Miracle-dz/UniFAR-FTUs huggingface dataset:
Proteasome activator PA200 maintains stability of histone marks during transcription and aging

The epigenetic inheritance relies on stability of histone marks, but various diseases, including aging-related diseases, are usually associated with alterations of histone marks. How the stability of histone marks is maintained still remains unclear. The core histones can be degraded by the atypical proteasome, which contains the proteasome activator PA200, in an acetylation-dependent manner during somatic DNA damage response and spermiogenesis. Here we show that PA200 promotes the transcription-coupled degradation of the core histones, and plays an important role in maintaining the stability of histone marks. Degradation of the histone variant H3.3, which is incorporated into chromatin during transcription, was much faster than that of its canonical form H3.1, which is incorporated during DNA replication. This degradation of the core histones could be suppressed by the transcription inhibitor, the proteasome inhibitor or deletion of PA200. The histone deacetylase inhibitor accelerated the degradation rates of H3 in general, especially its variant H3.3, while the mutations of the putative acetyl-lysine-binding region of PA200 abolished histone degradation in the G1-arrested cells, supporting that acetylation is involved in the degradation of the core histones. Deletion of PA200 dramatically altered deposition of the active transcriptional hallmarks (H3K4me3 and H3K56ac) and transcription, especially during cellular aging. Furthermore, deletion of PA200 or its yeast ortholog Blm10 accelerated cellular aging. Notably, the PA200-deficient mice displayed a range of aging-related deteriorations, including immune malfunction, anxiety-like behaviors and shorter lifespan. Thus, the proteasome activator PA200 is critical to the maintenance of the stability of histone marks during transcription and aging.
Lectotype Designations in the New World Myrmeleontidae

During the month of August, 1960, I had the opportunity of studying the extensive collection of ant-lions at the Museum of Comparative Zoology, Harvard University. This collection contains most of the type material of Nathan Ranks and Hermann Hagen. In many cases only syntypes were present which necessitated the desicgnation of lectotypes. Whenever possible I have selected and designated a male specimen as the lectotype since many of the specific characters are restricted to the males. Others of the syntypical series were labeled lectoparatypes. In many instances one specimen had a “type” label and the rest of the series “paratype” labels or no labels at all. However, these labels were not considered to be type designations as they were not indicated as such in the original descriptions. The specimens were therefore considered to be syntypes. Also, in checklng the specimens listed in the original descriptions with the available material, it was sometimes found that other specimens belonged to the type series, even though they were not so labeled. I have included such specimens in my decisions on lectotypes.
Gromov-Wasserstein optimal transport to align single-cell multi-omics data

Data integration of single-cell measurements is critical for understanding cell development and disease, but the lack of correspondence between different types of measurements makes such efforts challenging. Several unsupervised algorithms can align heterogeneous single-cell measurements in a shared space, enabling the creation of mappings between single cells in different data domains. However, these algorithms require hyperparameter tuning for high-quality alignments, which is difficult in an unsupervised setting without correspondence information for validation. We present Single-Cell alignment using Optimal Transport (SCOT), an unsupervised learning algorithm that uses Gromov Wasserstein-based optimal transport to align single-cell multi-omics datasets. We compare the alignment performance of SCOT with state-of-the-art algorithms on four simulated and two real-world datasets. SCOT performs on par with state-of-the-art methods but is faster and requires tuning fewer hyperparameters. Furthermore, we provide an algorithm for SCOT to use Gromov Wasserstein distance to guide the parameter selection. Thus, unlike previous methods, SCOT aligns well without using any orthogonal correspondence information to pick the hyperparameters. Our source code and scripts for replicating the results are available at https://github.com/rsinghlab/SCOT.
GeneTEFlow: A Nextflow-based pipeline for analysing gene and transposable elements expression from RNA-Seq data

Transposable elements (TEs) are mobile genetic elements in eukaryotic genomes. Recent research highlights the important role of TEs in the embryogenesis, neurodevelopment, and immune functions. However, there is a lack of a one-stop and easy to use computational pipeline for expression analysis of both genes and locus-specific TEs from RNA-Seq data. Here, we present GeneTEFlow, a fully automated, reproducible and platform-independent workflow, for the comprehensive analysis of gene and locus-specific TEs expression from RNA-Seq data employing Nextflow and Docker technologies. This application will help researchers more easily perform integrated analysis of both gene and TEs expression, leading to a better understanding of roles of gene and TEs regulation in human diseases. GeneTEFlow is freely available at https://github.com/zhongw2/GeneTEFlow.
Large-scale Inference of Population Structure in Presence of Missingness using PCA

