Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of salivary androgen-binding protein in reproductive isolation between two subspecies of house mouse: Mus musculus musculus and Mus musculus domesticus

Previous behavioural studies using inbred lines have suggested that the gene (Abpa) for the alpha subunit of salivary androgen-binding protein (ABP) plays a role in prezygotic isolation between house mouse Mus musculus subspecies. We tested this hypothesis in animals from wild allopatric (121 individuals from four samples) and parapatric (320 animals from 15 samples) populations sampled on the Czech-Bavarian transect across the hybrid zone between M. m. domesticus and M. m. musculus. The study did not reveal a consistent statistically significant trend of homosubspecific preferences in individual allopatric and parapatric populations. Nonetheless, the whole pattern of preference was skewed toward homosubspecific preference mostly on the M. m. musculus side of the hybrid zone. The pattern of homosubspecific preferences was stronger for the time spent sniffing than it was for the first choice of the signal (the ratio of homosubspecific vs. heterosubspecific preferences for both sexes was 6 : 2 in allopatric and 21 : 9 in parapatric populations, while the same rates were 4 : 4 and 16 : 14 for the first choice). To the extent that Y-maze tests reflect preference under wild conditions, we suggest that this slight preference may not in itself be sufficient to impede gene flow between the two subspecies and thus act as a reproductive barrier. ABP most probably participates in a complex system of subspecies-specific recognition in the hybrid zone, but the picture is far too complex at this time to allow a conclusive evaluation of the importance of this role.
Towards Understanding Label Smoothing

Label smoothing regularization (LSR) has a great success in training deep neural networks by stochastic algorithms such as stochastic gradient descent and its variants. However, the theoretical understanding of its power from the view of optimization is still rare. This study opens the door to a deep understanding of LSR by initiating the analysis. In this paper, we analyze the convergence behaviors of stochastic gradient descent with label smoothing regularization for solving non-convex problems and show that an appropriate LSR can help to speed up the convergence by reducing the variance. More interestingly, we proposed a simple yet effective strategy, namely Two-Stage LAbel smoothing algorithm (TSLA), that uses LSR in the early training epochs and drops it off in the later training epochs. We observe from the improved convergence result of TSLA that it benefits from LSR in the first stage and essentially converges faster in the second stage. To the best of our knowledge, this is the first work for understanding the power of LSR via establishing convergence complexity of stochastic methods with LSR in non-convex optimization. We empirically demonstrate the effectiveness of the proposed method in comparison with baselines on training ResNet models over benchmark data sets.
Cyclic dynamics of sympatric lemming populations on Bylot Island, Nunavut, Canada

We characterized the fluctuations (amplitude, periodicity) of two sympatric species, the brown lemming (Lemmus sibiricus (Kerr, 1792)) and the northern collared lemming (Dicrostonyx groenlandicus (Traill, 1823)), in a High Arctic area. Our objective was to determine if these populations were cyclic, and if fluctuations in numbers were synchronized between the two species temporally and spatially. An annual index of lemming abundance was obtained using snap-traps at two sites 30 km apart on Bylot Island (Nunavut, Canada) over 13 years (1993–2005) and 9 years (1997–2005), respectively. The time series were analyzed by spectral analyses and autoregressive modelling. At the site with the longest record, brown lemming showed regular population fluctuations of large amplitude (>40-fold), but collared lemming fluctuations were of much smaller amplitude (4-fold). At the other site, the collared lemming population was higher than at the main site, but brown lemmings were still most abundant in the peak year. Model.
HNHN: Hypergraph Networks with Hyperedge Neurons

Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolution network with nonlinear activation functions applied to both hypernodes and hyperedges, combined with a normalization scheme that can flexibly adjust the importance of high-cardinality hyperedges and high-degree vertices depending on the dataset. We demonstrate improved performance of HNHN in both classification accuracy and speed on real world datasets when compared to state of the art methods.
Automatic Recall Machines: Internal Replay, Continual Learning and the Brain

Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective, without extraneous buffers or generator networks. Instead the implicit memory of learned samples within the assessed model itself is exploited. Furthermore, whereas existing work focuses on reinforcing the full seen data distribution, we show that optimizing for not forgetting calls for the generation of samples that are specialized to each real training batch, which is more efficient and scalable. We consider high-level parallels with the brain, notably the use of a single model for inference and recall, the dependency of recalled samples on the current environment batch, top-down modulation of activations and learning, abstract recall, and the dependency between the degree to which a task is learned and the degree to which it is recalled. These characteristics emerge naturally from the method without being controlled for.
Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling

The reconstruction of phase spaces is an essential step to analyze time series according to Dynamical System concepts. A regression performed on such spaces unveils the relationships among system states from which we can derive their generating rules, that is, the most probable set of functions responsible for generating observations along time. In this sense, most approaches rely on Takens' embedding theorem to unfold the phase space, which requires the embedding dimension and the time delay. Moreover, although several methods have been proposed to empirically estimate those parameters, they still face limitations due to their lack of consistency and robustness, which has motivated this paper. As an alternative, we here propose an artificial neural network with a forgetting mechanism to implicitly learn the phase spaces properties, whatever they are. Such network trains on forecasting errors and, after converging, its architecture is used to estimate the embedding parameters. Experimental results confirm that our approach is either as competitive as or better than most state-of-the-art strategies while revealing the temporal relationship among time-series observations.
Real-time LCC-HVDC Maximum Emergency Power Capacity Estimation Based on Local PMU Measurements

The adjustable capacity of a line-commutated-converter High Voltage Direct Current (LCC-HVDC) connected to a power system, called the LCC-HVDC maximum emergency power capability or HVDC-MC for short, plays an important role in determining the response of that system to a large disturbance. However, it is a challenging task to obtain an accurate HVDC-MC due to system model uncertainties as well as to contingencies. To address this problem, this paper proposes to estimate the HVDC-MC using a Thevenin equivalent (TE) of the system seen at the HVDC terminal bus of connection with the power system, whose parameters are estimated by processing positive-sequences voltages and currents of local synchrophasor measurements. The impacts of TE potential changes on the impedance estimation under large disturbance have been extensively investigated and an adaptive screening process of current measurements is developed to reduce the error of TE impedance estimation. The uncertainties of phasor measurements have been further taken into account by resorting to the total least square estimation method. The limitations of the HVDC control characteristics, the voltage-dependent current order limit, the converter capacity, and the AC voltage on HVDC-MC estimation are also considered. The simulations show that the proposed method can accurately track the dynamics of the TE parameters and the real-time HVDC-MC after the large disturbances.
Experimental demonstration of an anomalous Floquet topological insulator based on negative-index media

Photonic and acoustic topological insulators exhibiting one-way transportation that is robust against defects and impurities are typically realized in coupled arrays of two-dimensional ring resonators. These systems have produced a series of applications, including optical isolators, delay lines, and lasers. However, the structures are complicated because an additional coupler ring between neighboring rings is needed to construct photonic pseudo-spin. In this work, a photonic analogue of the quantum spin Hall effect is proposed and experimentally demonstrated in an anomalous Floquet topological insulator in the microwave regime. This improved design takes advantage of the efficient and backward coupling of negative-index media. The results contribute to the understanding of topological structures in metamaterials and point toward a new direction for constructing useful topological photonic devices.
Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble

Despite neural networks have achieved prominent performance on many natural language processing (NLP) tasks, they are vulnerable to adversarial examples. In this paper, we propose Dirichlet Neighborhood Ensemble (DNE), a randomized smoothing method for training a robust model to defense substitution-based attacks. During training, DNE forms virtual sentences by sampling embedding vectors for each word in an input sentence from a convex hull spanned by the word and its synonyms, and it augments them with the training data. In such a way, the model is robust to adversarial attacks while maintaining the performance on the original clean data. DNE is agnostic to the network architectures and scales to large models for NLP applications. We demonstrate through extensive experimentation that our method consistently outperforms recently proposed defense methods by a significant margin across different network architectures and multiple data sets.
On improved convergence conditions and bounds for Markov chains

Improved rates of convergence for ergodic Markov chains and relaxed conditions for them, as well as analogous convergence results for non-homogeneous Markov chains are studied. The setting from the previous works is extended. Examples are provided where the new bounds are better and where they give the same convergence rate as in the classical Markov -- Dobrushin inequality (in the homogeneous case).
Confined Nano‐NMR Spectroscopy Using NV Centers

Nano nuclear magnetic resonance (nano‐NMR) spectroscopy with nitrogen‐vacancy (NV) centers holds the potential to provide high‐resolution spectra of minute samples. This is likely to have important implications for chemistry, medicine, and pharmaceutical engineering. One of the main hurdles facing the technology is that diffusion of unpolarized liquid samples broadens the spectral lines thus limiting resolution. Experiments in the field are therefore impeded by the efforts involved in achieving high polarization of the sample which is a challenging endeavor. Here, a scenario where the liquid is confined to a small volume is examined. It is shown that the confinement “counteracts” the effect of diffusion, thus overcoming a major obstacle to the resolving abilities of the NV‐NMR spectrometer.
Information theoretic limits of learning a sparse rule

