Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Imaging Methods for Improving Resolution in Biological Microscopy

Author(s): Chan, Kevin G. | Advisor(s): Liebling, Michael | Abstract: Optical microscopy is an essential tool for biological research, as it allows for non-invasive imaging of small animals. However, optical microscopy has its limits. Due to the low light level, fluorescence microscopy prohibits high speed imaging, making it difficult to study fast dynamic biological processes. In addition, optical blur due to the diffraction of light results in limited spatial resolution, particularly when using objective lenses with low numerical apertures. In this thesis, we propose computational imaging methods to overcome these limitations using a combination of novel image acquisition procedures and reconstruction algorithms.The first part of this thesis deals with improving temporal resolution in fluorescence microscopy to image rapid, repeating processes. We take advantage of multiple acquisitions, each taken with different time delays or temporally modulated illumination patterns, to recover high frequency information that is lost with traditional imaging. We demonstrate our method to image the beating heart in live embryonic zebrafish with reduced motion blur and high resolution in time.The second part of this thesis deals with reducing spatial blur in optical projection tomography, a form of optical microscopy that uses multiple 2D projections to reconstruct a 3D image of an object. We propose a method to reduce the optical distortion (as characterized by the system's optical point spread function) that can be implemented with a scanning acquisition approach combined with a modified filtered backprojection algorithm for reconstruction. We demonstrate our method to image blood vessels in larval zebrafish with high spatial resolution and reduced out-of-focus blur.The final part of this thesis deals with the dimensional limitation of 2D sensors for measuring 3D motion in microscopy. We propose a method to combine two-dimensional motion estimates from multiple views to recover out-of-plane velocity and reconstruct a divergence-free, three-dimensional velocity field. We demonstrate our method to measure, for the first time, dynamic blood flow in 3D inside the beating heart of a live zebrafish using optical microscopy.This thesis provides new tools that integrate custom image acquisition procedures and image reconstruction algorithms to overcome the resolution limitations -- temporal, spatial, and out-of-plane velocity resolution -- in optical microscopy. The methods presented in this thesis, in particular the single camera, active illumination method for temporal superresolution in fluorescence microscopy, will be directly applicable to a broad range of biological studies and will open up new perspectives for imaging small organisms in 3D (and time) with high spatio-temporal resolution.
Hierarchical Transfer Learning for Multi-label Text Classification

Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing documents into one or more topics organized in an hierarchical taxonomy. MLHTC can be formulated by combining multiple binary classification problems with an independent classifier for each category. We propose a novel transfer learning based strategy, HTrans, where binary classifiers at lower levels in the hierarchy are initialized using parameters of the parent classifier and fine-tuned on the child category classification task. In HTrans, we use a Gated Recurrent Unit (GRU)-based deep learning architecture coupled with attention. Compared to binary classifiers trained from scratch, our HTrans approach results in significant improvements of 1% on micro-F1 and 3% on macro-F1 on the RCV1 dataset. Our experiments also show that binary classifiers trained from scratch are significantly better than single multi-label models.
Multiple-filtering-process for the Edge Detection of High-dynamic-range Images

Edge detection is a basic image processing operation usually used in the first stage of the complex image processing systems, such as restoration, and its quality has a direct effect on the performance of the systems. The extraction of correct edges from a noise-contaminated image or an image with severe deformation is a challenging task. The objective of the work of this thesis is to develop an edge detection method to extract effectively edge signals from the images with the edge information seriously damaged while being acquired in high dynamic range (HDR) scenes. To achieve the objective, an edge detection method based on a multiple-high-pass-filtering process scheme has been proposed. Each of the filtering processes is designed to suit one of the signal deformation conditions, and is applied to the entire input image, instead of the designated regions, in order to spare the computation of image segmentation. A fusion process is then performed to merge the gradient maps generated by the multiple filtering processes into one. A detection procedure has been designed for a typical case of HDR images acquired with three different kinds of deformations due to the non-ideal characteristics of acquisition device. Based on the study of the characteristics, three high-pass filtering processes are designed to generate gradient signals with different modulations. A simple selection algorithm is developed for an easy fusion process. The results of the simulation with different types of HDR images have shown that, compared to some of most commonly used detection processes, the proposed one leads to a better quality of edge signals from severely deformed HDR images.
Determinism in GPU Programs - Real Time Applications on the NVIDIA Jetson TK1