Background Principal component analysis (PCA) is a commonly used tool in genetics to capture and visualize population structure. Due to technological advances in sequencing, such as the widely used non-invasive prenatal test, massive datasets of ultra-low coverage sequencing are being generated. These datasets are characterized by having a large amount of missing genotype information. We present EMU, a method for inferring population structure in the presence of rampant non-random missingness. Results We show through simulations that several commonly used PCA methods can not handle missing data arisen from various sources, which leads to biased results as individuals are projected into the PC space based on their amount of missingness. In terms of accuracy, EMU outperforms an existing method that also accommodates missingness while being competitively fast. We further tested EMU on around 100K individuals of the Phase 1 dataset of the Chinese Millionome Project, that were shallowly sequenced to around 0.08x. From this data we are able to capture the population structure of the Han Chinese and to reproduce previous analysis in a matter of CPU hours instead of CPU years. Conclusions EMU’s capability to accurately infer population structure in the presence of missingness will be of increasing importance with the rising number of large-scale genetic datasets. EMU is written in Python and is freely available at https://github.com/Rosemeis/emu/.
Activity Recognition in Egocentric Videos Using Bag of Key Action Units

In this paper we present a novel methodology for recognizing human activity in Egocentric video based on the Bag of Visual Features. The proposed technique is based on the assumption that, only a portion of the whole video can be sufficient to identify an activity. Rather, we argue that, for activity recognition in egocentric videos, the proposed approach performs better than any deep learning based method. Because, in egocentric videos, often the person wiring the sensor, becomes static for long time, or moves his head frequently. In both the cases, it becomes difficult to learn the spatiotemporal pattern of the video during action. The proposed approach divides the video into smaller video segments called Video Units. Spatio-temporal features extracted from the units, are clustered to construct the dictionary of Action Units (AU). The AUs are ranked based upon their score of likeliness. The scores are obtained by constructing a weighted graph with the AUs as vertices and edge weights calculated based on the frequencies of occurrences of the AUs during the activity. The less significant AUs are pruned out from the dictionary, and the revised dictionary of key AUs are used for activity classification. We test our approach on benchmark egocentric dataset and achieve a good accuracy.
Purchasing of Foreign Beauty Products in Mongolia: What Makes It and What Breaks It?

The purpose of this study was to investigate the impacts of internet celebrity endorsement on Mongolian purchase intention towards foreign beauty products. This study used a quantitative research methodology to examine the proposed hypotheses. A sample of 530 Mongolian social media community users aged 18 to 50 was collected by online questionnaire. Structural Equation Modeling (SEM) was used as statistical methods to analyze the relationships and mediate effects between variables. The finding showed that internet celebrity endorsement had significant positive influence on Word-of-Mouth, consumer buying behavior and purchase intention. This study will provoke new insights of future researchers and practitioners of beauty product industries, especially in Mongolian market. Moreover, beauty company will have a better understanding of the impacts of internet celebrity endorsement on consumers' purchasing intention and behavior towards foreign beauty products so as to improve their business practices.
Automatic Asset Selection and Allocation System with NSGA-II and Genetic Programming

Making a good investment decision is always difficult because of the large uncertainty, randomness, and unpredictability of asset price. However, a portfolio can help investors achieve a better result with a proper allocation of good quality assets and appropriate weights. In this study, we construct an automatic asset selection and allocation system. We first apply the genetic programing to design new risk factors which can bring abnormal return based on classical factors and then use the classical factors to select stocks. After that, weight of each stock is optimized by NSGA-II with three objective functions: Sharpe ratio, skewness and kurtosis. Our factors generated through by genetic programming successfully capture the abnormal return and NSGA-II helps us maximize Sharpe ratio and minimize drawdown and shortfall. In the last, though we have achieved remarkable cumulative return based on the optimized portfolio, more efforts are needed while applying it to the real market
Persistent memory and the rise of universal constructions

Non-Volatile Main Memory (NVMM) has brought forth the need for data structures that are not only concurrent but also resilient to non-corrupting failures. Until now, persistent transactional memory libraries (PTMs) have focused on providing correct recovery from non-corrupting failures without memory leaks. Most PTMs that provide concurrent access do so with blocking progress. The main focus of this paper is to design practical PTMs with wait-free progress based on universal constructions. We first present CX-PUC, the first bounded wait-free persistent universal construction requiring no annotation of the underlying sequential data structure. CX-PUC is an adaptation to persistence of CX, a recently proposed universal construction. We next introduce CX-PTM, a PTM that achieves better throughput and supports transactions over multiple data structure instances, at the price of requiring annotation of the loads and stores in the data structure---as is commonplace in software transactional memory. Finally, we propose a new generic construction, Redo-PTM, based on a finite number of replicas and Herlihy's wait-free consensus, which uses physical instead of logical logging. By exploiting its capability of providing wait-free ACID transactions, we have used Redo-PTM to implement the world's first persistent key-value store with bounded wait-free progress.
Autarky: closing controlled channels with self-paging enclaves

