Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Determinants of Serum Ascorbic Acid Concentrations

Background: The adequacy of serum ascorbic acid (vitamin C) concentrations in young Canadian adults is unknown. Individuals have varied serum ascorbic acid response to dietary vitamin C, possibly due to genetic variation. Objective: To investigate the prevalence of serum ascorbic acid deficiency in young Canadians and to determine whether common genotypes modify the association between dietary vitamin C and serum ascorbic acid. Methods: Subjects were 1277 men and women aged 20-29 years from the Toronto Nutrigenomics and Health study. Vitamin C intakes were estimated by a 196-item FFQ. Fasting blood was collected to measure serum ascorbic acid by HPLC and to genotype for common polymorphisms in genes that code for glutathione S-transferase (GST) (GSTM1, GSTT1 and GSTP1), haptoglobin (Hp), and vitamin C transporters (SLC23A1 and SLC23A2). Results: 53% of subjects had adequate, 33% had suboptimal and 14% had deficient serum ascorbic acid. Subjects with deficiency had higher mean C-reactive protein, waist circumference, BMI and blood pressure than subjects with adequate serum ascorbic acid. The odds ratio (95% confidence interval) for serum ascorbic acid
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman Filter

Variational inference (VI) combined with Bayesian nonlinear filtering produces state-of-the-art results for latent time-series modeling. A body of recent work has focused on sequential Monte Carlo (SMC) and its variants, e.g., forward filtering backward simulation (FFBSi). Although these studies have succeeded, serious problems remain in particle degeneracy and biased gradient estimators. In this paper, we propose Ensemble Kalman Variational Objective (EnKO), a hybrid method of VI and the ensemble Kalman filter (EnKF), to infer state space models (SSMs). Our proposed method can efficiently identify latent dynamics because of its particle diversity and unbiased gradient estimators. We demonstrate that our EnKO outperforms SMC-based methods in terms of predictive ability and particle efficiency for three benchmark nonlinear system identification tasks.
Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction

Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets. However, existing works on distantly supervised relation extraction suffer from the low quality of test set, which leads to considerable biased performance evaluation. These biases not only result in unfair evaluations but also mislead the optimization of neural relation extraction. To mitigate this problem, we propose a novel evaluation method named active testing through utilizing both the noisy test set and a few manual annotations. Experiments on a widely used benchmark show that our proposed approach can yield approximately unbiased evaluations for distantly supervised relation extractors.
UoB at SemEval-2020 Task 1: Automatic Identification of Novel Word Senses

Much as the social landscape in which languages are spoken shifts, language too evolves to suit the needs of its users. Lexical semantic change analysis is a burgeoning field of semantic analysis which aims to trace changes in the meanings of words over time. This paper presents an approach to lexical semantic change detection based on Bayesian word sense induction suitable for novel word sense identification. This approach is used for a submission to SemEval-2020 Task 1, which shows the approach to be capable of the SemEval task. The same approach is also applied to a corpus gleaned from 15 years of Twitter data, the results of which are then used to identify words which may be instances of slang.
Learning to Reconstruct and Segment 3D Objects

To endow machines with the ability to perceive the real-world in a three dimensional representation as we do as humans is a fundamental and long-standing topic in Artificial Intelligence. Given different types of visual inputs such as images or point clouds acquired by 2D/3D sensors, one important goal is to understand the geometric structure and semantics of the 3D environment. Traditional approaches usually leverage hand-crafted features to estimate the shape and semantics of objects or scenes. However, they are difficult to generalize to novel objects and scenarios, and struggle to overcome critical issues caused by visual occlusions. By contrast, we aim to understand scenes and the objects within them by learning general and robust representations using deep neural networks, trained on large-scale real-world 3D data. To achieve these aims, this thesis makes three core contributions from object-level 3D shape estimation from single or multiple views to scene-level semantic understanding.
Capturing Longer Context for Document-level Neural Machine Translation: A Multi-resolutional Approach

Discourse context has been proven useful when translating documents. It is quite a challenge to incorporate long document context in the prevailing neural machine translation models such as Transformer. In this paper, we propose multi-resolutional (MR) Doc2Doc, a method to train a neural sequence-to-sequence model for document-level translation. Our trained model can simultaneously translate sentence by sentence as well as a document as a whole. We evaluate our method and several recent approaches on nine document-level datasets and two sentence-level datasets across six languages. Experiments show that MR Doc2Doc outperforms sentence-level models and previous methods in a comprehensive set of metrics, including BLEU, four lexical indices, three newly proposed assistant linguistic indicators, and human evaluation.
Combining strong and weak lensing estimates in the Cosmos field

