Miracle-dz/UniFAR-FTUs huggingface dataset:
Stereo-based Multi-motion Visual Odometry for Mobile Robots

With the development of computer vision, visual odometry is adopted by more and more mobile robots. However, we found that not only its own pose, but the poses of other moving objects are also crucial for the decision of the robot. In addition, the visual odometry will be greatly disturbed when a significant moving object appears. In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects. In order to obtain the poses simultaneously, a continuous motion segmentation module and a coordinate conversion module are applied to the traditional visual odometry pipeline. As a result, poses of all moving objects can be acquired and transformed into the ground coordinate system. The experimental results show that the proposed multi-motion visual odometry can effectively eliminate the influence of moving objects on the visual odometry, as well as achieve 10 cm in position and 3{\deg} in orientation RMSE (Root Mean Square Error) of each moving object.
MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition

Recently, the end-to-end approach has proven its efficacy in monaural multi-speaker speech recognition. However, high word error rates (WERs) still prevent these systems from being used in practical applications. On the other hand, the spatial information in multi-channel signals has proven helpful in far-field speech recognition tasks. In this work, we propose a novel neural sequence-to-sequence (seq2seq) architecture, MIMO-Speech, which extends the original seq2seq to deal with multi-channel input and multi-channel output so that it can fully model multi-channel multi-speaker speech separation and recognition. MIMO-Speech is a fully neural end-to-end framework, which is optimized only via an ASR criterion. It is comprised of: 1) a monaural masking network, 2) a multi-source neural beamformer, and 3) a multi-output speech recognition model. With this processing, the input overlapped speech is directly mapped to text sequences. We further adopted a curriculum learning strategy, making the best use of the training set to improve the performance. The experiments on the spatialized wsj1-2mix corpus show that our model can achieve more than 60% WER reduction compared to the single-channel system with high quality enhanced signals (SI-SDR = 23.1 dB) obtained by the above separation function.
Liver segmentation and metastases detection in MR images using convolutional neural networks

Abstract. Primary tumors have a high likelihood of developing metastases in the liver, and early detection of these metastases is crucial for patient outcome. We propose a method based on convolutional neural networks to detect liver metastases. First, the liver is automatically segmented using the six phases of abdominal dynamic contrast-enhanced (DCE) MR images. Next, DCE-MR and diffusion weighted MR images are used for metastases detection within the liver mask. The liver segmentations have a median Dice similarity coefficient of 0.95 compared with manual annotations. The metastases detection method has a sensitivity of 99.8% with a median of two false positives per image. The combination of the two MR sequences in a dual pathway network is proven valuable for the detection of liver metastases. In conclusion, a high quality liver segmentation can be obtained in which we can successfully detect liver metastases.
Affect-aware thermal comfort provision in intelligent buildings

Predominant thermal comfort provision technologies are energy-hungry, and yet they perform crudely because they overlook the requisite precursors to thermal comfort. They also fail to exclusively cool or heat the parts of the body (e.g., the wrist, the feet, and the head) that influence the most a person's thermal comfort satisfaction. Instead, they waste energy by heating or cooling the whole room. This research investigates the influence of neck-coolers on people's thermal comfort perception and proposes an effective method that delivers thermal comfort depending on people's heart rate variability (HRV). Moreover, because thermal comfort is idiosyncratic and depends on unforeseeable circumstances, only person-specific thermal comfort models are adequate for this task. Unfortunately, using person-specific models would be costly and inflexible for deployment in, e.g., a smart building because a system that uses person-specific models would require collecting extensive training data from each person in the building. As a compromise, we devise a hybrid, cost-effective, yet satisfactory technique that derives a personalized person-specific-like model from samples collected from a large population. For example, it was possible to double the accuracy of a generic model (from 47.77% to 96.11%) using only 400 person-specific calibration samples. Finally, we propose a practical implementation of a real-time thermal comfort provision system that uses this strategy and highlighted its advantages and limitations.
Human Action Recognition with Multi-Laplacian Graph Convolutional Networks

