Miracle-dz/UniFAR-FTUs huggingface dataset:
Harnessing population-specific protein truncating variants to improve the annotation of loss-of-function alleles

Accurate annotation of putative loss-of-function (pLoF) variants is an important problem in human genomics and disease, which recently drew substantial attention. Since such variants in disease-related genes are under strong negative selection, their frequency across major ancestral groups is expected to be highly similar. In this study, we tested this assumption by systematically assessing the presence of highly population-specific protein-truncating variants (PTVs) in human genes using latest population-scale data. We discovered an unexpectedly high incidence of population-specific PTVs in all major ancestral groups. This does not conform to a recently proposed model, indicating either systemic differences in disease penetrance in different human populations, or a failure of current annotation criteria to accurately predict the loss-of-function potential of PTVs. We show that low-confidence pLoF variants are enriched in genes with non-uniform PTV count distribution, and developed a computational tool called LoFfeR that can efficiently predict tolerated pLoF variants. To evaluate the performance of LoFfeR, we use a set of known pathogenic and benign PTVs from the ClinVar database, and show that LoFfeR allows for a more accurate annotation of low-confidence pLoF variants compared to existing methods. Notably, only 4.4% of protein-truncating gnomAD SNPs in canonical transcripts can be filtered out using a recommended threshold value of the recently proposed pext score, while up to 10.9% of such variants are filtered using LoFfeR with the same false positive rate. Hence, we believe that LoFfeR can be used for additional filtering of low-confidence pLoF variants in population genomics and medical genetics studies.
Image and Video Understanding for Recommendation and Spam Detection Systems

Image and video-based content has become ever present in a variety of domains like news, entertainment and education. Users typically discover and engage with content via search and recommendation systems. It is also important to serve high quality data to users by filtering out irrelevant or harmful content. Thus, there is an increasing need to leverage the rich information in image and video content in order to power systems for search and recommendation. At the same time, the effectiveness and efficiency of these systems has been accelerated by the availability of large-scale labeled datasets and sophisticated deep learning-based models. This tutorial is aimed at providing an overview of image and video understanding, and their practical applications in the industry. We focus on deep learning-based state of the art techniques for image and video understanding. This includes tasks like image classification and segmentation, image-based content retrieval and video classification. We also focus on applications of these technologies to large-scale recommendation and low quality content detection systems. We present concrete examples from various LinkedIn production systems, and also discuss associated practical challenges.
Cutting Pattern Positioning Method Based on Improved ROI Pooling of R2CNN

It is of great significance for textile industry to realize automatic pattern detection and positioning. In this paper, combining with image processing technology and deep learning theory, an improved pattern location method based on R2CNN is proposed. Firstly, the multi-scale ROI pooling structure was designed on the basis of R2CNN network, the proportion of the suggestion window generated by RPN network was adjusted, and the pattern Angle prediction function was introduced. The experimental results show that the training on the self-made and labeled data sets achieves an average accuracy of 85%, which greatly improves the positioning accuracy of cut patterns.
The Hierarchical Structure of Temporal Modulations in Music is Universal across Genres and matches Infant-Directed Speech

Rhythm is essential in music. It is formed by the hierarchical structure of events such as notes, beat, accent and grouping. The core acoustic correlates of these hierarchical rhythmic events, however, remain unknown. This study examined the slow (<40 Hz) temporal modulation structure of music by applying two modeling approaches previously applied to infant- and child-directed speech. The first approach utilized a low-dimensional representation of the auditory signal based on the cochlear filterbank in the human brain, comprising 3 broad bands of amplitude modulation and 5 broad spectral bands; a “Spectral-Amplitude Modulation Phase Hierarchy (S-AMPH) modelling approach. The second utilized probabilistic amplitude demodulation (PAD), a signal-driven modelling approach that makes no adjustments for the brain. PAD has produced successful models of natural sounds like wind and rain, which are characterized by amplitude modulation ‘cascades’ correlated over long time scales and across multiple frequency bands. When applied to music, both models revealed a very similar hierarchically-nested amplitude modulation spectrum across different musical genres and instruments, including song. Accordingly, the core temporal architecture yielding musical rhythm appears universal. This same architecture is revealed by modelling infant- and child-directed speech, suggesting that both music and language may depend on a shared domain-general amplitude modulation architecture which, early in life, relies on shared neural processes. The demonstration that temporal modulation bands play a key role in rhythm hierarchies in music as well as in speech could suggest that the same evolutionary adaptations may underpin both music and language, explaining why both are human universals. Significance Statement Novel modelling across musical genres reveals a universal hierarchy of temporal modulations underpinning musical rhythm. The temporal modulation architecture of music shares fundamental commonalities with infant- and child-directed speech (IDS, CDS). As in IDS and CDS, temporal rhythm in delta- and theta-rate amplitude modulation bands shows mutual phase dependence.
CompactETA: A Fast Inference System for Travel Time Prediction

