Miracle-dz/UniFAR-FTUs huggingface dataset:
Male coercion and convenience polyandry in a calopterygid damselfly.

Copulation in odonates requires female cooperation because females must raise their abdomen to allow intromission. Nevertheless in Calopteryx haemorrhoidalis haemorrhoidalis (Odonata) males commonly grasp ovipositing females and apparently force copulations. This has been interpreted as a consequence of extreme population density and male-male competition. We studied this behavior at two sites on a river that had different densities over three years. As predicted, at high densities most matings were forced (i.e. not preceded by courtship), but at low density most were preceded by courtship. Courtship matings were shorter at high density, but density did not affect the duration of forced matings. Females cooperated in forced matings even if they had very few mature eggs. Furthermore, females mated more times if they experienced higher male harassment during oviposition, and at low density second and subsequent matings were more likely to be forced. We interpret these results to mean that females engage in “convenience polyandry”, because they gain more by accepting copulation than by resisting males. The results also suggest that females might trade copulations for male protection, because under extreme population density harassment by males is so intense that they can impede oviposition.
A LT-Based Distributed Codes Over Erasure Channel

— For the future space network scenarios, where multi-rovers can return science data through one obiter simultaneously, we design a new distributed LT (NDLT) code between two sources, one relay and a single sink, which could enhance the efficiency and reliability of packets transferring from source to sink. This paper proposes a method to decompose weaken robust soliton distribution (WRSD) exploited by relay node into two deconvolved weaken robust soliton distributions (DWSDs), which are used in the sources and have lower average degree than traditional RSD-based deconvolved soliton distributions (DSDs). Low operation complexity at sources and only including a simple XOR operation at relay node could be provided by the proposed degree distribution. This paper provides analytical results about the coding complexity of proposed and traditional distributed LT (DLT) code. The simulation results show that the proposed NDLT has a higher decoding probability than that of traditional DLT, under the conditions of recovering a certain proportion of original information, such as some space mission scenarios with specific data completeness requirement.
Effect of feeding rice distillers dried grains with solubles (rDDGS) and rice gluten meal (RGM) based diet on the gut health of broiler chicken

A biological experiment was conducted to evaluate the effect of feeding rice distillers dried grains with solubles (rDDGS) and rice gluten meal (RGM) combination as a replacement of soybean along with exogenous enzyme supplementation on the intestinal morphometry and microbiology of broiler chicken at 21 days of age. Following a 2x3 factorial design, the experimental diets were: T1 (no rDDGS/RGM/enzyme), T2 (no rDDGS/RGM, with multienzymes), T3 (12.5% rDDGS, 15% RGM, no enzyme), T4 (12.5% rDDGS, 15% RGM, with protease enzyme), T5 (10% rDDGS, 12.5% RGM, no enzyme), and T6 (10% rDDGS, 12.5% RGM, with protease enzyme). Each treatment was allocated 5 replicates of broiler chicken with 8 birds in each. The feeding of the dietary combination of 12.5% rDDGS and 15% RGM significantly (P<0.05) decreased the villus height (VH), crypt depth (CD), VH: CD ratio and villus width compared to the control and other rDDGS & RGM combination. The microbiological parameters revealed that TVC in crop and jejunum were increased significantly (P<0.01) in 12.5% rDDGS & 15% RGM as compared to control and other dietary combination. Lactobacillus count in crop was decreased significantly (P<0.05) in12.5% rDDGS & 15% RGM as compared control. It was concluded that the simultaneous inclusion of rDDGS and RGM in broiler chicken ration with or without enzyme supplementation had adverse effects on the intestinal histomorphometry and microbiology.
Capturing Service Versioning in Provenance Trace to Support Reproducibility

Reproducibility has long been a cornerstone of science. Underpinning reproducibility is provenance, which has the potential to provide scientists with a complete understanding of data generated in e-experiments, including the services that were produced and consumed. A key to reproducibility is the provenance model: a data model that structures information about an e-experiment. When all the entities in the experiment have been identified, they must be captured and recorded as a provenance trace. The provenance trace gives information about the actual execution of an experiment. Therefore, in running an experiment, the creation of the final results that are derived from the input data are documented in a provenance trace. This paper describes in greater detail the conceptualization of an experiment using the Open Provenance Model (OPM). As Open Provenance Model (OPM) is the provenance model standard, this paper explores whether the OPM is able to describe an experiment sufficiently precisely so as to support reproducibility. The paper also addresses the issue of how to ensure that the versions of services involved in the experiment can remain available, as service versioning is part of essential requirements in reproducibility.
Multi-objective differential evolution: theory and applications