Convolutional neural networks are nowadays witnessing a major success in different pattern recognition problems. These learning models were basically designed to handle vectorial data such as images but their extension to non-vectorial and semi-structured data (namely graphs with variable sizes, topology, etc.) remains a major challenge, though a few interesting solutions are currently emerging. In this paper, we introduce MLGCN; a novel spectral Multi-Laplacian Graph Convolutional Network. The main contribution of this method resides in a new design principle that learns graph-laplacians as convex combinations of other elementary laplacians each one dedicated to a particular topology of the input graphs. We also introduce a novel pooling operator, on graphs, that proceeds in two steps: context-dependent node expansion is achieved, followed by a global average pooling; the strength of this two-step process resides in its ability to preserve the discrimination power of nodes while achieving permutation invariance. Experiments conducted on SBU and UCF-101 datasets, show the validity of our method for the challenging task of action recognition.
Contraception in Cambodia : explaining unmet need

This thesis aims to explain why there is a high level of unmet need for contraception in Cambodia - a country where effective methods of birth control are cheaply available and morally acceptable. The research design takes a mixed methods approach, initially using data from the Cambodian Demographic and Health Surveys of 2000 and 2005 to assess trends in contraceptive use. Multivariate logistic regression is used to analyse factors associated with, firstly, unmet need, and secondly, use of traditional contraceptive methods. The likelihood of having an unmet need for contraception increased as education and wealth levels decreased; urban or rural residence had no significant effect. However, the likelihood of using traditional methods, rather than modern methods, increased as education and wealth increased. Taking these findings and the questions they raise as a departure point, 21 months of ethnographic fieldwork was conducted in one urban and one rural site in Northwest Cambodia between 2008 and 2010. The study looks at women’s and men’s reproductive decision making with a focus on their experiences of and meanings given to contraception, situating these understandings within the broader social context. Fear of side effects, stemming from both contraceptive experiences and notions of health and the body, was found to be the greatest obstacle to use of modern contraceptives. This related more broadly to the pluralistic medical systems operating simultaneously and the varying levels of medicalization and trust in both biomedicine and the Cambodian health system. Behaviour that seemed counter-intuitive at the outset - not wanting to become pregnant but not using contraception, and wealthy educated women choosing traditional over modern methods – becomes understandable in light of the context and meanings highlighted by the ethnographic data. This thesis provides a unique empirical study which contributes to the emerging field of anthropological demography. By bringing approaches and methods from medical anthropology to the typically demographic phenomenon of unmet need, the study provides a new insight for social policies regarding reproductive health as well as contributing to the body of ethnographic literature on Cambodia.
State of Compact Architecture Search For Deep Neural Networks

The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge and mobile scenarios. Due to the time-consuming and challenging nature of manually designing compact deep neural networks, there has been significant recent research interest into algorithms that automatically search for compact network architectures. A particularly interesting class of compact architecture search algorithms are those that are guided by baseline network architectures. Such algorithms have been shown to be significantly more computationally efficient than unguided methods. In this study, we explore the current state of compact architecture search for deep neural networks through both theoretical and empirical analysis of four different state-of-the-art compact architecture search algorithms: i) group lasso regularization, ii) variational dropout, iii) MorphNet, and iv) Generative Synthesis. We examine these methods in detail based on a number of different factors such as efficiency, effectiveness, and scalability. Furthermore, empirical evaluations are conducted to compare the efficacy of these compact architecture search algorithms across three well-known benchmark datasets. While by no means an exhaustive exploration, we hope that this study helps provide insights into the interesting state of this relatively new area of research in terms of diversity and real, tangible gains already achieved in architecture design improvements. Furthermore, the hope is that this study would help in pushing the conversation forward towards a deeper theoretical and empirical understanding where the research community currently stands in the landscape of compact architecture search for deep neural networks, and the practical challenges and considerations in leveraging such approaches for operational usage.
Design- and Model-Based Approaches to Small-Area Estimation in A Low- and Middle-Income Country Context: Comparisons and Recommendations

