Miracle-dz/UniFAR-FTUs huggingface dataset:
Redundant Packet Scheduling by Uncorrelated Paths in Heterogeneous Wireless Networks

Heterogeneous wireless networks operate under un-certainties as traffic load variation and failures. Redundant packet scheduling algorithms seek to timely cope with these uncertainties to meet the latency and throughput requirements for delay-sensitive applications, such as online gaming, voice and video streaming. The Multipath Transmission Control Protocol (MPTCP) can rely on a redundant scheduler to fulfill this demand, replicating data packets on multiple paths to mitigate negative effects of heterogeneity, such as fluctuations in delay, loss rate, and bandwidth. However, shared bottlenecks among paths can compromise the benefits of the redundant scheduler and restrain the MPTCP performance. Thus, this paper introduces a new scheduler, called RED (REdundant Diversity scheduling), to prioritize packet replication by uncorrelated paths. RED selects paths and replicates packets based on the Spearman's correlation coefficient. Results show that RED achieves low delay and enhances the usage of low correlated paths, improving transmission performance.
Neural Multi-task Learning for Citation Function and Provenance

Citation function and provenance are two cornerstone tasks in citation analysis. Given a citation, the former task determines its rhetorical role, while the latter locates the text in the cited paper that contains the relevant cited information. We hypothesize that these two tasks are synergistically related, and build a model that validates this claim. For both tasks, we show that a single-layer convolutional neural network (CNN) outperforms existing state-of-the-art baselines. More importantly, we show that the two tasks are indeed synergistic: by jointly training both tasks using multi-task learning, we demonstrate additional performance gains.
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights

While modern neural networks are making remarkable gains in terms of predictive accuracy, characterizing uncertainty over the parameters of these models (in a Bayesian setting) is challenging because of the high-dimensionality of the network parameter space and the correlations between these parameters. In this paper, we introduce a novel framework for variational inference for Bayesian neural networks that (1) encodes complex distributions in high-dimensional parameter space with representations in a low-dimensional latent space and (2) performs inference efficiently on the low-dimensional representations. Across a large array of synthetic and real-world datasets, we show that our method improves uncertainty characterization and model generalization when compared with methods that work directly in the parameter space.
Comparative Characterization of Osteoclasts Derived From Murine Bone Marrow Macrophages and RAW 264.7 Cells Using Quantitative Proteomics

Osteoclasts are bone‐resorbing cells differentiated from macrophage/monocyte precursors in response to macrophage colony‐stimulating factor (M‐CSF) and receptor activator of NF‐κB ligand (RANKL). In vitro models are principally based on primary bone marrow macrophages (BMMs), but RAW 264.7 cells are frequently used because they are widely available, easy to culture, and more amenable to genetic manipulation than primary cells. Increasing evidence, however, has shown that the vastly different origins of these two cell types may have important effects on cell behavior. In particular, M‐CSF is a prerequisite for the differentiation of BMMs, by promoting survival and proliferation and priming the cells for RANKL induction. RAW 264.7 cells readily form osteoclasts in the presence of RANKL, but M‐CSF is not required. Based on these key differences, we sought to understand their functional implications and how it might affect osteoclast differentiation and related signaling pathways. Using a robust and high‐throughput proteomics strategy, we quantified the global protein changes in osteoclasts derived from BMMs and RAW 264.7 cells at 1, 3, and 5 days of differentiation. Data are available via ProteomeXchange with the identifier PXD009610. Correlation analysis of the proteomes demonstrated low concordance between the two cell types (R2 ≈ 0.13). Bioinformatics analysis indicate that RANKL‐dependent signaling was intact in RAW 264.7 cells, but biological processes known to be dependent on M‐CSF were significantly different, including cell cycle control, cytoskeletal organization, and apoptosis. RAW 264.7 cells exhibited constitutive activation of Erk and Akt that was dependent on the activity of Abelson tyrosine kinase, and the timing of Erk and Akt activation was significantly different between BMMs and RAW 264.7 cells. Our findings provide the first evidence for major discrepancies between BMMs and RAW 264.7 cells, indicating that careful consideration is needed when using the RAW 264.7 cell line for studying M‐CSF‐dependent signaling and functions. © 2018 American Society for Bone and Mineral Research. © 2018 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.
Replication fork collapse at a protein-DNA roadblock leads to fork reversal, promoted by the RecQ helicase