Most decision support systems involve rnulti-objective optimization problems (MOOP). Previous research has usually treated these MOOPs by heuristically combining the multiple objectives into a single one. However, it is extremely difficult to incorporate a decision maker's (DM) preference structure and have an appropriate way for this combination. This has been a hurdle for the DM to obtain an optimal tradeoff solution. The fundamental theme of this research work is to develop the foundation of an efficient rnulti-objective optimization strategy from a theoretical perspective and demonstrate its applications as the core search engine of decision support systems for real-world problems. In this thesis, a new evolutionary rnulti-objective optimization strategy—Multi-Objective Differential Evolution (MODE) is proposed. The MODE is developed for both continuous (C-MODE) and discrete (D-MODE) domains. A set of performance metrics called Pareto Front Approximation Error (PFAE) is introduced to evaluate the non-dominated solutions obtained by optimization approaches. A theoretical foundation to explain the behaviors of MODE is developed through proofs of convergence of the MODE algorithms. The analysis of the D-MODE is treated based on a Markov model while the C-MODE is modeled in the global random search framework. The convergence of the population to the Pareto front with probability one is developed. A set of guidelines on the parameter setting of the C-MODE is derived based on both mathematical modeling and simulation analysis of its operators. A class of decision problems arising in integrated design, supplier, and manufacturing (DSM) planning involved in modular product development is formally modeled as a multi-objective optimization assignment problem. The MODE is applied to solving such MOOPs to generate the Pareto optimal solutions for further decision making. An object oriented framework of a multi-objective decision support system is proposed for such DSM planning with MODE as the core search engine. This can serve as a prototype for other applications. The routing problems in wireless networks is formally modeled as a MOOP that optimizes both power consumption and communication latency. The MODE concept is implemented with its internal data structure tailored to the network specifics.
Semiclassical approximation to virial coefficients beyond the leading order

We extend the calculation of virial coefficients of Fermi gases in the semiclassical approximation to next-to- and next-to-next-to-leading orders for $b_2$, $b_3$, $b_4$, and $b_5$. Our results feature relationships between interaction-induced changes $\Delta b_3$, $\Delta b_4$, and $\Delta b_5$ in terms of $\Delta b_2$, the latter being exactly known in many cases by virtue of the Beth-Uhlenbeck formula. We use that knowledge as a renormalization condition. We compare our results in $d=1,2,3$ spatial dimensions with known answers from Monte Carlo calculations and diagrammatic approaches. In particular, we find remarkable agreement for $\Delta b_3$ with previous calculations in all dimensions and we formulate predictions for $\Delta b_4$ and $\Delta b_5$ for attractive interactions.
Exact quantum search based on analytical multiphase matching for known number of target items and the experimental demonstration on IBM Q

In [Phys. Rev. Lett. 113, 210501 (2014)], to achieve the optimal fixed-point quantum search in the case of unknown fraction (denoted by $\lambda$) of target items, the analytical multiphase matching (AMPM) condition has been proposed. In this paper, we find out that the AMPM condition can also be used to design the exact quantum search algorithm in the case of known $\lambda$, and the minimum number of iterations reaches the optimal level of existing exact algorithms. Experiments are performed to demonstrate the proposed algorithm on IBM's quantum computer. In addition, we theoretically find two coincidental phases with equal absolute value in our algorithm based on the AMPM condition and that algorithm based on single-phase matching. Our work confirms the practicability of the AMPM condition in the case of known $\lambda$, and is helpful to understand the mechanism of this condition.
How Can Smart Buildings Be Price-Responsive?

The prospective participation of smart buildings in the electricity system is strongly related to the increasing active role of demand-side resources in the electrical grid. In addition, the growing penetration of smart meters and recent advances on home automation technologies will spur the development of new mathematical tools to help optimize the local resources of these buildings. Within this context, this paper first provides a comprehensive model to determine the electrical consumption of a single-zone household based on economic model predictive control. The goal of this problem is to minimize the electricity consumption cost while accounting for the heating dynamics of the building, smart home appliances, and comfort constraints. This paper then identifies and analyzes the key parameters responsible for the price-responsive behaviour of smart households.
An Asymptotically Optimal Channel Hopping Sequence with Maximum Rendezvous Diversity

In the literature, there are several well-known periodic channel hopping (CH) sequences that can achieve maximum rendezvous diversity in a cognitive radio network (CRN). For a CRN with $N$ channels, it is known that the period of such a CH sequence is at least $N^2$. The asymptotic approximation ratio, defined as the ratio of the period of a CH sequence to the lower bound $N^2$ when $N \to \infty$, is still 2.5 for the best known CH in the literature. An open question in the multichannel rendezvous problem is whether it is possible to construct a periodic CH sequence that has the asymptotic approximation ratio 1. In this letter, we solve such a question by proposing CH sequences that have the asymptotic approximation ratio 1.
The Study of Secure Congestion Control for TCP in Ad Hoc Networks