The need for rigorous and timely health and demographic summaries has provided the impetus for an explosion in geographic studies in low- and middle-income countries. Many of these studies present fine-scale pixel-level maps in an attempt to answer the needs of the current era of precision public health. However, even though household surveys with a two-stage cluster design stratified by region and urbanicity are a major source of data, cavalier approaches are taken to acknowledging the survey design. We investigate the extent to which accounting for the sample design affects the predictive performance at the aggregate level of interest for health policy decisions. We consider various commonly used models and introduce a new Bayesian cluster-level model with a discrete spatial smoothing prior. The investigation is performed through a simulation study in which realistic sampling frames are created for Kenya, based on the population and demographic information, with a survey design that mimics a Demographic Health Survey (DHS). We find that including stratification and cluster-level random effects can improve predictive performance. Spatially smoothed direct (weighted) estimates and area-level models accounting for stratification were robust to the underlying population and survey design. Continuous spatial models showed some promise in the presence of fine-scale variation; however, these models require the most “hand holding. ” Subsequently, we examine how the models perform on real data, estimating the prevalence of secondary education for women aged 20–29 and neonatal mortality rates, using data from the 2014 Kenya DHS.
Fast component-by-component construction of lattice algorithms for multivariate approximation with POD and SPOD weights

In a recent paper by the same authors, we provided a theoretical foundation for the component-by-component (CBC) construction of lattice algorithms for multivariate $L_2$ approximation in the worst case setting, for functions in a periodic space with general weight parameters. The construction led to an error bound that achieves the best possible rate of convergence for lattice algorithms. Previously available literature covered only weights of a simple form commonly known as product weights. In this paper we address the computational aspect of the construction. We develop fast CBC construction of lattice algorithms for special forms of weight parameters, including the so-called POD weights and SPOD weights which arise from PDE applications, making the lattice algorithms truly applicable in practice. With $d$ denoting the dimension and $n$ the number of lattice points, we show that the construction cost is $\mathcal{O}(d\,n\log(n) + d^2\log(d)\,n)$ for POD weights, and $\mathcal{O}(d\,n\log(n) + d^3\sigma^2\,n)$ for SPOD weights of degree $\sigma\ge 2$. The resulting lattice generating vectors can be used in other lattice-based approximation algorithms, including kernel methods or splines.
On the Use of Electromagnetic Inversion for Metasurface Design

We show that the use of the electromagnetic inverse source framework offers great flexibility in the design of metasurfaces. In particular, this approach is advantageous for antenna design applications where the goal is often to satisfy a set of performance criteria such as half power beamwidths and null directions, rather than satisfying a fully known complex field. In addition, the inverse source formulation allows the metasurface and the region over which the desired field specifications are provided to be of arbitrary shape. Some of the main challenges in solving this inverse source problem, such as formulating and optimizing a nonlinear cost functional, are addressed. Lastly, some 2-D and 3-D simulated examples are presented to demonstrate the method, followed by a discussion of the method’s current limitations.
Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling

We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given the past samples and future priors, the mother AR model can post-process the priors while the accompanied confidence predictor decides whether the current sample needs a resampling or not. Thanks to the i.i. d. assumption, the post-processing can update each sample in a parallel way, which remarkably accelerates the mother model. Our experiments on different data domains including sequences and images show that the proposed method can successfully capture the complex structures of the data and generate the meaningful future samples with lower computational cost while preserving the sequential relationship of the data.
A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads

Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact in electricity systems. We present a general methodology that is able to process and forecast a large number of smart meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and also consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model.
Bayesian inverse problems with heterogeneous variance

We consider inverse problems in Hilbert spaces under correlated Gaussian noise, and use a Bayesian approach to find their regularized solution. We focus on mildly ill‐posed inverse problems with fractional noise, using a novel wavelet‐based vaguelette–vaguelette approach. It allows us to apply sequence space methods without assuming that all operators are simultaneously diagonalizable. The results are proved for more general bases and covariance operators. Our primary aim is to study posterior contraction rate in such inverse problems over Sobolev classes and compare it to the derived minimax rate. Secondly, we study effect of plugging in a consistent estimator of variances in sequence space on the posterior contraction rate. This result is applied to the problem with error in forward operator. Thirdly, we show that empirical Bayes posterior distribution with a plugged‐in maximum marginal likelihood estimator of the prior scale contracts at the optimal rate, adaptively, in the minimax sense.
Deltoid Tangents with Evenly Distributed Orientations and Random Tilings

We study the construction of substitution tilings of the plane based on certain simplicial configurations of tangents of the deltoid with evenly distributed orientations. The random tiling ensembles are obtained as a result of tile rearrangements in the substitution rules associated to edge flips. Special types of random tilings for Euclidean, spherical and hyperbolic three-manifolds are also considered.
Solitary wave solutions to the Isobe‐Kakinuma model for water waves

