Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of supplementary feeding on interspecific dominance hierarchies in garden birds

Individuals often differ in competitive ability, which can lead to the formation of a dominance hierarchy that governs differential access to resources. Previous studies of dominance have predominently focussed on within-species interactions, while the drivers of between-species competitive hierarchies are poorly understood. The increasing prevalence of predictable anthropogenic food subsidies, such as that provided by garden bird feeders, is likely to intensify between-species competition. However, the consequences for resource acquisition await detailed study, and in particular, whether competitive interactions are influenced by food quality is not known. Here, we examine competitive interactions amongst ten passerine species of birds utilising supplementary food sources of differing quality. We show that dominance rank is strongly predicted by body mass across species. Socially dominant, heavier species monopolised access to a food that had a relatively short handling time (sunflower hearts), spent longer on supplementary feeders, and pecked at lower rates. In contrast subordinate, lighter species were constrained to feed on a food that had a relatively long handling time (sunflower seeds with the hull intact). Our findings suggest that differences in body mass may result in between-species dominance hierarchies that place the heaviest species in the greatest control of supplementary feeding sites, gaining superior access to higher value foods. This may have important implications for the use of supplementary feeding as a conservation tool.
DNN-Based Speech Enhancement via Integrating NMF and CASA

In this paper, we come up with a novel speech enhancement method, which integrates nonnegative matrix factorization (NMF) and computational auditory scene analysis (CASA) using deep neural network (DNN). Firstly, we can obtain the basis matrices of speech and noise respectively via NMF and get the ideal ratio mask (IRM) that is based on CASA by using deep neural network. Then, a linear minimum mean square error (LMMSE) filter in fast Fourier transform (FFT) domain is constructed and transformed to the Gammatone domain. Finally, an integrated Wiener-like filter is obtained by combining the filter of NMF with the mask of CASA. By comparing with NMF and CASA methods, the experiments present the superiority of the proposed method.
Numerical Analysis near Singularities in RBF Networks

The existence of singularities often affects the learning dynamics in feedforward neural networks. In this paper, based on theoretical analysis results, we numerically analyze the learning dynamics of radial basis function (RBF) networks near singularities to understand to what extent singularities influence the learning dynamics. First, we show the explicit expression of the Fisher information matrix for RBF networks. Second, we demonstrate through numerical simulations that the singularities have a significant impact on the learning dynamics of RBF networks. Our results show that overlap singularities mainly have influence on the low dimensional RBF networks and elimination singularities have a more significant impact to the learning processes than overlap singularities in both low and high dimensional RBF networks, whereas the plateau phenomena are mainly caused by the elimination singularities. The results can also be the foundation to investigate the singular learning dynamics in deep feedforward neural networks.
Toward the Coexistence of 5G MmWave Networks with Incumbent Systems beyond 70 GHz

Cellular networks are on the verge of enabling mobile millimeter-wave access beyond the traditional sub-6 GHz spectrum. Indeed, standardization of 5G-NR has already started, with an eye on enabling gigabit connectivity over 28 GHz and 39 GHz bands. Nevertheless, there remains a plethora of spectrum beyond these bands, and specifically at 70 GHz (71-76 GHz) and 80 GHz (81-86 GHz). A key issue in the aforementioned bands is the presence of licensed incumbent systems that provide point-to-point services. This article provides an overview of recent efforts toward the coexistence of 5G systems with these incumbents. Specifically, we analyze the 5G interference into incumbent receivers and present practical active and passive interference management techniques. We validate the 5G coexistence with detailed network simulations and show the feasibility of 5G connectivity beyond 70 GHz using an experimental prototype. Finally, we highlight future research venues for 5G coexistence with incumbent systems.
Semantic Sequential Query Expansion for Biomedical Article Search

The conventional sequential dependence model (SDM) has been proved to perform better than the bag of words model for biomedical article search because it pays attention to the sequence information within queries. Meanwhile, introducing lexical semantic relations into query expansion becomes a hot topic in IR research. However, a few research have been conducted on combining semantic and sequence information together. Hence, we propose the semantic sequential dependence model in this paper, which provides an innovative combination of semantic information and the conventional SDM. Specifically, our synonyms are obtained automatically through the word embeddings which are trained on the domain-specific corpus by selecting an appropriate language model. Then, these synonyms are utilized to generate possible sequences with the same semantics as the original query and these sequences are fed into SDM to obtain the final retrieval results. The proposed approach is evaluated on 2016 and 2017 BioASQ benchmark test sets and the experimental results show that our query expansion approach outperforms the baseline and other participants in the BioASQ competitions.
A Soft Robotic Gripper Module with 3D Printed Compliant Fingers for Grasping Fruits