We consider generalized linear models in regimes where the number of nonzero components of the signal and accessible data points are sublinear with respect to the size of the signal. We prove a variational formula for the asymptotic mutual information per sample when the system size grows to infinity. This result allows us to derive an expression for the minimum mean-square error (MMSE) of the Bayesian estimator when the signal entries have a discrete distribution with finite support. We find that, for such signals and suitable vanishing scalings of the sparsity and sampling rate, the MMSE is nonincreasing piecewise constant. In specific instances the MMSE even displays an all-or-nothing phase transition, that is, the MMSE sharply jumps from its maximum value to zero at a critical sampling rate. The all-or-nothing phenomenon has previously been shown to occur in high-dimensional linear regression. Our analysis goes beyond the linear case and applies to learning the weights of a perceptron with general activation function in a teacher-student scenario. In particular, we discuss an all-or-nothing phenomenon for the generalization error with a sublinear set of training examples.
Automated Optical Multi-layer Design via Deep Reinforcement Learning

Optical multi-layer thin films are widely used in optical and energy applications requiring photonic designs. Engineers often design such structures based on their physical intuition. However, solely relying on human experts can be time-consuming and may lead to sub-optimal designs, especially when the design space is large. In this work, we frame the multi-layer optical design task as a sequence generation problem. A deep sequence generation network is proposed for efficiently generating optical layer sequences. We train the deep sequence generation network with proximal policy optimization to generate multi-layer structures with desired properties. The proposed method is applied to two energy applications. Our algorithm successfully discovered high-performance designs, outperforming structures designed by human experts in task 1, and a state-of-the-art memetic algorithm in task 2.
Breeding and Recruitment Phenology of Amphibians in Missouri Oak-Hickory Forests

ABSTRACT Knowing the phenologies of species in a region helps guide management and conservation activities in breeding ponds and surrounding terrestrial habitats. We examined the phenology of pond-breeding amphibians in central Missouri oak-hickory forests. Two ponds were monitored for 4 y from 2000–2003 and five other ponds were monitored for 1 y during 2004 using drift fences with pitfall traps. We found 15 species of pond-breeding amphibians, nine of which we captured in sufficient abundance to evaluate breeding phenology. Among the nine species, breeding migrations occur from Feb. to Nov., while subsequent metamorph emigration occurred primarily from May to Oct. Our ponds were nearly permanent, resulting in salamander-dominated communities in these oak-hickory forests. Pond-use was partitioned by species that differed in their timing of reproduction and post-metamorphic emergence. For example, breeding in the fall gives larval ringed salamanders a potential size advantage over the spring-breeding spotted salamander larvae. However, the fall breeding strategy requires ponds with long hydroperiods that hold water continuously from Aug. through May. Green frogs and central newts also required long hydroperiods for their larval stage (>160 d). American toads, however, are adapted to exploit ponds with shorter, less predictable hydroperiods as they only required ponds to hold water for as little as 60 d. Management for multiple species of pond-breeding amphibians in a landscape benefits from the inclusion of wetlands with a variety of hydroperiods.
QOPT: Optimistic Value Function Decentralization for Cooperative Multi-Agent Reinforcement Learning

We propose a novel value-based algorithm for cooperative multi-agent reinforcement learning, under the paradigm of centralized training with decentralized execution. The proposed algorithm, coined QOPT, is based on the "optimistic" training scheme using two action-value estimators with separate roles: (i) true action-value estimation and (ii) decentralization of optimal action. By construction, our framework allows the latter action-value estimator to achieve (ii) while representing a richer class of joint action-value estimators than that of the state-of-the-art algorithm, i.e., QMIX. Our experiments demonstrate that QOPT newly achieves state-of-the-art performance in the StarCraft Multi-Agent Challenge environment. In particular, ours significantly outperform the baselines for the case where non-cooperative behaviors are penalized more aggressively.
Efeito de níveis de fósforo não-fítico e de fitase sobre o fêmur de frangos de corte

Esse experimento foi conduzido para avaliar o efeito de niveis de fosforo nao-fitico (FNF) e de fitase sobre peso, morfometria e indice peso/comprimento (IPC) de femures de frangos aos 21 e 42 dias de idade. Foram utilizados 1200 pintos de corte alojados em delineamento inteiramente casualizado e arranjo fatorial 4 x 3 (niveis de FNF x niveis de fitase), com quatro repeticoes. Os niveis de FNF, em cada fase, foram de 0,45; 0,37; 0,29 e 0,21% na fase inicial; 0,41; 0,33; 0,25 e 0,17% na fase de crescimento e de 0,37; 0,29; 0,21 e 0,13% na fase final. Os niveis de fitase utilizados foram 0,500 e 1000U/kg de dieta. Aos 21 e 42 dias de idade, 48 aves foram sacrificadas para a retirada dos femures. Aos 21 dias, nao houve efeito (P > 0,05) da interacao FNF x fitase sobre parâmetros osseos, porem a reducao do FNF dietetico diminuiu (P < 0,05) o peso, o comprimento e o IPC dos femures e a inclusao de 500U/kg de fitase melhorou (P < 0,05) o peso e o IPC dos ossos. Aos 42 dias de criacai, a interacao FNF x fitase foi significativa (P < 0,05) para peso e comprimento dos femures, em que aves que ingeriram dietas sem fitase tiveram femures mais leves e mais curtos quando o menor nivel de FNF foi avaliado, porem, a inclusao de 500 ou 1000U/kg de fitase tornou o peso e o comprimento similares aos do tratamento com niveis recomendados de FNF e o menor nivel de FNF avaliado, promoveu reducao (P < 0,05) no diâmetro e no IPC dos femures. Concluiu-se que dietas com 0,29, 0,25 e 0,21% de FNF, suplementadas com 500U/kg de fitase, podem ser usadas sem prejuizos para a qualidade do femur dos frangos de corte de um a 42 dias de idade.
Computational Enhancement of Molecularly Targeted Contrast-Enhanced Ultrasound: Application to Human Breast Tumor Imaging