Ad hoc networks are vulnerable to various attacks. In addition, congestion caused by limited resources may occur at any time in the transmission of the packets at intermediate nodes. This paper proposes a dynamic congestion control method of the selection of a secure path. By estimating the average queue length at the nodes, the congestion level at present is detected. If the occurrence of the possible congestion is predicted, the network will select a new path where all nodes have been certified as trusted nodes, generating session keys in the TCP three-way handshake to prevent the denial of service attacks. Simulation results show that the new algorithm is superior to TCP Reno algorithm in terms of security, packets loss rate, throughput, and end-to-end delay.
Estimating Color-Concept Associations from Image Statistics

To interpret the meanings of colors in visualizations of categorical information, people must determine how distinct colors correspond to different concepts. This process is easier when assignments between colors and concepts in visualizations match people's expectations, making color palettes semantically interpretable. Efforts have been underway to optimize color palette design for semantic interpretablity, but this requires having good estimates of human color-concept associations. Obtaining these data from humans is costly, which motivates the need for automated methods. We developed and evaluated a new method for automatically estimating color-concept associations in a way that strongly correlates with human ratings. Building on prior studies using Google Images, our approach operates directly on Google Image search results without the need for humans in the loop. Specifically, we evaluated several methods for extracting raw pixel content of the images in order to best estimate color-concept associations obtained from human ratings. The most effective method extracted colors using a combination of cylindrical sectors and color categories in color space. We demonstrate that our approach can accurately estimate average human color-concept associations for different fruits using only a small set of images. The approach also generalizes moderately well to more complicated recycling-related concepts of objects that can appear in any color.
The effect of mechanical stress on lithium distribution and geometry optimisation for multi-material lithium-ion anodes

A model is presented for predicting the open-circuit voltage (OCV) and lithium distribution within lithium-ion anodes containing multiple materials, coupling linear elasticity with a stress-dependent chemical potential. The model is applied to a spherical radially-symmetric nano-particle with a silicon core and a graphite shell, highlighting the large effect on lithium distribution and OCV caused by the stress-coupling. Various performance measures based on the expanded volume, the amount of lithium intercalated and the maximum stress induced, are calculated for a silicon core with a graphite shell to enable optimisation of the volume of the silicon core.
Pilot-Based Channel Estimation Design in Covert Wireless Communication

In this work, for the first time, we tackle channel estimation design with pilots in the context of covert wireless communication. Specifically, we consider Rayleigh fading for the communication channel from a transmitter to a receiver and additive white Gaussian noise (AWGN) for the detection channel from the transmitter to a warden. Before transmitting information signals, the transmitter has to send pilots to enable channel estimation at the receiver. Using a lower bound on the detection error probability, we first prove that transmitting pilot and information signals with equal power can minimize the detection performance at the warden, which is confirmed by the minimum detection error probability achieved by the optimal detector based on likelihood ratio test. This motivates us to consider the equal transmit power in the channel estimation and then optimize channel use allocation between pilot and information signals in covert wireless communication. Our analysis shows that the optimal number of the channel uses allocated to pilots increases as the covertness constraint becomes tighter. In addition, our examination shows that the optimal percentage of all the available channel uses allocated to channel estimation decreases as the total number of channel uses increases.
Hot, dense HeII outflows during the 2017 outburst of the X-ray transient Swift J1357.2-0933

Time-resolved SALT spectra of the short-period, dipping X-ray transient, Swift J1357.2-0933, during its 2017 outburst has revealed broad Balmer and HeII λ4686 absorption features, blue-shifted by ∼600 km s−1. Remarkably these features are also variable on the ∼500s dipping period, indicating their likely association with structure in the inner accretion disc. We interpret this as arising in a dense, hot (≳ 30, 000K) outflowing wind seen at very high inclination, and draw comparisons with other accretion disc corona sources. We argue against previous distance estimates of 1.5kpc and favour a value ≳ 6kpc, implying an X-ray luminosity LX ≳ 4 × 1036 erg s−1. Hence it is not a very faint X-ray transient. Our preliminary 1D Monte-Carlo radiative transfer and photoionization calculations support this interpretation, as they imply a high intrinsic LX, a column density NH ≳ 1024cm−2 and a low covering factor for the wind. Our study shows that Swift J1357.2-0933 is truly remarkable amongst the cohort of luminous, galactic X-ray binaries, showing the first example of HeII λ4686 absorption, the first (and only) variable dip period and is possibly the first black hole ‘accretion disc corona’ candidate.
Nijenhuis-type variants of Local Theory of Background Independence.

