Miracle-dz/UniFAR-FTUs huggingface dataset:
Consistent Significance Testing for Nonparametric Regression

This article presents a framework for individual and joint tests of significance employing nonparametric estimation procedures. The proposed test is based on nonparametric estimates of partial derivatives, is robust to functional misspecification for general classes of models, and employs nested pivotal bootstrapping procedures. Two simulations and one application are considered to examine size and power relative to misspecified parametric models, and to test for the linear unpredictability of exchange-rate movements for G7 currencies.
Your activity tracker knows when you quit smoking

This paper discusses outcomes of our exploratory research aiming to discover ways of utilising continuous long term respiratory rate data collected from actigraphy (wrist-worn accelerometers.) We show that by monitoring changes in respiratory rate during sleep, we can detect and visualise various physical conditions that were previously not detectable using such simple wearable sensors, namely; the subjective level of drunkenness, fever, and smoking cessation. This study provides valuable insight into the potential of actigraphy, not simply as a tool for detecting common daily activities, but as a base for building a generic lifelog system that can evaluate the more qualitative aspects of your life.
The Phoenix Deep Survey: The Clustering and Environment of Extremely Red Objects

In this paper we explore the clustering properties and environment of the extremely red objects (EROs; I - K > 4 mag) detected in a ≈180 arcmin2 deep (Ks ≈ 20 mag) Ks-band survey of a region within the Phoenix Deep Survey, an ongoing multiwavelength program aiming to investigate the nature and evolution of faint radio sources. Using our complete sample of 289 EROs brighter than Ks = 20 mag, we estimate a statistically significant (≈3.7 σ) angular correlation function signal with amplitude Aw = 8.7 × 10-3 (assuming w(θ) = Awθ-0.8, with θ in degrees), consistent with earlier work based on smaller samples. This amplitude suggests a clustering length in the range ro = 12-17 h-1 Mpc, implying that EROs trace regions of enhanced density. Using a novel method, we further explore the association of EROs with galaxy overdensities by smoothing the K-band galaxy distribution using the matched filter algorithm of Postman et al. (1996) and then cross-correlating the resulting density maps with the ERO positions. Our analysis provides direct evidence that EROs are associated with overdensities at redshifts z ≳ 1. We also exploit the available deep radio 1.4 GHz data (limiting flux 60 μJy) to explore the association of EROs and faint radio sources and whether the two populations trace similar large-scale structures. Cross-correlation of the two samples (after excluding 17 EROs with radio counterparts) gives a 2 σ signal only for the subsample of high-z radio sources (z > 0.6). Although the statistics are poor, this suggests that it is the high-z radio subsample that traces similar structures with EROs.
Auditory Mismatch Negativity and Repetition Suppression Deficits in Schizophrenia Explained by Irregular Computation of Prediction Error

Background The predictive coding model is rapidly gaining attention in schizophrenia research. It posits the neuronal computation of residual variance (‘prediction error’) between sensory information and top-down expectation through multiple hierarchical levels. Event-related potentials (ERP) reflect cortical processing stages that are increasingly interpreted in the light of the predictive coding hypothesis. Both mismatch negativity (MMN) and repetition suppression (RS) measures are considered a prediction error correlates based on error detection and error minimization, respectively. Methods Twenty-five schizophrenia patients and 25 healthy controls completed auditory tasks designed to elicit MMN and RS responses that were investigated using repeated measures models and strong spatio-temporal a priori hypothesis based on previous research. Separate correlations were performed for controls and schizophrenia patients, using age and clinical variables as covariates. Results MMN and RS deficits were largely replicated in our sample of schizophrenia patients. Moreover, MMN and RS measures were strongly correlated in healthy controls, while no correlation was found in schizophrenia patients. Single-trial analyses indicated significantly lower signal-to-noise ratio during prediction error computation in schizophrenia. Conclusions This study provides evidence that auditory ERP components relevant for schizophrenia research can be reconciled in the light of the predictive coding framework. The lack of any correlation between the investigated measures in schizophrenia patients suggests a disruption of predictive coding mechanisms in general. More specifically, these results suggest that schizophrenia is associated with an irregular computation of residual variance between sensory input and top-down models, i.e. prediction error.
A Study on a Secure Wireless Link Creation Based on Multi-path Routing Combined with Transmit Antenna Directivity Control

This paper proposes a secure wireless link creation scheme based on a multi-path routing in a single-hop wireless transmission environment, where multiple paths are created by directive transmission using the multipath component of wireless link which the link possesses naturally on its own. Moreover a secret sharing method is employed to divide the transmitted information into pieces of shared information and sending each pieces of information separately through the multiple paths. Computer simulation clarifies that with the proposed scheme, information recovery of the transmitted information can be confined to a limited area in the wireless space, i.e., the transmitted information is less exposed to the adversary around the legitimate terminals.
Lie-Access Neural Turing Machines