We present a combined cosmic shear analysis of the modeling of line-of-sight distortions on strongly lensed extended arcs and galaxy shape measurements in the COSMOS field. We develop a framework to predict the covariance of strong lensing and galaxy shape measurements of cosmic shear on the basis of the small scale matter power-spectrum. The weak lensing measurement is performed using data from the COSMOS survey calibrated with a cloning scheme using the Ultra Fast Image Generator UFig [1]. The strong lensing analysis is performed by forward modeling the lensing arcs with a main lensing deflector and external shear components from the same Hubble Space Telescope imaging data set. With a sample of three strong lensing shear measurements we present a 2-sigma detection of the cross-correlation signal between the two complementary measurements of cosmic shear along the identical line of sight. With large samples of lenses available with the next generation ground and space based observatories, the covariance of the signal of the two probes with large samples of lenses allows for systematic checks, cross-calibration of either of the two measurement and the measurement of the small scale shear power-spectrum.
Mean curvature motion of point cloud varifolds

This paper investigates a discretization scheme for mean curvature motion on point cloud varifolds with particular emphasis on singular evolutions. To define the varifold a local covariance analysis is applied to compute an approximate tangent plane for the points in the cloud. The core ingredient of the mean curvature motion model is the regularization of the first variation of the varifold via convolution with kernels with small stencil. Consistency with the evolution velocity for a smooth surface is proven provided that a sufficiently small stencil and a regular sampling are considered. Furthermore, an implicit and a semi-implicit time discretization are derived. The implicit scheme comes with discrete barrier properties known for the smooth, continuous evolution, whereas the semi-implicit still ensures in all our numerical experiments very good approximation properties while being easy to implement. It is shown that the proposed method is robust with respect to noise and recovers the evolution of smooth curves as well as the formation of singularities such as triple points in 2D or minimal cones in 3D.
Unexpected Information Leakage of Differential Privacy Due to the Linear Property of Queries

Differential privacy is a widely accepted concept of privacy preservation, and the Laplace mechanism is a famous instance of differentially private mechanisms used to deal with numerical data. In this paper, we find that differential privacy does not take the linear property of queries into account, resulting in unexpected information leakage. Specifically, the linear property makes it possible to divide one query into two queries, such as <inline-formula> <tex-math notation="LaTeX">$q(D)=q(D_{1})+q(D_{2})$ </tex-math></inline-formula> if <inline-formula> <tex-math notation="LaTeX">$D=D_{1}\cup D_{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$D_{1}\cap D_{2}=\emptyset $ </tex-math></inline-formula>. If attackers try to obtain an answer to <inline-formula> <tex-math notation="LaTeX">$q(D)$ </tex-math></inline-formula>, they can not only issue the query <inline-formula> <tex-math notation="LaTeX">$q(D)$ </tex-math></inline-formula> but also issue <inline-formula> <tex-math notation="LaTeX">$q(D_{1})$ </tex-math></inline-formula> and calculate <inline-formula> <tex-math notation="LaTeX">$q(D_{2})$ </tex-math></inline-formula> by themselves as long as they know <inline-formula> <tex-math notation="LaTeX">$D_{2}$ </tex-math></inline-formula>. Through different divisions of one query, attackers can obtain multiple different answers to the same query from differentially private mechanisms. However, from the attackers’ perspective and differentially private mechanisms’ perspective, the total consumed privacy budget is different if divisions are delicately designed. This difference leads to unexpected information leakage because the privacy budget is the key parameter for controlling the amount of information that is legally released from differentially private mechanisms. To demonstrate unexpected information leakage, we present a membership inference attack against the Laplace mechanism. Specifically, under the constraints of differential privacy, we propose a method for obtaining multiple independent identically distributed samples of answers to queries that satisfy the linear property. The proposed method is based on a linear property and some background knowledge of the attackers. When the background knowledge is sufficient, the proposed method can obtain a sufficient number of samples from differentially private mechanisms such that the total consumed privacy budget can be made unreasonably large. Based on the obtained samples, a hypothesis testing method is used to determine whether a target record is in a target dataset.
Freetures: Localization in Signed Distance Function Maps

Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps. Existing techniques for geometry-based localization have focused on the description of local surface geometry, usually using pointclouds as the underlying representation. We propose a system for geometry-based localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF). The SDF varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space. Through evaluations on public datasets, we demonstrate the flexibility of this approach, and show an increase in localization performance over state-of-the-art handcrafted surfaces-only descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and ~18% on a LiDAR-based dataset. Finally, we demonstrate our system for localizing a LiDAR-equipped MAV within a previously built map of a search and rescue training ground.
Large fluctuations and transport properties of the Lévy–Lorentz gas

The Levy-Lorentz gas describes the motion of a particle on the real line in the presence of a random array of scattering points, whose distances between neighboring points are heavy-tailed i.i. d. random variables with finite mean. The motion is a continuous-time, constant-speed interpolation of the simple symmetric random walk on the marked points. In this paper we study the large fluctuations of the continuous-time process and the resulting transport properties of the model, both annealed and quenched, confirming and extending previous work by physicists that pertain to the annealed framework. Specifically, focusing on the particle displacement, and under the assumption that the tail distribution of the interdistances between scatterers is regularly varying at infinity, we prove a uniform large deviation principle for the annealed fluctuations and present the asymptotics of annealed moments, demonstrating annealed superdiffusion. Then, we provide an upper large deviation estimate for the quenched fluctuations and the asymptotics of quenched moments, showing that, unexpectedly, the asymptotically stable diffusive regime conditional on a typical arrangement of the scatterers is normal diffusion. Although the Levy-Lorentz gas seems to be accepted as a model for anomalous diffusion, our findings lead to the conclusion that superdiffusion is a metastable behavior, which develops into normal diffusion on long timescales.
Reduce and Reconstruct: Improving Low-resource End-to-end ASR Via Reconstruction Using Reduced Vocabularies

End-to-end automatic speech recognition (ASR) systems are increasingly being favoured due to their direct treatment of the problem of speech to text conversion. However, these systems are known to be data hungry and hence underperform in low-resource settings. In this work, we propose a seemingly simple but effective technique to improve low-resource end-to-end ASR performance. We compress the output vocabulary of the end-to-end ASR system using linguistically meaningful reductions and then reconstruct the original vocabulary using a standalone module. Our objective is two-fold: to lessen the burden on the low-resource end-to-end ASR system by reducing the output vocabulary space and to design a powerful reconstruction module that recovers sequences in the original vocabulary from sequences in the reduced vocabulary. We present two reconstruction modules, an encoder decoder-based architecture and a finite state transducer-based model. We demonstrate the efficacy of our proposed techniques using ASR systems for two Indian languages, Gujarati and Telugu.
Integrating multiple data sources for assessing blue whale abundance and distribution in Chilean Northern Patagonia

Species distribution models are useful tools for depicting important habitat, assessing abundance and orienting conservation efforts. For small populations in poorly studied ecosystems, available data are often scarce and patchy. To overcome this limitation, we aim to evaluate the use of different data types within a hierarchical Bayesian framework with the goal of modelling the abundance and distribution of a small and highly migratory population of blue whale (BW, Balaenoptera musculus) summering in Chilean Northern Patagonian (CNP).
Proportional Representation under Single-Crossing Preferences Revisited

We study the complexity of determining a winning committee under the Chamberlin-Courant voting rule when voters' preferences are single-crossing on a line, or, more generally, on a tree. For the line, Skowron et al. (2015) describe an O(n^2mk) algorithm (where n, m, k are the number of voters, the number of candidates and the committee size, respectively); we show that a simple tweak improves the time complexity to O(nmk). We then improve this bound for k=Ω(log n) by reducing our problem to the k-link path problem for DAGs with concave Monge weights, obtaining a nm2^O(√(log k log log n)) algorithm for the general case and a nearly linear algorithm for the Borda misrepresentation function. For trees, we point out an issue with the algorithm proposed by Clearwater, Puppe and Slinko (2015), and develop a O(nmk) algorithm for this case as well.
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models

We study knowledge-grounded dialogue generation with pre-trained language models. To leverage the redundant external knowledge under capacity constraint, we propose equipping response generation defined by a pre-trained language model with a knowledge selection module, and an unsupervised approach to jointly optimizing knowledge selection and response generation with unlabeled dialogues. Empirical results on two benchmarks indicate that our model can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment.
Incorporate Semantic Structures into Machine Translation Evaluation via UCCA