Computing estimated time of arrival (ETA) is one of the most important services for online ride-hailing platforms like DiDi and Uber. With billions of service queries per day on such platforms, a fast inference ETA module ensures the efficiency of the overall decision system to guarantee satisfied user experience, as well as saving significant operating cost. In this paper, we develop a novel ETA learning system named as CompactETA, which provides an accurate online travel time inference within 100 microseconds. In the proposed method, we encode high order spatial and temporal dependency into sophisticated representations by applying graph attention network on a spatiotemporal weighted road network graph. We further encode the sequential information of the travel route by positional encoding to avoid the recurrent network structure. The properly learnt representations enable us to apply a very simple multi-layer perceptron model for online real-time inference. Evaluation of both offline experiments and online A/B testing verifies that CompactETA reduces the inference latency by more than 100 times compared to a state-of-the-art system, while maintains competing prediction accuracy.
Ads Allocation in Feed via Constrained Optimization

Social networks and content publishing platforms have newsfeed applications, which show both organic content to drive engagement, and ads to drive revenue. This paper focuses on the problem of ads allocation in a newsfeed to achieve an optimal balance of revenue and engagement. To the best of our knowledge, we are the first to report practical solutions to this business-critical and popular problem in industry. The paper describes how large-scale recommender system like feed ranking works, and why it is useful to consider ads allocation as a post-operation once the ranking of organic items and (separately) the ranking of ads are done. A set of computationally lightweight algorithms are proposed based on various sets of assumptions in the context of ads on the LinkedIn newsfeed. Through both offline simulation and online A/B tests, benefits of the proposed solutions are demonstrated. The best performing algorithm is currently fully deployed on the LinkedIn newsfeed and is serving all live traffic.
On Sampling Top-K Recommendation Evaluation

Recently, Rendle has warned that the use of sampling-based top-k metrics might not suffice. This throws a number of recent studies on deep learning-based recommendation algorithms, and classic non-deep-learning algorithms using such a metric, into jeopardy. In this work, we thoroughly investigate the relationship between the sampling and global top-K Hit-Ratio (HR, or Recall), originally proposed by Koren[2] and extensively used by others. By formulating the problem of aligning sampling top-k ($SHR@k$) and global top-K (HR@K) Hit-Ratios through a mapping function f, so that SHR@k~ HR@f(k), we demonstrate both theoretically and experimentally that the sampling top-k Hit-Ratio provides an accurate approximation of its global (exact) counterpart, and can consistently predict the correct winners (the same as indicate by their corresponding global Hit-Ratios).
Computer Vision: Deep Dive into Object Segmentation Approaches

Image segmentation is the task of associating pixels in an image with their respective object class labels. It has a wide range of applications in many industries including healthcare, transportation, robotics, fashion, home improvement, and tourism. Many deep learning-based approaches have been developed for image-level object recognition and pixel-level scene understanding - with the latter requiring a much denser annotation of scenes with a large set of objects. This tutorial provides an end-to-end pipeline for performing image segmentation using the state-of-art deep learning approaches and public datasets. The hands-on session will provide instructions for dataset customization, transformation, and training, validating, and testing segmentation models. The goal of this tutorial is to provide participants with a strong understanding of building image segmentation models for downstream applications.
Truth Discovery against Strategic Sybil Attack in Crowdsourcing

Crowdsourcing is an information system for recruiting online workers to perform human intelligent tasks (HITs) that are hard for computers. Due to the openness of crowdsourcing, dynamic online workers with different knowledge backgrounds might give conflicting labels to a task. With the assumption that workers provide their labels independently, most existing works aggregate worker labels in a voting manner, which is vulnerable to Sybil attack where the attacker earns easy rewards by coordinating several Sybil workers to share a randomized label on each task for dominating the aggregation result. A strategic Sybil attacker also attempts to evade Sybil detection. In this paper, we propose a novel approach, called TDSSA (Truth Discovery against Strategic Sybil Attack), to defend against strategic Sybil attack. Experimental results on real-world and synthetic datasets indicate that TDSSA ensures more accurate inference of true labels under various Sybil attacking scenarios, as compared to state-of-the-art methods.
A Multiobjective Evolutionary Approach for Influence Maximization in Multilayer Networks

