Miracle-dz/UniFAR-FTUs huggingface dataset:
Committing the primordial germ cell: An updated molecular perspective

The germ line is a crucial cell lineage that is distinct from somatic cells, and solely responsible for the trans‐generational transmission of hereditary information in metazoan sexual reproduction. Primordial germ cells (PGCs)—the precursors to functional germ cells—are among the first cell types to be allocated in embryonic development, and this lineage commitment is a critical event in partitioning germ line and somatic tissues. Classically, mammalian PGC development has been largely informed by investigations on mouse embryos and embryonic stem cells. Recent findings from corresponding nonrodent systems, however, have indicated that murine PGC specification may not be fully archetypal. In this review, we outline the current understanding of molecular mechanisms in PGC specification, emphasizing key transcriptional events, and focus on salient differences between early human and mouse PGC commitment. Beyond these latest findings, we also contemplate the future outlook of inquiries in this field, highlighting the importance of comprehensively understanding early fate decisions that underlie the segregation of this unique lineage.
Investigation of a Bipedal Platform for Rapid Acceleration and Braking Manoeuvres

Rapid acceleration manoeuvres have been avoided by researchers due to the aperiodicity and complexities of this motion. With the recent improvements in optimal control, this paper presents the first examination of a biped completing a time optimal sprint, starting and ending in rest, to provide insight for parameter choices of a robotic platform. In particular, a realistic linkage morphology is used with the limitation of a pre-specified actuator to choose the nominal leg length and gear ratio. Due to the size of the optimisation problem, a brute force approach is used rather than including these parameters as free variables. The results provided unique motion trajectories for time optimal behaviour with the models reaching near steady state motion and performing manoeuvres that are seen in a biped's biological counterpart. We then show that access to a higher mass-specific force does not improve the rapid acceleration manoeuvres, rather the friction coefficient and keeping the feet near the ground act as the limiting factor given sufficiently powerful actuators. A parabolic relationship emerged for sprint time versus linkage lengths providing valuable insight into the parameters to use for the platform design. To the authors knowledge, no prior research has focused on rapid acceleration and braking manoeuvres of a biped in one optimisation problem, let alone providing insight for the physical bipedal robotic platform.
Identification of novel haplotypes and interpretation of gene flow of mitochondrial DNA control region of Eurasian otter (Lutra lutra) for the effective conservation

The number and distribution of Eurasian otters have declined during twentieth century due to human activity and water pollution. The global conservation status of Eurasian otter is presently ‘Near Threatened (NT)’ and strictly protected by being listed on the international legislation and conventions. A number of studies using the mitochondrial DNA (mtDNA) control region (CR) have been conducted in order to effectively apply conservation and reintroduction programs, especially in Europe. However, aside from Europe, there have been few studies concerning genetic diversity and phylogeny of Eurasian otters. Therefore, in this study, we sequenced partial mtDNA CR sequences (232 bp) from five South Korean Eurasian otters and analyzed 27 otters originating from parts of northeast Asia (South Korea, China, Japan and Russia (Sakhalin)), and Europe. Out of 232 bp partial mtDNA CR sequences, 13 polymorphic sites (5.6%) were identified and 4 novel mtDNA CR haplotypes (Lut16–19) were discovered from 12 Eurasian otters originating from northeast Asian region. In this study, a comprehensive analysis of genetic diversity and population structure of Eurasian otter between Europe and northeast Asia continents were conducted. Of these, different past demographic histories in Pleistocene period might have largely impacted the genetic structure of each population differently. In addition, low degree of gene flow, isolation by distance (IBD) pattern from geographically wide distanced dataset and analysis of molecular variance (AMOVA) also represented distinct genetic characteristics of Eurasian otter between Europe and northeast Asia.
Explicit dynamic and MTJ Control of a 3-RRS Parallel Manipulator with mass Compensator

In this paper, a 3-RRS ball and plate parallel manipulator is discussed to obtain the exact kinematics and kinetics of the robot. Numerous types of ball and plate systems, reckoned as multivariable and nonlinear workbenches, are exploited to conduct researches to evaluate advanced control algorithms. Although most of these equipment have been presented as two degrees of freedom (DOF), the presented mechanism possesses 3 DOF. Floating plate of this system has three independent motion, two rotational movements and a translation along the vertical axis. Moreover, three dependent motion, two translational and one rotational, are also available. In order to reduce the required torques produced by servomotors, a mass compensator is designed and exploited. Control of the system has been investigated after deriving kinematics and dynamics equations of motion. In the control section, due to the high complexity of kinematics and dynamics equations, Modified Transpose Jacobian (MTJ) control algorithm which is based on feedback linearization approach is used. The goal is to cause the ball follow the chosen trajectories with properly controlled inputs. Simulation results indicate the ability of the suggested controller to accomplish its task.
Player Activity and Popularity in Online Social Games and their Implications for Player Retention