This study presents a soft robotic gripper module with 3D printed compliant fingers for grasping fruits. The proposed compliant gripper module is an underactuated two-finger sensor-less design which can be actuated by one linear actuator. A topology optimization method is used to synthesize the monolithic compliant finger. The topology optimized compliant finger is prototyped by 3D printing using flexible thermoplastic filament. The developed gripper module is mounted on a robot arm to pick and place a variety of fruits automatically via machine vision to demonstrate the effectiveness of the design. The results show the developed system can be used for automated grasping of fruits without causing surface damages. The proposed design is expected to be used to resolve the challenging issue for robotic automation of irregular and fragile objects.
2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map

In this paper, we propose a novel sub-pixel motion extraction method, called as Two Dimensional Spatial Keystone Transform (2DS-KST), for the motion detection and estimation from successive noisy Occupancy Grid Maps (OGMs). It extends the KST in radar imaging or motion compensation to 2D real spatial case, based on multiple hypotheses about possible directions of moving obstacles. Simulation results show that 2DS-KST has a good performance on the extraction of sub-pixel motions in very noisy environment, especially for those slowly moving obstacles.
Control of Five-Phase Dual Stator-Winding Induction Generator With an Open Phase

Dual stator-winding induction generator (DWIG) is attractive in stand-alone power systems, and the aim of this paper is to develop the dc generating system of five-phase DWIG with an open phase. First, the relationship between flux and torque is constructed based on the reduced-order transformation, modified flux, and current. Second, the relation between modified voltage and current on the dq plane is constructed. The current of fundamental plane is used for the control loop of the power-winding voltage and control-winding voltage, respectively. Third, the current of the modified secondary subspace plane is used to control the five-phase DWIG operation at minimum control-winding loss. Finally, the simulation and experimental results are given to verify that the amplitude of phase current and output voltage can be regulated effectively by the proposed control strategy.
(WIP) CloudCAMP: Automating the Deployment and Management of Cloud Services

Users of cloud platforms often must expend significant manual efforts in the deployment and orchestration of their services on cloud platforms due primarily to having to deal with the high variabilities in the configuration options for virtualized environment setup and meeting the software dependencies for each service. Despite the emergence of many DevOps cloud automation and orchestration tools, users must still rely on specifying low-level scripting details for service deployment and management. Using these tools required domain expertise along with a steep learning curve. To address these challenges in a tool-and-technology agnostic manner, which helps promote interoperability and portability of services hosted across cloud platforms, we present initial ideas on a GUI based cloud automation and orchestration framework called CloudCAMP. CloudCAMP uses model-driven engineering techniques to provide users with intuitive and higher-level modeling abstractions that preclude the need to specify all the low-level details. CloudCAMP's generative capabilities leverage a built-in knowledge-base to automate the synthesis of Infrastructure-as-Code (IAC) solution that subsequently can be used to deploy and orchestrate services in the cloud. Preliminary results from a small user study are presented in the paper.
Integrating Software Defined Networks with Traditional Hardware Networks

This paper describes the behavior of software defined networking components connected through traditional hardware equipment and the compatibility between software elements combined with hardware elements. In order to study the compatibility of the two types of networks, two methods are proposed using switching hardware and router hardware. The readings made for each method present data packaging, packet processing delays, and the time it takes for the data to be transferred through the network. In the paper will be presented the differences between the virtual switches and the real hardware components of the network.
A Simple Piecewise Model of Reset/Set Transitions in Bipolar ReRAM Memristive Devices

In this paper, we develop and test a piecewise model for the reset and set transitions of a bipolar ReRAM memristive device in the flux-charge space, instead of the usual voltage–current one. To do so, we consider the devices as memristors. The model used is very simple and provides accurate simulation results. It also allows the development of simple expressions for the conductance and power consumption, as well as the characterization of the ReRAM memristive device in voltage–current domain by using two points for any reset or set cycle. We consider the case of a ramp input signal with different slopes to obtain the model parameters, and we compare the predictions of our model with experimental results.
Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery

The synergistic use of spatial features with spectral properties of satellite images enhances thematic land cover information, which is of great significance for complex land cover mapping. Incorporating spatial features within the classification scheme have been mainly carried out by applying just low-level features, which have shown improvement in the classification result. By contrast, the application of high-level spatial features for classification of satellite imagery has been underrepresented. This study aims to address the lack of high-level features by proposing a classification framework based on convolutional neural network (CNN) to learn deep spatial features for wetland mapping using optical remote sensing data. Designing a fully trained new convolutional network is infeasible due to the limited amount of training data in most remote sensing studies. Thus, we applied fine tuning of a pre-existing CNN. Specifically, AlexNet was used for this purpose. The classification results obtained by the deep CNN were compared with those based on well-known ensemble classifiers, namely random forest (RF), to evaluate the efficiency of CNN. Experimental results demonstrated that CNN was superior to RF for complex wetland mapping even by incorporating the small number of input features (i.e., three features) for CNN compared to RF (i.e., eight features). The proposed classification scheme is the first attempt, investigating the potential of fine-tuning pre-existing CNN, for land cover mapping. It also serves as a baseline framework to facilitate further scientific research using the latest state-of-art machine learning tools for processing remote sensing data.
Open-Circuit Fault-Tolerant Control of Five-Phase PM Machine Based on Reconfiguring Maximum Round Magnetomotive Force

This paper investigates the open-circuit fault-tolerant current control for a five-phase fault-tolerant permanent-magnet (PM) machine used for electric vehicles. By relieving the common constraint of zero neutral point current in the existing fault-tolerant control strategies and reconfiguring the maximum round rotating magnetomotive force under different fault conditions, the new current sets, which enable the five-phase PM machine to output the maximum smooth torque, are obtained by the analytical method. Compared with the existing fault-tolerant control strategy, larger torque and lower torque ripple can be obtained with the proposed fault-tolerant control strategy, and the five-phase PM machine can safely operate with the loss of up to three phase windings. The proposed fault-tolerant control strategy is verified by both finite-element analysis and experimental results. The developed fault-tolerant current control strategy can be generalized into any multiphase PM machines.
Exploiting context for people detection in crowded scenes

Abstract. The effective use of context information can significantly improve the effect of object detection. This paper proposes a method to exploit the context of a hard example searched by online hard example mining for improving the detection effect for people in crowded scenes. As shown by these experiments, this method can improve the effectiveness of faster R-CNN networks on people detection with a smaller convolutional neural network model. Because smaller convolutional neural network model is used, both the running memory consumption and the computing time can be reduced. Hence, our method can be implemented more easily on embedded devices.
FloodShield: Securing the SDN Infrastructure Against Denial-of-Service Attacks

Software-Defined Networking (SDN) has attracted great attention from both academia and industry. However, the deployment of SDN has faced some critical security issues, such as Denial-of-Service (DoS) attacks on the SDN infrastructure. One such DoS attack is the data-to-control plane saturation attack, where an attacker floods a large number of packets to trigger massive table-misses and packet-in messages in the data plane. This attack can exhaust resources of different components of the SDN infrastructure, including TCAM and buffer memory in the data plane, bandwidth of the control channel, and CPU cycles of the controller. In this paper, we analyze the vulnerability of SDN against the data-to-control plane saturation attack extensively and design FloodShield, a comprehensive, deployable and lightweight SDN defense framework to mitigate this dedicated DoS attack. FloodShield combines the following two techniques: 1) source address validation which filters forged packets directly in the data plane, and 2) stateful packet supervision which monitors traffic states of real addresses and performs dynamic countermeasures based on evaluation scores and network resource usages. Implementations and experiments demonstrate that, compared with previous defense frameworks, FloodShield provides effective protection for all three components of the SDN infrastructure – data plane, control channel and control plane – with less resource consumption.
Nonlinear Distortion Calibration of an Optical Flow Sensor for Monocular Visual Odometry

With increasing interest in robotics and automation, there exist a wide range of work on visual odometry. Most literature while capable of showing the state-of-the-art, are inaccessible to engineers outside the domain of computer vision. Some companies minimize this gap by providing these advanced algorithms in an integrated solution, however these solutions do not provide the level of accuracy desired as a single sensor solution. By breaking an assumption in the pinhole model, the paper has shown that it is able to increase the accuracy of a commercially available optical flow sensor by more than tenfold. The sensor was calibrated using a high precision magnetic linear actuator without requiring access to the image frame. Performance test conducted in various environments such as canals and corridors show that accurate visual odometry can be achieved with a small and lightweight commercially available sensor.
Age-based cooperative caching in information-centric networking

Information-Centric Networking (ICN) provides substantial flexibility for users. One of the most important features of ICN is the universal in-network caching. The characteristics of ICN make it substantially different from traditional caching systems. In this paper we propose an age-based cooperative caching scheme in response to the special characteristics of ICN. We leverage the coupling between routing and caching in ICN to develop a light-weight collaboration mechanism that adaptively pushes popular contents to the network edge. We evaluate our approach using real traces and realistic network topology. Results show that our approach can significantly reduce network delay and traffic, and outperforms existing schemes.
SUMO-1 modification of FEN1 facilitates its interaction with Rad9–Rad1–Hus1 to counteract DNA replication stress

