Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition errors and probability of parasitism determine whether reed warblers should accept or reject mimetic cuckoo eggs

Reed warblers sometimes make recognition errors when faced with a mimetic cuckoo egg in their nest and reject one or more of their own eggs rather than the foreign egg. Using the framework of signal detection theory, we analyse responses to model eggs to quantify the costs and benefits of acceptance versus rejection in parasitized and unparasitized nests. We show that below a threshold of 19-41% parasitism, the warblers should accept mimetic cuckoo eggs because the costs of rejection outweigh the benefits, whereas above this threshold they should reject. The warblers behaved as predicted; when they saw a cuckoo at their nest they usually showed rejection, but without the sight of the cuckoo they behaved appropriately for the average parasitism rate in Britain (6%) and tended to accept.
Seed‐borne cucumber mosaic virus infection of narrow‐leafed lupin (Lupinus angustifolius) in Western Australia

SUMMARY In 1986 in Western Australia, cucumber mosaic virus (CMV) infection was widespread in breeders' selections of narrow-leafed lupin (Lupinus angustifolius), and in collections of lupin cvs and wild L. angustifolius lines. When seed of some of these selections and cvs was sown, seed-borne CMV was detected in seedlings. Infection of F1 progenies was traced to use of infected parent plants. CMV was also widespread in 25 seed crops of the new lupin cv. Wandoo but not in 42 seed crops of the new cv. Danja. When samples of the seed sown in 1986 were tested, CMV was detected in 3 - 34% of seedlings of cv. Wandoo but in none of cv. Danja. Following intensive roguing of symptom-bearing plants in the 1986 seed crop of new lupin cv. Gungurru, the level of seedling infection with CMV in seed samples after harvest was 0·1-0·2%. CMV was detected in 6 - 8%, 0·6-5% and 0 - 18% of seedlings from seed samples of established lupin cvs Chittick, Yandee and Illyarrie respectively. Highest levels of seed transmission were in seed from crops grown in high rainfall areas. When a sample of cv. Wandoo seed was graded for size by sieving, CMV was detected in seedlings grown from seed in all grades, but the smallest grade contained the highest level of infection. When seed was collected from pods at different positions on plants in a CMV-infected crop of cv. Illyarrie, seed from primary pods transmitted the virus to seedlings at a 3% rate, seed from first order lateral pods at 8% while seed from second and third order lateral pods transmitted at 13%. Examination of CMV-infected lupin crops indicated that seed-infected plants competed poorly and tended to be shaded out in dense crops but to survive in sparse crops. In 1987 during drought conditions after seeding, plant mortality was greater with seed-infected seedlings than with healthy seedlings despite wide plant spacing. An isolate of CMV from subterranean clover (Trifolium subterraneum) induced severer symptoms in lupins than four isolates from lupin; only the subterranean clover isolate prevented seed production. In tests at one lupin breeding site, CMV was found in 15 species of weeds and volunteer legumes. Fumaria officinalis, Stachys arvensis and volunteer lupins were most frequently infected.
A Dynamical Approach to Two-Block Separable Convex Optimization Problems with Linear Constraints

Abstract The aim of this manuscript is to approach by means of first order differential equations/inclusions convex programming problems with two-block separable linear constraints and objectives, whereby (at least) one of the components of the latter is assumed to be strongly convex. Each block of the objective contains a further smooth convex function. We investigate the dynamical system proposed and prove that its trajectories converge weakly to a saddle point of the Lagrangian of the convex optimization problem. The dynamical system provides through time discretization the alternating minimization algorithm AMA and also its proximal variant recently introduced in the literature.
VLA–ALMA Spectroscopic Survey in the Hubble Ultra Deep Field (VLASPECS): Total Cold Gas Masses and CO Line Ratios for z = 2–3 Main-sequence Galaxies