Copying mechanism has been commonly used in neural paraphrasing networks and other text generation tasks, in which some important words in the input sequence are preserved in the output sequence. Similarly, in machine translation, we notice that there are certain words or phrases appearing in all good translations of one source text, and these words tend to convey important semantic information. Therefore, in this work, we define words carrying important semantic meanings in sentences as semantic core words. Moreover, we propose an MT evaluation approach named Semantically Weighted Sentence Similarity (SWSS). It leverages the power of UCCA to identify semantic core words, and then calculates sentence similarity scores on the overlap of semantic core words. Experimental results show that SWSS can consistently improve the performance of popular MT evaluation metrics which are based on lexical similarity.
Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks

This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly charge energy-limited users to collect data from them. We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables. Unlike traditional centralized optimization algorithms which require global information collected at a central unit, the proposed MADRL technique models an H-AP as an agent producing its action based only on its own locally observable states. Numerical results verify that the proposed approach can achieve comparable performance of the centralized algorithms.
Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism

Non-autoregressive models generate target words in a parallel way, which achieve a faster decoding speed but at the sacrifice of translation accuracy. To remedy a flawed translation by non-autoregressive models, a promising approach is to train a conditional masked translation model (CMTM), and refine the generated results within several iterations. Unfortunately, such approach hardly considers the sequential dependency among target words, which inevitably results in a translation degradation. Hence, instead of solely training a Transformer-based CMTM, we propose a Self-Review Mechanism to infuse sequential information into it. Concretely, we insert a left-to-right mask to the same decoder of CMTM, and then induce it to autoregressively review whether each generated word from CMTM is supposed to be replaced or kept. The experimental results (WMT14 En ↔ De and WMT16 En ↔ Ro) demonstrate that our model uses dramatically less training computations than the typical CMTM, as well as outperforms several state-of-the-art non-autoregressive models by over 1 BLEU. Through knowledge distillation, our model even surpasses a typical left-to-right Transformer model, while significantly speeding up decoding.
Improvement of nutritional properties of Cassva (Manihot esculenta) through massive analysis of gene expression

Motivation: Currently, the cassava is the basis of food for more than 1 billion people in the world. In this instance, the modification of the nutritional composition of plant foods is an urgent matter, since the basic nutritional needs of the world population are not yet covered. The identification of the transcription factors that regulate oil biosynthesis could give tools to re-direct sucrose to oil in the root of tuberculous cultures. In cassava, most of the research that has been done has focused on the tuber, with little study of the seeds and their characteristics. In Cassava the factors that direct the flow of carbon towards the different reserve tissues and determine the final composition of the tissue in the plant are not known. The proposed research line aims to deepen the knowledge of the fatty acid biosynthesis in cassava and increase the oil content of the roots. Fundamental knowledge to be able to reach the final objective of increasing the oil content. In addition to improving its nutritional value, while the amount of nutrients that contribute to the body. Methods: There are two lines of research. The computer methods are based on searching for genes involved in the biosynthesis pathway of lipid substrates through databases such as Uniref and UniProt. After a series of genes obtained from other trials, we compared their presence in the Manihot Esculenta genome and verified their function. With homologous genes, we can expand the number of candidate genes. The laboratory methods aim to develop the method of genetic transformation of cassava (Ima M Zainuddin et al., Plant Methods 2012, 8:24) in combination with Agrobacterium. From a horizontal cuttings and an in vitro culture of their apical buds, we get a mother plant from which we can obtain somatic embryos and create new seedlings to be able to transform with different Results: The following results were obtained after a first search for genes obtained from a compilation base don papers previously made in other related organisms. From that list of candidates genes, there are genes that cease to be candidates because they are not found in Manihot esculenta, these being susy, ATP-PFK and FBAGenes that continue as candidates, for being in the genome of cassava, being PK,GPT2 and PPT1. And for the latter, new genes can be obtained, such as APE2 with identifier 16G010700.1 in JGI Phytozome. It is a translocator of triose-phosphate located in the chloroplast. Conclusions: There is a vision of the future in this line of research to be able to start from a new and broader list of genes involved in the synthesis routes of sucrose and oil that are susceptible to modification. There is still a lot to study to reach the goal, but positive results will come out.