Online Social Games (OSGs) like World of Tanks (WoT) have attracted hundreds of millions of users to game and to view games shared online, and yet player retention is a key challenge for game design. Qualitative studies have shown three major motivations for players to stay—achievement, social and immersion. In this paper, based on a dataset that is orders of magnitude larger than previous analyses, we provide a quantitative analysis on the player retention problem. We examine traditional factors including the activity level, the gaming performance, and the social relationships of players, and we include additional features, such as how many users have watched a player, that are provided uniquely by the game replay sharing communities. And we build machine-learned classifiers that can accurately predict active players. Among other results, we find that (i) the activity level and the popularity of players are highly skewed; (ii) the influence between the activity and the gaming performance is mutual; (iii) while the majority players do not form long-term relationships, a small fraction of players connect very closely; and (iv) the gaming performance and the player popularity are very informative for predicting highly active players.
Data Fusion for MaaS: Opportunities and Challenges

Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.
Solid State Transformer for Electrical System: Challenges and Solution

After the evolution of SST, it plays vital role in the field of modern electrical power system. It has numerous benefits of small volume, weight, size, fault segregation and added some others functions and enhanced power quality such as voltage regulation, reactive power compensation, fault isolation, voltage unbalance, sag and swell. The idea of SST and its topology has been discussed in earlier papers and their main work focused on advancement of its topology, with the advancement of technology and how it can be used for improvement of exiting technology. In this paper discuss about the features of power electronic based solid state transformer and its application in different electrical field, where conventional transformer is replaced by the solid state transformer.
Efficient Multicore Scaling in Software Packet Forwarding Engines

Network softwarization has accelerated the research and development of network functions on commercial off-the-shelf hardware. Multicore scalability has been a key to achieve high-performance packet forwarding engines. However, resource efficiency and bottlenecks in multicore scaling have not been discussed in the past research. Therefore, different hardware configurations sometimes encounter serious performance degradation. This paper presents a design and implementation of the forwarding engine that efficiently scales on multicore systems. Exclusive CPU core utilization and buffer management enable concurrent processing without synchronization overheads between multiple CPU cores. Our experiments demonstrate that memory access in buffer handling is the bottleneck in multicore scaling, and thus, thorough buffer management to reduce cache misses of shared CPU caches is required to debottleneck it. We also exhibit that the efficiency of CPU resource utilization could be enhanced by taking the advantage of hyperthreading that alleviate the overheads of memory access latency. The implemented forwarding engine efficiently scales and achieves 0% loss routing against 143 mega-packets per second traffic on the backbone router's IPv4 routing table using twelve CPU cores.
Image-Based PM2.5 Estimation and its Application on Depth Estimation

Air pollution is still a big threat to human health particularly for developing countries. It is highly demanding to measure air quality with daily-used devices such as smartphones. On the other hand, it is difficult to estimate the scene depth under the foul weather using traditional vision-based methods. This paper proposes an image-based method for PM2.5 estimation by capturing a single image. We extract high-level features based on convolutional neural network (CNN) and learn the mapping between the features and PM2.5 by support vector regression (SVR). Given a captured image, we can estimate the PM2.5 value in real time. With the estimated PM2.5, we can estimate the depth of scene using sparse prior and nonlocal bilateral kernel. Experimental results demonstrate that the proposed method achieves the same accuracy of PM2.5 estimation as commodity measurement devices, and estimates the accurate depth information that is even better than the “ground-truth” captured by a laser in the no-haze condition.
Knockdown of pleiotrophin increases the risk of preeclampsia following vitrified-thawed embryo transfer

