Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex ratio dynamics of Gonatocerus ashmeadi, a parasitoid of the glassy‐winged sharpshooter in California

We examined whether Gonatocerus ashmeadi Girault (Hymenoptera: Mymaridae), a quasi‐gregarious egg parasitoid of Homalodisca vitripennis (Germar) (Hemiptera: Cicadellidae), produces precise sex ratios under a field setting. Under laboratory conditions, previous studies have shown that G. ashmeadi exhibits strongly female‐biased sex ratios with low variance in the number of males produced per host. Field‐collected G. ashmeadi tend to produce much less female‐biased sex ratios with high variance in male numbers. We found significant positive effects of proportion parasitism and host density on sex ratio. Proportion parasitism also had a positive effect on sex ratio variance. The findings of this study are discussed in the context of theoretical predictions.
PARENT-OFFSPRING REGRESSION IN REED CANARYGRASS: METHODS FOR PARENT AND OFFSPRING EVALUATION AND THEIR EFFECT ON HERITABILITY ESTIMATES

Parent-offspring regression is a common technique for estimating narrow sense heritability of quantitative characters in crop species. However, regression of progeny means on parent values evaluated in the same or similar environments may lead to biased heritability estimates. This bias arises from genotype × environment and error covariances between parents and progenies. This research dealt with the effects of these covariances on parent-offspring regression heritability estimates for several characters of reed canarygrass (Phalaris arundinacea L.). Biases to expected values of heritability due to these covariances were usually in the positive direction, supporting previous research. In this study, the genotype × location interaction covariance usually had the greatest bias effect, indicating that parents and progenies should be evaluated at least at separate locations to improve parent-offspring heritability estimates.
When Machine Unlearning Jeopardizes Privacy

The right to be forgotten states that a data owner has the right to erase their data from an entity storing it. In the context of machine learning (ML), the right to be forgotten requires an ML model owner to remove the data owner's data from the training set used to build the ML model, a process known asmachine unlearning. While originally designed to protect the privacy of the data owner, we argue that machine unlearning may leave some imprint of the data in the ML model and thus create unintended privacy risks. In this paper, we perform the first study on investigating the unintended information leakage caused by machine unlearning. We propose a novel membership inference attack that leverages the different outputs of an ML model's two versions to infer whether a target sample is part of the training set of the original model but out of the training set of the corresponding unlearned model. Our experiments demonstrate that the proposed membership inference attack achieves strong performance. More importantly, we show that our attack in multiple cases outperforms the classical membership inference attack on the original ML model, which indicates that machine unlearning can have counterproductive effects on privacy. We notice that the privacy degradation is especially significant for well-generalized ML models where classical membership inference does not perform well. We further investigate four mechanisms to mitigate the newly discovered privacy risks and show that releasing the predicted label only, temperature scaling, and differential privacy are effective. We believe that our results can help improve privacy protection in practical implementations of machine unlearning. \footnoteOur code is available at \urlhttps://github.com/MinChen00/UnlearningLeaks.
Designing Data Augmentation for Simulating Interventions

Machine learning models trained with purely observational data and the principle of empirical risk minimization (Vapnik, 1992) can fail to generalize to unseen domains. In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels. We find that many domain generalization methods do not explicitly take this spurious correlation into account. Instead, especially in more application-oriented research areas like medical imaging or robotics, data augmentation techniques that are based on heuristics are used to learn domain invariant features. To bridge the gap between theory and practice, we develop a causal perspective on the problem of domain generalization. We argue that causal concepts can be used to explain the success of data augmentation by describing how they can weaken the spurious correlation between the observed domains and the task labels. We demonstrate that data augmentation can serve as a tool for simulating interventional data. Lastly, but unsurprisingly, we show that augmenting data improperly can cause a significant decrease in performance.
Small, sparse, but substantial: techniques for segmenting small agricultural fields using sparse ground data