External neural memory structures have recently become a popular tool for algorithmic deep learning (Graves et al. 2014, Weston et al. 2014). These models generally utilize differentiable versions of traditional discrete memory-access structures (random access, stacks, tapes) to provide the storage necessary for computational tasks. In this work, we argue that these neural memory systems lack specific structure important for relative indexing, and propose an alternative model, Lie-access memory, that is explicitly designed for the neural setting. In this paradigm, memory is accessed using a continuous head in a key-space manifold. The head is moved via Lie group actions, such as shifts or rotations, generated by a controller, and memory access is performed by linear smoothing in key space. We argue that Lie groups provide a natural generalization of discrete memory structures, such as Turing machines, as they provide inverse and identity operators while maintaining differentiability. To experiment with this approach, we implement a simplified Lie-access neural Turing machine (LANTM) with different Lie groups. We find that this approach is able to perform well on a range of algorithmic tasks. HarvardNLP landing page
Kinematic modeling and observer based control of soft robot using real-time Finite Element Method

This paper aims at providing a novel approach to modeling and controlling soft robots. Based on real-time Finite Element Method (FEM), we obtain a globally defined discrete-time kinematic model in the workspace of soft robots. From the kinematic equations, we deduce the soft-robot Jacobian matrix and discuss the conditions to avoid singular configurations. Then, we propose a novel observer based control methodology where the observer is built by Finite Element Model in this paper to deal with the control problem of soft robots. A closed-loop controller for position control of soft robot is designed based on the discrete-time model with feedback signal being extracted by means of visual servoing. Finally, experimental results on a parallel soft robot show the efficiency and performance of our proposed controller.
Low-rank matrix recovery based on smooth function approximation

Recently, smooth rank function was proposed for matrix completion problem. The main idea of this method was based on a continuous and differentiable approximation of the rank function. In this paper, a new approach for solving low-rank matrix recovery problem based on smooth function is proposed. It not only uses a smooth function to approximate the rank function, but also approximates the l0-norm with a continuous and differentiable function. In addition, gradient decreasing approach can be used to solve the minimization problem. Finally, experimental results show that this new algorithm provides accurate results in almost all of our testing scenarios with a reasonable running time. Especially, it has higher approximation performance than other methods.
A New Approach for Inversion of Large Random Matrices in Massive MIMO Systems

We report a novel approach for inversion of large random matrices in massive Multiple-Input Multiple Output (MIMO) systems. It is based on the concept of inverse vectors in which an inverse vector is defined for each column of the principal matrix. Such an inverse vector has to satisfy two constraints. Firstly, it has to be in the null-space of all the remaining columns. We call it the null-space problem. Secondly, it has to form a projection of value equal to one in the direction of selected column. We term it as the normalization problem. The process essentially decomposes the inversion problem and distributes it over columns. Each column can be thought of as a node in the network or a particle in a swarm seeking its own solution, the inverse vector, which lightens the computational load on it. Another benefit of this approach is its applicability to all three cases pertaining to a linear system: the fully-determined, the over-determined, and the under-determined case. It eliminates the need of forming the generalized inverse for the last two cases by providing a new way to solve the least squares problem and the Moore and Penrose's pseudoinverse problem. The approach makes no assumption regarding the size, structure or sparsity of the matrix. This makes it fully applicable to much in vogue large random matrices arising in massive MIMO systems. Also, the null-space problem opens the door for a plethora of methods available in literature for null-space computation to enter the realm of matrix inversion. There is even a flexibility of finding an exact or approximate inverse depending on the null-space method employed. We employ the Householder's null-space method for exact solution and present a complete exposition of the new approach. A detailed comparison with well-established matrix inversion methods in literature is also given.
Research on constructing 3D scenelite model based on 2D structure drawings of buildings

3D building model is the important foundation of constructing virtual landscape of 3D city. This paper will focus on building models and construct buildings in small scene with existing survey data based on the secondary development of Google SketchUp. The concept of scenelite will be illustrated. The research shows that the method can effectively reduce the redundancy of repeated work in modeling. It is useful for querying, managing the building objects, and performing 3D topological analysis with the scenelite model.
Study on Interactions between the Major Apple Valsa Canker Pathogen Valsa mali and Its Biocontrol Agent Saccharothrix yanglingensis Hhs.015 Using RT-qPCR

