Miracle-dz/UniFAR-FTUs huggingface dataset:
On generalized {\Phi}-strongly monotone mappings and algorithms for the solution of equations of Hammerstein type

In this paper, we consider the class of generalized {\Phi}-strongly monotone mappings and the methods of approximating a solution of equations of Hammerstein type. Auxiliary mapping is defined for nonlinear integral equations of Hammerstein type. The auxiliary mapping is the composition of bounded generalized {\Phi}-strongly monotone mappings which satisfy the range condition. Suitable conditions are imposed to obtain the boundedness and to show that the auxiliary mapping is a generalized {\Phi}-strongly which satisfies the range condition. A sequence is constructed and it is shown that it converges strongly to a solution of equations of Hammerstein type. The results in this paper improve and extend some recent corresponding results on the approximation of a solution of equations of Hammerstein type.
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible

Machine learning is predicated on the concept of generalization: a model achieving low error on a sufficiently large training set should also perform well on novel samples from the same distribution. We show that both data whitening and second order optimization can harm or entirely prevent generalization. In general, model training harnesses information contained in the sample-sample second moment matrix of a dataset. For a general class of models, namely models with a fully connected first layer, we prove that the information contained in this matrix is the only information which can be used to generalize. Models trained using whitened data, or with certain second order optimization schemes, have less access to this information; in the high dimensional regime they have no access at all, producing models that generalize poorly or not at all. We experimentally verify these predictions for several architectures, and further demonstrate that generalization continues to be harmed even when theoretical requirements are relaxed. However, we also show experimentally that regularized second order optimization can provide a practical tradeoff, where training is still accelerated but less information is lost, and generalization can in some circumstances even improve.
Very Deep Transformers for Neural Machine Translation

We explore the application of very deep Transformer models for Neural Machine Translation (NMT). Using a simple yet effective initialization technique that stabilizes training, we show that it is feasible to build standard Transformer-based models with up to 60 encoder layers and 12 decoder layers. These deep models outperform their baseline 6-layer counterparts by as much as 2.5 BLEU, and achieve new state-of-the-art benchmark results on WMT14 English-French (43.8 BLEU and 46.4 BLEU with back-translation) and WMT14 English-German (30.1 BLEU). The code and trained models will be publicly available at: this https URL.
Universality of the geodesic tree in last passage percolation

In this paper we consider the geodesic tree in exponential last passage percolation. We show that for a large class of initial conditions around the origin, the line-to-point geodesic that terminates in a cylinder of width $o(N^{2/3})$ and length $o(N)$ agrees in the cylinder, with the stationary geodesic sharing the same end point. In the case of the point-to-point model, we consider width $\delta N^{2/3}$ and length up to $\delta^{3/2} N/(\log(\delta^{-1}))^3$ and provide lower and upper bound for the probability that the geodesics agree in that cylinder.
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action space to a higher level in the form of subgoals for a motion generator (a combination of motion planner and trajectory executor). We argue that, by lifting the action space and by leveraging sampling-based motion planners, we can efficiently use RL to solve complex, long-horizon tasks that could not be solved with existing RL methods in the original action space. We propose ReLMoGen -- a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. To validate our method, we apply ReLMoGen to two types of tasks: 1) Interactive Navigation tasks, navigation problems where interactions with the environment are required to reach the destination, and 2) Mobile Manipulation tasks, manipulation tasks that require moving the robot base. These problems are challenging because they are usually long-horizon, hard to explore during training, and comprise alternating phases of navigation and interaction. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. In all settings, ReLMoGen outperforms state-of-the-art Reinforcement Learning and Hierarchical Reinforcement Learning baselines. ReLMoGen also shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
A quantitative study of neuronal calcium signaling