Molecularly targeted contrast enhanced ultrasound (mCEUS) is a clinically promising approach for early cancer detection through targeted imaging of VEGFR2 (KDR) receptors. We have developed computational enhancement techniques for mCEUS tailored to address the unique challenges of imaging contrast accumulation in humans. These techniques utilize dynamic analysis to distinguish molecularly bound contrast agent from other contrast-mode signal sources, enabling analysis of contrast agent accumulation to be performed during contrast bolus arrival when the signal due to molecular binding is strongest. Applied to the 18 human patient examinations of the first-in-human molecular ultrasound breast lesion study, computational enhancement improved the ability to differentiate between pathology-proven lesion and pathology-proven normal tissue in real-world human examination conditions that involved both patient and probe motion, with improvements in contrast ratio between lesion and normal tissue that in most cases exceed an order of magnitude (10x). Notably, computational enhancement eliminated a false positive result in which tissue leakage signal was misinterpreted by radiologists to be contrast agent accumulation.
Sarcasm Detection in Tweets with BERT and GloVe Embeddings

Sarcasm is a form of communication in which the person states opposite of what he actually means. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses all of the context provided in the dataset to which the user is reacting along with his actual response.
Ultrastructure of Gnathostomaria lutheri Ax (Gnathostomulida: Scleroperalia).I. An Hypothesis About the Origin of ­Micropodia in Scleroperalian Sperm

Initially described as an aberrant taxon of turbellarian flatworms, the Gnathostomulida were recognized as a distinct phylum by Rupert Riedl already in 1969. Yet these microscopic, non-segmented marine worms remain organisms of enigmatic phylogenetic affiliation, and although they are often the dominant invertebrate taxon in detritus-rich, oxygen-poor sands, our knowledge of their biology is still very scanty. About 90 gnathostomulid species are known to date; they are grouped in two orders, one with two suborders. Sperm structure, often a useful instrument for clarifying evolutionary pathways in metazoan taxa, differs so widely between the orders and suborders that sperm phylogeny within the Gnathostomulida also remains uncertain. We here report on the ultrastructural features of a peculiar type of sperm, the aflagellar “megasperm” of Gnathostomaria lutheri, and discuss new and older interpretations of sperm cytology of the taxon.
Learning with AMIGo: Adversarially Motivated Intrinsic Goals

A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation. We propose AMIGo, a novel agent incorporating a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals to train a goal-conditioned "student" policy in the absence of (or alongside) environment reward. Specifically, through a simple but effective "constructively adversarial" objective, the teacher learns to propose increasingly challenging---yet achievable---goals that allow the student to learn general skills for acting in a new environment, independent of the task to be solved. We show that our method generates a natural curriculum of self-proposed goals which ultimately allows the agent to solve challenging procedurally-generated tasks where other forms of intrinsic motivation and state-of-the-art RL methods fail.
Scalable Assessment and Mitigation Strategies for Fairness in Rankings

Motivated by industrial-scale applications, we consider two specific areas of fairness, one connected to the notion of equality of opportunity, and the other one generally tied to fair model performance. Throughout the paper, we consider only methods that can be scaled to Internet-industry size datasets. With this in mind, we propose a simple post-processing method to achieve equality of opportunity and discuss challenges and some solutions in the specific cases of recommendation systems and rankings. We then discuss a class of model performance fairness measures based on conditional ROC curves. We propose both scalable uncertainty assessment tools (that improve upon recent research) as well as scalable penalized methods to improve fairness with respect to these metrics. We provide fast algorithms with an emphasis on making few passes over the data when possible.
Disruption of pheromone communication in moths: is the natural blend really most efficacious?

It is widely assumed that a blend of pheromone components, that is qualitatively and quantitatively similar to the natural sex attractant, is the most effective mating disruptant for moths. However, the literature contains only limited evidence supporting this assumption. The authors discuss the importance of comparing the relative effectiveness of complete and incomplete pheromone blends when evaluating the potential of mating disruption for controlling pest moths.