ABSTRACT The recent thrust on digital agriculture (DA) has renewed significant research interest in the automated delineation of agricultural fields. Most prior work addressing this problem has focused on detecting medium to large fields, while there is strong evidence that around 40% of the fields worldwide and 70% of the fields in Asia and Africa are small. The lack of adequate labelled images for small fields, huge variations in their colour, texture, and shape, and faint boundary lines separating them make it difficult to develop an end-to-end learning model for detecting such fields. Hence, in this paper, we present a multi-stage approach that uses a combination of machine learning and image processing techniques. In the first stage, we leverage state-of-the-art edge-detection algorithms such as holistically nested edge detection (HED) to extract first-level contours and polygons. In the second stage, we propose image-processing techniques to identify polygons that are non-fields, over-segmentations, or noise and eliminate them. The next stage tackles under-segmentations using a combination of a novel ‘cut-point’ based technique and localized second-level edge detection to obtain individual parcels. Since a few small, non-cropped but vegetated or constructed pockets can be interspersed in areas that are predominantly croplands, in the final stage, we train a classifier for identifying each parcel from the previous stage as an agricultural field or not. In an evaluation using high-resolution 1.19 m imagery sourced from DigitalGlobe, we show that our approach has a high F-Score of 0.84 in areas with large fields and reasonable accuracy with an F-Score of 0.73 in areas with small fields, which is encouraging. We also apply our techniques without any additional specialized processing to coarser resolution open satellite imagery and find the approach to be reasonable for delineating large fields with such imagery.
Inorganic carbon acquisition by Chlamydomonas acidophila across a pH range

Chlamydomonas acidophila Negoro had a higher maximum growth rate upon aeration with 5% CO2 (v/v) than in nonaerated conditions at an external pH above 2. In medium with a pH of 1.0 or 2.0, a decrease in the maximum growth rate was observed upon CO2 aeration in comparison with nonaerated conditions. At both very low and very high external pH conditions, an induction of external carbonic anhydrase was detected; this being more pronounced in CO2-aerated cells than in nonaerated cells. It is therefore suggested that the induction of carbonic anhydrase is part of a stress response in Chlamydomonas acidophila. Comparison of some physiological characteristics of Chlamydomonas acidophila acclimated at pH 2.65 and at pH 6.0, revealed that CO2 aeration increased gross maximum photosynthesis at both pHs, whereas respiration, light acclimation, and photoinhibition were not effected. At pH 2.65, Chlamydomonas acidophila was found to have a carbon-concentrating mechanism under nonaerated conditions, whereas it did not under CO2-aerated conditions at pH 6. The affinity for CO2 use in O-2 production was not dependent on CO2 aeration, but it was much lower at pH 6 than it was at pH 2.65. CO2 kinetic characteristics indicate that the photosynthesis of Chlamydomonas acidophila in its natural environment is not limited by inorganic carbon
Elevated CO2 increases biomass and tuber yield in potato even at high ozone concentrations.

• Changes in the growth and yield of field-grown potato (Solanum tuberosum cv. Bintje) induced by season-long elevated CO2 and/or ozone concentrations are reported. • Open-top chambers and unchambered field plots were used to examine crop responses to three CO2 (ambient, 550 and 680 µmol mol-1 ) and two ozone (ambient and 65 nmol mol-1 , 8 h d-1 seasonal mean) treatments applied throughout the 105 d growing season. • Elevated CO2 increased both above- and below-ground biomass at intermediate and final harvests. Tuber yield at final harvest was increased by c. 40% due to an increase in mean tuber weight rather than tuber number; tuber yield did not differ significantly between the 550 and 680 µmol mol-1 CO2 treatments. Elevated ozone had no significant effect on growth or yield except for the largest size category of tubers, despite extensive visible foliar injury. Significant CO2 × ozone interactions were detected only for senescent leaf number and green leaf ratio. • Elevated CO2 increases biomass and tuber yield in S. tuberosum cv. Bintje even at high ozone concentrations; these findings are discussed in relation to predicted future atmospheric changes.
ExpBERT: Representation Engineering with Natural Language Explanations

Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow model developers to specify these types of inductive biases as natural language explanations. We use BERT fine-tuned on MultiNLI to “interpret” these explanations with respect to the input sentence, producing explanation-guided representations of the input. Across three relation extraction tasks, our method, ExpBERT, matches a BERT baseline but with 3–20x less labeled data and improves on the baseline by 3–10 F1 points with the same amount of labeled data.
RadarSLAM: Radar based Large-Scale SLAM in All Weathers

Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall.
Implications of the mild gas motion found with Hitomi in the core of the Perseus cluster

