Miracle-dz/UniFAR-FTUs huggingface dataset:
Bogdanov-Takens Bifurcation in a General Conductance-Based Neuron Model with the M-Current and Switching of Neuron Excitability Class

In this work, we consider a general conductance-based neuron model with the inclusion of the acetycholine sensitive, M-current. We study bifurcations in the parameter space consisting of the applied current, $I_{app}$ the maximal conductance of the M-current, $g_M$, and the conductance of the leak current, $g_L$. We give precise conditions for the model that ensure the existence of a Bogdanov-Takens (BT) point and show such a point can occur by varying $I_{app}$ and $g_{M}$. We discuss the case when the BT point becomes a Bogdanov-Takens-Cusp (BTC) point and show that such a point can occur in the three dimensional parameter space. The results of the bifurcation analysis are applied to different neuronal models and are verified and supplemented by numerical bifurcation diagrams generated using the package MATCONT. We conclude that there is a transition in the neuronal excitability type organized by the BT point and the neuron switches from Class-I to Class-II as conductance of the M-current increases.
Double layer from least action principle

We derived the equations for the double layers in quadratic gravity, using solely the least action principle. The advantage of our approach is that, in the process of calculation, the δ′-function does not appear at all, and the δ-functions appear for a moment and are mutually canceled prior to integration. We revealed the peculiar structure of the obtained equations, namely, that the surface energy–momentum tensor of the matter fields (constituents of the thin shells) does not play a role in the determination of the trajectory of the double layer. Also, we suggested that the space-like double layers may provide us with the adequate description of the creation of the Universe from the black hole singularity. The related topics, including the Gauss–Bonnet term and F(R)-theories, are shortly discussed.
Meta Continual Learning via Dynamic Programming

Meta continual learning algorithms seek to train a model when faced with similar tasks observed in a sequential manner. Despite promising methodological advancements, there is a lack of theoretical frameworks that enable analysis of learning challenges such as generalization and catastrophic forgetting. To that end, we develop a new theoretical approach for meta continual learning~(MCL) where we mathematically model the learning dynamics using dynamic programming, and we establish conditions of optimality for the MCL problem. Moreover, using the theoretical framework, we derive a new dynamic-programming-based MCL method that adopts stochastic-gradient-driven alternating optimization to balance generalization and catastrophic forgetting. We show that, on MCL benchmark data sets, our theoretically grounded method achieves accuracy better than or comparable to that of existing state-of-the-art methods.
Jointly Sparse Signal Recovery and Support Recovery via Deep Learning With Applications in MIMO-Based Grant-Free Random Access

In this article, we investigate jointly sparse signal recovery and jointly sparse support recovery in Multiple Measurement Vector (MMV) models for complex signals, which arise in many applications in communications and signal processing. Recent key applications include channel estimation and device activity detection in MIMO-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT). Utilizing techniques in compressive sensing, optimization and deep learning, we propose two model-driven approaches, based on the standard auto-encoder structure for real numbers. One is to jointly design the common measurement matrix and jointly sparse signal recovery method, and the other aims to jointly design the common measurement matrix and jointly sparse support recovery method. The proposed model-driven approaches can effectively utilize features of sparsity patterns in designing common measurement matrices and adjusting model-driven decoders, and can greatly benefit from the underlying state-of-the-art recovery methods with theoretical guarantee. Hence, the obtained common measurement matrices and recovery methods can significantly outperform the underlying advanced recovery methods. We conduct extensive numerical results on channel estimation and device activity detection in MIMO-based grant-free random access. The numerical results show that the proposed approaches provide pilot sequences and channel estimation or device activity detection methods which can achieve higher estimation or detection accuracy with shorter computation time than existing ones. Furthermore, the numerical results explain how such gains are achieved via the proposed approaches.
Stabilizing Deep Tomographic Reconstruction Networks

Tomographic image reconstruction with deep learning is an emerging field of applied artificial intelligence but a recent study reveals that deep reconstruction networks, such as well-known AUTOMAP, are unstable for computed tomography (CT) and magnetic resonance imaging (MRI). Specifically, three kinds of instabilities were identified: (1) strong output artefacts from tiny perturbation, (2) poor detection of small features, and (3) decreased performance with increased input data. These instabilities are believed to be from lacking kernel awareness and nontrivial to overcome, but compressed sensing (CS) reconstruction was reported to be stable due to its kernel awareness. Since deep reconstruction may potentially become the main driving force to achieve better image quality, stabilizing deep reconstruction networks is an urgent challenge. Here we propose an Analytic, Compressive, Iterative Deep (ACID) network to fundamentally address this challenge. Instead of only using deep learning or compressed sensing, ACID consists of four modules including deep reconstruction, CS, analytic mapping, and iterative refinement. In our experiments, ACID eliminated all three kinds of instabilities and significantly improved image quality relative to the methods in the aforementioned PNAS study. ACID is only an example of integrating diverse algorithmic ingredients but it has clearly demonstrated that data-driven reconstruction can be stabilized to outperform reconstruction using CS alone. The power of ACID comes from a unique combination of a deep reconstruction network trained on big data, CS via advanced optimization, and iterative refinement that stabilizes the whole workflow. We anticipate that this integrative closed-loop data driven approach will add great value to clinical and other applications.
Bubble wall correlations in cosmological phase transitions