The mechanism of biocontrol agent Saccharothrix yanglingensis Hhs.015 action against Valsa mali, a major apple Valsa canker pathogen, was examined using a novel, sensitive (minimum detection limit 100 pg/μL) and reliably RT-qPCR technique. Prior to lesion formation, total concentration of V. mali in the bark showed a significant decrease (p<0.05) after 24 h of Hhs.015 treatment. This was more pronounced at 48 and 96 h post treatment. After lesion formation, levels of V. mali remained constant at the boundary between infected and uninfected bark tissues, although the relative expansion rate of the lesion was significantly reduced (p<0.05). Gene expression levels of endo-polygalacturonase, a marker for fungal pathogenicity, were sharply reduced while host induced resistance callose synthase levels increased significantly (p<0.05) at the boundary bark at 9 d after Hhs.015 treatment. The results showed that biocontrol agent Hhs.015 prevented infection of V. mali by inhibiting pathogen growth, down-regulating pathogenicity factor expression and inducing a high level of host resistance.
Niederhauser's model for epilepsy and wavelet methods

Wavelets and wavelet transforms (WT) could be a very useful tool to analyze electroencephalogram (EEG) signals. To illustrate the WT method we make use of a simple electric circuit model introduced by Niederhauser, which is used to produce EEG-like signals, particularly during an epileptic seizure. The original model is modified to resemble the 10-20 derivation of the EEG measurements. WT is used to study the main features of these signals
An improved interconnect noise methodology for reduced pessimism using dAP model

Noise simulators were never accurate estimators of interconnect noise values as the switching pattern, slew and delay in a cluster of adjacent nets were never addressed. In modern signal integrity flows, a very high level of pessimism is incorporated into the noise methodology to augment the lack of accuracy, such as lumped aggression, noise ramps and lumped slews. Desirably, such pessimistic assumptions are much easier to compute hence efficient on simulation runtime, but often than ever it will result in an overdesigned chip. This paper discusses a recipe to reduce pessimism in interconnect noise methodologies by effectively modeling the downstream degradation on the aggression line. We will be presenting an aggression slew estimation method derived from the Elmore delay, which has less than 0.8% error compared to SPICE simulations and is inclined towards the higher slew bound when compared to both Elmore's delay and SPICE. We also show that the dAP method reduces the average reported noise by 5% even when we account for victim-aggressor reverse aggression issues.
Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent

Ms Pac-Man is one of the most challenging test beds in game artificial intelligence (AI). Genetic programming and Monte Carlo Tree Search (MCTS) have already been successful applied to several games including Pac-Man. In this paper, we use Monte Carlo Tree Search to create a Ms Pac-Man playing agent before using genetic programming to enhance its performance by evolving a new default policy to replace the random agent used in the simulations. The new agent with the evolved default policy was able to achieve an 18% increase on its average score over the agent with random default policy.
Integrating network coding into heterogeneous wireless networks

We investigate the problem of integrating network coding into heterogeneous wireless networks where a number of coding nodes are to be placed among legacy nodes that do not handle network coding operations well. In particular, we seek to understand better the following questions: 1) how many coding nodes are needed, 2) where should the coding nodes be located, and 3) how should the coding nodes interact with other non-coding nodes? To this aim, we apply our previously proposed evolutionary algorithm that operates as a distributed protocol and present quantifiable results through various simulations. We also consider the algorithmpsilas operation in lossy environments and show that the temporally distributed structure of our algorithm offers a significant advantage in overcoming the adverse effect of packet erasures.
On the Importance of Temporal Context in Proximity Kernels: A Vocal Separation Case Study

Musical source separation methods exploit source-specific spectral characteristics to facilitate the decomposition process. Kernel Additive Modelling (KAM) models a source applying robust statistics to time-frequency bins as specified by a source-specific kernel, a function defining similarity between bins. Kernels in existing approaches are typically defined using metrics between single time frames. In the presence of noise and other sound sources information from a single-frame, however, turns out to be unreliable and often incorrect frames are selected as similar. In this paper, we incorporate a temporal context into the kernel to provide additional information stabilizing the similarity search. Evaluated in the context of vocal separation, our simple extension led to a considerable improvement in separation quality compared to previous kernels.
Why User Comments Affect the Perceived Quality of Journalistic Content: The Role of Judgment Processes

User comments on news websites are frequently uncivil and are not supported by reasoned argumentation. These characteristics can have negative effects on the perceived quality of the commented-on journalistic content, yet to date, it remains unclear how such effects occur. We propose three mechanisms that assume that the effect of user comments depends on how deliberately and elaborately the quality of the commented-on news item is judged. We conducted an experiment (N = 633) in which we varied the level of civility and reasoning in the comments accompanying a news article and the brand of the news website on which it was presented. The results showed that a lack of reasoning in the comments decreased the perceived quality of the news item irrespective of brand awareness, but only with high elaboration during judgment. Incivility in the comments decreased the perceived quality of the journalistic content, but only with low elaboration, and only with an unknown news brand. We discuss different psychological mechanisms that can explain this pattern of effects.
Quiz-Based Evaluation of Machine Translation