As the first widely-deployed secure enclave hardware, Intel SGX shows promise as a practical basis for confidential cloud computing. However, side channels remain SGX's greatest security weakness. Inparticular, the "controlled-channel attack" on enclave page faults exploits a longstanding architectural side channel and still lacks effective mitigation. We propose Autarky: a set of minor, backward-compatible modifications to the SGX ISA that hide an enclave's page access trace from the host, and give the enclave full control over its page faults. A trusted library OS implements an enclave self-paging policy. We prototype Autarky on current SGX hardware and the Graphene library OS, implementing three paging schemes: a fast software oblivious RAM system made practical by leveraging the proposed ISA, a novel page cluster abstraction for application-aware secure self-paging, and a rate-limiting paging mechanism for unmodified binaries. Overall, Autarky provides a comprehensive defense for controlled-channel attacks which supports efficient secure demand paging, and adds no overheads in page-fault free execution.
Dual Domain Approach for Colour Enhancement of Underwater Images

Scattering and Absorption of light in water are the two main problems in underwater scenario which result in low contrast, high level of blurring and dull colours in underwater images. To deal with these issues and to provide better visual quality image, the present paper proposes contrast stretching in approximation band of discrete wavelet transformed image. This is followed by intensity adjustment of different colour channels in spatial domain. To further improve the colour quality, image is processed in HSV (Hue, Saturation and Value) colour space. Different level of histogram stretching is applied on the R, G and B colour channels depending on their composition in underwater image. Result analysis indicates better results as compared to the state of art methods. Subjective results of proposed method show minimization of the bluish-green effect and reduction of non-uniform illumination leading to enhanced colours and image details. Quantitative results show that UIQM (Underwater Image Quality Measure) values between 1and 2 are achieved which signifies that images have been enhanced efficiently. Also, the Entropy values between 7 and 8 depict improvement in image details. Present method has application in underwater scenario for detection of corroded and leaked subsea pipelines.
Improvement of Trademark Retrieval Using Pseudo Relevance Feedback

As the number of registered trademarks become larger, the demand for trademark retrieval systems with high accuracy has grown to reduce the burden of human searchers. In particular, precision at high recall is important to avoid erroneous registration of newly submitted trademarks. In this paper we propose a method to realize this by using pseudo relevance feedback, which is a method to search again by using an expanded query formed based on top ranked results of the first search. From the experimental results with the database of about 620, 000 trademarks, we have achieved the improvement of precision by 2.4% at 100% recall as compared to the method without the feedback.
Vision-based Steering Angle Prediction by the Fusion of Depth and Intensity Deep Features

In monocular camera-based end-to-end driving, the vehicle driving parameters such as steering angle, speed etc are directly estimated from the camera using deep learning. On the other hand, in traditional autonomous driving, these parameters are estimated using multiple modules such as sensing, behaviour generation, path planning and control. Owing to its ability to directly estimate the driving parameters, the end-to-end driving framework has received significant attention from the research community. In this paper, we present a novel stereo-based deep learning framework for end-to-driving, where the depth and appearance information generated using the stereo camera, are integrated to improve the steering angle prediction accuracy, especially for varying illumination conditions. Validation of the proposed algorithm is performed using multiple sequences of pre-defined driving routes with an expert driver. Each pre-defined driving route is acquired over multiple days with varying illumination conditions. Utilizing the acquired dataset, we show that the steering angle prediction accuracy of stereo-based end-to-end driving is better than monocular camera-based end-to-end driving.
Examining the Influence of Customer-to-Customer Electronic Word of Mouth on Purchase Intention in Social Networking Sites

This paper discusses factors that drive perception of eWOM usefulness. Quantitative approach was employed for this research. Respondents were individuals who have made an online purchase using Instagram as its platform. The finding suggest that both perceived persuasiveness and source trustworthiness are significant predictors of e-WOM usefulness. Secondly, source trustworthiness was shown to have a significant positive impact on e-WOM credibility.
Estimation of Ball Possession Statistics in Soccer Video