We study statistical relationships between bubble walls in cosmological first-order phase transitions. We consider the conditional and joint probabilities for different points on the walls to remain uncollided at given times. We use these results to discuss space and time correlations of bubble walls and their relevance for the consequences of the transition. In our statistical treatment, the kinematics of bubble nucleation and growth is characterized by the nucleation rate and the wall velocity as functions of time. We obtain general expressions in terms of these two quantities, and we consider several specific examples and applications.
Out-of-Distribution Generalization with Maximal Invariant Predictor

Out-of-Distribution (OOD) generalization problem is a problem of seeking the predictor function whose performance in the worst environments is optimal. This paper makes two contributions to OOD problem. We first use the basic results of probability to prove maximal Invariant Predictor(MIP) condition, a theoretical result that can be used to identify the OOD optimal solution. We then use our MIP to derive inner-environmental Gradient Alignment(IGA) algorithm that can be used to help seek the OOD optimal predictor. Previous studies that have investigated the theoretical aspect of the OOD-problem use strong structural assumptions such as causal DAG. However, in cases involving image datasets, for example, the identification of hidden structural relations is itself a difficult problem. Our theoretical results are different from those of many previous studies in that it can be applied to cases in which the underlying structure of a dataset is difficult to analyze. We present an extensive comparison of previous theoretical approaches to the OODproblems based on the assumptions they make. We also present an extension of the colored-MNIST that can more accurately represent the pathological OOD situation than the original version, and demonstrate the superiority of IGA over previous methods on both the original and the extended version of Colored-MNIST.
Can grassland plant communities be preserved on road and railway verges

Semi-natural grasslands are a threatened biotope type in many countries. Typical grassland plant species are adapted to continuous grazing or mowing that keeps the envi- ronment open. With the decline in grassland area, these spe- cies are ever more reliant on alternative habitats such as road verges. To find out whether plant communities comparable to those of semi-natural grasslands can be maintained on modern road and railway sides, the vegetation of 92 grasslands and 90 road and railway verges was studied. These biotope types were compared with each other according to their community structure, total number of species, number of grassland species and restricted-range diversity. Further study of the vegetation of road and railway verges was carried out in order to identify the treatments and environments which are most likely to support diverse plant communities. The species number and the restricted-range diversity proved to be higher next to roads and railways than on grasslands. Grassland species were, however, most abundant on grasslands. Furthermore, the com- munity structure of these biotope types was totally divergent. In their present state, road and railway verges are not a substi- tute for semi-natural grasslands. Nevertheless, the occurrence of grassland species in verges may be enhanced by a suitable mowing regime, by giving up the use of de-icing salt and herbicides and by allowing natural establishment of vegeta- tion on the verges.
Inertia-gravity-wave scattering by geostrophic turbulence

In rotating stratified flows including in the atmosphere and ocean, inertia-gravity waves (IGWs) often coexist with a geostrophically balanced turbulent flow. Advection and refraction by this flow lead to wave scattering, redistributing IGW energy in the position--wavenumber phase space. We give a detailed description of this process by deriving a kinetic equation governing the evolution of the IGW phase-space energy density. The derivation relies on the smallness of the Rossby number characterising the geostrophic flow, which is treated as a random field with known statistics, and makes no assumption of spatial scale separation. The kinetic equation describes energy transfers that are restricted to IGWs with the same frequency, as a result of the timescale separation between waves and flow. We formulate the kinetic equation on the constant-frequency surface -- a double cone in wavenumber space -- using polar spherical coordinates, and we examine the form of the two scattering cross sections involved, which quantify energy transfers between IGWs with, respectively, the same and opposite directions of vertical propagation. The kinetic equation captures both the horizontal isotropisation and the cascade of energy across scales that result from scattering. We focus our attention on the latter to assess the predictions of the kinetic equation against direct simulations of the three-dimensional Boussinesq equations, finding good agreement.
Analysis of the TiO isotopologues in stellar optical spectra

