Miracle-dz/UniFAR-FTUs huggingface dataset:
Future applications of lucerne for efficient livestock production in southern Australia

Abstract. Lucerne is a deep-rooted herbaceous perennial legume with high levels of summer production and adaptation to a broad range of agro-ecological environments in southern Australia. The ability of lucerne to extend the growing season of winter-based pasture and respond quickly to rainfall after periods of drought makes it one of the most valuable plants in our feed base. However, for all the advantages of lucerne, it remains underutilised. Lucerne is often considered to be a speciality fodder crop, requiring careful management to achieve high levels of production and persistence. This paper investigates the opportunity of whole-farm integration of lucerne; from speciality fodder crop to traditional pasture. The future trends of lucerne production in temperate grazing and intensive dairy systems are discussed in relation to breeding objectives identified to meet these demands. If lucerne is to be used more commonly as a pasture, the plant and systems must adapt. This paper investigates the plant traits and management principles that are important for growing lucerne in mixtures with other plants and improving the integration of lucerne into the whole-farm plan.
G-Image Segmentation: Similarity-Preserving Fuzzy C-Means With Spatial Information Constraint in Wavelet Space

G-images refer to image data defined on irregular graph domains. This article elaborates on a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback–Leibler divergence term on partition matrix is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, the proposed FCM is performed in this space rather than the Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art segmentation algorithms. Moreover, it requires less computation than most of them.
Derivation of analytical expressions for optical gain coefficient in bulk semiconductors

An analytical model is proposed to determine the expressions of the semiconductor optical gain coefficient by using the special functions. These expressions differ from the standard numerical calculations approaches and they involve sums of the polylogarithm and incomplete gamma functions. One of the calculation results presented here reproduces formulae developed in the study. The new formulae obtained here provide a sufficient way to calculate the semiconductor optical gain on the total carrier density and temperature in the semiconductor lasers. The obtained analytical expression gives an accurate and precise evaluation of the semiconductor gain function. The performance of the proposed formula is confirmed by its implementation of the different values of the parameter. The sufficiency obtained results of this approach was confirmed by the other literature data.
Low host specificity of beetles associated with fruit falls in lowland tropical rainforest of north‐east Australia

Most host‐specificity studies of tropical rainforest insects have focused on those species feeding on leaves. Apart from the fruit flies, the level of specialisation among fruit‐associated insects is poorly known. The relative contribution to local species richness made by insects feeding or associated with fallen fruits is also unknown. Beetles from fruit falls over a 5‐year period in lowland tropical rainforest at Cape Tribulation, Australia were sampled. A total of 5157 individual beetles of 73 species were sampled from the fruits of 18 different plant species. Only a few species were of frugivorous families, and most species are likely utilising resources associated with the breakdown of the fruits. The size of the fruit fall‐associated beetle assemblage (73 spp) was small compared with the number of species collected during a 4‐year sampling program conducted at the same site prior to the current study using 10 combined Malaise‐Flight Interception Traps (1473 spp, 77 families). The number of beetles and species collected from fruit that were very strongly correlated with the number of times fruit falls from a particular species of plant were sampled. The locally common palm, Normanbya normanbyi, produced fruit throughout the year and supported the largest number of fruit‐associated species. It is suggested that this might be a keystone resource for local fruit fall‐associated insect species. Although most beetle species showed a preference for the fruits of a particular plant species, overall host specificity for beetles was low. This is probably because fruit resources at this site are spatially and temporally patchy. Our results challenge the notion that most insects associated with fruit falls in tropical rainforests are highly host‐specific.
Learning Minimax Estimators via Online Learning

We consider the problem of designing minimax estimators for estimating the parameters of a probability distribution. Unlike classical approaches such as the MLE and minimum distance estimators, we consider an algorithmic approach for constructing such estimators. We view the problem of designing minimax estimators as finding a mixed strategy Nash equilibrium of a zero-sum game. By leveraging recent results in online learning with non-convex losses, we provide a general algorithm for finding a mixed-strategy Nash equilibrium of general non-convex non-concave zero-sum games. Our algorithm requires access to two subroutines: (a) one which outputs a Bayes estimator corresponding to a given prior probability distribution, and (b) one which computes the worst-case risk of any given estimator. Given access to these two subroutines, we show that our algorithm outputs both a minimax estimator and a least favorable prior. To demonstrate the power of this approach, we use it to construct provably minimax estimators for classical problems such as estimation in the finite Gaussian sequence model, and linear regression.
Bandit algorithms: Letting go of logarithmic regret for statistical robustness