Using the NSF’s Karl G. Jansky Very Large Array (VLA), we report six detections of CO(J = 1 → 0) emission and one upper limit in z = 2–3 galaxies originally detected in higher-J CO emission in the Atacama Large Millimeter/submillimeter Array Spectroscopic Survey in the Hubble Ultra Deep Field (ASPECS). From the CO(J = 1 → 0) line strengths, we measure total cold molecular gas masses of Mgas = (2.4–11.6) × 1010 (αCO/3.6)M⊙. We also measure a median CO(J = 3 → 2) to CO(J = 1 → 0) line brightness temperature ratio of r31 = 0.84 ± 0.26, and a CO(J = 7 → 6) to CO(J = 1 → 0) ratio range of r71 < 0.05 to r71 = 0.17. These results suggest that CO(J = 3 → 2) selected galaxies may have a higher CO line excitation on average than CO(J = 1 → 0) selected galaxies, based on the limited, currently available samples from the ASPECS and VLA CO Luminosity Density at High Redshift (COLDz) surveys. This implies that previous estimates of the cosmic density of cold gas in galaxies based on CO(J = 3 → 2) measurements should be revised down by a factor of ≃2 on average based on assumptions regarding CO excitation alone. This correction further improves the agreement between the best currently existing constraints on the cold gas density evolution across cosmic history from line scan surveys, and the implied characteristic gas depletion times.
Some Physical and Chemical Factors on Formation of Sclerotia in Sclerotinia libertiana Fuckel

Formation of sclerotia in a strain of Sclerotinia libertiana Fuckel using Czapek-Dox agar medium was highest at pH 4.0~6.0 and at 22~25°C. The response was better under darkness than under light. However, when a potato-extract medium was used the optimum temperature range extended, and even at 15~27°C mature sclerotia formed. The addition of indole-3-acetic acid (IAA) to the Czapek-Dox medium containing riboflavine stimulated the formation of sclerotia under fluorescent light. Though iodoacetic acid, a −SH reagent, also had a stimulatory effect, cysteine had little inhibitory effect on sclerotial formation. Formation was markedly inhibited by p-aminobenzoic acid (PABA), especially in the presence of tyrosine or tryptophan, and excess ammonium salts in the medium also produced an inhibitory effect. It was assumed that accumulation of an intermediary metabolite with high reactivity with −SH groups was necessary for sclerotial formation, but PABA and excess ammonium salts inhibited the accumulation.
Rethinking Performance Estimation in Neural Architecture Search

Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space. Since searching an optimal BPE is extremely time-consuming as it requires to train a large number of networks for evaluation, we propose a Minimum Importance Pruning (MIP) approach. Given a dataset and a BPE search space, MIP estimates the importance of hyper-parameters using random forest and subsequently prunes the minimum one from the next iteration. In this way, MIP effectively prunes less important hyper-parameters to allocate more computational resource on more important ones, thus achieving an effective exploration. By combining BPE with various search algorithms including reinforcement learning, evolution algorithm, random search, and differentiable architecture search, we achieve 1, 000× of NAS speed up with a negligible performance drop comparing to the SOTA. All the NAS search codes are available at: https: //github.com/zhengxiawu/rethinking_performance_ estimation_in_NAS
Plastic reproductive allocation as a buffer against environmental stochasticity : linking life history and population dynamics to climate

Empirical work suggest that long-lived organisms have adopted risk sensitive reproductive strategies where individuals trade the amount of resources spent on reproduction versus survival according to expected future environmental conditions. Earlier studies also suggest that climate affects population dynamics both directly by affecting population vital rates and indirectly through long-term changes in individual life histories. Using a seasonal and state-dependent individual-based model we investigated how environmental variability affects the selection of reproductive strategies and their effect on population dynamics. We found that: (1) dynamic, i.e. plastic, reproductive strategies were optimal in a variable climate. (2) Females in poor and unpredictable climatic regimes allocated fewer available resources in reproduction and more in own somatic growth. This resulted in populations with low population densities, and a high average female age and body mass. (3) Strong negative density dependence on offspring body mass and survival, along with co-variation between climatic severity and population density, resulted in no clear negative climatic effects on reproductive success and offspring body mass. (4) Time series analyses of population growth rates revealed that populations inhabiting benign environments showed the clearest response to climatic perturbations as high population density prohibited an effective buffering of adverse climatic effects as individuals were not able to gain sufficient body reserves during summer. Regularly occurring harsh winters ‘harvested’ populations, resulting in persistent low densities, and released them from negative density dependent effects, resulting in high rewards for a given resource allocation.
Error mitigation with Clifford quantum-circuit data

Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data {Xinoisy,Xiexact} via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where Xinoisy and Xiexact are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.
Identification of AFLP markers closely linked to the powdery mildew resistance genes Pm1c and Pm4a in common wheat (Triticum aestivum L.)