Based mainly on X-ray observations, we study the interactions between the intracluster medium (ICM) in clusters of galaxies and their member galaxies. Through (magneto)hydrodynamic and gravitational channels, moving galaxies are expected to drag the ICM around them, and then transfer some fraction of their dynamical energies on cosmological timescales to the ICM. This hypothesis is in line with several observations, including the possible cosmological infall of galaxies toward the cluster center, found over redshifts of z ∼ 1 to z ∼ 0. Further assuming that the energy lost by these galaxies is first converted into ICM turbulence and then dissipated, this picture can explain the subsonic and uniform ICM turbulence, measured with Hitomi in the core region of the Perseus cluster. The scenario may also explain several other unanswered problems regarding clusters of galaxies, such as what prevents the ICM from underoing the expected radiative cooling, how the various mass components in nearby clusters have attained different radial distributions, and how a thermal stability is realized between hot and cool ICM components that co-exist around cD galaxies. This view is also considered to pertain to the general scenario of galaxy evolution, including their environmental effects.
An investigation into the minimum number of tissue groups required for 7T in‐silico parallel transmit electromagnetic safety simulations in the human head

Safety limits for the permitted specific absorption rate (SAR) place restrictions on pulse sequence design, especially at ultrahigh fields (≥ 7 tesla). Due to intersubject variability, the SAR is usually conservatively estimated based on standard human models that include an applied safety margin to ensure safe operation. One approach to reducing the restrictions is to create more accurate subject‐specific models from their segmented MR images. This study uses electromagnetic simulations to investigate the minimum number of tissue groups required to accurately determine SAR in the human head.
Printing and Scanning Attack for Image Counter Forensics

Examining the authenticity of images has become increasingly important as manipulation tools become more accessible and advanced. Recent work has shown that while CNN-based image manipulation detectors can successfully identify manipulations, they are also vulnerable to adversarial attacks, ranging from simple double JPEG compression to advanced pixel-based perturbation. In this paper we explore another method of highly plausible attack: printing and scanning. We demonstrate the vulnerability of two state-of-the-art models to this type of attack. We also propose a new machine learning model that performs comparably to these state-of-the-art models when trained and validated on printed and scanned images. Of the three models, our proposed model outperforms the others when trained and validated on images from a single printer. To facilitate this exploration, we create a dataset of over 6,000 printed and scanned image blocks. Further analysis suggests that variation between images produced from different printers is significant, large enough that good validation accuracy on images from one printer does not imply similar validation accuracy on identical images from a different printer.
Spin-imbalanced ultracold Fermi gases in a two-dimensional array of tubes

Motivated by a recent experiment [Revelle et al. Phys. Rev. Lett. 117, 235301 (2016)] that characterized the one- to three-dimensional crossover in a spin-imbalanced ultracold gas of $^6$Li atoms trapped in a two-dimensional array of tunnel-coupled tubes, we calculate the phase diagram for this system using Hartree-Fock Bogoliubov-de Gennes mean-field theory, and compare the results with experimental data. Mean-field theory predicts fully spin-polarized normal, partially spin-polarized normal, spin-polarized superfluid, and spin-balanced superfluid phases in a homogeneous system. We use the local density approximation to obtain density profiles of the gas in a harmonic trap. We compare these calculations with experimental measurements in Revelle {\em et al.} as well as previously unpublished data. Our calculations qualitatively agree with experimentally-measured densities and coordinates of the phase boundaries in the trap, and quantitatively agree with experimental measurements at moderate-to-large polarizations. Our calculations also reproduce the experimentally-observed universal scaling of the phase boundaries for different scattering lengths at a fixed value of scaled inter-tube tunneling. However, our calculations have quantitative differences with experimental measurements at low polarization, and fail to capture important features of the one- to three-dimensional crossover observed in experiments. These suggest the important role of physics beyond-mean-field theory in the experiments. We expect that our numerical results will aid future experiments in narrowing the search for the FFLO phase.
Credible Identification in Economies with Frictions -- The role of Survey Data

The paper demonstrates that identification in heterogeneous agent economies can be robust to alternative assumptions regarding the underlying structure using identifying restrictions that are consistent with a variety of mechanisms. These restrictions generate moment inequalities and therefore set identification, which can lead to wide confidence sets for the structural parameters. The paper shows that employing aggregated survey data that are informative about the extensive margin of adjustment i.e. the proportion of agents whose behavior is distorted over time due to financial frictions can provide additional information that can tighten the corresponding bounds. The paper provides identification analysis both in an extended partial equilibrium analytical example and in a general equilibrium setting. I apply this approach to the Spanish economy, where the proportion of constrained consumers is identified by combining information from different surveys. The results suggest that the extensive margin is empirically informative.
It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information