We consider the Isobe‐Kakinuma model for two‐dimensional water waves in the case of a flat bottom. The Isobe‐Kakinuma model is a system of Euler‐Lagrange equations for a Lagrangian approximating Luke's Lagrangian for water waves. We show theoretically the existence of a family of small amplitude solitary wave solutions to the Isobe‐Kakinuma model in the long wave regime. Numerical analysis for large amplitude solitary wave solutions is also provided and suggests the existence of a solitary wave of extreme form with a sharp crest.
AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network

Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather. These proteins bind to the ice crystals, hindering their growth into large ice lattice that could cause physical damage. There are variety of AFPs found in numerous organisms and due to the heterogeneous sequence characteristics, AFPs are found to demonstrate a high degree of diversity, which makes their prediction a challenging task. Herein, we propose a machine learning framework to deal with this vigorous and diverse prediction problem using the manifolding learning through composition of k-spaced amino acid pairs. We propose to use the deep neural network with skipped connection and ReLU non-linearity to learn the non-linear mapping of protein sequence descriptor and class label. The proposed antifreeze protein prediction method called AFP-CKSAAP has shown to outperform the contemporary methods, achieving excellent prediction scores on standard dataset. The main evaluater for the performance of the proposed method in this study is Youden's index whose high value is dependent on both sensitivity and specificity. In particular, AFP-CKSAAP yields a Youden's index value of 0.82 on the independent dataset, which is better than previous methods.
A Population of Neutron Star Ultraluminous X-Ray Sources with a Helium Star Companion

It was recently proposed that a significant fraction of ultraluminous X-ray sources (ULXs) actually host a neutron star (NS) accretor. We have performed a systematic study on the NS ULX population in Milky Way–like galaxies, by combining binary population synthesis and detailed stellar evolution calculations. Besides a normal star, the ULX donor can be a helium star (the hydrogen envelope of its progenitor star was stripped during previous common envelope evolution) if the NS is accreting at a super-Eddington rate via Roche lobe overflow. We find that the NS−helium star binaries can significantly contribute to the ULX population, with the overall number of about several in a Milky Way–like galaxy. Our calculations show that such ULXs are generally close systems with orbital period distribution peaked at ∼0.1 day (with a tail up to ∼100 days), and the helium stars have relatively low masses distributing with a maximum probability at ∼1M⊙.
The binomial transform of p-recursive sequences and the dilogarithm function

Using a generalized binomial transform and a novel binomial coefficient identity, we will show that the set of p-recursive sequences is closed under the binomial transform. Using these results, we will derive a new series representation for the dilogarithm function that converges on $\mathbf{C} \, \backslash \, [1,\infty)$. Finally, we will show that this series representation results in a scheme for numerical evaluation of the dilogarithm function that is accurate, efficient, and stable.
Molybdenum Disulfide Nanoflakes Grown by Chemical Vapor Deposition on Graphite: Nucleation, Orientation, and Charge Transfer

Two-dimensional molybdenum disulphide on graphene grown by chemical vapour deposition is a promising van der Waals system for applications in optoelectronics and catalysis. To extend the fundamental understanding of growth and intrinsic properties of molybdenum disulphide on graphene, molybdenum disulphide on highly oriented pyrolytic graphite is a suitable model system. Here we show, experimentally and by density-functional-theory calculations, that molybdenum disulphide flakes grow in two orientations. One of the orientations is energetically preferred, the other one is rotated by 30 degree. Because of a high energy barrier confirmed by our calculations both orientations are stable at room temperature and their switching can only be forced by external stimuli, i.e. by a scanning tunneling microscope tip. Combined Kelvin probe microscopy and Raman spectroscopy measurements show that the flakes with a typical size of a few hundred nanometers are less doped than the often studied exfoliated molybdenum disulphide single layer.
Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation

Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays sparked new interest in the potential of 3D visualization for immersive network analytics. Nevertheless, most solutions do not scale well with the number of nodes and edges and rely on conventional fly-or walk-through navigation. In this paper, we present a novel approach for the exploration of large dynamic graphs in virtual reality that interweaves two navigation metaphors: overview exploration and immersive detail analysis. We thereby use the potential of state-of-the-art VR headsets, coupled with a web-based 3D rendering engine that supports heterogeneous input modalities to enable ad-hoc immersive network analytics. We validate our approach through a performance evaluation and a case study with experts analyzing medical data.