We study regret minimization in a stochastic multi-armed bandit setting and establish a fundamental trade-off between the regret suffered under an algorithm, and its statistical robustness. Considering broad classes of underlying arms' distributions, we show that bandit learning algorithms with logarithmic regret are always inconsistent and that consistent learning algorithms always suffer a super-logarithmic regret. This result highlights the inevitable statistical fragility of all `logarithmic regret' bandit algorithms available in the literature---for instance, if a UCB algorithm designed for $\sigma$-subGaussian distributions is used in a subGaussian setting with a mismatched variance parameter, the learning performance could be inconsistent. Next, we show a positive result: statistically robust and consistent learning performance is attainable if we allow the regret to be slightly worse than logarithmic. Specifically, we propose three classes of distribution oblivious algorithms that achieve an asymptotic regret that is arbitrarily close to logarithmic.
Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness

Neural Tensor Networks (NTNs), which are structured to encode the degree of relationship among pairs of entities, are used in Logic Tensor Networks (LTNs) to facilitate Statistical Relational Learning (SRL) in first-order logic. In this paper, we propose Randomly Weighted Tensor Networks (RWTNs), which incorporate randomly drawn, untrained tensors into an NTN encoder network with a trained decoder network. We show that RWTNs meet or surpass the performance of traditionally trained LTNs for Semantic Image Interpretation (SII) tasks that have been used as a representative example of how LTNs utilize reasoning over first-order logic to exceed the performance of solely data-driven methods. We demonstrate that RWTNs outperform LTNs for the detection of the relevant part-of relations between objects, and we show that RWTNs can achieve similar performance as LTNs for object classification while using fewer parameters for learning.
Seasonal activity patterns and habitats in Solifugae (Arachnida) in the southern Karoo, South Africa

Solifuges were caught with pitfall traps over a 29-month period in three different habitats at Tierberg, near Prince Albert. A total of 134 individuals of at least nine species of four families (Ceromidae, Daesiidae, Gylippidae and Solpugidae) were trapped in the three habitats. Blossia karrooica, B. unquicornis and Hemiblossia idioceras were caught only on plains, Blossia sp. nov. was caught only on 'heuweltjies', Lipophaga trispinosa was caught only on old fields, and Hemiblossia oneili and Solpugiba lineata were caught in all three habitats. Most species were active in the summer (October-March) but two species, Blossia karrooica and Lipophaga trispinosa were active throughout the year.
HORSE HEART CYTOCHROME c AS AN ELECTRON ACCEPTOR FROM CHLOROPHYLL TRIPLET IN NEGATIVELY CHARGED LIPID BILAYER VESICLES *

Abstract— Cytochrome c has been shown to bind via electrostatic interactions to egg phosphatidylcholine vesicles which contain 5–30 mol percent of negatively‐charged surfactant (dihexadecylphosphate) in a low ionic strength medium. Under these conditions the oxidized cytochrome can function as a direct one‐electron acceptor from membrane‐bound triplet state chlorophyll to produce chlorophyll cation radical and reduced cytochrome. Kinetic experiments using laser flash photolysis have demonstrated that triplet quenching and the yield of electron transfer products increase, and product lifetime decreases, with an increase in the magnitude of the negative charge on the vesicles, and with a decrease in the ionic strength of the medium. Both triplet quenching and product formation rates and yields showed saturation behavior as the cytochrome concentration was increased, and reached limiting values at 20–30 μM cytochrome when the vesicle contained 20 mol percent of the negatively‐charged surfactant. This behavior is interpreted in terms of saturation of the vesicle surface binding sites. Under optimum conditions in this system, approximately 20% of the chlorophyll triplet molecules could be converted to electron transfer products which had a halftime for the reverse reaction of approximately 1.5 ms.
Regret of Age-of-Information Bandits