In recent years, increases in computing power have reached the automotive industry in the form of advanced driver assistance systems (ADAS). Cars that support these systems require a variety of sensors and powerful computers. Sensor data must be processed and acted upon in real time so that the car can react to changes in its environment as they occur. This is especially important when the car must take action to avoid a collision and potentially prevent loss of life. GPUs offer a large speedup for processing sensor data but their runtime performance can be unpredictable in some cases. In this paper we evaluate the predictability of GPU programs on the NVIDIA Jetson TK1 and make them perform more predictably by using Zero Copy memory.
Carrier Attitude Adjustment Strategy of a Space Robot for On-Orbit Detection

A strategy for attitude adjustment of a free-floating space robot is proposed to reduce fuel consumption and time required to operate a space manipulator and its carrier before and after the detection of an on-orbit spacecraft. This strategy can be used in such a way that the carrier attitude and space manipulator joint angle reach their expected values at the same time. In order to obtain an advanced effect of carrier attitude adjustment, a novel parameterization method of joint trajectory is proposed, which extends the search scope of the joint trajectory by the fusion of the 4-3-4 and 3-5-3 planning methods. An objective function is defined according to the difference of the expected and initial carrier attitudes. A genetic algorithm is used to search for an optimal solution of the parameters. The effectiveness of the proposed strategy is verified through simulations for a six-degree-of-freedom (6-DOF) space robot system.
Semantically enhanced cross-domain recommender systems

Memory-based collaborative filtering use past activities of a group of similar users to recommend future preferences for a target user in the group. Recommender systems based on this type of technique are prone to errors when there are too few historic interactions (e.g. rating, likes, transaction-history, visit-frequencies) between users of the system. The sparsity in users’ historic data render the memory-based algorithm less effective at finding similar users for the personalisation process. In contrast, model-based collaborative filtering techniques such as matrix factorisation (MF) use predictive models. In single-domain recommender systems, one problem prevalent to all techniques is the cold start problem. A cold start situation happens when there is no historical information about new users or items that have just been introduced to the system. Several recommender techniques have used semantic knowledge extracted from additional user and item information to build profiles that reflect otherwise implicit user preferences. This semantic representation of the user is then used to find other similar users and address sparsity and cold-start problems in single domain recommender systems. Recent attempts to resolve cold-start and sparsity problems are considering cross-domain collaborative filtering techniques. Cross-domain recommender systems exploit additional user and item information from domains that are unique but related to the target recommendation domain. Extending predictive models to include parameters that model the semantic similarity in user and item information across the domains constitute a genuine approach in cross-domain recommendation. The contents of this thesis centre around the use of semantically enhanced cross-domain recommender systems as a solution to cold start and sparsity problems. The contributions to cross-domain recommender systems are in three folds. First, we investigate and analyse the performance of a cross-domain recommender model as we vary the size of intersecting user/item information from the target and auxiliary domains. Secondly, we proposed a predictive model that adds semantically related tags as additional parameters to a matrix factorisation model. Thirdly, we present a model that incorporates category similarity
Training Hybrid Language Models by Marginalizing over Segmentations

In this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words. Using such models, multiple potential segmentations usually exist for a given string, for example one using words and one using characters only. Thus, the probability of a string is the sum of the probabilities of all the possible segmentations. Here, we show how it is possible to marginalize over the segmentations efficiently, in order to compute the true probability of a sequence. We apply our technique on three datasets, comprising seven languages, showing improvements over a strong character level language model.
EEG Classification using Semi Supervised Learning