A Quantitative Study of Neuronal Calcium Signaling by Jane Hartsfield Neurons have both a fast and slow mode of signaling. Fast signals are communicated by transmembrane voltage changes, while calcium levels within the cell communicate information on a much slower time scale. Calcium acts as a second messenger responsible for modulating neuronal excitability in many ways including the mediation of gene transcription in the cell and the sensitivity of the cell to further stimulus. I propose a means of determining calcium conductance density of the cell membrane from intracellular calcium concentration measurement data using a two step process. The first step is the inference of calcium current density from calcium concentration measurements using a least squares fit to the data. Once an estimate of the calcium current density is determined, the minimum value over time is used to determine the calcium conductance density. I develop a voltage model of the neuron’s electrical signal with ion diffusion and drift which includes voltage-gated calcium currents and calcium-dependent potassium currents. The influx of calcium resulting from the voltage model will prime the endoplasmic reticulum with calcium. A model of the dynamics of calcium induced calcium release from the endoplasmic reticulum via IP3 receptors which includes diffusion of calcium and IP3 as well as calcium buffering by the mitochondria results in a calcium wave similar to what has iii been observed experimentally. Finally, I use a branch structure together with IP3 generation, calcium buffers in the cytosol and ER, cell membrane calcium transports (voltage-gated calcium channels, pumps, exchangers, and store-operated channels), and ER calcium transports (IP3 receptors, ryanodine receptors, pumps, leak channels) to show that calcium waves initiate in the apical trunk at the point where the stimulated oblique branches off.
The Massive and Distant Clusters of WISE Survey. VIII. Radio Activity in Massive Galaxy Clusters

We present a study of the central radio activity of galaxy clusters at high redshift. Using a large sample of galaxy clusters at 0.7 < z < 1.5 from the Massive and Distant Clusters of Wide-field Infrared Survey Explorer Survey and the Faint Images of the Radio Sky at Twenty-Centimeters 1.4 GHz catalog, we measure the fraction of clusters containing a radio source within the central 500 kpc, which we term the cluster radio-active fraction, and the fraction of cluster galaxies within the central 500 kpc exhibiting radio emission. We find tentative (2.25σ) evidence that the cluster radio-active fraction increases with cluster richness, while the fraction of cluster galaxies that are radioluminous ( W Hz−1) does not correlate with richness at a statistically significant level. Compared to that calculated at 0 < z < 0.6, the cluster radio-active fraction at 0 < z < 1.5 increases by a factor of 10. This fraction is also dependent on the radio luminosity. Clusters at higher redshift are much more likely to host a radio source of luminosity W Hz−1 than are lower-redshift clusters. We compare the fraction of radioluminous cluster galaxies to the fraction measured in a field environment. For 0.7 < z < 1.5, we find that both the cluster and field radioluminous galaxy fraction increases with stellar mass, regardless of environment, though at fixed stellar mass, cluster galaxies are roughly 2 times more likely to be radioluminous than field galaxies.
Glancing Transformer for Non-Autoregressive Neural Machine Translation

Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. We propose the Glancing Language Model (GLM) for single-pass parallel generation models. With GLM, we develop Glancing Transformer (GLAT) for machine translation. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8×-15× speedup. Note that GLAT does not modify the network architecture, which is a training method to learn word interdependency. Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive methods, and is nearly comparable to Transformer, reducing the gap to 0.25-0.9 BLEU points.
Efficient Bayesian Structural Equation Modeling in Stan

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or hidden variables), often leading to slow and inefficient MCMC samples. In this paper, we describe and illustrate a general, efficient approach to Bayesian SEM estimation in Stan, contrasting it with previous implementations in R package blavaan (Merkle & Rosseel, 2018). After describing the approaches in detail, we conduct a practical comparison under multiple scenarios. The comparisons show that the new approach is clearly better. We also discuss ways that the approach may be extended to other models that are of interest to psychometricians.
Intercalation of Six β-Carboline Derivatives into DNA

The absorption spectra, ethidium bromide fluorescent spectra and circular dichroism (CD) spectra indicate that six β-carboline derivatives, norharman, harman, harmol, harmine, harmalol and harmaline intercalate into DNA. This result is supported by unwinding experiments using the superhelical plasmid pBR322 DNA, and the magnitude of intercalation except for harmalol decreased in the order of harmol>harmine>harmaline>harman>norharman. The potency of the interactions was further determined by spectrophotometric titration. The Scatchard plots were then analyzed using the Langmuir equation including two terms for specific and non-specific interactions. The interaction of the β-carboline derivatives with DNA is characterized as follows : 1) Substitution into the β-carboline skeleton rings decreases the number of binding sites. 2) The polar groups OH and OCH3 increase the binding affinity at the first specific term. 3) A decrease in the planarity of the β-carboline skeleton decreases the binding affinity. 4) The interaction of harmalol with DNA may be less potent than that of harmine.
Deploying Lifelong Open-Domain Dialogue Learning

