Miracle-dz/UniFAR-FTUs huggingface dataset:
Hox, Parahox, Ehgbox, and NK Genes in Bivalve Molluscs: Evolutionary Implications

ABSTRACT Molluscs belong to one of the two protostome superphyla, the Lophotrochozoa. Among molluscs, bivalves show a characteristic shell morphology separated bilaterally into two plates. The connection between the modification of the development and evolution of the molluscan body plan can be study identifying and obtaining information on the genes that regulate the development of bivalves. Homeobox genes are involved in body plan formation and in the regulation of many developmental processes in bilateria. For this reason, the evolutionary history of Hox, ParaHox, EHGbox, and NK homeobox gene families could be crucial to understand the evolution of molluscan body plans and phylogeny. The aim of this work was to compare these genes from different Bivalvia families. In this study, 22 homeobox gene fragments from five bivalve species were identified, and then these homeodomain sequences were compared with those available from other bivalves. The Hox cluster in bivalve molluscs has 11 genes. Regarding the ParaHox cluster, current data suggest that the ParaHox genes are also conserved in bivalves. Two EHGbox genes are well conserved in bivalves (en and Gbx); however, there are no data about the presence of the third one (Mnx). The NK cluster has not been examined in depth in Bivalvia; however, the conservation of Tlx and NK2 genes in different species can be confirmed. The study of the genes reported here might contribute to the understanding of their evolutionary history within the phylum and the involvement of these genes in the body plan of bivalves.
A Model-driven Migration Approach among Cloud Providers

Cloud computing has become the primary model of pay-per-use to obtain cloud services in a short time. Companies are using the cloud services to get access to computing resources located in a virtualized environment. However, the traditional method of using a single cloud provider has numerous limitation in terms of privacy, security, performance, and geography reach. Furthermore, companies are focusing their efforts on avoiding dependent on a single vendor for products and services. As a result, companies start to use multiple clouds and look for methods to move or migrate their infrastructure from a cloud provider to another one. In previous work, we have presented ARGON, which is an infrastructure modeling tool for cloud provisioning. In this paper, we propose an extension of ARGON to provide a model-driven migration approach among cloud providers.
Possible contribution of TED6 and TED7, secondary cell wall-related membrane proteins, to evolution of tracheary element in angiosperm lineage

The Tracheary Element Differentiation-Related6 (TED6) and TED7 membrane proteins function in the differentiation of xylem vessel elements, the cellular units for water conduction in angiosperm plants. Functional analysis of TED6 and TED7 had suggested that these proteins directly bind to a subunit of the secondary cell wall (SCW)-related cellulose synthase complex, to promote SCW formation in xylem vessel elements. However, whether TED6 and TED7 function in SCW formation of xylem vessel elements only, or function broadly in other cell types has remained unclear. To clarify this, we conducted detailed expression analysis of TED6 and TED7 genes in Arabidopsis thaliana. This showed that TED6 and TED7 are expressed in differentiating vessel elements of all organs examined here, including roots, leaves, and inflorescence stems. We detected no TED6 and TED7 promoter activity in other types of cells with SCW thickening, such as fiber cells and anther endothecium, indicating that TED6 and TED7 have specific roles in SCW formation of vessel elements. Homology searches identified TED6/7-like proteins only in the angiosperm lineage. These data suggest that development of TED6/7 proteins could have coincided with the emergence of the angiosperm lineage, and that TED6/7 may have made key contributions to the evolution of water-conducting cells from tracheids to vessels.
Functional Analysis of the RatN-Methyl-D-aspartate Receptor 2A Promoter: MULTIPLE TRANSCRIPTION START POINTS, POSITIVE REGULATION BY Sp FACTORS, AND TRANSLATIONAL REGULATION

Abstract N-Methyl-d-aspartate (NMDA) receptor subunit 2A (NR2A) is an important modulatory component of the NMDA subtype of glutamate receptors. To investigate the transcription mechanism of the NR2A gene, we cloned the 5′-flanking sequence from a rat genomic library. RNA mapping with rat brain RNA revealed two sets of major and several minor transcription start points in a single exon of 1140 bp. Reporter gene and mutation studies indicated that core promoter activity resided in exon 1, whereas the 5′-flanking sequence up to 1.5 kb showed no significant impact on promoter activity. Fragments containing minor transcription start points were able to drive a reporter gene in transfected cells and produce nascent RNAs in an in vitro transcription system. All fragments tested showed more promoter activity in dissociated neurons of the rat embryonic cerebrocortex and cell lines expressing NR2A mRNA than that in glial cultures and non-neuronal cells. Within exon 1 there are three GC-box elements that displayed distinct binding affinity to both Sp1- and Sp4-like factors. Overexpression of Sp1 or Sp4, but not Sp3, significantly increased the activity of the promoter containing these elements. Inclusion of exon 2 and 3 sequences, which contain five short open-reading frames, attenuated promoter-driven reporter activity more than 3-fold but attenuated the level of reporter mRNA less than 1.4-fold. Our results suggest that the core promoter of the rat NR2A gene requires exon 1, that Sp factors positively regulate this core promoter, and that a post-transcriptional mechanism may negatively regulate expression of the gene.
Learning Dynamical Systems with Side Information