A total of 7654 DNA fragments were screened for linkage to wheat powdery mildew resistance gene Pm1c employing fluorescently based AFLP analysis and phenotypic pools from F3 families. F3 and derived F4 families were used for segregation analysis. Pool screening revealed several cosegregating and tightly linked (0.9 cM) AFLP markers for the Pm1c resistance gene. The previously reported RFLP locus Xwhs178 was integrated into the AFLP map in the vicinity of Pm1c. One AFLP marker, 18M2, was determined to be highly specific for the Pm1c gene in diverse genetic backgrounds. As Pm1c allele confers an effective resistance to powdery mildew, the marker 18M2 provides a valuable tool for enhancing marker assisted selection and pyramiding of powdery mildew resistance genes in wheat.Key words: Triticum aestivum, powdery mildew, disease resistance, AFLP, bulked segregant analysis
Molecular and morphological characterization of Colletotrichum gloeosporioides from native Mexican Stylosanthes species

A total of 264 Stylosanthes spp. plants collected from 78 Stylosanthes spp. populations in seven southern Mexican states were analysed for the presence of Colletotrichum spp. Isolates were obtained from 64 plants collected from 36 Stylosanthes populations; 198 isolates produced straight conidia, while 72 isolates produced falcate conidia. Molecular identification was performed to confirm the identity of C. gloeosporioides for the straight-spored isolates. PCR amplifications using the primer CgInt, synthesized from an ITS1 fragment specific to C. gloeosporioides, and the universal primer ITS4 generated the target fragment for 120 Mexican isolates with straight conidia. The endonucleases Ava II and Sma I were used for restriction of the entire amplified ITS1 region of these 120 isolates. The tree constructed from the restriction data grouped 118 Mexican C. gloeosporioides isolates into three clusters containing reference isolates from Africa and Australia, and generated two additional clusters for two Mexican isolates. Conidial shape and growth rate on solid medium were used as the major morphological criteria for distinguishing types A and B. On the basis of 32 other morphological characteristics, a phenogram grouped the colonies into three main clusters. These clusters were partially related to the Stylosanthes species from which they were isolated, and to the molecular groups.
Relative Positional Encoding for Speech Recognition and Direct Translation

Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for modeling positions in this model was tailored for text modeling, and thus is less ideal for acoustic inputs. In this work, we adapt the relative position encoding scheme to the Speech Transformer, where the key addition is relative distance between input states in the self-attention network. As a result, the network can better adapt to the variable distributions present in speech data. Our experiments show that our resulting model achieves the best recognition result on the Switchboard benchmark in the non-augmentation condition, and the best published result in the MuST-C speech translation benchmark. We also show that this model is able to better utilize synthetic data than the Transformer, and adapts better to variable sentence segmentation quality for speech translation.
Element Stratification in the Middle-aged SN Ia Remnant G344.7–0.1

Despite their importance, a detailed understanding of Type Ia supernovae (SNe Ia) remains elusive. X-ray measurements of the element distributions in supernova remnants (SNRs) offer important clues for understanding the explosion and nucleosynthesis mechanisms for SNe Ia. However, it is challenging to observe the entire ejecta mass in X-rays for young SNRs, because the central ejecta may not have been heated by the reverse shock yet. Here we present over 200 kilosecond Chandra observations of the Type Ia SNR G344.7–0.1, whose age is old enough for the reverse shock to have reached the SNR center, providing an opportunity to investigate the distribution of the entire ejecta mass. We reveal a clear stratification of heavy elements with a centrally peaked distribution of the Fe ejecta surrounded by intermediate-mass elements (IMEs: Si, S, Ar Ca) with an arc-like structure. The centroid energy of the Fe K emission is marginally lower in the central Fe-rich region than in the outer IME-rich regions, suggesting that the Fe ejecta were shock-heated more recently. These results are consistent with the prediction for standard SN Ia models, where the heavier elements are synthesized in the interior of an exploding white dwarf. We find, however, that the peak location of the Fe K emission is slightly offset to the west with respect to the geometric center of the SNR. This apparent asymmetry is likely due to the inhomogeneous density distribution of the ambient medium, consistent with our radio observations of the ambient molecular and neutral gas.
Quotient-AdS/BCFT: Holographic Boundary CFT$_2$ on AdS$_3$ Quotients

The holographic principle, being a generic feature of quantum gravity, should allow for the consideration of dualities other than AdS/CFT. The AdS/BCFT correspondence, in which the dual field theory has local conformal symmetry and is defined on a manifold with boundary, is one such example. Inspired by the quotienting of AdS$_3$ by spacetime isometries in order to construct multiboundary wormholes dual to multipartite CFT$_2$ states, we find that this correspondence can be understood by combining AdS/CFT with some appropriate quotient procedure. Furthermore, in three bulk dimensions, we find example quotient spaces of AdS$_3$ in order to construct "natural" bulk duals for specific BCFT$_2$ states, one of which appears to describe a novel, time-dependent BCFT$_2$ solution. We call this particular refinement of AdS/BCFT, in which we use quotients, the quotient-AdS/BCFT correspondence.
Learning Undirected Graphs in Financial Markets