The major challenge in the current brain–computer interface research is the accurate classification of time-varying electroencephalographic (EEG) signals. The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and easy to collect in real applications. Semi-supervised learning (SSL) methods can utilize both labeled and unlabeled data to improve performance over supervised approaches. However, it has been reported that the unlabeled data may undermine the performance of SSL in some cases. This study proposes a three stages technique for automatic detection of epileptic seizure in EEG signals. In practical application of pattern recognition, there are often diverse features extracted from raw data which needs to be recognized. Proposed method is based on time series signal, spectral analysis and recurrent neural networks (RNNs). Decision making was performed in three stages:(i)feature extraction using Welch method power spectrum density estimation (PSD) (ii)dimensionality reduction using statistics over extracted features and time series signal samples (iii)EEG classification using recurrent neural networks. This study shows that Welch method power spectrum density estimation is an appropriate feature which well represents EEG signals. We achieved higher classification accuracy (specificity, sensitivity, classification accuracy) in comparison with other researches to classify EEG signals exactly 100% in this study. To improve the safety of SSL, we proposed a new safety-control mechanism by analyzing the differences between unlabeled data analysis in supervised and semi-supervised learning. We then develop and implement a safe classification method based on the semi-supervised extreme learning machine (SS-ELM). Following this approach, the Wasserstein distance is used to measure the similarities between the predictions obtained from ELM and SS-ELM algorithms, and a different risk degree is thereby calculated for each unlabeled data instance. A risk-based regularization term is then constructed and embedded into the objective function of the SS-ELM. Extensive experiments were conducted using benchmark and EEG datasets to evaluate the effectiveness of the proposed method. Experimental results show that the performance of the new algorithm is comparable to SS-ELM and superior to ELM on average. It is thereby shown that the proposed method is safe and efficient for the classification of EEG signals.
Optogenomic Interfaces: Bridging Biological Networks With the Electronic Digital World

The development of optical nano–bio interfaces is a fundamental step toward connecting biological networks and traditional electronic computing systems. Compared to conventional chemical and electrical nano–bio interfaces, the use of light as a mediator enables new type of interfaces with unprecedented spatial and temporal resolutions. In this paper, the state of the art and future research directions in optogenomic interfaces are discussed. Optogenomic interfaces are light-mediated nano–bio interfaces that allow the control of the genome, i.e., the genes and their interactions in the cell nucleus (and, thus, of all the cell functionalities) with (sub) cellular resolution and high temporal accuracy. Given its fundamental role in the process of cell development, the study is focused on the interactions with the fibroblast growth factor receptor 1 (FGFR1) gene and the integrative nuclear FGFR1 signaling (INFS) module in stem cells and in neuronal cells, whose control opens the door to transformative applications, including reconstructive medicine and cancer therapy. Three stages of optogenomic interfaces are described, ranging from already experimentally validated interfaces activating broad cellular responses and expressing individual genes to more advanced interfaces able to regulate and correct DNA topology, chromatin structure, and cellular development.
Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System

Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and largely overlook situational context. In this work, we propose Hydra, a recommendation system that offers multi-modal transportation planning and is adaptive to various situational context (e.g., nearby point-of-interest (POI) distribution and weather). We leverage the availability of existing routing engines and big urban data, and design a novel two-level framework that integrates uni-modal and multi-modal (e.g., taxi-bus, bus-cycle) routes as well as heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features constructed from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs and transportation modes based on user implicit feedbacks, which captures the collaborative transportation mode preferences of users and OD pairs. A gradient boosting tree based model is then introduced to recommend the proper route among various uni-modal and multi-modal transportation routes. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy Hydra on Baidu Maps, one of the world's largest map services. Real-world urban-scale experiments demonstrate the effectiveness and efficiency of our proposed system. Since its deployment in August 2018, Hydra has answered over a hundred million route recommendation queries made by over ten million distinct users with 82.8% relative improvement of user click ratio.
Fast Word Lattice Generation in Morphological Analysis

This paper proposes a fast word lattice generation algorithm for Japanese morphological analysis. We conducted experiments on three Japanese data sets to demonstrate that the previously proposed pruning-based algorithm is in fact not efficient enough, and that the pipeline algorithm, which is introduced in this paper, achieves considerable speed-up without loss of accuracy. Moreover, the compactness of the lattice generated by the pipeline algorithm was investigated from both theoretical and empirical perspectives.
A comparison of elaborated and restricted feedback in LogEx, a tool for teaching rewriting logical formulae