We present a mathematical and computational framework for the problem of learning a dynamical system from noisy observations of a few trajectories and subject to side information. Side information is any knowledge we might have about the dynamical system we would like to learn besides trajectory data. It is typically inferred from domain-specific knowledge or basic principles of a scientific discipline. We are interested in explicitly integrating side information into the learning process in order to compensate for scarcity of trajectory observations. We identify six types of side information that arise naturally in many applications and lead to convex constraints in the learning problem. First, we show that when our model for the unknown dynamical system is parameterized as a polynomial, one can impose our side information constraints computationally via semidefinite programming. We then demonstrate the added value of side information for learning the dynamics of basic models in physics and cell biology, as well as for learning and controlling the dynamics of a model in epidemiology. Finally, we study how well polynomial dynamical systems can approximate continuously-differentiable ones while satisfying side information (either exactly or approximately). Our overall learning methodology combines ideas from convex optimization, real algebra, dynamical systems, and functional approximation theory, and can potentially lead to new synergies between these areas.
Topological estimation of arbitrary unknown latent geometries of complex networks.

Many networks are embedded in their latent geometries. If two nodes are embedded closely in the latent geometry, there is a disproportionately high probability that they are connected by a link. Latent geometry has a wide range of practical applications, and some of the epidemic process spreads through the latent geometry. Although extensive studies have been devoted to the embedding of networks in latent hyperbolic space, little research has been conducted to develop a method to estimate general unknown latent geometry of complex networks. Here, we develop methods to estimate unknown latent geometry of a given network by removing links with high loads according to certain criteria. Our methods estimate the homology of the latent geometry and provide a simplified map of the latent geometry.
Waves in screeching jets

Abstract The interaction between various wave-like structures in screeching jets is considered via both experimental measurements and linear stability theory. Velocity snapshots of screeching jets are used to produce a reduced-order model of the screech cycle via proper orthogonal decomposition. Streamwise Fourier filtering is then applied to isolate the negative and positive wavenumber components, which for the waves of interest in this jet correspond to upstream- and downstream-travelling waves. A global stability analysis on an experimentally derived base flow is conducted, demonstrating a close match to the results obtained via experiment, indicating that the mechanisms considered here are well represented in a linear framework. In both the global stability analysis and the experimental decomposition, three distinct wave-like structures are evident; these waves are also solutions to the cylindrical vortex-sheet dispersion relation. One of the waves is the well-known downstream-travelling Kelvin–Helmholtz mode. Another is the upstream-travelling guided jet mode that has been a topic of recent discussion by a number of authors. The third component, with positive phase velocity, has not previously been identified in screeching jets. Via a local stability analysis, we provide evidence that this downstream-travelling wave is a duct-like mode similar to that recently identified in high-subsonic jets. We further demonstrate that both of the latter two waves are generated by the interaction between the Kelvin–Helmholtz wavepacket and the shock cells in the flow. Finally, we consider the periodic spatial modulation of the coherent velocity fluctuation evident in screeching jets, and show that this modulation can be at least partially explained by the superposition of the three wave-like structures, in addition to any possible modulation of the Kelvin–Helmholtz wavepacket by the shocks themselves.
Stochastic Hybrid Combining Design for Quantized Massive MIMO Systems

Both the power-dissipation and cost of massive multiple-input multiple-output (mMIMO) systems may be substantially reduced by using low-resolution analog-to-digital converters (LADCs) at the receivers. However, both the coarse quantization of LADCs and the inaccurate instantaneous channel state information (ICSI) degrade the performance of quantized mMIMO systems. To overcome these challenges, we propose a novel stochastic hybrid analog-digital combiner (SHC) scheme for adapting the hybrid combiner to the long-term statistics of the channel state information (SCSI). We seek to minimize the transmit power by jointly optimizing the SHC subject to average rate constraints. For the sake of solving the resultant nonconvex stochastic optimization problem, we develop a relaxed stochastic successive convex approximation (RSSCA) algorithm. Simulations are carried out to confirm the benefits of our proposed scheme over the benchmarkers.
Robust Vision Challenge 2020 - 1st Place Report for Panoptic Segmentation