We investigate the problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market data. We show that Laplacian constraints have meaningful physical interpretations related to the market index factor and to conditional correlations between stocks. Those interpretations lead to a set of guidelines that users should be aware of when estimating graphs in financial markets. In addition, we propose algorithms to learn undirected graphs that account for stylized facts and tasks intrinsic to financial data such as non-stationarity and stock clustering.
An LSTM approach to Predict Migration based on Google Trends

Being able to model and predict international migration as precisely as possible is crucial for policy making. Recently Google Trends data in addition to other economic and demographic data have been shown to improve the prediction quality of a gravity linear model for the one-year ahead predictions. In this work, we replace the linear model with a long short-term memory (LSTM) approach and compare it with two existing approaches: the linear gravity model and an artificial neural network (ANN) model. Our LSTM approach combined with Google Trends data outperforms both these models on various metrics in the task of predicting the one-year ahead incoming international migration to 34 OECD countries: the root mean square error has been divided by 5 on the test set and the mean average error by 4. This positive result demonstrates that machine learning techniques constitute a serious alternative over traditional approaches for studying migration mechanisms.
What has quenched the massive spiral galaxies?

Quenched massive spiral galaxies have attracted great attention recently, as more data is available to constrain their environment and cold gas content. However, the quenching mechanism is still uncertain, as it depends on the mass range and baryon budget of the galaxy. In this letter, we report the identification of a rare population of very massive, quenched spiral galaxies with stellar mass $\gtrsim10^{11}{\rm~M_\odot}$ and halo mass $\gtrsim10^{13}{\rm~M_\odot}$ from the Sloan Digital Sky Survey at redshift $z\sim0.1$. Our CO observations using the IRAM-30m telescope show that these galaxies contain only a small amount of molecular gas. Similar galaxies are also seen in the state-of-the-art semi-analytical models and hydro-dynamical simulations. It is found from these theoretical models that these quenched spiral galaxies harbor massive black holes, suggesting that feedback from the central black holes has quenched these spiral galaxies. This quenching mechanism seems to challenge the popular scenario of the co-evolution between massive black holes and massive bulges.
What makes for good views for contrastive learning

Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning. Despite its success, the influence of different view choices has been less studied. In this paper, we use empirical analysis to better understand the importance of view selection, and argue that we should reduce the mutual information (MI) between views while keeping task-relevant information intact. To verify this hypothesis, we devise unsupervised and semi-supervised frameworks that learn effective views by aiming to reduce their MI. We also consider data augmentation as a way to reduce MI, and show that increasing data augmentation indeed leads to decreasing MI and improves downstream classification accuracy. As a by-product, we also achieve a new state-of-the-art accuracy on unsupervised pre-training for ImageNet classification ($73\%$ top-1 linear readoff with a ResNet-50). In addition, transferring our models to PASCAL VOC object detection and COCO instance segmentation consistently outperforms supervised pre-training. Code:this http URL
Feature Purification: How Adversarial Training Performs Robust Deep Learning

Despite the empirical success of using adversarial training to defend deep learning models against adversarial perturbations, so far, it still remains rather unclear what the principles are behind the existence of adversarial perturbations, and what adversarial training does to the neural network to remove them. In this paper, we present a principle that we call feature purification, where we show one of the causes of the existence of adversarial examples is the accumulation of certain small dense mixtures in the hidden weights during the training process of a neural network; and more importantly, one of the goals of adversarial training is to remove such mixtures to purify hidden weights. We present both experiments on the CIFAR-10 dataset to illustrate this principle, and a theoretical result proving that for certain natural classification tasks, training a two-layer neural network with ReLU activation using randomly initialized gradient descent indeed satisfies this principle. Technically, we give, to the best of our knowledge, the first re-sult proving that the following two can hold simultaneously for training a neural network with ReLU activation. (1) Training over the original data is indeed non-robust to small adversarial perturbations of some radius. (2) Adversarial training, even with an empirical perturbation algorithm such as FGM, can in fact be provably robust against any perturbations of the same radius. Finally, we also prove a complexity lower bound, showing that low complexity models such as linear classifiers, low-degree polynomials, or even the neural tangent kernel for this network, cannot defend against perturbations of this same radius, no matter what algorithms are used to train them. 11The full version of this paper can be found at https://arxiv.org/abs/-2005.l0190.