This article describes an experiment with LogEx, an e‐learning environment that supports students in learning how to prove the equivalence between two logical formulae, using standard equivalences such as DeMorgan. In the experiment, we compare two groups of students. The first group uses the complete learning environment, including hints, next steps, worked solutions, and informative timely feedback. The second group uses a version of the environment without hints or next steps, but with worked solutions, and delayed flag feedback. We use pretest and posttest to measure the performance of both groups with respect to error rate and completion of the exercises. We analyse the loggings of the student activities in the learning environment to compare its use by the different groups. Both groups score significantly better on the posttest than on the pretest. We did not find significant differences between the groups in the posttest, although the group using the full learning environment performed slightly better than the other group. In the examination, which took place 5 weeks after the experiment, the group of students who used the complete learning environment scored significantly better than a control group of students who did not participate in the experiment.
USRP Testbed and Performance Analysis of New Reconfigurable LDACS In Presence of DME Interference

Existing VHF band based air-to-ground communication (A2GC) is suffering from severe congestion especially in Europe and the US where air-traffic has increased significantly in the last decade. To overcome this problem, a new L-band digital aeronautical communication system (LDACS) is being explored by ICAO. Recently, we proposed reconfigurable filtered OFDM based LDACS (Ref-OFDM) which has the potential to improve the spectrum efficiency of existing OFDM based LDACS. In this paper, we develop an experimental setup using universal software radio peripherals (USRPs) and analyze the performance of these two LDACS. The comparison is made in terms of interference to the legacy distance measuring equipment (DME) channels in L-band and BER for various channel conditions. Specifically, we analyze the performance by varying three parameters related to the LDACS/DME transmitter and receiver: 1) Altitude, 2) Distance, and 3) DME antenna gain. We also consider different channels such as Rayleigh fading channel (LOS path) and Rician channel (non-LOS paths). The proposed Ref-OFDM performs superior in all considered scenarios, and experimental results closely match the simulation results making Ref-OFDM based LDACS as an attractive alternative to the next generation LDACS.
Integrating Prepositions in Wordnets: Relations, Glosses and Visual Description

Lexicology and lexicon models are necessarily concerned with content words, being grammatical and functional categories often set aside. Currently, however, lexicographers work for real needs and, in a NLP perspective, the nature of computational lexicons reflects a necessary match between what we know about the mental lexicon and what we need to encode about the set of words of a given language. Prepositions, in many languages, combine these two prototypes of words – lexical and functional –, as they can have full meaning or serve solely as structural aids. Based on the analysis of Portuguese prepositions related to the expression of movement, this paper describes how the integration of prepositions in wordnets is possible and quite easy, requiring mainly the linguistic adaptation of the tests and conditions that mediate the establishment of the relations of synonymy, antonymy, hyperonymy and cause. In what concerns lexicographic strategies, the integration of prepositions shows the difficulty of establishing equivalences between the concepts denoted by prepositions in different languages, as well as the difficulty of using glosses in natural language to describe their meaning. The use of visual information may obviate this issue, while posing issues on implementation.
Nexus: Small Worlds and the Groundbreaking Science of Networks

From the Publisher: As Chaos explained the science of disorder, Nexus reveals the new science of connection and the odd logic of six degrees of separation. "If you ever wanted to know how many links connect you and the Pope, or why when the U.S. Federal Reserve Bank sneezes the global economy catches cold, read this book," writes John L. Casti (Santa Fe Institute). This "cogent and engaging" (Nature) work presents the fundamental principles of the emerging field of "small-worlds" theorythe idea that a hidden pattern is the key to how networks interact and exchange information, whether that network is the information highway or the firing of neurons in the brain. Mathematicians, physicists, computer scientists, and social scientists are working to decipher this complex organizational system, for it may yield a blueprint of dynamic interactions within our physical as well as social worlds. Highlighting groundbreaking research behind network theory, "Mark Buchanan's graceful, lucid, nontechnical and entertaining prose" (Mark Granovetter) documents the mounting support among various disciplines for the small-worlds idea and demonstrates its practical applications to diverse problemsfrom the volatile global economy or the Human Genome Project to the spread of infectious disease or ecological damage. Nexus is an exciting introduction to the hidden geometry that weaves our lives so inextricably together. Author Biography: Mark Buchanan is a science writer and holds a doctorate in physics. He has been an editor at Nature and New Scientist. He lives in France.