In this technical report, we present key details of our winning panoptic segmentation architecture EffPS_b1bs4_RVC. Our network is a lightweight version of our state-of-the-art EfficientPS architecture that consists of our proposed shared backbone with a modified EfficientNet-B5 model as the encoder, followed by the 2-way FPN to learn semantically rich multi-scale features. It consists of two task-specific heads, a modified Mask R-CNN instance head and our novel semantic segmentation head that processes features of different scales with specialized modules for coherent feature refinement. Finally, our proposed panoptic fusion module adaptively fuses logits from each of the heads to yield the panoptic segmentation output. The Robust Vision Challenge 2020 benchmarking results show that our model is ranked #1 on Microsoft COCO, VIPER and WildDash, and is ranked #2 on Cityscapes and Mapillary Vistas, thereby achieving the overall rank #1 for the panoptic segmentation task.
MLD: An Intelligent Memory Leak Detection Scheme Based on Defect Modes in Smart Grids

With the expansion of the software scale and complexity of smart grid systems, the detection of smart grid software defects has become a research hotspot. Because of the large scale of the existing smart grid software code, the efficiency and accuracy of the existing smart grid defect detection algorithms are not high. We propose an intelligent memory leak detection scheme based on defect modes MLD in smart grid. Based on the analysis of existing memory leak defect modes, we summarize memory operation behaviors (allocation, release and transfer) and present a state machine model. We employ a fuzzy matching algorithm based on regular expression to determine the memory operation behaviors and then analyze the change in the state machine to assess the vulnerability in the source code. To improve the efficiency of detection and solve the problem of repeated detection at the function call point, we propose a function summary method for memory operation behaviors. The experimental results demonstrate that the method we proposed has high detection speed and accuracy. The algorithm we proposed can identify the defects of the smart grid operation software and ensure the safe operation of the grid.
AMRConvNet: AMR-Coded Speech Enhancement Using Convolutional Neural Networks

Speech is converted to digital signals using speech coding for efficient transmission. However, this often lowers the quality and bandwidth of speech. This paper explores the application of convolutional neural networks for Artificial Bandwidth Expansion (ABE) and speech enhancement on coded speech, particularly Adaptive Multi-Rate (AMR) used in 2G cellular phone calls. In this paper, we introduce AMRConvNet: a convolutional neural network that performs ABE and speech enhancement on speech encoded with AMR. The model operates directly on the time-domain for both input and output speech but optimizes using combined time-domain reconstruction loss and frequency-domain perceptual loss. AMRConvNet resulted in an average improvement of 0.425 Mean Opinion Score - Listening Quality Objective (MOS-LQO) points for AMR bitrate of 4.75k, and 0.073 MOS-LQO points for AMR bitrate of 12.2k. AMRConvNet also showed robustness in AMR bitrate inputs. Finally, an ablation test showed that our combined time-domain and frequency-domain loss leads to slightly higher MOS-LQO and faster training convergence than using either loss alone.
A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild

In this work, we investigate the problem of lip-syncing a talking face video of an arbitrary identity to match a target speech segment. Current works excel at producing accurate lip movements on a static image or videos of specific people seen during the training phase. However, they fail to accurately morph the lip movements of arbitrary identities in dynamic, unconstrained talking face videos, resulting in significant parts of the video being out-of-sync with the new audio. We identify key reasons pertaining to this and hence resolve them by learning from a powerful lip-sync discriminator. Next, we propose new, rigorous evaluation benchmarks and metrics to accurately measure lip synchronization in unconstrained videos. Extensive quantitative evaluations on our challenging benchmarks show that the lip-sync accuracy of the videos generated by our Wav2Lip model is almost as good as real synced videos. We provide a demo video clearly showing the substantial impact of our Wav2Lip model, and also publicly release the code, models, and evaluation benchmarks on our website.
Optimization of operation parameters towards sustainable WWTP based on deep reinforcement learning

A large amount of wastewater has been produced nowadays. Wastewater treatment plants (WWTPs) are designed to eliminate pollutants and alleviate environmental pollution resulting from human activities. However, the construction and operation of WWTPs still have negative impacts. WWTPs are complex to control and optimize because of high nonlinearity and variation. This study used a novel technique, multi-agent deep reinforcement learning (DRL), to optimize dissolved oxygen (DO) and dosage in a hypothetical WWTP. The reward function is specially designed as LCA-based form to achieve sustainability optimization. Four scenarios: baseline, LCA-oriented, cost-oriented and effluent-oriented are considered. The result shows that optimization based on LCA has lowest environmental impacts. The comparison of different SRT indicates that a proper SRT can reduce negative impacts greatly. It is worth mentioning that the retrofitting of WWTPs should be implemented with the consideration of other environmental impacts except cost. Moreover, the comparison between DRL and genetic algorithm (GA) indicates that DRL can solve optimization problems effectively and has great extendibility. In a nutshell, there are still limits and shortcomings of this work, future studies are required.
LT4REC: A Lottery Ticket Hypothesis Based Multi-task Practice for Video Recommendation System