There are numerous impediments that DNA replication can encounter while copying a genome, including the many proteins that bind DNA. Collapse of the replication fork at a protein roadblock must be dealt with to enable replication to eventually restart; failure to do so efficiently leads to mutation or cell death. Several prospective models have been proposed that process a stalled or collapsed replication fork. This study shows that replication fork reversal (RFR) is the preferred pathway for dealing with a collapsed fork in Escherichia coli, along with exonuclease activity that digests the two nascent DNA strands. RFR moves the Y-shaped replication fork DNA away from the site of the blockage and generates a four-way DNA structure, the Holliday junction (HJ). Direct endo-nuclease activity at the replication fork is either slow or does not occur. The protein that had the greatest effect on HJ processing/RFR was found to be the RecQ helicase. RecG and RuvABC both played a lesser role, but did affect the HJ produced: mutations in these known HJ processing enzymes produced longer-lasting HJ intermediates, and delayed replication restart. The SOS response is not induced by the protein-DNA roadblock under these conditions and so does not affect fork processing. Author Summary To transfer genetic material to progeny, a cell must replicate its DNA accurately and completely. If a cell does not respond appropriately to inhibitors of the DNA replication process, genetic mutation and cell death will occur. Previous works have shown that protein-DNA complexes are the greatest source of replication fork stalling and collapse in bacteria. This work examines how the cell deals with replication fork collapse at a persistent protein blockage, at a specific locus on the chromosome of Escherichia coli. Cells were found to process the DNA at the replication fork, moving the branch point away from the site of blockage by replication fork reversal and exonuclease activity. Our data indicate that it is the RecQ helicase that has the main controlling role in this process, and not the proteins RecG and RuvABC, as currently understood. RecQ homologs have been shown to be involved in replication fork processing in eukaryotes and their mutation predisposes humans to genome instability and cancer. Our findings suggest that RecQ proteins could play more important role in replication fork reversal than previously understood, and that this role could be conserved across domains.
Enhanced cecropin B2 production via chitin‐binding domain and intein self‐cleavage system

In this study, various constructs and hosts were used to produce high levels of cecropin B2 (cecB2). To mitigate cecB2's toxic inhibition of host cells, various cecB2 constructs were built. Results showed that the combination of a chitin‐binding domain and an intein self‐cleavage motif in front of cecropin B2, without a His‐tag, was best for cecB2 expression. E. coli ER2566 was the best host, and 2YT was the best medium for cultivation. Under these conditions, a cecB2 yield of 98.2 mg/L could be obtained after purification. The purified cecB2 expressed a wide antimicrobial effect on most Gram‐negative strains, including multidrug‐resistant Acinetobactor baumannii and Staphylococcus aureus. This study provides a systematic approach to the efficient production of the antimicrobial peptide (AMP) cecB2 via the recombinant E. coli process, which is expected to be an efficient way for the production of other AMPs.
Impact of Physical Computing on Learner Motivation

Intrinsic motivation is a key element of successful and effective learning. In computer science lessons, some students lack this kind of motivation. Physical computing requires learner initiative and activity, places high demands on their skills for self-determined learning and appeals to different senses. These characteristics suggest that physical computing activities are suitable for promoting intrinsic motivation. With the aim of investigating motivational aspects of physical computing activities in different classroom settings, questionnaire data from students are evaluated both as a whole and in comparison with each other. A short scale of intrinsic motivation was adapted and used in this cross-sectional study. The data was evaluated and compared to qualitative data from the same classrooms (e. g. gathered in learner reports or teacher interviews) to identify sources for success or failure with regard to the aims. Overall, physical computing activities result in higher motivational values than the average of any other activities in computer science classroom, especially when integrating suggested design principles for physical computing lessons that particularly focus on promoting creative and constructionist learning.
NBWGuard: Realizing Network QoS for Kubernetes

Kubernetes is a very popular and fast-growing container orchestration platform that automates the process of deploying and managing multi-container applications at scale. Users can specify required and maximum values of resources they need for their containers and Kubernetes realizes them by interfacing with lower levels (container runtime which in turn can use OS capabilities) of the stack for enforcing them. Kubernetes supports differentiated QoS classes - Guaranteed, Burstable, and Best-effort - in order of decreasing priority based on the resource size specifications for CPU and memory capacity. This allows many applications to obtain a desired level of QoS (performance isolation and throughput) when CPU or memory capacity management can provide them. However, when workloads may be critically dependent for their performance on another resource, namely network bandwidth, Kubernetes has no means to meet their QoS needs. Networking between pods in Kubernetes is supported with plug-ins and the network resource is not managed directly. In this work, we propose NBWGuard, a design for network bandwidth management and evaluate its implementation. NBWGuard lets Kubernetes manage network bandwidth as a resource (like CPU or memory capacity) while still using plug-ins for realizing the network specification desired by users. Consistent with Kubernetes approach to application QoS based on resource allocation NBWGuard also supports the 3 QoS classes: Guaranteed, Burstable, and Best-effort with respect to network bandwidth. NBWGuard is evaluated with iperf benchmark on real cloud environment, and the evaluation results demonstrate that it is able to provide network bandwidth isolation without impact on overall throughput.
Influence of a growth hormone transgene on the genetic architecture of growth‐related traits: A comparative analysis between transgenic and wild‐type coho salmon