Influence Maximization (IM) is one key algorithmic problems in information diffusion research; it aims to select a set of users from a social network and, by following a specific model, maximize the number of users influenced (the influence spread). Yet despite its immense potential, relatively little research is dedicated to IM for multilayer networks. Conversely, most existing IM studies that rely on a greedy algorithm strategy only obtain a single solution that provides limited insights on the target networks' core organization. With that in mind, we focus on studying the Influence Maximization Problem (IMP) in multilayer networks. Specifically, we define novel concepts, such as the pairwise reciprocal length and pairwise influence, with respect to the information-diffusion process in multilayer networks. Then we formulate the IM in multilayer networks as a multiobjective optimization problem and employ the classic Nondominated Sorting Genetic Algorithm II (NSGA-II) to find a set of Pareto-optimal solutions that provide a wide range of options for decision makers. To maintain population diversity and accelerate the algorithm's convergence, we combine a heuristic population initialization strategy and an efficient two-point crossover operation. Extensive experiments show that our approach has competitive performance when compared to off-the-shelf IM algorithms with regard to influence spread and running time.
Edge-consensus Learning: Deep Learning on P2P Networks with Nonhomogeneous Data

An effective Deep Neural Network (DNN) optimization algorithm that can use decentralized data sets over a peer-to-peer (P2P) network is proposed. In applications such as medical data analysis, the aggregation of data in one location may not be possible due to privacy issues. Hence, we formulate an algorithm to reach a global DNN model that does not require transmission of data among nodes. An existing solution for this issue is gossip stochastic gradient descend (SGD), which updates by averaging node models over a P2P network. However, in practical situations where the data are statistically heterogeneous across the nodes and/or where communication is asynchronous, gossip SGD often gets trapped in local minimum since the model gradients are noticeably different. To overcome this issue, we solve a linearly constrained DNN cost minimization problem, which results in variable update rules that restrict differences among all node models. Our approach can be based on the Primal-Dual Method of Multipliers (PDMM) or the Alternating Direction Method of Multiplier (ADMM), but the cost function is linearized to be suitable for deep learning. It facilitates asynchronous communication. The results of our numerical experiments using CIFAR-10 indicate that the proposed algorithms converge to a global recognition model even though statistically heterogeneous data sets are placed on the nodes.
Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns

Many real-world time series data exhibit complex patterns with trend, seasonality, outlier and noise. Robustly and accurately decomposing these components would greatly facilitate time series tasks including anomaly detection, forecasting and classification. RobustSTL is an effective seasonal-trend decomposition for time series data with complicated patterns. However, it cannot handle multiple seasonal components properly. Also it suffers from its high computational complexity, which limits its usage in practice. In this paper, we extend RobustSTL to handle multiple seasonality. To speed up the computation, we propose a special generalized ADMM algorithm to perform the decomposition efficiently. We rigorously prove that the proposed algorithm converges approximately as standard ADMM while reducing the complexity from O(N2) to O(N log N) for each iteration. We empirically study our proposed algorithm with other state-of-the-art seasonal-trend decomposition methods, including MSTL, STR, TBATS, on both synthetic and real-world datasets with single and multiple seasonality. The experimental results demonstrate the superior performance of our decomposition algorithm in terms of both effectiveness and efficiency.
Bilateral Intracranial Beta Activity during Forced and Spontaneous Movements in a Hemi-PD Rat Model

Cortico-basal ganglia beta oscillations (13-30Hz) are assumed to be involved in motor impairments in Parkinson’s Disease (PD), especially in bradykinesia and rigidity. Various studies have utilized the unilateral 6-OHDA rat PD model to further investigate PD and test novel treatments. However, a detailed behavioral and electrophysiological characterization of the model, including analyses of popular PD treatments such as DBS, has not been documented in the literature. We hence challenged the 6-OHDA rat PD model with a series of experiments (i.e. cylinder test, open field test and rotarod test) aimed at assessing the motor impairments, analyzing the effects of Deep Brain Stimulation (DBS), and identifying under which conditions excessive beta oscillations occur. We found that hemi-PD rats presented an impaired performance in all experiments compared to the sham group, and DBS could improve their overall performance. Across all the experiments and behaviors, the power in the high beta band was observed to be an important biomarker for PD as it showed differences between healthy and lesioned hemispheres and between PD and sham rats. This all shows that the 6-OHDA PD model accurately represents many of the motor and electrophysiological symptoms of PD and makes it a useful tool for the pre-clinical testing of new treatments and further investigations into this disease.
Determination of environments and genes on the interaction effects of fixed factors on biometrical traits of local chicken ecotypes, Ethiopia

An exploratory field survey was conducted in the three districts of Quara, Alefa and Tachearmacheho, Ethiopia to determine the Role of Environments and Genes on the Interaction Effects of Fixed factors on Biometrical Traits of Local Chicken Ecotypes. Interaction effect results of live body weight and linear body measurements in the fixed effect of sexes, ecotype and their interactions are main effect. Males is significantly (P 0.05) sex by ecotype interaction effects on wingspan, shank length, body length, comb length and beak length among the three breeds. The other quantitative traits had significant (P<0.05) effects and this implies the significant variation were indicted that stable parameters are highly associated or governed by genotype rather than environmental effects.
Delivery Scope: A New Way of Restaurant Retrieval for On-demand Food Delivery Service