This study is based on the use of the new ExoMol TiO rovibronic line lists to identify and model TiO isotopologue features in spectra of M-dwarfs. We aim to investigate problems involving the computation of electronic bands for different isotopologues of TiO by modelling optical spectra of late-type stars and to determine their Ti isotopic abundances, to compare the TiO isotopologues spectra computed using line lists by different authors. We fit theoretical synthetic spectra to the observed stellar molecular bands of TiO. We model spectra of two M-dwarfs: GJ 15A (M1V) and GJ 15B (M3 V) to determine Ti isotopic ratios in their atmospheres. We demonstrate the accuracy of the ExoMol {\sc Toto} line list for differentisotopologues of TiO and the possibility of determining accurate Ti isotope abundances in a numberof spectral ranges. The 7580--7594 A spectral range seems particularly useful, with two atomic linesof Fe I and molecular band heads of $^{50}Ti$O, $^{49}Ti$O, $^{48}Ti$O, $^{47}Ti$O clearly observable in our two M-dwarf spectra. We determine non-solar Ti isotopic ratios of $^{46}Ti$ : $^{47}Ti$ : $^{48}Ti$ : $^{49}Ti$ : $^{50}Ti$ = 7.9 : 5.2 : 72.8 : 7.9 :6.2 for GJ 15A and 7.4 : 4.2 : 76.6 : 5.8 : 6.0 for GJ 15B with accuracy of order $\pm$ 0.2. [Ti] = 0.040 and 0.199 within accuracy $\pm$ 0.10 were also determined for GJ 15A and GJ 15B, respectively. We find that the ExoMol {\sc Toto} TiO line list: a) describes the fine details in line position and intensities of the M-dwarf spectra better than other available TiO line lists; b) correctly reproduces the positions and intensities of the TiO isotopologue band heads observed in M-dwarf spectra; c) can be used to determine Ti isotope abundances in atmospheres of M-stars.
Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds

This paper introduces novel results for the score function gradient estimator of the importance weighted variational bound (IWAE). We prove that in the limit of large $K$ (number of importance samples) one can choose the control variate such that the Signal-to-Noise ratio (SNR) of the estimator grows as $\sqrt{K}$. This is in contrast to the standard pathwise gradient estimator where the SNR decreases as $1/\sqrt{K}$. Based on our theoretical findings we develop a novel control variate that extends on VIMCO. Empirically, for the training of both continuous and discrete generative models, the proposed method yields superior variance reduction, resulting in an SNR for IWAE that increases with $K$ without relying on the reparameterization trick. The novel estimator is competitive with state-of-the-art reparameterization-free gradient estimators such as Reweighted Wake-Sleep (RWS) and the thermodynamic variational objective (TVO) when training generative models.
Microbial Metabolism of Alicyclic Hydrocarbons: Cyclohexane Catabolism by a Pure Strain of Pseudomonas sp.

A micro-organism capable of growth on cyclohexane as the sole carbon source has been isolated from soil of an ash wood; the organism has been identified as a pseudomonad. Growth, respiration and enzymic studies with the organism are consistent with an oxidation route for cyclohexane proceeding via cyclohexanol, cyclohexanone, e-caprolactone, 6-hydroxycaproate and adipate. Cell-free extracts of the organism grown on cyclohexane demonstrated cyclohexane hydroxylase activity which was found to be very labile, dependent on molecular oxygen and specific for NADH; the product of this reaction was identified as cyclohexanol.
Análise do desempenho produtivo de diversos grupos genéticos Holandês x Gir no Brasil

Total milk yield per lactation (PL), milk yield in 305 days (PL305) and lactation length (DL) were estimated based on records of the Brazilian Association of Girolando Breeders (3,574 lactations). Milk production was recorded monthly and 10.8% of the herds were raised under an extensive feeding system, 67.9% under a semi-intensive feeding system and 21.3% under an intensive feeding system. Data were analyzed through the least square method by GLM procedure (SAS, 1996). Preliminary analysis indicated a high interaction between genetic group and feeding system. Thus, the performance of genetic groups was evaluated within each feeding system. Genetic effects of additive difference between (g) Holstein and Gir breeds, dominance (d) and additive x additive epistatic interactions (gg) were also studied. It was observed that, in some cases, the effects of epistatic interactions should be taken into account. Moreover, increasing the proportion of Holstein-Friesian genes did not have any significant effect on milk yield when herds were raised under poor management conditions. However, as such conditions improved, crosses with higher proportion of Holstein tended to show better performance.
Conservation of Avian Genetic Resources