Much of NLP research has focused on crowdsourced static datasets and the supervised learning paradigm of training once and then evaluating test performance. As argued in de Vries et al. (2020), crowdsourced data has the issues of lack of naturalness and relevance to real-world use cases, while the static dataset paradigm does not allow for a model to learn from its experiences of using language (Silver et al., 2013). In contrast, one might hope for machine learning systems that become more useful as they interact with people. In this work, we build and deploy a role-playing game, whereby human players converse with learning agents situated in an open-domain fantasy world. We show that by training models on the conversations they have with humans in the game the models progressively improve, as measured by automatic metrics and online engagement scores. This learning is shown to be more efficient than crowdsourced data when applied to conversations with real users, as well as being far cheaper to collect.
Anomaly Detection With Convolutional Autoencoders for Fingerprint Presentation Attack Detection

In recent years, the popularity of fingerprint-based biometric authentication systems significantly increased. However, together with many advantages, biometric systems are still vulnerable to presentation attacks (PAs). In particular, this applies for unsupervised applications, where new attacks unknown to the system operator may occur. Therefore, presentation attack detection (PAD) methods are used to determine whether samples stem from a bona fide subject or from a presentation attack instrument (PAI). In this context, most works are dedicated to solve PAD as a two-class classification problem, which includes training a model on both bona fide and PA samples. In spite of the good detection rates reported, these methods still face difficulties detecting PAIs from unknown materials. To address this issue, we propose a new PAD technique based on autoencoders (AEs) trained only on bona fide samples (i.e., one-class), which are captured in the short wave infrared domain. On the experimental evaluation over a database of 19,711 bona fide and 4,339 PA images including 45 different PAI species, a detection equal error rate (D-EER) of 2.00% was achieved. Additionally, our best performing AE model is compared to further one-class classifiers (support vector machine, Gaussian mixture model). The results show the effectiveness of the AE model as it significantly outperforms the previously proposed methods.
Dataset Bias in Few-Shot Image Recognition

The goal of few-shot image recognition (FSIR) is to identify novel categories with a small number of annotated samples by exploiting transferable knowledge from training data (base categories). Most current studies assume that the transferable knowledge can be well used to identify novel categories. However, such transferable capability may be impacted by the dataset bias, and this problem has rarely been investigated before. Besides, most of few-shot learning methods are biased to different datasets, which is also an important issue that needs to be investigated deeply. In this paper, we first investigate the impact of transferable capabilities learned from base categories. Specifically, we use the relevance to measure relationships between base categories and novel categories. Distributions of base categories are depicted via the instance density and category diversity. The FSIR model learns better transferable knowledge from relevant training data. In the relevant data, dense instances or diverse categories can further enrich the learned knowledge. Experimental results on different sub-datasets of Imagenet demonstrate category relevance, instance density and category diversity can depict transferable bias from distributions of base categories. Second, we investigate performance differences on different datasets from the aspects of dataset structures and different few-shot learning methods. Specifically, we introduce image complexity, intra-concept visual consistency, and inter-concept visual similarity to quantify characteristics of dataset structures. We use these quantitative characteristics and eight few-shot learning methods to analyze performance differences on multiple datasets. Based on the experimental analysis, some insightful observations are obtained from the perspective of both dataset structures and few-shot learning methods. We hope these observations are useful to guide future few-shot learning research on new datasets or tasks. Our data is available at http://123.57. 42.89/dataset-bias/dataset-bias.html.
Equivalent Classification Mapping for Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization is a newly emerging yet widely studied topic in recent years. The existing methods can be categorized into two localization-by-classification pipelines, i.e., the pre-classification pipeline and the post-classification pipeline. The pre-classification pipeline first performs classification on each video snippet, and then, aggregates the snippet-level classification scores to obtain the video-level classification score. In contrast, the post-classification pipeline aggregates the snippet-level features first and then predicts the video-level classification score based on the aggregated feature. Although the classifiers in these two pipelines are used in different ways, the role they play is exactly the same—to classify the given features to identify the corresponding action categories. To this end, an ideal classifier can make both pipelines work. This inspires us to simultaneously learn these two pipelines in a unified framework to obtain an effective classifier. Specifically, in the proposed learning framework, we implement two parallel network streams to model the two localization-by-classification pipelines simultaneously and make the two network streams share the same classifier. This achieves the novel Equivalent Classification Mapping (ECM) mechanism. Moreover, we discover that an ideal classifier may possess two characteristics: 1) the frame-level classification scores obtained from the pre-classification stream and the feature aggregation weights in the post-classification stream should be consistent; and 2) the classification results of these two streams should be identical. Based on these two characteristics, we further introduce a weight-transition module and an equivalent training strategy into the proposed learning framework, which assists to thoroughly mine the equivalence mechanism. Comprehensive experiments are conducted on three benchmarks and ECM achieves accurate action localization results.
Search for Nearby Earth Analogs .III. Detection of 10 New Planets, 3 Planet Candidates, and Confirmation of 3 Planets around 11 Nearby M Dwarfs