Preeclampsia (PE) in pregnancy is associated with vitrified-thawed embryo transfer. Pleiotrophin (PTN) is important in inflammation via its receptors. The aim of the present study was to determine the effect of PTN on the risk of PE following embryo transfer. An enzyme-linked immunosorbent assay was performed to determine the levels of tumor necrosis factor (TNF)-α and PTN in serum. The knockdown of PTN was conditionally induced by tamoxifen (tax) treatment. The tail-cuff method and Bradford assay were used to monitor blood pressure and the level of urine protein, respectively. The expression patterns of PTN, receptor protein tyrosine phosphatase β/ζ, (RPTPβ/ζ), syndecan-1 (SDC1), syndecan-3 (SDC3) and anaplastic lymphoma kinase (ALK) were determined by immunohistochemistry (IHC). Western blot analysis was performed to evaluate the expression level of PTN and its receptors. The risk of PE was elevated following embryo transfer in clinical and in the tax/PTN−/− group. It was found that the level of PTN increased when pregnancy progressed in normal conditions, however, the level of PTN was reduced in the PE mice. In addition, increases in TNF-α, blood pressure and urine protein were more marked in the PE mice that lacked PTN, compared with those in other PE mice. In addition, overlapping expression of PTN and its receptors in villous mesenchyme and fetal macrophages were identified using an IHC assay. However, the positive staining of PTN and its receptors was weaker or even absent in the PE mice. The protein level of RPTPβ/ζ was lower in the PE mice that lacked PTN than that in the other PE mice. The knockdown of PTN increased the risk of PE following vitrified-thawed embryo transfer, in which its receptors, particularly RPTPβ/ζ, may be involved.
Not All Words Are Equal: Video-specific Information Loss for Video Captioning

An ideal description for a given video should fix its gaze on salient and representative content, which is capable of distinguishing this video from others. However, the distribution of different words is unbalanced in video captioning datasets, where distinctive words for describing video-specific salient objects are far less than common words such as 'a' 'the' and 'person'. The dataset bias often results in recognition error or detail deficiency of salient but unusual objects. To address this issue, we propose a novel learning strategy called Information Loss, which focuses on the relationship between the video-specific visual content and corresponding representative words. Moreover, a framework with hierarchical visual representations and an optimized hierarchical attention mechanism is established to capture the most salient spatial-temporal visual information, which fully exploits the potential strength of the proposed learning strategy. Extensive experiments demonstrate that the ingenious guidance strategy together with the optimized architecture outperforms state-of-the-art video captioning methods on MSVD with CIDEr score 87.5, and achieves superior CIDEr score 47.7 on MSR-VTT. We also show that our Information Loss is generic which improves various models by significant margins.
TOP2B: The First Thirty Years

Type II DNA topoisomerases (EC 5.99.1.3) are enzymes that catalyse topological changes in DNA in an ATP dependent manner. Strand passage reactions involve passing one double stranded DNA duplex (transported helix) through a transient enzyme-bridged break in another (gated helix). This activity is required for a range of cellular processes including transcription. Vertebrates have two isoforms: topoisomerase IIα and β. Topoisomerase IIβ was first reported in 1987. Here we review the research on DNA topoisomerase IIβ over the 30 years since its discovery.
Neural Networks and Quantifier Conservativity: Does Data Distribution Affect Learnability?

All known natural language determiners are conservative. Psycholinguistic experiments indicate that children exhibit a corresponding learnability bias when faced with the task of learning new determiners. However, recent work indicates that this bias towards conservativity is not observed during the training stage of artificial neural networks. In this work, we investigate whether the learnability bias exhibited by children is in part due to the distribution of quantifiers in natural language. We share results of five experiments, contrasted by the distribution of conservative vs. non-conservative determiners in the training data. We demonstrate that the aquisitional issues with non-conservative quantifiers can not be explained by the distribution of natural language data, which favors conservative quantifiers. This finding indicates that the bias in language acquisition data might be innate or representational.
Spermatic mitochondria: role in oxidative homeostasis, sperm function and possible tools for their assessment

Summary Despite sperm mitochondrial relevance to the fertilization capacity, the processes involved in the production of ATP and functional dynamics of sperm mitochondria are not fully understood. One of these processes is the paradox involved between function and formation of reactive oxygen species performed by the organelle. Therefore, this review aimed to provide data on the role of sperm mitochondria in oxidative homeostasis and functionality as well the tools to assess sperm mitochondrial function.
Quality Enhancement for Intra Frame Coding Via Cnns: An Adversarial Approach

Lossy compression is an indispensable technique in image/video processing, due to its highly desirable ability of reducing the huge data volume. However, lossy compression introduces complex compression artifacts. To reduce these artifacts, post-processing techniques have been extensively studied. In this paper, we propose a novel post-processing technique using multi-level progressive refinement network via an adversarial training approach, called MPRGAN, for artifacts reduction and coding efficiency improvement in intra frame coding. Furthermore, our network generates multi-level residues in one feed-forward pass through the progressive reconstruction. This coarse-to-fine work fashion, which makes our network have high flexibility, can make trade-off between enhanced quality and computational complexity. Thereby facilitates the resource-aware applications. Extensive evaluations on benchmark datasets verify the superiority of our proposed MPRGAN model over the latest state-of-the-art methods with fast deployment running speed.
Advanced Multimedia Power-Saving Method Using a Dynamic Pixel Dimmer on AMOLED Displays