Quiz-Based Evaluation of Machine Translation This paper proposes a new method of manual evaluation for statistical machine translation, the so-called quiz-based evaluation, estimating whether people are able to extract information from machine-translated texts reliably. We apply the method to two commercial and two experimental MT systems that participated in WMT 2010 in English-to-Czech translation. We report inter-annotator agreement for the evaluation as well as the outcomes of the individual systems. The quiz-based evaluation suggests rather different ranking of the systems compared to the WMT 2010 manual and automatic metrics. We also see that overall, MT quality is becoming acceptable for obtaining information from the text: about 80% of questions can be answered correctly given only machine-translated text.
The Role of Attention and Perception in the Control of Visually Guided and Memory-Guided Actions

Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission. Abstract I investigated the attentional and perceptional mechanisms involved in the control of visually guided and memory‐guided actions in two experiments using event‐related potentials (ERPS). In the first dual task experiment, participants performed reciprocal aiming in visually guided and memory‐guided modes while simultaneously performing a dichotic listening task from which ERPs were collected. A decrease in mismatch negativity (MMN) component amplitude during visually guided aiming suggests that it requires more automatic attention, and an increase in P300 component latency during memory‐guided aiming suggests that it requires more voluntary attention. In the second experiment, ERPs were collected while participants were presented with pictures of tools and, after a delay, either pantomimed how each tool was used (go) or did nothing (no‐go). No differences in perception‐related N170 amplitude were found between go and no‐go trials but limitations of this experiment impede conclusions as to the role of N170 mechanisms in delayed action planning.
Blood Transcriptional Profiling Reveals Immunological Signatures of Distinct States of Infection of Humans with Leishmania infantum

Visceral leishmaniasis (VL) can be lethal if untreated; however, the majority of human infections with the etiological agents are asymptomatic. Using Illumina Bead Chip microarray technology, we investigated the patterns of gene expression in blood of active VL patients, asymptomatic infected individuals, patients under remission of VL and controls. Computational analyses based on differential gene expression, gene set enrichment, weighted gene co-expression networks and cell deconvolution generated data demonstrating discriminative transcriptional signatures. VL patients exhibited transcriptional profiles associated with pathways and gene modules reflecting activation of T lymphocytes via MHC class I and type I interferon signaling, as well as an overall down regulation of pathways and gene modules related to myeloid cells, mainly due to differences in the relative proportions of monocytes and neutrophils. Patients under remission of VL presented heterogeneous transcriptional profiles associated with activation of T lymphocytes via MHC class I, type I interferon signaling and cell cycle and, importantly, transcriptional activity correlated with activation of Notch signaling pathway and gene modules that reflected increased proportions of B cells after treatment of disease. Asymptomatic and uninfected individuals presented similar gene expression profiles, nevertheless, asymptomatic individuals exhibited particularities which suggest an efficient regulation of lymphocyte activation and a strong association with a type I interferon response. Of note, we validated a set of target genes by RT-qPCR and demonstrate the robustness of expression data acquired by microarray analysis. In conclusion, this study profiles the immune response during distinct states of infection of humans with Leishmania infantum with a novel strategy that indicates the molecular pathways that contribute to the progression of the disease, while also providing insights into transcriptional activity that can drive protective mechanisms.
Segmentation of vowel-initial words is facilitated by function words*

Abstract Within the first year of life, infants learn to segment words from fluent speech. Previous research has shown that infants at 0;7·5 can segment consonant-initial words, yet the ability to segment vowel-initial words does not emerge until the age of 1;1–1;4 (0;11 in some restricted cases). In five experiments, we show that infants aged 0;11 but not 0;8 are able to segment vowel-initial words that immediately follow the function word the [ði], while ruling out a bottom-up, phonotactic account of these results. Thus, function words facilitate the segmentation of vowel-initial words that appear sentence-medially for infants aged 0;11.
A new method to building simulation model for Space-Vector-Modulation and two-level three-phase inverter

The computer simulation is a powerful tool for assessment of the SVM (Space-Vector-Modulation) strategy and inverter. The methods to building the simulation model for them are analyzed, and a new modeling method is proposed and discussed. The new method models the SVM and inverter as a whole based on the start point of the SVM. The method to computing the DC link current is given based on the three-phase current. Finally the typical application of SVM and inverter in motor control is simulated, and the results verify the simulation method and model.