We consider a system with a single source that measures/tracks a time-varying quantity and periodically attempts to report these measurements to a monitoring station. Each update from the source has to be scheduled on one of $K$ available communication channels. The probability of success of each attempted communication is a function of the channel used. This function is unknown to the scheduler. The metric of interest is the Age-of-Information (AoI), formally defined as the time elapsed since the destination received the recent most update from the source. We model our scheduling problem as a variant of the multi-arm bandit problem with communication channels as arms. We characterize a lower bound on the AoI regret achievable by any policy and characterize the performance of UCB, Thompson Sampling, and their variants. Our analytical results show that UCB and Thompson sampling are order-optimal for AoI bandits. In addition, we propose novel policies which, unlike UCB and Thompson Sampling, use the current AoI to make scheduling decisions. Via simulations, we show the proposed AoI-aware policies outperform existing AoI-agnostic policies.
Learning to Generate Noise for Robustness against Multiple Perturbations

Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations. However, the majority of existing defense methods are tailored to defend against a single category of adversarial perturbation (e.g. $\ell_\infty$-attack). In safety-critical applications, this makes these methods extraneous as the attacker can adopt diverse adversaries to deceive the system. To tackle this challenge of robustness against multiple perturbations, we propose a novel meta-learning framework that explicitly learns to generate noise to improve the model's robustness against multiple types of attacks. Specifically, we propose Meta Noise Generator (MNG) that outputs optimal noise to stochastically perturb a given sample, such that it helps lower the error on diverse adversarial perturbations. However, training on multiple perturbations simultaneously significantly increases the computational overhead during training. To address this issue, we train our MNG while randomly sampling an attack at each epoch, which incurs negligible overhead over standard adversarial training. We validate the robustness of our framework on various datasets and against a wide variety of unseen perturbations, demonstrating that it significantly outperforms the relevant baselines across multiple perturbations with marginal computational cost compared to the multiple perturbations training.
GASP XXVI. HI Gas in Jellyfish Galaxies: The case of JO201 and JO206

We present HI observations of the jellyfish galaxy, JO201. This massive galaxy (M$_{\ast} = 3.5 \times 10^{10}$ M$_\odot$) is falling along the line-of-sight towards the centre of a rich cluster (M$_{200} \sim 1.6 \times 10^{15}$ M$_\odot$, $\sigma_{cl} \sim 982$ km/s) at a high velocity $\geq$3363 km/s. Its H$\alpha$ emission shows a $\sim$40 kpc tail confined closely to its stellar disc and a $\sim$100 kpc tail extending further out. We find HI emission coinciding only with the shorter clumpy H$\alpha$ tail. In total, we measure an HI mass of M$_{\rm HI} = 1.65 \times 10^{9}$ M$_\odot$, which is about 60% lower than expected based on its stellar mass and stellar surface density. We compared JO201 to another jellyfish in the GASP sample, JO206 (of similar mass but residing in a 10$\times$ less massive cluster), and find that they are similarly HI-deficient. Of the total HI mass in JO201, about 30% lies outside the galaxy disc in projection. This HI fraction is probably a lower limit since most of the HI is redshifted relative to the stellar disc and could be outside the disc. The global star formation rate (SFR) analysis of JO201 suggests that its observed SFR would be expected if it had 10$\times$ its current HI mass. The disc is the main contributor of the high star formation efficiency at a given HI gas density for both galaxies, but their tails also show higher star formation efficiencies compared to the outer regions of field galaxies. Generally, we find that JO201 and JO206 are similar based on their HI content, stellar mass and star formation rate. This finding is unexpected considering their different environments. A toy model comparing the ram pressure of the ICM versus the restoring forces of these galaxies suggests that the ram pressure strength exerted on them could be comparable if we consider their 3D orbital velocities and radial distances relative to the clusters.
Counterfactually Guided Policy Transfer in Clinical Settings

Domain shift, encountered when using a trained model for a new patient population, creates significant challenges for sequential decision making in healthcare since the target domain may be both data-scarce and confounded. In this paper, we propose a method for off-policy transfer by modeling the underlying generative process with a causal mechanism. We use informative priors from the source domain to augment counterfactual trajectories in the target in a principled manner. We demonstrate how this addresses data-scarcity in the presence of unobserved confounding. The causal parametrization of our sampling procedure guarantees that counterfactual quantities can be estimated from scarce observational target data, maintaining intuitive stability properties. Policy learning in the target domain is further regularized via the source policy through KL-divergence. Through evaluation on a simulated sepsis treatment task, our counterfactual policy transfer procedure significantly improves the performance of a learned treatment policy when assumptions of"no-unobserved confounding"are relaxed.
Laser-induced ultrafast insulator-metal transition in 
BaBiO3