The performance of neural machine translation systems is commonly evaluated in terms of BLEU. However, due to its reliance on target language properties and generation, the BLEU metric does not allow an assessment of which translation directions are more difficult to model. In this paper, we propose cross-mutual information (XMI): an asymmetric information-theoretic metric of machine translation difficulty that exploits the probabilistic nature of most neural machine translation models. XMI allows us to better evaluate the difficulty of translating text into the target language while controlling for the difficulty of the target-side generation component independent of the translation task. We then present the first systematic and controlled study of cross-lingual translation difficulties using modern neural translation systems. Code for replicating our experiments is available online at https://github.com/e-bug/nmt-difficulty.
Stochastic Stackelberg games

In this paper, we consider a discrete-time stochastic Stackelberg game where there is a defender (also called leader) who has to defend a target and an attacker (also called follower). Both attacker and defender have conditionally independent private types, conditioned on action and previous state, that evolve as controlled Markov processes. The objective is to compute the stochastic Stackelberg equilibrium of the game where defender commits to a strategy. The attacker's strategy is the best response to the defender strategy and defender's strategy is optimum given the attacker plays the best response. In general, computing such equilibrium involves solving a fixed-point equation for the whole game. In this paper, we present an algorithm that computes such strategies by solving smaller fixed-point equations for each time $t$. This reduces the computational complexity of the problem from double exponential in time to linear in time. Based on this algorithm, we compute stochastic Stackelberg equilibrium of a security example.
A Two-moment Radiation Hydrodynamics Scheme Applicable to Simulations of Planet Formation in Circumstellar Disks

We present a numerical code for radiation hydrodynamics designed as a module for the freely available PLUTO code. We adopt a gray approximation and include radiative transfer following a two-moment approach by imposing the M1 closure to the radiation fields. This closure allows for a description of radiative transport in both the diffusion and free-streaming limits, and is able to describe highly anisotropic radiation transport as can be expected in the vicinity of an accreting planet in a protoplanetary disk. To reduce the computational cost caused by the timescale disparity between radiation and matter fields, we integrate their evolution equations separately in an operator-split way, using substepping to evolve the radiation equations. We further increase the code’s efficiency by adopting the reduced speed of light approximation (RSLA). Our integration scheme for the evolution equations of radiation fields relies on implicit-explicit schemes, in which radiation-matter interaction terms are integrated implicitly while fluxes are integrated via Godunov-type solvers. The module is suitable for general astrophysical computations in one, two, and three dimensions in Cartesian, spherical, and cylindrical coordinates, and can be implemented on rotating frames. We demonstrate the algorithm performance on different numerical benchmarks, paying particular attention to the applicability of the RSLA for computations of physical processes in protoplanetary disks. We show 2D simulations of vertical convection in disks and 3D simulations of gas accretion by planetary cores, which are the first of their kind to be solved with a two-moment approach.
A faster Fourier transform? Computing small-scale power spectra and bispectra for cosmological simulations in 𝒪(N2) time

We present $\mathcal {O}(N^2)$ estimators for the small-scale power spectrum and bispectrum in cosmological simulations. In combination with traditional methods, these allow spectra to be efficiently computed across a vast range of scales, requiring orders of magnitude less computation time than Fast Fourier Transform based approaches alone. These methods are applicable to any tracer; simulation particles, haloes or galaxies, and take advantage of the simple geometry of the box and periodicity to remove almost all dependence on large random particle catalogues. By working in configuration-space, both power spectra and bispectra can be computed via a weighted sum of particle pairs up to some radius, which can be reduced at larger k, leading to algorithms with decreasing complexity on small scales. These do not suffer from aliasing or shot-noise, allowing spectra to be computed to arbitrarily large wavenumbers. The estimators are rigorously derived and tested against simulations, and their covariances discussed. The accompanying code, hipster, has been publicly released, incorporating these algorithms. Such estimators will be of great use in the analysis of large sets of high-resolution simulations.
Can gender inequality be created without inter-group discrimination?

Understanding human societies requires knowing how they develop gender hierarchies, which are ubiquitous. We test whether a simple agent-based dynamic process could create gender inequality. Relying on evidence of gendered status concerns, self-construals, and cognitive habits, our model included a gender difference in how responsive male-like and female-like agents are to others’ opinions about the level of esteem for someone. We simulate a population who interact in pairs of randomly selected agents to influence each other about their esteem judgments of self and others. Half the agents are more influenced by their relative status rank during the interaction than the others. Without prejudice, stereotypes, segregation, or categorization, our model produces inter-group inequality of self-esteem and status that is stable, consensual, and exhibits characteristics of glass ceiling effects. Outcomes are not affected by relative group size. We discuss implications for group orientation to dominance and individuals’ motivations to exchange.