Earth-sized planets in the habitable zones of M dwarfs are good candidates for the study of habitability and detection of biosignatures. To search for these planets, we analyze all available radial velocity data and apply four signal detection criteria to select the optimal candidates. We find 10 strong candidates satisfying these criteria and three weak candidates showing inconsistency over time due to data samplings. We also confirm three previous planet candidates and improve their orbital solutions through combined analyses of updated data sets. Among the strong planet candidates, HIP 38594 b is a temperate super-Earth with a mass of 8.2 ± 1.7 M ⊕ and an orbital period of 60.7 ± 0.1 days, orbiting around an early-type M dwarf. Early-type M dwarfs are less active and thus are better hosts for habitable planets than mid-type and late-type M dwarfs. Moreover, we report the detection of five two-planet systems, including two systems made up of a warm or cold Neptune and a cold Jupiter, consistent with a positive correlation between these two types of planets. We also detect three temperate Neptunes, four cold Neptunes, and four cold Jupiters, contributing to a rarely explored planet population. Due to their proximity to the Sun, these planets on wide orbits are appropriate targets for direct imaging by future facilities such as the Habitable Exoplanet Observatory and the Extremely Large Telescope.
Antimicrobial Activity on Food-Borne Pathogensof Extracellular Fermented Products from Bacillus amyloliquefaciens C-1

Bacillus amyloliquefaciens C-1 was a newly isolated strain.C-1 supernatant was collected from the 72h fermentation culture.It showed high antimicrobial activity against Bacillus cereus MS10362R,Clostridium difficile QB21and Escherichia coli ATCC25922 by culture dish assay.In growth curve analysis,4 mL of C-1 supernatant in 150mL medium inhibited the growth of B.cereus MS10362R at the lag phase,and the pH changing of culture medium by the addition of 4 mL C-1 supernatant were excluded.At the same time,the production of melanin,secondary metabolite of MS10362R,was reduced,which might be the effect of QS(quorum sensing) system.Meanwhile,exopolysaccharides extracted and purified from C-1 supernatant failed to show any antimicrobial activity.
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers

We study the problem of robustly estimating the mean of a $d$-dimensional distribution given $N$ examples, where $\varepsilon N$ examples may be arbitrarily corrupted and most coordinates of every example may be missing. Assuming each coordinate appears in a constant factor more than $\varepsilon N$ examples, we show algorithms that estimate the mean of the distribution with information-theoretically optimal dimension-independent error guarantees in nearly-linear time $\widetilde O(Nd)$. Our results extend recent work on computationally-efficient robust estimation to a more widely applicable incomplete-data setting.
An unsupervised deep learning method for multi-coil cine MRI

Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the undersampled k-space to the fully sampled images. Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied. In this paper, we propose an unsupervised deep learning method for multi-coil cine MRI via a time-interleaved sampling strategy. Specifically, a time-interleaved acquisition scheme is utilized to build a set of fully encoded reference data by directly merging the k-space data of adjacent time frames. Then these fully encoded data can be used to train a parallel network for reconstructing images of each coil separately. Finally, the images from each coil are combined via a CNN to implicitly explore the correlations between coils. The comparisons with classic k-t FOCUSS, k-t SLR, L+S and KLR methods on in vivo datasets show that our method can achieve improved reconstruction results in an extremely short amount of time.
Residual Learning from Demonstration

Contacts and friction are inherent to nearly all robotic manipulation tasks. Through the motor skill of insertion, we study how robots can learn to cope when these attributes play a salient role. In this work we propose residual learning from demonstration (rLfD), a framework that combines dynamic movement primitives (DMP) that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy. The proposed solution is applied directly in task space and operates on the full pose of the robot. We show that rLfD outperforms alternatives and improves the generalisation abilities of DMPs. We evaluate this approach by training an agent to successfully perform both simulated and real world insertions of pegs, gears and plugs into respective sockets.