We investigate ultra-fast coherent quantum dynamics of undoped $\text{BaBiO}_{3}$ driven by a strong laser pulse. Our calculations demonstrate that in a wide range of radiation frequencies and intensities the system undergoes a transient change from the insulating to the metallic state, where the charge density wave and the corresponding energy spectrum gap vanish. The transition takes place on the ultra-fast time scale of tens femtoseconds, comparable to the period of the corresponding lattice vibrations. The dynamics are determined by a complex interplay of the particle-hole excitation over the gap and of the tunnelling through it, giving rise to the highly non-trivial time evolution which comprises high harmonics and reveals periodic reappearance of the gap. The time evolution is obtained by solving the dynamical mean-field theory equations with the realistic parameters for the system and radiation. Results are summarized in the phase diagram, helpful for a possible experimental setup to achieve a dynamical control over the conduction state of this and other materials with the similarly strong electron-phonon interaction.
Improving Image Captioning with Better Use of Caption

Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community. In this paper, we present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. The representation is then enhanced with neighbouring and contextual nodes with their textual and visual features. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences. We perform extensive experiments on the MSCOCO dataset, showing that the proposed framework significantly outperforms the baselines, resulting in the state-of-the-art performance under a wide range of evaluation metrics. The code of our paper has been made publicly available.
Evaluating the Toxicity of Organophosphate Flame Retardants in Zebrafish

Flame retardants are in many home products including couches, chairs, and electronics. The most commonly used flame retardants contained polybrominated diphenyl ethers (PBDEs) until 2004, when the majority of these chemicals were phased out. PBDEs were found to be persistent, bioaccumulative, and toxic to both humans and the environment. Organophosphate flame retardants have become an option for alternative flame retardants, but have not been tested extensively. There is concern that many of these organophosphate flame retardants may be carcinogenic, neurotoxic, or reproductive toxicants. Using zebrafish as a model, three organophosphate flame retardants (TDCPP, TCPP, and TCEP) were tested for their developmental and neurotoxic effects. The results of this study indicated that there are both physical and behavioral effects of all three of these chemicals. With further testing, it may be found that these negative effects are linked to various neurological disorders in humans such as ADD and ADHD. Other neurological disorders, such as autism or depression, may also be affected, but additional studies would need to be performed in order to determine this.
Habitat Productivity and Temporal Patterns of Monarch Butterfly Egg Densities in the Eastern United States

ABSTRACT There is compelling evidence that numbers of eastern North American monarchs are declining, as documented by the area they occupy in their Mexican overwintering grounds. Decreasing availability of breeding habitat has been implicated in this decline. However, it is difficult to document population size during the breeding phase of the annual cycle because monarchs are dispersed over a large area, and eggs and larvae are only monitored in remaining milkweed patches. We assess monarch egg densities on a per host plant basis from 1997 to 2014, using data from the Monarch Larva Monitoring Project (MLMP), a citizen science program. We analyzed the effects of habitat characteristics and overall trends in monarch egg densities within and between years. Gardens and sites with fewer milkweed plants tended to have higher egg densities, and natural areas tended to have lower densities. While there was a great deal of year-to-year variation in monarch egg densities, MLMP data document declining densities after 2006. Finally, breeding area egg density is a significant positive predictor of the area occupied in the Mexican overwintering grounds. Additionally, the area planted in herbicide-tolerant crops is a significant negative predictor of the area occupied in the Mexican overwintering grounds. Our findings suggest that monarchs are not compensating for the loss of breeding habitat by laying more eggs in the remaining habitat. We conclude that declining egg densities over the past eight years could indicate a lack of critical mass needed to ensure that monarchs find remaining habitat.
Speed Analysis of Body Biased TSPC and ETSCPC Flip Flops

: In this paper the Performance of body biased True Single Phase Clock (BBTSPC) and body biased Extended True Single Phase Clock (BBETSPC) are investigated. The delay of BBTSPC and BBETSPC are analyzed, simulated and compared with the existing TSPC and ETSPC. A high speed divide-by-2/3 unit of prescaler with the body biased is proposed and validated that this prescaler can operate with higher frequency of 4 GHz stably on a 180 nm technology. This prescaler with the body bias design can be widely used in Communication data analysis probe systems.