Recently on-demand food delivery service has become very popular in China. More than 30 million orders are placed by eaters of Meituan-Dianping everyday. Delicacies are delivered to eaters in 30 minutes on average. To fully leverage the ability of our couriers and restaurants, delivery scope is proposed as an infrastructure product for on-demand food delivery area. A delivery scope based retrieval system is designed and built on our platform. In order to draw suitable delivery scopes for millions of restaurant partners, we propose a pioneering delivery scope generation framework. In our framework, a single delivery scope generation algorithm is proposed by using spatial computational techniques and data mining techniques. Moreover, a scope scoring algorithm and decision algorithm are proposed by utilizing machine learning models and combinatorial optimization techniques. Specifically, we propose a novel delivery scope sample generation method and use the scope related features to estimate order numbers and average delivery time in a period of time for each delivery scope. Then we formalize the candidate scopes selection process as a binary integer programming problem. Both branch&bound algorithm and a heuristic search algorithm are integrated in our system. Results of online experiments show that scopes generated by our new algorithm significantly outperform manual generated ones. Our algorithm brings more orders without hurt of users' experience. After deployed online, our system has saved thousands of hours for operation staff, and it is considered to be one of the most useful operation tools to balance demand of eaters and supply of restaurants and couriers.
Genes ptz and idi, Coding for Cytokinin Biosynthesis Enzymes, Are Essential for Tumorigenesis and In Planta Growth by P. syringae pv. savastanoi NCPPB 3335

The phytopathogenic bacterium Pseudomonas syringae pv. savastanoi elicits aerial tumors on olive plants and is also able to synthesize large amounts of auxins and cytokinins. The auxin indoleacetic acid was shown to be required for tumorigenesis, but there is only correlational evidence suggesting a role for cytokinins. The model strain NCPPB 3335 contains two plasmid-borne genes coding for cytokinin biosynthesis enzymes: ptz, for an isopentenyl transferase and idi, for an isopentenyl-diphosphate delta-isomerase. Phylogenetic analyses showed that carriage of ptz and idi is not strictly associated with tumorigenic bacteria, that both genes were linked when first acquired by P. syringae, and that a different allele of ptz has been independently acquired by P. syringae pv. savastanoi and closely related bacteria. We generated mutant derivatives of NCPPB 3335 cured of virulence plasmids or with site-specific deletions of genes ptz and/or idi and evaluated their virulence in lignified and micropropagated olive plants. Strains lacking ptz, idi, or both produced tumors with average volumes up to 29 times smaller and reached populations up to two orders of magnitude lower than those induced by strain NCPPB 3335; these phenotypes reverted by complementation with the cloned genes. Trans-zeatin was the most abundant cytokinin in culture filtrates of NCPPB 3335. Deletion of gene ptz abolished biosynthesis of trans-zeatin and dihydrozeatin, whereas a reduced but significant amount of isopentenyladenine was still detected in the medium, suggesting the existence of other genes contributing to cytokinin biosynthesis in P. syringae. Conversely, extracts from strains lacking gene idi contained significantly higher amounts of trans-zeatin than extracts from the wild-type strain but similar amounts of the other cytokinins. This suggests that Idi might promote tumorigenesis by ensuring the biosynthesis of the most active cytokinin forms, their correct balance in planta, or by regulating the expression of other virulence genes. Therefore, gene ptz, but not gene idi, is essential for the biosynthesis of high amounts of cytokinins in culture; however, both ptz and idi are individually essential for the adequate development of tumors on olive plants by Psv NCPPB 3335.
The Reversal of Memory Deficits in an Alzheimer’s Disease Model Using Physical and Cognitive Exercise

Alzheimer’s disease (AD) is the leading cause of dementia in the world, accounting for 50–75% of cases. Currently, there is limited treatment for AD. The current pharmacological therapy minimizes symptom progression but does not reverse brain damage. Studies focused on nonpharmacological treatment for AD have been developed to act on brain plasticity and minimize the neurotoxicity caused by the amyloid-beta (Aβ) peptide. Using a neurotoxicity model induced by Aβ in rats, the present study shows that physical (PE) and cognitive exercise (CE) reverse recognition memory deficits (with a prominent effect of long-term object recognition memory), decrease hippocampal lipid peroxidation, restore the acetylcholinesterase activity altered by Aβ neurotoxicity, and seems to reverse, at least partially, hippocampal tissue disorganization.