mapped onto programmable logic or ASICs in a heterogeneous system-on-chip for a mobile device or server. Unlike fixed-function accelerators, LINQits accelerates a domain-specific query language called LINQ. LINQits does not provide coverage for all possible applications---however, existing applications (re-)written with LINQ in mind benefit extensively from hardware acceleration. Furthermore, the LINQits framework offers a graceful and transparent migration path from software to hardware. LINQits is prototyped on a 2W heterogeneous SoC called the ZYNQ processor, which combines dual ARM A9 processors with an FPGA on a single die in 28nm silicon technology. Our physical measurements show that LINQits improves energy efficiency by 8.9 to 30.6 times and performance by 10.7 to 38.1 times compared to optimized, multithreaded C programs running on conventional ARM A9 processors.
Connectedness to Campus and Likelihood of Help-Seeking for Suicidality among College Women

ABSTRACT The goal of the present study was to examine the link between connectedness to campus and the likelihood of help-seeking from a mental health professional/school counselor for thoughts of suicide among college women. An online survey was administered to college students to assess perceived likelihood of seeking help, feelings of connectedness to campus, sense of togetherness with peers, and presence of a supportive group of friends on campus. Findings indicated that feeling connected to campus was positively associated with perceived likelihood of help-seeking for suicidal ideation. Furthermore, this association was stronger among participants reporting either a sense of togetherness with peers or a supportive group of friends. Efforts to increase students’ sense of connection to the larger campus community and build peer relationships may promote help-seeking for suicidal ideation among college women.
The musical environment and auditory plasticity: hearing the pitch of percussion

Although musical skills clearly improve with training, pitch processing has generally been believed to be biologically determined by the behavior of brain stem neural mechanisms. Two main classes of pitch models have emerged over the last 50 years. Harmonic template models have been used to explain cross-channel integration of frequency information, and waveform periodicity models have been used to explain pitch discrimination that is much finer than the resolution of the auditory nerve. It has been proposed that harmonic templates are learnt from repeated exposure to voice, and so it may also be possible to learn inharmonic templates from repeated exposure to inharmonic music instruments. This study investigated whether pitch-matching accuracy for inharmonic percussion instruments was better in people who have trained on these instruments and could reliably recognize their timbre. We found that adults who had trained with Indonesian gamelan instruments were better at recognizing and pitch-matching gamelan instruments than people with similar levels of music training, but no prior exposure to these instruments. These findings suggest that gamelan musicians were able to use inharmonic templates to support accurate pitch processing for these instruments. We suggest that recognition mechanisms based on spectrotemporal patterns of afferent auditory excitation in the early stages of pitch processing allow rapid priming of the lowest frequency partial of inharmonic timbres, explaining how music training can adapt pitch processing to different musical genres and instruments.
Timely and Continuous Machine-Learning-Based Classification for Interactive IP Traffic

Machine Learning (ML) for classifying IP traffic has relied on the analysis of statistics of full flows or their first few packets only. However, automated QoS management for interactive traffic flows requires quick and timely classification well before the flows finish. Also, interactive flows are often long-lived and should be continuously monitored during their lifetime. We propose to achieve this by using statistics derived from sub-flows—a small number of most recent packets taken at any point in a flow's lifetime. Then, the ML classifier must be trained on a set of sub-flows, and we investigate different sub-flow selection strategies. We also propose to augment training datasets so that classification accuracy is maintained even when a classifier mixes up client-to-server and server-to-client directions for applications exhibiting asymmetric traffic characteristics. We demonstrate the effectiveness of our approach with the Naive Bayes and C4.5 Decision Tree ML algorithms, for the identification of first-person-shooter online game and VoIP traffic. Our results show that we can classify both applications with up to 99% Precision and 95% Recall within less than 1 s. Stable results are achieved regardless of where within a flow the classifier captures the packets and the traffic direction.
Towards Multi-camera Human Motion Capture and Low Bit-rate Video

In this paper we describe an approach taken towards applying human motion capture to low bit-rate video. Our objective is to generate parameters defining a person’s motion, and with these parameters to create a model of a human performing the same movement. A video sequence of a person walking is obtained from multiple CCD cameras. Key points on the person’s body are tracked in 3D without the use of markers or sensors. Spatio-temporal tracking is done by the method of least squares matching. A simple model is fitted to the tracked points in each frame, and the pose is described by the parameters defined in the MPEG-4 standard.
Time Domain Equalizer Design Using Bit Error Rate Minimization for UWB Systems

Ultra-wideband (UWB) communication systems occupy huge bandwidths with very low power spectral densities. This feature makes the UWB channels highly rich in resolvable multipaths. To exploit the temporal diversity, the receiver is commonly implemented through a Rake. The aim to capture enough signal energy to maintain an acceptable output signal-to-noise ratio (SNR) dictates a very complicated Rake structure with a large number of fingers. Channel shortening or time domain equalizer (TEQ) can simplify the Rake receiver design by reducing the number of significant taps in the effective channel. In this paper, we first derive the bit error rate (BER) of a multiuser and multipath UWB system in the presence of a TEQ at the receiver front end. This BER is then written in a form suitable for traditional optimization. We then present a TEQ design which minimizes the BER of the system to perform efficient channel shortening. The performance of the proposed algorithm is compared with some generic TEQ designs and other Rake structures in UWB channels. It is shown that the proposed algorithm maintains a lower BER along with efficiently shortening the channel.
Inducing Differences among Documents using Aleph with Construction of Background Knowledge

This paper presents a case study in which an Inductive Logic Programming (ILP) technique is applied to natural language processing. Aleph, an ILP system, is used to induce differences among documents. A Case-Based Reasoning (CBR) system is proposed for the purpose of compiling the background knowledge inputted into Aleph. In the CBR system, lexical and syntactic information concerning words in close proximity to the target word(s) in training sentences is provided in order to infer new cases effectively. The compiled background knowledge is used to determine the semantic differences in documents that are written in natural language. Tentative experiments with this technique have produced encouraging results.
A comparison of PCA and ICA for object recognition under varying illumination

An experiment is performed to evaluate the ability of two different subspace methods to recognize objects under different illumination conditions. The principal component analysis (PCA) and independent component analysis (ICA) are compared for classifying 25 different objects with varying degrees of specularity under different illumination. Each object was sampled under three widely different lighting conditions to form a set of training images used to create subspaces with dimensions ranging from 10 to 30 basis vectors. The efficacy of ICA and PCA to correctly classify the objects was tested using two test images for each object under unique lighting conditions not included in the training set. The results were also determined when the images were pre-filtered with a Laplacian of Gaussian filter. Results show that ICA techniques show promise for object recognition under varying illumination conditions.