In this work, we have estimated ball possession statistics from the video of a soccer match. The ball possession statistics is calculated based on the valid pass counts of two playing teams. We propose a player-ball interaction energy function to detect ball pass event. Based on position and velocity of the ball and players, a model for interaction energy is defined. The energy increases when the ball is closer and about to collide with a player. Lower energy denotes that the ball is freely moving and not near to any player. The interaction energy generates a binary state sequence which determines a valid pass or a miss-pass. We assess the performance of our model on publicly available soccer videos and have achieved close to 83% accuracy.
Accessible near-storage computing with FPGAs

Data transfers impose a major bottleneck in heterogenous system architectures. As a mitigation strategy, compute resources can be introduced in places where data occurs naturally. The increased diversity of compute resources in turn affects programming models and practicalities of software development for near-data compute kernels and raises the question of how those resources can be made accessible to users and applications. We introduce the Metal FS framework to improve the accessibility of FPGA-based near-storage accelerators: Firstly, we present a near-storage-compute-aware file system that enables self-contained, reusable compute kernels to operate on the granularity of file data streams. Secondly, we provide an integrated build process for FPGA overlay images that starts with the acquisition of compute kernels through a package manager and finally allows to dynamically configure near-storage compute pipelines consisting of them. Thirdly, we integrate the framework into Linux as a file system driver and repurpose Unix Pipes as a well-known operating system primitive to orchestrate near-storage compute pipelines.
The ACM SIGSOFT Paper and Peer Review Quality Initiative

Scholarly peer review is crucial to science: it not only determines what is published where, but also, indirectly, who is hired, funded and promoted. Yet, virtually every academic has peer review horror stories. Empirical evidence suggests that "peer review is prejudiced, capricious, inefficient, ineffective, and generally unscientific" [1]. An experiment at a major machine learning conference found that peer review was unreliable highlighted that the outcome of peer review can be very noisy [2, 3]. In May 2019, ACM SIGSOFT launched an initiative to improve the quality of research papers and peer reviews at software engineering venues. It has two main components: empirical standards and recommendations for improving review processes.
Dynamic Gesture Recognition with Pose-based CNN Features derived from videos using LSTM

In this work, we address the problem of dynamic gesture recognition using a pose based video descriptor. The proposed approach takes as input video frames and extracts pose-specific image regions which are further processed by a pre-trained Convolutional Neural Network (CNN) to derive a pose-based descriptor for each frame. A Long Short Term Memory (LSTM) network is trained from scratch for dynamic gesture classification by learning long-term spatiotemporal relations among features. We also demonstrate that only using video data (RGB frames and optical flow) one can design an effective model for recognizing dynamic gestures. We utilize ChaLearn multi-modal gesture challenge dataset [13] and Cambridge hand gesture dataset [18] for evaluation of the proposed algorithm achieving an accuracy of 91.27% and 96% respectively using only RGB data.
Game Element Preferences and Engagement of Different Hexad Player Types in a Gamified Physics Course

Recently, the research community has taken interest on the use of gamification to engage and motivate students. However, researches reveal mixed results on its effect on students which suggest that individual differences should be considered in designing gamified systems. As an initial step towards this goal, this study investigated on the game element preferences and engagement of students with different player types. The participants in the study were seventy-three (73) students taking up College Physics 1 course in a private university. The player type of the students was profiled based on the Gamification User Type Hexad Model. The gamified course was designed based on the suggested game element preferences of each player type. To determine the game element preferences and engagement of students, self-report instruments were used. Students were asked to answer a survey on game element preferences and the Student Course Engagement Questionnaire (SCEQ). They were also asked to participate in an interview at the end of the course to support quantitative results. The results revealed that game elements preferences were not in accordance to the suggested elements for each type. Furthermore, though a high level of engagement was found both overall and per subscale of the SCEQ, there was no significant differences found among the engagement of various Hexad player types. These findings suggest that further research work is required to explore the relationship between player types and behavior and interaction with a gamified system.
Provable multicore schedulers with Ipanema: application to work conservation

Recent research and bug reports have shown that work conservation, the property that a core is idle only if no other core is overloaded, is not guaranteed by Linux's CFS or FreeBSD's ULE multicore schedulers. Indeed, multicore schedulers are challenging to specify and verify: they must operate under stringent performance requirements, while handling very large numbers of concurrent operations on threads. As a consequence, the verification of correctness properties of schedulers has not yet been considered. In this paper, we propose an approach, based on a domain-specific language and theorem provers, for developing schedulers with provable properties. We introduce the notion of concurrent work conservation (CWC), a relaxed definition of work conservation that can be achieved in a concurrent system where threads can be created, unblocked and blocked concurrently with other scheduling events. We implement several scheduling policies, inspired by CFS and ULE. We show that our schedulers obtain the same level of performance as production schedulers, while concurrent work conservation is satisfied.