Abstract Human flap endonuclease 1 (FEN1) is a structure-specific, multi-functional endonuclease essential for DNA replication and repair. We and others have shown that during DNA replication, FEN1 processes Okazaki fragments via its interaction with the proliferating cell nuclear antigen (PCNA). Alternatively, in response to DNA damage, FEN1 interacts with the PCNA-like Rad9–Rad1–Hus1 complex instead of PCNA to engage in DNA repair activities, such as homology-directed repair of stalled DNA replication forks. However, it is unclear how FEN1 is able to switch between these interactions and its roles in DNA replication and DNA repair. Here, we report that FEN1 undergoes SUMOylation by SUMO-1 in response to DNA replication fork-stalling agents, such as UV irradiation, hydroxyurea, and mitomycin C. This DNA damage-induced SUMO-1 modification promotes the interaction of FEN1 with the Rad9–Rad1–Hus1 complex. Furthermore, we found that FEN1 mutations that prevent its SUMO-1 modification also impair its ability to interact with HUS1 and to rescue stalled replication forks. These impairments lead to the accumulation of DNA damage and heightened sensitivity to fork-stalling agents. Altogether, our findings suggest an important role of the SUMO-1 modification of FEN1 in regulating its roles in DNA replication and repair.
A Phylogenomic Study of Acanthamoeba polyphaga Draft Genome Sequences Suggests Genetic Exchanges With Giant Viruses

Acanthamoeba are ubiquitous phagocytes predominant in soil and water which can ingest many microbes. Giant viruses of amoebae are listed among the Acanthamoeba-resisting microorganisms. Their sympatric lifestyle within amoebae is suspected to promote lateral nucleotide sequence transfers. Some Acanthamoeba species have shown differences in their susceptibility to giant viruses. Until recently, only the genome of a single Acanthamoeba castellanii Neff was available. We analyzed the draft genome sequences of Acanthamoeba polyphaga through several approaches, including comparative genomics, phylogeny, and sequence networks, with the aim of detecting putative nucleotide sequence exchanges with giant viruses. We identified a putative sequence trafficking between this Acanthamoeba species and giant viruses, with 366 genes best matching with viral genes. Among viruses, Pandoraviruses provided the greatest number of best hits with 117 (32%) for A. polyphaga. Then, genes from mimiviruses, Mollivirus sibericum, marseilleviruses, and Pithovirus sibericum were best hits in 67 (18%), 35 (9%), 24 (7%), and 2 (0.5%) cases, respectively. Phylogenetic reconstructions showed in a few cases that the most parsimonious evolutionary scenarios were a transfer of gene sequences from giant viruses to A. polyphaga. Nevertheless, in most cases, phylogenies were inconclusive regarding the sense of the sequence flow. The number and nature of putative nucleotide sequence transfers between A. polyphaga, and A. castellanii ATCC 50370 on the one hand, and pandoraviruses, mimiviruses and marseilleviruses on the other hand were analyzed. The results showed a lower number of differences within the same giant viral family compared to between different giant virus families. The evolution of 10 scaffolds that were identified among the 14 Acanthamoeba sp. draft genome sequences and that harbored ≥ 3 genes best matching with viruses showed a conservation of these scaffolds and their 46 viral genes in A. polyphaga, A. castellanii ATCC 50370 and A. pearcei. In contrast, the number of conserved genes decreased for other Acanthamoeba species, and none of these 46 genes were present in three of them. Overall, this work opens up several potential avenues for future studies on the interactions between Acanthamoeba species and giant viruses.
ThunderSVM: A Fast SVM Library on GPUs and CPUs

Support Vector Machines (SVMs) are classic supervised learning models for classiﬁcation, regression and distribution estimation. A survey conducted by Kaggle in 2017 shows that 26% of the data mining and machine learning practitioners are users of SVMs. However, SVM training and prediction are very expensive computationally for large and complex problems. This paper presents an eﬃcient and open source SVM software toolkit called ThunderSVM which exploits the high-performance of Graphics Processing Units (GPUs) and multi-core CPUs. ThunderSVM supports all the functionalities—including classiﬁcation (SVC), regression (SVR) and one-class SVMs—of LibSVM and uses identical command line options, such that existing LibSVM users can easily apply our toolkit. ThunderSVM can be used through multiple language interfaces including C/C++, Python, R and MAT-LAB. Our experimental results show that ThunderSVM is generally an order of magnitude faster than LibSVM while producing identical SVMs. In addition to the high eﬃciency, we design our convex optimization solver in a general way such that SVC, SVR, and one-class SVMs share the same solver for the ease of maintenance. Documentation, examples, and more about ThunderSVM are available at https://github.com/zeyiwen/thundersvm .