As an emissive display, the active matrix organic light-emitting diodes (AMOLEDs) endure lower power efficiency at the higher level of pixel intensities. In the existing techniques, the power consumption is lowered with a significant loss of details and hue alterations. This paper proposes a hue-preserving pixel-dimming technique using subtractive coefficients based on the decomposed hue–saturation–value color map that reduces the power consumption on AMOLED displays. An entropy-based scene detection is adopted to maintain the computational efficiency of the proposed method on video input. Experimental results on a 5.5-in 1080p AMOLED displays show that the proposed method conserves up to 73% of the displaying power with high perceptual qualities as compared with the existing methods.
Stability Properties of the 3-Level Flying Capacitor Buck Converter Under Peak or Valley Current-Programmed-Control

In this paper, stability of the current-programmed-controlled three-level flying-capacitor dc-dc buck converter is studied in detail. The proposed analysis is developed for both peak-current-mode-control (P-CMC) and valley-current-mode-control (V-CMC), and addresses both static stability of the inductor current and stability of the flying capacitor voltage. Conditions for avoiding current subharmonic oscillations are first derived, along with the expressions of the minimal compensation ramp slope which guarantees current stability throughout the entire operating range. As for the stability of the flying capacitor voltage, P-CMC results to be inherently unstable unless the converter operates with a relatively large peak-to-peak inductor current ripple. On the other hand, V-CMC is inherently stabilized once the static instability of the current is compensated with an external ramp – a property which has been highlighted in the literature but never formally proven. The proposed analysis is verified through a simulation model and experimentally using a 3.3 V, 500 mA, and 500 kHz prototype.
Statistical Nearest Neighbors for Image Denoising

Non-local-means image denoising is based on processing a set of neighbors for a given reference patch. few nearest neighbors (NN) can be used to limit the computational burden of the algorithm. Resorting to a toy problem, we show analytically that sampling neighbors with the NN approach introduces a bias in the denoised patch. We propose a different neighbors’ collection criterion to alleviate this issue, which we name statistical NN (SNN). Our approach outperforms the traditional one in case of both white and colored noise: fewer SNNs can be used to generate images of superior quality, at a lower computational cost. A detailed investigation of our toy problem explains the differences between NN and SNN from a grounded point of view. The intuition behind SNN is quite general, and it leads to image quality improvement also in the case of bilateral filtering. The MATLAB code to replicate the results presented in the paper is freely available.
A Matrix Decomposition Method for Odd-Type Gaussian Normal Basis Multiplication

Normal basis is used in many applications because of the efficiency of the implementation. However, most space complexity reduction techniques for binary field multiplier are applicable for only optimal normal basis or Gaussian normal basis of even type. There are 187 binary fields GF 2k for k from 2 to 1,000 that use odd-type Gaussian normal basis. This paper presents a method to reduce the space complexity of odd-type Gaussian normal basis multipliers over binary field GF 2k. The idea is adapted from the matrix decomposition method for optimal normal basis. The result shows that our space complexity reduction method can reduce the number of XOR gates used in the implementation comparing to previous works with a small trade-off in critical path delay.
Low synchronization GMRES algorithms

Communication-avoiding and pipelined variants of Krylov solvers are critical for the scalability of linear system solvers on future exascale architectures. We present low synchronization variants of iterated classical (CGS) and modified Gram-Schmidt (MGS) algorithms that require one and two global reduction communication steps. Derivations of low synchronization iterated CGS algorithms are based on previous work by Ruhe. Our main contribution is to introduce a backward normalization lag into the compact $WY$ form of MGS resulting in a ${\cal O}(\eps)\kappa(A)$ stable GMRES algorithm that requires only one global synchronization per iteration. The reduction operations are overlapped with computations and pipelined to optimize performance. Further improvements in performance are achieved by accelerating GMRES BLAS-2 operations on GPUs.
Precise Regression for Bounding Box Correction for Improved Tracking Based on Deep Reinforcement Learning

In this paper, we propose a precise regression approach for correcting imprecise bounding box using deep reinforcement learning. Object tracking task essentially builds trajectory of a moving object based on detection and tracking algorithms and its current state is indicated by having the object encapsulated with a bounding box corresponding to its position and size. However due to the imperfect detection and tracking algorithms operating in complex scene, it is difficult to obtain the precise bounding box as errors frequently occur producing oversized, partial, and false bounding box, respectively. To correct the error, we train an intelligent agent that move the bounding box to the right position and scale it to its correct size matching to that of the true target. The agent is trained by deep Q-Iearning and evaluated on several state-of-the-art multiple object tracking approaches. The experimental results demonstrate that our proposed framework can effectively eliminate the object tracking bounding box error and its robustness is verified by realizing improved tracking performance in complex scene.