A local resolution of the Problem of Time has recently been given, alongside reformulation as a local theory of Background Independence. The classical part of this can be viewed as requiring just Lie's Mathematics, albeit entrenched in subsequent Topology and Differential Geometry developments and extended to the setting of contemporary Physics' state spaces. We now generalize this approach by mild recategorization to one based on Nijenhuis' generalization of Lie's Mathematics, as follows. 1) Relationalism is encoded using the Nijenhuis-Lie derivative. 2) Closure is assessed using the Schouten-Nijenhuis bracket, and a `Schouten-Nijenhuis Algorithm' analogue of the Dirac and Lie Algorithms. This produces a class of Gerstenhaber algebraic structures of generators or of constraints. 3) Observables are defined by a Schouten--Nijenhuis brackets relation, reformulating the constrained canonical case as explicit PDEs to be solved using the Flow Method, and forming their own Gerstenhaber algebras of observables. Lattices of Schouten-Nijenhuis-Gerstenhaber constraint or generator algebraic substructures furthermore induce dual lattices of Gerstenhaber observables subalgebras. 4) Deformation of Gerstenhaber algebraic structures of generators or constraints encountering Rigidity gives a means of Constructing more structure from less. 5) Reallocation of Intermediary-Object Invariance gives the general Schouten-Nijenhuis-Gerstenhaber algebraic structure's analogue of posing Refoliation Invariance for GR. We finally point to general Gerstenhaber bracket and Vinogradov bracket generalizations, with the former likely to play a significant role in Backgound-Independent Deformation Quantization and Quantum Operator Algebras.
A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network

The fast and accurate prediction of unsteady flow becomes a serious challenge in fluid dynamics, due to the high-dimensional and nonlinear characteristics. A novel hybrid deep neural network (DNN) architecture was designed to capture the unsteady flow spatio-temporal features directly from the high-dimensional unsteady flow fields. The hybrid deep neural network is constituted by the convolutional neural network (CNN), convolutional Long Short Term Memory neural network (ConvLSTM) and deconvolutional neural network (DeCNN). The flow around a cylinder at various Reynolds numbers and the flow around an airfoil at higher Reynolds number are carried out to establish the datasets used to train the networks separately. The trained hybrid DNNs were then tested by the prediction of the flow fields at future occasions. The predicted flow fields using the trained hybrid DNNs are in good agreement with the flow fields calculated directly by the computational fluid dynamic solver.
Predicting Graph Signals Using Kernel Regression Where the Input Signal is Agnostic to a Graph

We propose a kernel regression method to predict a target signal lying over a graph when an input observation is given. The input and the output could be two different physical quantities. In particular, the input may not be a graph signal at all or it could be agnostic to an underlying graph. We use a training dataset to learn the proposed regression model by formulating it as a convex optimization problem, where we use a graph-Laplacian based regularization to enforce that the predicted target is a graph signal. Once the model is learnt, it can be directly used on a large number of test data points one-by-one independently to predict the corresponding targets. Our approach employs kernels between the various input observations, and as a result the kernels are not restricted to be functions of the graph adjacency/Laplacian matrix. We show that the proposed kernel regression exhibits a smoothing effect, while simultaneously achieving noise-reduction and graph-smoothness. We then extend our method to the case when the underlying graph may not be known apriori, by simultaneously learning an underlying graph and the regression coefficients. Using extensive experiments, we show that our method provides a good prediction performance in adverse conditions, particularly when the training data is limited in size and is noisy. In graph signal reconstruction experiments, our method is shown to provide a good performance even for a highly under-determined subsampling.
Falls Prediction Based on Body Keypoints and Seq2Seq Architecture

This paper presents a novel approach for predicting the falls of people in advance from monocular video. First, all persons in the observed frames are detected and tracked with the coordinates of their body keypoints being extracted meanwhile. A keypoints vectorization method is exploited to eliminate irrelevant information in the initial coordinate representation. Then, the observed keypoint sequence of each person is input to the pose prediction module adapted from sequence-to-sequence(seq2seq) architecture to predict the future keypoint sequence. Finally, the predicted pose is analyzed by the falls classifier to judge whether the person will fall down in the future. The pose prediction module and falls classifier are trained separately and tuned jointly using Le2i dataset, which contains 191 videos of various normal daily activities as well as falls performed by several actors. The contrast experiments with mainstream raw RGB-based models show the accuracy improvement of utilizing body keypoints in falls classification. Moreover, the precognition of falls is proved effective by comparisons between models that with and without the pose prediction module.