Click-through rate prediction (CTR) and post-click conversion rate prediction (CVR) play key roles across all industrial ranking systems, such as recommendation systems, online advertising, and search engines. Different from the extensive research on CTR, there is much less research on CVR estimation, whose main challenge is extreme data sparsity with one or two orders of magnitude reduction in the number of samples than CTR. People try to solve this problem with the paradigm of multi-task learning with the sufficient samples of CTR, but the typical hard sharing method can't effectively solve this problem, because it is difficult to analyze which parts of network components can be shared and which parts are in conflict, i.e., there is a large inaccuracy with artificially designed neurons sharing. In this paper, we model CVR in a brand-new method by adopting the lottery-ticket-hypothesis-based sparse sharing multi-task learning, which can automatically and flexibly learn which neuron weights to be shared without artificial experience. Experiments on the dataset gathered from traffic logs of Tencent video's recommendation system demonstrate that sparse sharing in the CVR model significantly outperforms competitive methods. Due to the nature of weight sparsity in sparse sharing, it can also significantly reduce computational complexity and memory usage which are very important in the industrial recommendation system.
Periods and classifications of RR Lyrae stars in the globular cluster M15

We present measurements of the periods, amplitudes, and types of 78 RR Lyrae stars in the globular cluster M15 derived from Nickel 1 m telescope observations conducted at Lick Observatory in 2019 and 2020. Of these, two were previously reported but without a determination of the period. In addition, we identify five Cepheid variable stars for which we report three novel period determinations, and a further 35 stars with uncertain classifications and periods. We discuss the development and subsequent application to our data of a new Python package, Period-determination and Identification Pipeline Suite (PIPS), based on a new adaptive free-form fitting technique to detect the periods of variable stars with a clear treatment of uncertainties.
A Large Fraction of Hydrogen-rich Supernova Progenitors Experience Elevated Mass Loss Shortly Prior to Explosion

Spectroscopic detection of narrow emission lines traces the presence of circumstellar mass distributions around massive stars exploding as core-collapse supernovae. Transient emission lines disappearing shortly after the supernova explosion suggest that the material spatial extent is compact and implies an increased mass loss shortly prior to explosion. Here, we present a systematic survey for such transient emission lines (Flash Spectroscopy) among Type II supernovae detected in the first year of the Zwicky Transient Facility survey. We find that at least six out of ten events for which a spectrum was obtained within two days of the estimated explosion time show evidence for such transient flash lines. Our measured flash event fraction (>30% at 95% confidence level) indicates that elevated mass loss is a common process occurring in massive stars that are about to explode as supernovae.
LMSCNet: Lightweight Multiscale 3D Semantic Completion

We introduce a new approach for multiscale 3Dsemantic scene completion from voxelized sparse 3D LiDAR scans. As opposed to the literature, we use a 2D UNet backbone with comprehensive multiscale skip connections to enhance feature flow, along with 3D segmentation heads. On the SemanticKITTI benchmark, our method performs on par on semantic completion and better on occupancy completion than all other published methods – while being significantly lighter and faster. As suchit provides a great performance/speed trade-off for mobile-robotics applications. The ablation studies demonstrate our method is robust to lower density inputs, and that it enables very high speed semantic completion at the coarsest level. Our code is available at https://github.com/cv-rits/LMSCNet.
Networks with degree-degree correlations is a special case of edge-coloured random graphs

In complex networks the degrees of adjacent nodes may often appear dependent -- which presents a modelling challenge. We present a working framework for studying networks with an arbitrary joint distribution for the degrees of adjacent nodes by showing that such networks are a special case of edge-coloured random graphs. We use this mapping to study bond percolation in networks with assortative mixing and show that, unlike in networks with independent degrees, the sizes of connected components may feature unexpected sensitivity to perturbations in the degree distribution. The results also indicate that degree-degree dependencies may feature a vanishing percolation threshold even when the second moment of the degree distribution is finite. These results may be used to design artificial networks that efficiently withstand link failures and indicate possibility of super spreading in networks without clearly distinct hubs
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs.

This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices. We approach these tasks via the adaptation of statistically principled methods for joint graph inference, specifically multiple adjacency spectral embedding (MASE) and omnibus embedding (OMNI). We demonstrate that these two methods are effective for our inference tasks. Moreover, we assess the performance of these methods in terms of the underlying nature of detectable anomalies. Our results delineate the relative strengths and limitations of these procedures, and provide insight into their use. Applied to a large-scale commercial search engine time series of graphs, our approaches demonstrate their applicability and identify the anomalous vertices beyond just large degree change.