There is an acute necessity to develop a systematic methodology to conserve avian genetic resources, since 1,313 species out of 10,064 known avian species are listed as endangered (IUCN2013). However, limited information is available on the application of germline chimera and semen cryopreservation technologies to secure endangered wild birds. In the presentation, our recent progress and future prospectus on the conservation of avian genetic resources will be discussed focusing on following three topics: 1. Semen cryopreservation : Glycerol, DMSO and ethylene glycol have been widely used for cryopreserving chicken semen. A new method of cryopreserving chicken semen by using N-Methylacetamide (MA) as a cryoprotective agent (Sasaki et al. 2010). It is now possible to cryopreserve chicken semen that can retains high fertilizing fertility and duration of fertility by using MA as cryoprotective agent. 2. New method of isolating gonadal germ cells (GGCs): PBS(-) method has been developed to recover GGCs from early chick embryos (Nakajima et al. 2011). Factors involved in spontaneous GGC discharge from developing chick gonad into PBS(-) has been examined. It has been shown that PBS(-) methods can be applied to collect viable GGCs from some endangered wild birds. 3. Production of germline chimera in chicken using GGCs: It is possible to produce germline chimeras by transferring GGCs recovered from 5, 7 and 9 day old chick transferred into male embryos. However, no female germline chimera has been produced so far by transferring GGCs recovered from 9 day-old chick embryos. References (2~5 references) 1. Sasaki K, Tatsumi T, Tsutsui M, Niinomi T, Naito M, Tajima A, and Nishi Y. A method of cryopreserving semen from Yakido roosters using N-Methylacetamide as a cryoprotective agent. Journal of Poultry Science 47:297-301, 2010. 2. Nakajima Y, Minematsu T, Naito M and Tajima A. A New Method for Isolating Viable Gonadal Germ Cells from 7-day-old Chick Embryos. Journal of Poultry Science 48:106-111, 2011. 3. Tajima A, Conservation of avian genetic resources (Review) Journal of Poultry Science 50:1-8, 2013.
PIC methods in astrophysics: PIC simulations of relativistic jets

The Particle-In-Cell (PIC) method has been developed by Oscar Buneman, Charles Birdsall, Roger W. Hockney, and John Dawson in the 1950s and, with the advances of computing power, has been further developed for several fields such as astrophysical, magnetospheric as well as solar plasmas and recently also for atmospheric and laser physics. Currently more than 15 semi-public PIC codes are available. Its applications have grown extensively with increasing computing power available on high performance computing facilities around the world. These systems allow the study of various topics of astrophysical plasmas, such as magnetic reconnection, pulsars and black hole.
Novel High-Throughput Decoding Algorithms for Product and Staircase Codes Based on Error-and-Erasure Decoding

Product codes (PCs) and staircase codes (SCCs) are conventionally decoded based on bounded distance decoding (BDD) of the component codes and iterating between row and column decoders. The performance of iterative BDD (iBDD) can be improved using soft-aided (hybrid) algorithms. Among these, iBDD with combined reliability (iBDD-CR) has been recently proposed for PCs, yielding sizeable performance gains at the expense of a minor increase in complexity compared to iBDD. In this paper, we first extend iBDD-CR to SCCs. We then propose two novel decoding algorithms for PCs and SCCs which improve upon iBDD-CR. The new algorithms use an extra decoding attempt based on error and erasure decoding of the component codes. The proposed algorithms require only the exchange of hard messages between component decoders, making them an attractive solution for ultra high-throughput fiber-optic systems. Simulation results show that our algorithms based on two decoding attempts achieve gains of up to 0.88 dB for both PCs and SCCs. This corresponds to a 33% optical reach enhancement over iBDD with bit-interleaved coded modulation using 256 quadrature amplitude modulation.
Hopfield Networks is All You Need

We show that the transformer attention mechanism is the update rule of a modern Hopfield network with continuous states. This new Hopfield network can store exponentially (with the dimension) many patterns, converges with one update, and has exponentially small retrieval errors. The number of stored patterns is traded off against convergence speed and retrieval error. The new Hopfield network has three types of energy minima (fixed points of the update): (1) global fixed point averaging over all patterns, (2) metastable states averaging over a subset of patterns, and (3) fixed points which store a single pattern. Transformer and BERT models operate in their first layers preferably in the global averaging regime, while they operate in higher layers in metastable states. The gradient in transformers is maximal for metastable states, is uniformly distributed for global averaging, and vanishes for a fixed point near a stored pattern. Using the Hopfield network interpretation, we analyzed learning of transformer and BERT models. Learning starts with attention heads that average and then most of them switch to metastable states. However, the majority of heads in the first layers still averages and can be replaced by averaging, e.g. our proposed Gaussian weighting. In contrast, heads in the last layers steadily learn and seem to use metastable states to collect information created in lower layers. These heads seem to be a promising target for improving transformers. Neural networks with Hopfield networks outperform other methods on immune repertoire classification, where the Hopfield net stores several hundreds of thousands of patterns. We provide a new PyTorch layer called "Hopfield", which allows to equip deep learning architectures with modern Hopfield networks as a new powerful concept comprising pooling, memory, and attention. GitHub: this https URL