Genetic engineering has been increasingly applied to many commercially important plant and animal species, generating phenotypic changes that are not observed in natural populations and creating genetic interactions that have not experienced natural selection. The degree to and way in which such human‐induced genetic variation interacts with the rest of the genome is currently largely unknown. Integrating such information into ecological and risk assessment frameworks is crucial to understand the potential effects of genetically modified organisms in natural environments. Here, we performed QTL mapping to investigate the genetic architecture of growth‐related traits in nontransgenic (NT) and growth hormone transgenic (T) coho salmon with large changes in growth and related physiology, with the aim of identifying how an inserted transgene might influence the opportunity for selection. These fish shared the same parental genetic background, thus allowing us to determine whether the same or different loci influence these traits within the two groups. The use of over 1,700 loci, derived from restriction site‐associated DNA sequencing, revealed that different genomic regions were linked with growth over time between the two groups. Additionally, the effect sizes of detected QTL appear to have been influenced by the transgene. Direct comparison of QTL between the T and NT fish during two size‐matched periods identified little overlap in their location. Taken together, the results showed that the transgene altered the genetic basis of growth‐related traits in this species. The study has important implications for effective conservation and management of wild populations experiencing introduction of transgenes. Evolutionary changes and their ecological consequences may occur at different rates and in different directions in NT versus T individuals in response to selection. Thus, assessments of phenotypic change, and hence ecological risk, should be determined periodically to evaluate whether initial estimates made with founder strains remain valid.
Automatic L3 slice detection in 3D CT images using fully-convolutional networks

The analysis of single CT slices extracted at the third lumbar vertebra (L3) has garnered significant clinical interest in the past few years, in particular in regards to quantifying sarcopenia (muscle loss). In this paper, we propose an efficient method to automatically detect the L3 slice in 3D CT images. Our method works with images with a variety of fields of view, occlusions, and slice thicknesses. 3D CT images are first converted into 2D via Maximal Intensity Projection (MIP), reducing the dimensionality of the problem. The MIP images are then used as input to a 2D fully-convolutional network to predict the L3 slice locations in the form of 2D confidence maps. In addition we propose a variant architecture with less parameters allowing 1D confidence map prediction and slightly faster prediction time without loss of accuracy. Quantitative evaluation of our method on a dataset of 1006 3D CT images yields a median error of 1mm, similar to the inter-rater median error of 1mm obtained from two annotators, demonstrating the effectiveness of our method in efficiently and accurately detecting the L3 slice.
Genome size‐dependent pcna gene copy number in dinoflagellates and molecular evidence of retroposition as a major evolutionary mechanism

Proliferating cell nuclear antigen (PCNA) plays critical roles in eukaryotic DNA replication and replication‐associated processes. It is typically encoded by one or two gene copies (pcna) in eukaryotic genomes. Recently reported higher copy numbers of pcna in some dinoflagellates raised a question of how this gene has uniquely evolved in this phylum. Through real‐time PCR quantification, we found a wide range of pcna copy number (2–287 copies) in 11 dinoflagellate species (n = 38), and a strong positive correlation between pcna copy number and genome size (log10–log10 transformed). Intraspecific pcna diverged up to 21% and are dominated by nonsynonymous substitutions, indicating strong purifying selection pressure on and hence functional necessity of this gene. By surveying pcna copy numbers in eukaryotes, we observed a genome size threshold at 4 pg DNA, above which more than two pcna copies are found. To examine whether retrotransposition is a mechanism of pcna duplication, we measured the copy number of retroposed pcna, taking advantage of the 22‐nt dinoflagellate‐specific spliced leader (DinoSL) capping the 5′ end of dinoflagellate nuclear‐encoded mRNAs, which would exist in the upstream region of a retroposed gene copy. We found that retroposed pcna copy number increased with total pcna copy number and genome size. These results indicate co‐evolution of dinoflagellate pcna copy number with genome size, and retroposition as a major mechanism of pcna duplication in dinoflagellates. Furthermore, we posit that the demand of faithful replication and maintenance of the large dinoflagellate genomes might have favored the preservation of the retroposed pcna as functional genes.
Association between Sleep Quality and Body Composition in Sedentary Middle-Aged Adults

Background: Ageing is associated with sleep pattern changes and body composition changes, which are related to several diseases. Purpose: This study aimed to analyse the association between sleep quality and an extensive set of body composition parameters (waist-hip ratio, body mass index, bone mineral content, bone mineral density, lean mass, lean mass index, fat mass, fat mass percentage, fat mass index, visceral adipose tissue) and sleep quality in sedentary middle-aged adults. We also aimed to evaluate whether the possible associations accord between subjective and objective measurements of sleep quality. Methods: 74 (39 women) middle-aged sedentary adults (40–65 years old) participated in the present study. The sleep quality was assessed using the Pittsburgh sleep quality index (PSQI) scale and accelerometers. A PSQI global score more than 5 indicates poor sleep quality. Weight, height, waist and hip circumferences were measured, and body mass index and waist-hip ratio were also calculated. Body composition was assessed with a dual-energy X-ray absorptiometry scanner. Results: The PSQI global score was negatively associated with bone mineral content, bone mineral density, lean mass, lean mass index and positively associated with fat mass percentage. No association was found between accelerometer parameters and body composition variables. Conclusion: We showed that a subjective poor sleep quality was negatively associated with bone mineral content (BMC), bone mineral density (BMD), lean mass and lean mass index (LMI) whereas was positively associated with fat mass percentage in middle-aged adults. We also observed that these associations did not accord with objective sleep quality measurements.
Reliable Positioning and mmWave Communication via Multi-Point Connectivity †

One of the key elements of future 5G and beyond mobile technology is millimeter-wave (mmWave) communications, which is targeted to extreme high-data rate services. Furthermore, combining the possibility of a wideband signal transmission with the capability of pencil-beamforming, mmWave technology is key for accurate cellular-based positioning. However, it is also well-known that at the mmWave frequency band the radio channel is very sensitive to line-of-sight blockages giving rise to unstable connectivity and inefficient communication. In this paper, we tackle the blockage problem and propose a solution to increase the communication reliability by means of a coordinated multi-point reception. We also investigate the advantage of this solution in terms of positioning quality. More specifically, we describe a robust hybrid analog–digital receive beamforming strategy to combat the unavailability of dominant links. Numerical examples are provided to validate the efficiency of our proposed method.
SNS Door Phone as Robotic Process Automation

We developed SNS Door Phone by making an interphone system an IoT device. We integrated SNS and QR-code recognition function with an interphone system. Thanks to connection with SNS, we can know the visit of the parcel delivery service anytime through SNS even if during going out. Thanks to introduction of QR-code recognition function, if a parcel deliveryman only showed the QR-code of the parcel in front of SNS Door Phone, the re-delivery operation information would be sent to a user automatically through SNS. Then, the user can call or ask re-delivery arrangement using smart phone without inputting any additional data. We can consider this kind of seamless re-delivery operation to be a good example of Robotic Process Automation.
Accelerating SuperBE with Hardware/Software Co-Design

Background Estimation is a common computer vision task, used for segmenting moving objects in video streams. This can be useful as a pre-processing step, isolating regions of interest for more complicated algorithms performing detection, recognition, and identification tasks, in order to reduce overall computation time. This is especially important in the context of embedded systems like smart cameras, which may need to process images with constrained computational resources. This work focuses on accelerating SuperBE, a superpixel-based background estimation algorithm that was designed for simplicity and reducing computational complexity while maintaining state-of-the-art levels of accuracy. We explore both software and hardware acceleration opportunities, converting the original algorithm into a greyscale, integer-only version, and using Hardware/Software Co-design to develop hardware acceleration components on FPGA fabric that assist a software processor. We achieved a 4.4× speed improvement with the software optimisations alone, and a 2× speed improvement with the hardware optimisations alone. When combined, these led to a 9× speed improvement on a Cyclone V System-on-Chip, delivering almost 38 fps on 320 × 240 resolution images.
On Learning Association of Sound Source and Visual Scenes

The sight (vision) and hearing (audition) senses are the most important sources that humans use to understand their surroundings. Visual events are typically associated with sounds and they are combined. For instance; When we see that a car is moving, we hear the engine sound at the same time, i.e., co-occurrence. Humans observe tremendous number of combined visual-audio examples and learn the correlation between them throughout life-long observations unconsciously. Because of the correlation between the sound and the visual events, humans can understand the object or the event that causes sound and can localize the sound source even without separate education. Naturally, videos and their corresponding sounds also come together in a synchronized way. Given a plenty of video and sound clip pairs, can a machine model learn to associate the sound with visual scene to reveal the sound source location without any supervision in a way similar to human perception to localize sound sources in visual scenes? In this paper, we are interested in exploring whether computational models can learn the spatial correspondence between visual and audio information by leveraging the correlation between visuals and sound based on simply watching and listening to videos in unsupervised way. We address this challenge by designing our model with a two-stream network architecture (sound and visual networks [2]) where each network leverages each modality and a localization module which contains attention mechanism [4] as in Figure 2.